copyright 2014, janani r. thapa

ESSAYS ON

COMMUNITY-BASED BEHAVIOR CHANGE INTERVENTIONS

FOR OBESITY PREVENTION

by

Janani R. Thapa, M.S., M.P.A.

A Dissertation

In

Agricultural and Applied Economics

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Conrad Lyford

Chair of Committee

Dr. Jaime Malaga

Dr. Barbara Pence

Dr. Eduardo Segarra

Dr. Ryan Williams

Mark Sheridian

Dean of the Graduate School

August 2014

Texas Tech University, Janani R. Thapa, August 2014

ii

ACKNOWLEDGEMENTS

Earning a Ph.D. degree and serving in academia is a dream come true which

wouldn’t have been possible without the support, patience, and guidance of the following

people. It is to them that I owe my deepest gratitude.

To Dr. Conrad Lyford, my major advisor, for his endless encouragements,

invaluable and uncountable suggestions, constant guidance, and crucial understanding

throughout my Ph.D. study. I highly acknowledge his wisdom, knowledge and

commitment that always inspired and motivated me. I am indebted and will forever

continue to be his advisee. To Dr. Eduardo Segarra for his unending confidence and

support since before I started my degree to this day. To my committee members: Dr.

Jaime Malaga for his countless encouragements, Dr. Barbara Pence for her invaluable

time, and Dr. Ryan Williams for his helpful comments. This dissertation took its current

shape due to the detailed attention and feedback I received from my major advisor and

my committee members. Also, to Dr. Eric Belasco, Tyra Carter, Dr. Conrad Lyford, Dr.

Barent McCool, Dr. Audrey McCool, and Dr. Barbara Pence for their work in bringing

the research grant from the Cancer Prevention Research Institute of Texas.

To my support system: guardian Drs. Devendra and Ratna Khatri, siblings (Sujata

and Siddhartha), father- and mother-in law, very close ones (Beth-Thys, Roji-Kaushal,

Sameer-Simran, Resham-Durga, and Chandra-Sirjana), colleagues (Drs. Adhikari, Nair

and Tiwari), friends (Michael, Sanja, Haiyan, Abbas, Abbes, Dacheng, Kishor, Mira,

Dipti, Bishnu, Shivani, Nilam, Erina, Shailee), office staff (Cindy, DeeAnn and Neal),

advisors (Drs. Nauriddine Abidi, Durga D. Dhakal, Durga M. Gautam, Kathryn March,

Megha Parajulee, and Norman Uphoff), and above all I acknowledge all the faculty of the

Department of Agricultural and Applied Economics at Texas Tech University.

To Rajeev Rajbhandari, my husband, for making finishing so important and for

making every tough times easy and to Rishav, my son, for spending countless hours

without ‘mamu.’ Without them this effort would have worth nothing.

Finally, I dedicate this dissertation to Ms. Subhadra Kumari, my mother. It is only

due to her devotion and perseverance that I have been able to reach this far.

Unfortunately I fell short by few months and lost her just before I could tell her I have

attained what she had set me forth for. This is for her.


iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ______________________________________________ ii

ABSTRACT ___________________________________________________________ ix

LIST OF TABLES ______________________________________________________x

LIST OF FIGURES ___________________________________________________ xii

CHAPTER I: BEHAVIORAL INTERVENTIONS TO ADDRESS OBESITY AS A

MARKET FAILURE ____________________________________________________1

Abstract _______________________________________________________________1

1.1 Background _________________________________________________________2

1.2 Obesity: a market failure ______________________________________________4

1.2.1 Negative externality in production____________________________________5

1.2.2 Negative externality in consumption __________________________________8

1.3 Addressing the market failure __________________________________________9

1.3.1 Taxation ________________________________________________________9

1.3.2 Behavioral interventions __________________________________________10

1.4 Addressing obesity as an externality ____________________________________12

1.5 Problem statement and research objective _______________________________16

1.5.1 Essay I ________________________________________________________18

1.5.2 Essay II________________________________________________________18

1.5.3 Essay III _______________________________________________________19


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CHAPTER II: THE IMPACT OF A MULTI-TIERED COMMUNITY-BASED

OBESITY AND CANCER PREVENTION PROGRAM IN A SMALL

COMMUNITY ________________________________________________________20

Abstract ______________________________________________________________20

2.1 Introduction ________________________________________________________21

2.2 The project concept and model ________________________________________23

2.3 Review of literature__________________________________________________24

2.3.1 Obesity and obesity in the US ______________________________________24

2.3.1.1 Obesity and overweight _____________________________________24

2.3.1.2 Obesity in the United States __________________________________25

2.3.2 Obesity and cancer _______________________________________________27

2.3.2.1 Evidence of obesity and cancer _______________________________27

2.3.2.2 Epidemiology _____________________________________________28

2.3.3 Behavior _______________________________________________________28

2.3.3.1 Behavior and cancer ________________________________________28

2.3.3.2 Food behavior ____________________________________________29

2.3.3.3 Theories of behavior change _________________________________30

2.3.4 Community-based participatory research (CBPR) ______________________31

2.3.4.1 Community interventions measuring BMI as the outcome variable ___32

2.4 Conceptual framework _______________________________________________33

2.4.1 The mediating variable model ______________________________________35


v

2.5 Methods ___________________________________________________________36

2.5.1 Data __________________________________________________________36

2.5.1.1 Anthropometric measurements _______________________________39

2.5.1.2 Health assessment survey ___________________________________40

2.5.1.3 Project participation survey __________________________________42

2.5.1.4 Missing data ______________________________________________42

2.5.2 Study designs ___________________________________________________43

2.5.3 Data analysis ___________________________________________________45

2.5.3.1 Project impact and sustainability through group comparisons _______45

2.5.3.2 Simple intervention effect on cancer knowledge score _____________47

2.5.3.3 Effect of intervention on BMI ________________________________49

2.6 Results ____________________________________________________________52

2.6.1 Project impact and sustainability through group comparisons _____________52

2.6.1.1 Cancer risk awareness ______________________________________52

2.6.1.2 Cancer risk behavior including nutrition awareness _______________55

2.6.1.3 Food behavior and other cancer risk behavior ____________________56

2.6.2 Simple intervention effect on cancer knowledge score ___________________59

2.6.3 Effect of intervention on BMI ______________________________________62

2.6.3.1 Multiple linear regressions ___________________________________67

2.7 Conclusions ________________________________________________________72

Appendix _____________________________________________________________74


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CHAPTER III: THE EFFECT ON SUPERMARKET FOOD PURCHASES FROM

POINT OF SALE NUDGES WITH COMMUNITY REINFORCEMENT _______83

Abstract ______________________________________________________________83

3.1 Introduction ________________________________________________________84

3.2 Review of literature__________________________________________________87

3.2.1 Risk factors for obesity ___________________________________________87

3.2.2 Supermarket interventions _________________________________________88

3.2.3 Use of scanner data ______________________________________________89

3.2.4 Influence of personal characteristics in food choice decisions _____________90

3.3 Conceptual framework _______________________________________________91

3.4 Methods ___________________________________________________________94

3.4.1 Research setting _________________________________________________94

3.4.2 Data __________________________________________________________95

3.4.2.1 Produce _________________________________________________95

3.4.2.2 Point of sale nudges ________________________________________96

3.4.2.3 Community awareness and project activities data _________________97

3.4.3 Data analysis ___________________________________________________98

3.4.3.1 Produce data analysis _______________________________________99

3.4.3.2 Point of sale nudges data analysis ____________________________101

3.4.3.3 Community awareness and use of project activities ______________101


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3.5 Results ___________________________________________________________102

3.5.1 Produce sales __________________________________________________102

3.5.1.1 Paired comparison in intervention community __________________102

3.5.1.2 Change in produce sales and overall comparison ________________104

3.5.2 Point of sale nudges _____________________________________________108

3.5.2.1 Change in sales and overall comparison _______________________108

3.5.3 Community awareness and use of project activities ____________________113

3.5.3.1 Community’s perception on project activities ___________________113

3.5.3.2 Awareness by demographics ________________________________116

3.6 Conclusions _______________________________________________________124

Appendix ____________________________________________________________126

CHAPTER IV: COMMUNITY-BASED BEHAVIOR CHANGE INTERVENTION

FOR OBESITY PREVENTION: A SYSTEMATIC REVIEW AND META-

ANALYSIS __________________________________________________________130

Abstract _____________________________________________________________130

4.1 Introduction _______________________________________________________131

4.2 Review of literature_________________________________________________134

4.3 Conceptual framework ______________________________________________136


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4.4 Methods __________________________________________________________140

4.4.1 Search strategy _________________________________________________140

4.4.2 Language and data restrictions _____________________________________141

4.4.3 Selection criteria _______________________________________________141

4.4.4 Data extraction and synthesis______________________________________142

4.4.5 Validation of systematic review ___________________________________1423

4.4.6 Meta-analysis __________________________________________________145

4.5 Results ___________________________________________________________145

4.5.1 Trend in community-based intervention evaluation research _____________147

4.5.2 Narrative synthesis ______________________________________________148

4.5.2.1 Small-scale relatively more-intense interventions ________________148

4.5.2.2 Large-scale relatively less-intense interventions _________________151

4.5.3 Meta-analysis __________________________________________________152

4.5.4 Large-scale, less-intense or small-scale, more-intense interventions? ______162

4.6 Conclusions _______________________________________________________163

Appendix ____________________________________________________________165

CHAPTER V: SUMMARY AND CONCLUSIONS _________________________174

BIBLIOGRAPHY _____________________________________________________178


ix

ABSTRACT

Unlike the traditional way of addressing obesity as an individual’s concern, it is now

often addressed as a community’s problem for a societal level effect. A conceptual

framework, by studying obesity as a market failure, was developed for the potential of

behavior change interventions in obesity prevention. Then, a multi-tiered supermarket

and community-based obesity prevention model, implemented in a small community, was

evaluated for its effect on 1) cancer knowledge and BMI, and 2) supermarket sales of

fresh fruits and vegetables (produce) and selected healthier items. The first evaluation

was done by comparing food behavior and attitude before and after the project and by

analyzing the intervention effect on cancer knowledge and BMI. The results suggests that

this community-based model has a good potential for bringing long term change in

obesity through targeting the behaviors associated with obesity and should have a

sustainable impact. The second evaluation was done by comparing the change in sales

from the pre-trial to trial period in the intervention store with the change in the control

store. The findings suggest that supermarket interventions have potential in changing

food behavior. Finally, a systematic review and meta-analysis of community-based

behavior change interventions was done. A narrative analysis and meta-analysis suggests

that community-based behavior change interventions have promise for changing

behaviors towards obesity prevention and to some extent changing (decreasing) BMI.

Additionally, the difference in project impact between large scale less intense and small

scale more intense interventions suggests that designers of community-based obesity

prevention intervention should carefully consider between large scale less intense and

small scale more intense interventions for desired outcomes.


x

LIST OF TABLES

Table 2.1 Description of explanatory variables ________________________________51

Table 2.2 Comparison of cancer risk awareness in community group _______________53

Table 2.3 Comparison of cancer risk awareness in paired group ___________________53

Table 2.4 Sustainability of cancer risk awareness in the intervention community ______54

Table 2.5 Comparison of project impact indicator variables in the community ________55

Table 2.6 Comparison of project impact indicator variables in the paired group _______56

Table 2.7 Food behavior and other cancer risk behavior _________________________58

Table 2.8 Statistical relationship on cancer knowledge score (CKS) ________________60

Table 2.9 Effect of intervention on cancer knowledge score (N=106) _______________61

Table 2.10 Body mass index (BMI) and waist circumference (WC) outcomes ________63

Table 2.11 Summary statistics of dependent and explanatory variables _____________67

Table 2.12 Statistical relationship on BMI ____________________________________70

Table 2.13 Self-perception of body weight (N=1067) ___________________________71

Table 3.1 Sales of produce during pre-trial and trial period ______________________103

Table 3.3 Change from pre-trial to trial period for produce (mean no. of units sold) __107

Table 3.4 Total percentage change (units sold) from the pre-trial to trial period ______110

Table 3.5 Change from pre-trial to trial period (mean units sold per month) _________112

Table 3.6 Community views of the project during the trial period _________________114


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Table 3.7 Community views of the project from the follow-up survey _____________116

Table 3.8a Use of NuVal score system by education level_______________________117

Table 3.8b Use of in-store signs by education level in the intervention community ___118

Table 3.9a Use of NuVal score system by income groups _______________________118

Table 3.9b Use of in-store signs by income groups in the intervention community ___119

Table 3.10a Use of NuVal score system by gender ____________________________120

Table 3.10b Use of in-store signs by gender in the intervention community _________120

Table 3.11a Use of NuVal score system by race ______________________________121

Table 3.11b Use of in-store signs by race in the intervention community ___________121

Table 3.12a Use of NuVal score system by language __________________________122

Table 3.12b Use of in-store healthy eating signs by race ________________________122

Table 3.13a Use of NuVal score system by age group __________________________123

Table 3.13b Use of in-store signs by age group _______________________________123

Table 4.2 Body weight comparison from selected interventions __________________155

Table 4.3 BMI comparison in selected interventions ___________________________157

Table 4.4 Data for meta-analysis __________________________________________159


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LIST OF FIGURES

Figure 1.1a Negative Externality in Production _________________________________6

Figure 1.1b Leftward Shift in the Demand to Decrease the Social Cost _____________11

Figure 1.2 Externality of High-Fat, Sugar and Salt Food Products _________________13

Figure 2.1 Self-reported Obesity among US Adults (BRFSS, 2012) ________________26

Figure 2.2 Norms for Cancer Prevention _____________________________________34

Figure 2.3 Mediating Variable Model _______________________________________35

Figure 2.4 Project Intervention and Control Site _______________________________37

Figure 2.5 Survey Respondents ____________________________________________38

Figure 2.6a Mean BMI Trend in Total Male Respondents ________________________64

Figure 2.6b Mean BMI Trend in Total Female Respondents ______________________64

Figure 2.7a Mean BMI Trend in Male Community Respondents’ Group ____________66

Figure 2.7b Mean BMI Trend in Female Community Respondents’ Group __________66

Figure 3.1 Conceptual Framework for the Supermarket Intervention _______________91

Figure 4.1 The Systematic Review Process Used ______________________________144

Figure 4.2 Trend in Community-based Evaluation Research _____________________147

Figure 4.3 Forest Plot of the Standard Difference in Mean BMI __________________161


1

CHAPTER I

BEHAVIORAL INTERVENTIONS TO ADDRESS OBESITY

AS A MARKET FAILURE

Abstract

Obesity in the US is pervasive across gender, ethnic origin, and socioeconomic status.

The cause of obesity at the individual level is the result of genetics, environment and

choices. Additionally evidence increasingly suggests that an environment conducive to

obesity at a personal level results in obesity and the associated energy imbalance. A key

factor affecting energy imbalance is a market system that has increasingly supplied food

high in calories including the foods that are sweeter and saltier. Obesity outcomes from

this market could be considered to be an externality of the demand and supply of high

calorie foods. This paper discusses obesity as a negative externality in food production

and consumption as it develops a framework for behavioral interventions to decrease the

demand of unhealthy foods. This could be used to conceptually evaluate for example

taxation to reduce demand for unhealthy products such as cigarettes, alcohols and

perhaps unhealthy foods that are high in fat, sugar and salt. This paper looks at the

potential of behavioral interventions as a complement to the ongoing efforts in changing

the supply and demand of products that are of concern to public health.

Key words: obesity, market failure, behavior change, community-based.


2

1.1 Background

Obesity in the US is pervasive across gender, ethnic origin, and socioeconomic

status. Associated statistics and comorbidity have been cited and reported in many

scholarly works (Guh et al., 2009; Ogden et al., 2012 and NCI, 2013). According to the

Center for Disease Control and Prevention (CDC, 2012), the percent of adults (age 20

years and over) who were obese was 35.9% in 2010. Obesity is among the leading

modifiable behavior risk for morbidity, mortality and disability in Americans (Mokdad et

al., 2004). Obesity prevalence remains high according to the most recent obesity trend

analysis (Ogden et al., 2014).

The cause of obesity at individual level is the result of genetics, environment and

choices. Additionally an increasing area of evidence suggests that an environment

conducive to obesity at a personal level is the result of energy imbalance. An energy

imbalance is when more calorie is consumed than is burned over a number of time

periods (Martinez, 2000; Chou et al., 2002 and Martínez et al., 2002). The World Health

Organization (WHO) has defined energy imbalance as the fundamental cause of obesity

and overweight status (WHO, 2013). Further, WHO (2013) has pointed out that globally

there has been an increased intake of energy dense foods that are high in fat and a

decrease in physical activity due to increasingly sedentary nature of many forms of work,

changing modes of transportation, and increasing urbanization. Research has been

conducted in different disciplines regarding the effect of energy imbalance on obesity:

nutrition and physical activity (Dionne et al., 1997; Seale and Rumpler, 1997;

Murgatroyd et al., 1999 and Mobasheri et al., 2005) and psychology (Williamson and

Stewart, 2005 and Sluis et al., 2010).


3

In the realm of demand and supply, a key factor affecting energy imbalance is the

increasing consumption of food high in calories. In particular, foods that are sweeter and

saltier have been increasingly demanded and supplied. As the US food system

transformed from small farm production and home cooking to efficient and specialized

large scale production and manufacturing of ready to eat foods obesity has increased.

With the food system transformation came value addition in foods, keeping cost low and

extensive advertising (Apple, 2003). US consumers adopted with the transformation in

the food system by changing their food (and lifestyle) behavior. Americans now purchase

more of their foods away from home (Stewart, 2011). The sequence of food system

transformation has led to path dependency in food choice decision towards increased

unhealthiness. Swinburn (2011) suggests that the global obesity pandemic is the outcome

of these changes in global food system.

The focus of this paper is looking at obesity as a path dependent outcome of

increasing demand and supply of high calorie foods. Several behavioral and economic

factors affect food consumption decisions (Finkelstein et al., 2005). The market success

of high calorie foods (sweeter and saltier foods) have been said to have contributed to the

obesity epidemic (Finkelstein et al., 2005; Moodie et al., 2006 and Anand and Gray,

2009). A key issue in the debate on the best method of obesity prevention is the need to

identify key points that should be considered in obesity prevention. Evaluating the

problem of obesity from a theoretical approach should lead to potential solutions by

framing the opportunity set. The paper discusses obesity as a market failure and then

discusses community-based behavioral interventions to be a potential method to prevent


4

and curb obesity. Studying obesity as at least partly a market failure based on commercial

success has potential to trigger much needed discussion to better combat obesity.

1.2 Obesity: a market failure

Using a market failure approach in health service research is not new. The market

failure concept in chronic disease markets has been used by Watts and Segal (2009). In

obesity research, childhood obesity has been described to be a “market failure” of

obesogenic-calorie dense and activity limiting products by Moodie et al. (2006). This

view has been supported by Lawrence (2009). Lawrence (2009) reported that the

expanded profile of nutrition is exploited to benefit food sales rather than public health.

Further, Swinburn (2009) pointed out that the market failure leading to obesity should be

best addressed by considering food marketing to children as a central policy choice.

Market failure exists when a free market allocation is not efficient. A free and

competitive market allocates resources efficiently. However, in the real world the

markets often are inefficient for various reasons including when there are externalities.

An externality is an unintended consequence, positive or negative, of an economic

activity born by consumers or producers. The concept of obesity as a market failure is

also explained in Moodie et al. (2006). They have argued that the market has sustained

and promoted unhealthy food choices. The current market has led to overconsumption of

food products, particularly unhealthy products. They suggest that significant government

intervention may be needed to correct this market failure.

In addition to what has been proposed by Moodie et al. (2006), an additional

argument to consider is that marketing channels and marketers are more informed than

the consumers because the industry develops sophisticated market research. As stated by


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Nelson (1970) “consumers are continually making choices among products, the

consequences of which they are but dimly aware.” This is still true. The information set

available to consumer before a food purchase decision is made is constrained by the

consumer’s socioeconomic status. Further behaviors are influenced by marketing

strategies including price, place, advertising and promotion.

There is a high social cost of excessive unhealthy food consumption. Obesity can

be considered to be a negative externality of unhealthy food market. There is evidence in

the literature on obesity being a negative externality of production, but it is less

developed. Finkelstein et al. (2005) has examined economic causes such as advancement

in technology behind the causes of obesity epidemic. This paper will discuss obesity as a

negative externality in production and consumption.

1.2.1 Negative externality in production

A negative externality in production occurs when there are costs caused by the

production of a good which are not borne by the producer. That is the private marginal

cost of production which determines the supply is below the social marginal cost. Since

the producer does not bear the full cost factor input and production are higher than the

socially optimal level of production. The effect of a negative externality in production is

described in Figure 1. The case of a negative externality in the production of unhealthy

food products is based on the fact that unhealthy food consumption is at odds with the

assumption of perfect competition, particularly full information and rational decisions

based on preferences. However, assumption of rational decision is affected by several

psychological factors, commonly studied by behavioral economists. Behavioral

economics seeks to explain common and systematic deviations from the behavior implied


6

by a fully rational economics model (Just, 2014). Hence, with increasing evidence from

behavioral economics, weight outcomes from consumption of unhealthy food can be

considered negative externality in consumption. Consumers’ lack of awareness and

irrational decisions will be discussed as resulting in a negative externality in

consumption. Moreover, producer’s role in promotion and marketing of foods that are

high in fat sugar and salt play a role and hence it becomes useful to consider obesity as an

externality in production.

Figure 1.1a Negative Externality in Production

In Figure 1.1a, the vertical and the horizontal axis are the price and quantity of

unhealthy foods that are high in fat, sugar and salt. The current competitive market

equilibrium is at 1, is the aggregate demand curve, is the marginal private cost

curve. The quantity demanded is 1 at equilibrium 1.

However, consider the social cost of 1 quantity of unhealthy food items being

produced and consumed. This social cost is not paid by producers and could be

considered to be a net cost to society. represents the supply curve considering this


7

marginal social cost. The social cost is the negative health impact of consuming excessive

amounts of food high in fat, sugar and salt. While quantifying this cost is not easy, it

shows the social cost from the production and consumption of unhealthy foods. Triangle

in the figure is the obesity burden and is the cost of the negative externality in

production.

When unhealthy foods are produced the marginal private cost of the producers

determines the supply. However, there are costs caused by the production of these

unhealthy foods, the costs that form the negative externality in production are the social

costs of production. The social costs of unhealthy food production are the costs born to

the society due to obesity for example increasing expenditure in health related issues

caused due to obesity. The economic costs of obesity which allows to estimate triangle A

have been summarized by Mccormick et al. (2007). Similarly, the consequences of

obesity in terms of annual medical costs, lifetime medical costs, nonmedical

expenditures, and occupation choice and wages have been summarized in Finkelstein et

al. (2005). These costs are born directly by the consumers but also by society. Evidence

of the costs of obesity to the society is abundant. In 2008 the national estimated cost of

obesity totaled about $147 billion (Finkelstein et al., 2009).

The negative externality in production due to unhealthy foods, unlike such

externalities in pollution for example comes only after excessive consumption of the

unhealthy food. The argument is if pollution destroying the environment is an externality

then the excessive marketing and consumption of unhealthy foods destroying the food

environment can also be considered to be externality. Hence, obesity is a market failure


8

caused both due to negative externality in production and negative externality in

consumption.

1.2.2 Negative externality in consumption

If the marginal social benefit of consumption is less than the marginal private

benefits, it is considered to be a negative externality in consumption. Smoking and

alcohol consumption has been widely explained as a case of negative externality in

consumption. A question is whether foods high in fat, sugar and salt are parallel to

smoking and alcohol consumption? A case of obesity as a negative externality in

consumption was explained in (Camerer et al., 2003). According to Camerer et al. when

consumers make errors, it imposes costs on themselves because the decisions they make

(reflected by their demand) which do not fully reflect the benefits and costs they derive.

Individual consumption also has an external effect on society. The outcomes of

individual decisions are also faced by society in the societal obesity burden (see section

1.2.1).


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1.3 Addressing the market failure

1.3.1 Taxation

In scenarios such as demonstrated in Figure 1.1a, the traditional approach is for

governments to intervene to fix the market failure. A natural response from economists

for externalities is to impose a Pigouvian tax. Using a Pigouvian tax in theory internalizes

the cost of the externality including both externality in consumption and production. The

evidence of imposing tax to reduce the negative externality of production is questionable.

As the result of a tax, consumers can shift their spending towards cheaper substitutes (see

Edwards, 2011). In general, a tax is not effective unless the benefit of the tax offsets the

cost of taxation born by consumers, producers and the tax collectors. In addition taxation

distorts behavior and reduces well-being, especially for low income families from a

regressive tax (Cawley, 2010). Sturm et al. (2010) reported that a greater impact of small

taxes could come from the dedication of the revenues they generate to other obesity

prevention efforts rather than through their direct effect on consumption. Or, at the least

taxation of soda should accompany taxing candy, ice cream and fried foods along with

subsidizing broccoli, gym membership and dental floss (Mankiw, 2010). Other studies

have looked at soda tax and its effect on BMI but have seen an effect small in magnitude

(e.g. Fletcher et al., 2010).


10

1.3.2 Behavioral interventions

An alternate or complement to taxation is applying behavioral interventions to

shift supply and demand towards increasing the demand of healthy foods and hence

creating a new supply channel to meet the increasing demand. An approach recently

being used to overcome obesity in health promotion activities is to promote healthy food

habits and physical activity.

The recent trend has been on adopting translational evidence based community-

based participatory models. Community-based participatory models have shown to be

effective in reversing the obesity trend and in disease prevention (e.g. Brownson et al.,

1996; Chou et al., 1998 and Allen et al., 2013). Other studies like Baker and Brownson

(1998), Merzel and D’Afflitti (2003), and Corda et al. (2010) including the American

Cancer Society (2012) have emphasized community-based programs which encompass

multiple interventions as the model with best potential to achieve behavior change that

will reduce person’s health risk. Also, several approaches of behavioral economics have

shown success in combating obesity trends in schools, as reported in Just and Payne

(2009).

The research concept is to shift the demand for unhealthy foods to the left and

encourage demand, supply and consumption of healthier products. This paper develops a

research framework to look at the possibility of providing information and awareness to

encourage the leftward shift in the demand of unhealthy foods. The Dietary Guidelines

for Americans 2010 recommend stronger environmental strategies for improving the

population’s eating practices, including interventions to influence food purchasing

behavior in stores (USDA-USDHH, 2010). The Food Marketing Institute (FMI) reported


11

that consumers averaged 2.2 trips per week to the supermarket in 2010 (FMI, 2011).

Hence, supermarkets play an important role in food purchasing (Glanz and Mullis, 1988).

Gittelsohn and Lee (2013) also recommended that the grocery store interventions should

be coordinated with community reinforcement to encourage awareness and to shift

purchases towards healthier products.

Figure 1.1b Leftward Shift in the Demand to Decrease Social Cost

In Figure 1.1b, an integrated effort to shift D to the new demand curve D and at

the new equilibrium 2 where the quantity demanded is 2 can in the long run decrease

the marginal social cost to . This framework shows potential to decrease the obesity

burden to triangle by intervening to shift the demand curve leftward.


12

1.4 Addressing obesity as an externality

The relative cost of changing behavior to shift the demand and supply curve is

very important. Additionally the market does not identify obesity or other health risk

from foods high in fat, sugar and salt in the production and marketing of product.

Therefore, an added dimension is needed to explain externality. As such, a two panel

diagram has been used to address obesity as an externality in Figure 1.2. Mccormick et al.

(2007) cited individuals not bearing the full cost of their health costs as a type of

externality of obesity. Moreover, the paper has cited obese people not being in

employment as an externality. The individual bears the foregone earnings, the costs of

unemployment benefit, incapacity benefit and foregone tax revenues are borne by

society. A conceptual model has been developed to look at the unhealthy food market’s

externalities.


13

Figure 1.2 Externality of High-Fat, Sugar and Salt Food Products


14

In Figure 1.2, the top panel shows the high calorie food’s market equilibrium 1.

At this equilibrium the quantity demanded is 1. The area above the supply curve and

below the demand curve left to the equilibrium is the net welfare, which is the sum of

producers’ surplus and consumers’ surplus in the current market. The new demand curve

which has shifted leftwards (decreased) due to the application of behavior change

interventions is and the equilibrium is 2. The total welfare now has decreased to the

area under and above S, and right to 2. Under the framework of negative externality

of unhealthy food market there are externalities of unhealthy food products’ market

depicted in the bottom panel of Figure1.2.

The bottom panel of figure 1.2 shows the autonomous level of externality 0 in

the form of genetic causes of obesity, other health problems caused regardless of the food

habit. The line E is the supply function of externality that is depicted by the supply of

unhealthy food products in the top panel. As awareness about the effect of the externality

increases, this function can be more elastic. However, unlike the externality effect in

production supply function, in this framework the externality is caused only when what is

produced is consumed. Similarly, the line is the externality with the new decreased

demand . Therefore, the bigger triangle B is the externality caused by 1 level of

production and consumption. The correct welfare measure then will be the welfare in top

panel minus the triangle B in bottom panel. This will give the net welfare that accounts

for obesity as externality. Similar partial equilibrium framework was developed by

Segarra et al. (1991) to develop optimal input use in agriculture.


15

Therefore the gain of decreasing the demand of foods high in fat, sugar and salt

can be measured by the gain in externality (which is a decrease in the externalities social

cost). As long as the decrease in externality effect is higher than the welfare loss in the

unhealthy foods market, collaborative approach must be applied to decrease demand of

such foods. How big is the externality effect, what we know? Mccormick et al. (2007)

have listed available estimates of the financial costs of obesity. The two primary costs

they have listed are health costs and employment costs.

The shift in demand shown in Figure 1.1b from to incurs cost. The present

basket of goods that is chosen by consumers is not necessarily the basket chosen under a

full informed decision. The decision is path dependent of the change in food system and

lifestyle behavior. There has been success in influencing this choice and demand set by

translational and community-based research. However from a policy perspective the cost

of these interventions should be less than the decrease in the externality cost. The cost of

intervention on changing the demand should be lower than or equal to decrease in

externality. The interventions are cost effective only as long as the decrease in externality

is higher than the welfare loss in the obesogenic market and the decrease of the

externality should also offset the cost of behavior change intervention. Hence it is very

crucial to develop low cost intervention that has high impact. Behavioral interventions

including the tools’ of behavioral economics have shown success and promise (Thomson

and Ravia, 2011).

An additional option is that government should intervene when market correction

is needed. However, there are cases of failed government intervention suggesting that not

all market forces can be affected by government intervention. A potential way to fix a


16

market failure could be by encouraging the market to fix itself by impacting the supply

and demand through behavioral interventions. This dissertation is based on the

conceptual framework of allowing the unhealthy food market to fix itself by intervening

in the area of behavior change to increase the demand for healthy food products which

will cause suppliers to gradually switch selling sugar loaded products by healthier

products. This will also gradually close the gap between the social and private costs of

unhealthy foods being demanded and supplied.

1.5 Problem statement and research objective

Obesity is increasingly seen as a societal problem rather than only an individual’s

problem. It is increasingly viewed as a problem resulting from the interactions of

individual behavior, genetic predisposition and environment (Hernandez et al., 2006).

The environmental factors can be influenced for individuals to improve physical activity

and eating behaviors that encourage individuals to achieve and maintain healthy weight.

One approach to the obesity epidemic is Community-based Participatory Research

(CBPR). In this type of effort, community works together towards a common goal of

obesity prevention. CBPR may be the answer to the need for practical, affordable, and

scalable intervention strategies that effectively induce weight loss and prevent weight

gain.


17

Community-based obesity prevention project

A public-private partnership for cancer prevention in rural communities was

implemented in a small West Texas community employing a multi-tiered CBPR

approach. The program was designed with potential to develop a community-based

obesity and cancer prevention model which would be feasible for adoption by small

communities. The model was unique in having a large supermarket serving the

community at the center of program activities. One potential to use the supermarket is to

nudge community’s food choice towards healthy food choices. The supermarket can be a

key part of CBPR-approach also because it serves as a community meeting place and an

information center in sparsely populated areas and in other contexts. A key to success in

the CBPR approach is to enable community residents to more actively participate in the

full effort to promote healthier living.

The three essays in this dissertation address the above discussed issues

independently, each employing its own set of methodologies. A brief description for each

of the essays is provided in the following section, the description will include the need

and appropriateness of each study in tackling the obesity epidemic through community-

based behavior change interventions.


18

1.5.1 Essay I

The first essay focuses entirely on the impact of the multi-tiered CBPR model

developed to reduce obesity risk factors by changing cancer knowledge, health attitudes

and food behavior. The objective of the CBPR project was to raise cancer knowledge,

nutrition awareness and change food behavior while building positive health attitudes to

reduce obesity and cancer risk. The model developed in this project accounts for

community heterogeneity and reinforces behavior change at multiple levels. Changing

individual’s decision making within a community context was central to this model.

The model delivered educational interventions to a small community using

several means to encourage healthy eating and healthy weight maintenance. The

interventions included articles in the local newspaper, television, public service

announcements, posters at community sites, presentations to community groups and the

supermarket. Earlier research has shown that the effect of exercise, smoking, occupation,

and race vary by sizeable amounts from high to low BMI quantiles (Belasco et al., 2012).

The impact of this model was analyzed using health promotion impact evaluation

methods.

1.5.2 Essay II

The second essay is on the effect of the project including supermarket

interventions on food purchases. Since the local supermarket is the largest source of daily

food needs it can be an excellent source of information and reinforcement for healthy

behavior. Earlier research on behavioral change has reported reinforcements for behavior

change that include different aspects of the environment are more effective than simple

reinforcement methods. Nudges in the form of shelf talkers were placed on relatively


19

healthier items in a particular food category to promote healthy food choice decision. The

supermarket was used to: 1) conduct healthy food demonstrations, 2) put shelf talkers on

the comparatively healthy food product, 3) access food purchase data to analyze purchase

behavior as affected by project intervention, and 4) provide an active site of information

flow including posters on healthy eating that were placed in the store. Thus, the

supermarket was used to nudge participants towards healthy food behavior. The

outcomes of this supermarket intervention were evaluated using comparative analysis.

1.5.3 Essay III

The third essay focuses on the development of a cohesive and assimilative

research paper on the learnings from community-based behavior change interventions. A

systematic review and meta-analysis of research on community-based behavior change

interventions implemented for obesity prevention was done to this end. An assimilation

of literature on impact evaluation of community-based interventions designed to address

obesity has potential in guiding the design, implementation, and evaluation of future

community-based behavior change interventions.


20

CHAPTER II

THE IMPACT OF A MULTI-TIERED COMMUNITY-BASED OBESITY AND

CANCER PREVENTION PROGRAM IN A SMALL COMMUNITY

Abstract

There is an increasing interest and effort in reducing obesity but relatively few of these

efforts focus on small communities. A multi-tiered model to reduce obesity risk factors

was developed and implemented to change food behavior and health awareness in a small

community over a one year period. This paper evaluates the project outcomes based on

the following: 1) mean and frequency comparisons of self-reported health behavior and

attitude response before and after the intervention, 2) intervention effects on cancer

knowledge score, and 3) intervention effects on body mass index (BMI). Findings show a

significant increase in obesity as cancer risk awareness and food nutrition awareness as

well as a significant intervention effect on cancer knowledge score. This increased food

nutrition awareness should lead to reduced obesity over time as indicated by a regression

analysis of obesity risk factors on BMI. Overall, this adds to evidence that community-

based interventions such as these have potential in bringing long term change in obesity

and awareness towards cancer inducing behaviors.

Key words: body mass index (BMI), cancer risk, food behavior, health education, multi-

tier approach, obesity, small community


21

2.1 Introduction

The prevalence of overweight and obesity in the United States continues to be

high. Ogden et al. (2012) have tracked the prevalence of obesity among the US adults.

According to the report there was no change in the prevalence of obesity among adults or

children from 2007-2008 to 2009-2010. Obesity is also among the leading modifiable

behavior risk for morbidity, mortality and disability in the Americans (Mokdad et al.,

2004). Obesity also causes heart disease and cancer which are also among the top five

preventable causes of death (Yoon, 2014). The prevalence of obesity is now not only in

developed countries but includes emerging economies like China and India that have

reported increased prevalence of obesity. For example, the overweight rate in India

increased by 20% between 1998 and 2005 (Sinha, 2010). The prevalence of obesity and

overweight is increasing to epidemic proportions at an alarming rate in China (Gao et al.,

2011 and Shang et al., 2012).

There is relatively little evidence available in the obesity literature that shows

programs developed and tested to bring healthful obesity prevention services to small

communities and limited success is seen in larger communities. Programs developed and

tested in the past (e.g. Rodgers et al., 1994 and Economos et al., 2007) have been located

in larger urban areas. Interventions with even a moderate success would be an

improvement in small community context. In addition, a key benefit of working in a

small community is the ability to control the media and food environment. Also the

overall community context is much easier, requires fewer actors and much lower cost.

Moreover, innovative public approaches including a variety of environmental initiatives

designed to increase fruit and vegetable consumption have been recommended by


22

previous research in obesity prevention (Giang et al., 2008 and Flegal et al., 2012).

Gittelsohn and Lee (2013) also recommended that the grocery store interventions should

be coordinated with community reinforcement to encourage awareness and to hopefully

shift purchase to healthier products.

Obesity has been found to be associated with certain socioeconomic status (SES).

Though there are individual variation, there is a predictable pattern (Swinburn,

2011).There is a significant variation in obesity trend by race and ethnicity (Flegal et al.,

2012). Researchers have also found that residents in small communities are more obese

than their urban counterparts (Blankenau, 2009). Similarly, an individual’s SES affects

timely access to health messages and health care services. The American Cancer Society

has outlined that uninsured patients from ethnic minorities are substantially more likely to

be diagnosed with cancer at a later stage when treatment is extensive and costly

(Gosschalk and Carozza, 2010; Philips et al., 2011and Gong et al., 2012).

A multi-tiered community-based obesity prevention project was launched in

June of 2011 in a small community in West Texas. The objective of the project was to

raise nutrition awareness and reduce obesity induced health risks by changing food

behavior. Multiple community-based and reinforced efforts were used to provide

healthier food and activity choice information and improve health and nutrition

awareness. In particular, the supermarket, given its relatively prominent role in the

community and lack of choice in the community, provided a means to promote healthy

food choices. This paper evaluates the success of this approach and analyzes its outcome

by comparing food behavior and attitude before and after the project and by analyzing the

intervention effect on cancer knowledge and BMI.


23

2.2 The project concept and model

Research has shown that the effect of exercise, smoking, occupation, and race

vary by sizeable amounts from high to low BMI-quantiles (Belasco et al., 2012).

Research has also shown that, in the US, higher BMI is ubiquitous for groups with lower

SES and specially minorities (Clarke et al., 2009 and Grabner, 2012). Communities with

a high percentage of minorities are generally medically underserved and have high rates

of disease incidence (Gosschalk and Carozza, 2010 and Gong et al., 2012).

To address this concern, a multi-tiered behavior change model was developed to

account for community heterogeneity and reinforce behavior change at multiple levels.

The concept of changing individual’s decision making within a community context was

central to this behavior change project model. The model delivered educational

interventions using several means to encourage healthy eating and healthy weight

maintenance. This approach included articles in the local newspaper, television, posters at

community sites, presentations to community groups and the supermarket.

Identifying local community leaders was another important component of this

model as suggested by Macaulay et al. (1998), Corda et al. (2010), and Hystad and

Carpiano (2012). As such, community leaders participated in the project advisory board.

Community leaders included leaders from the local school district, churches, city and

county government, extension service, media (local newspaper and television station),

and community organizations, such as 4-H clubs, the Boy and Girl Scouts, and the local

senior center, along with the manager of the local supermarket. These all participated as

local community leaders during the implementation of this project. We have described


24

this supermarket based community-based multi-tiered model more fully in McCool et al.

(2013).

In regards to the primary goal of individual behavior change this research model

can be related to behavior change models like the Trans-theoretical Model (TM). Stages

of change are central in TM where views and behavior changes through a process over

time, as individuals move from pre-contemplation stage to maintenance stage

(Zimmerman et al., 2000 and Taylor et al., 2004). The underlying research objective is

attained when individuals believe that benefits of performing a behavior outweigh its

costs and they are persuaded to change their behavior reinforced by the multi-tiered

approach.

2.3 Review of literature

2.3.1 Obesity and obesity in the US

2.3.1.1 Obesity and overweight

Overweight and obesity are both labels for ranges of weight that are greater than

what is generally considered healthy for a given height (CDC, 2013). Overweight and

obesity ranges are determined by using weight and height to calculate a number called

“body mass index (BMI).” BMI is a measure of an adult’s weight in relation to an

individual’s height and is calculated with this formula:

( )

An individual is considered obese when BMI is more than 30, overweight when

BMI ranges from 25 to 30, normal when BMI is less than 25, and a person with a BMI

under 18.5 is considered underweight. This range was used throughout this research

regardless of sex. The National Heart, Lung and Blood Institute guidelines also


25

recommends using other two predictors: a) waist circumference (WC) because abdominal

fat is a predictor of risk of obesity-related diseases, and b) other risk factors like high

blood pressure and physical inactivity. WC has also been found to be more strongly

associated with morbidity and mortality than BMI ( Janssen et al., 2004 and Brown, 2009

and Seidell, 2011). However, despite the American Heart Association’s only recent

endorsement of both BMI and WC as primary tools for assessing adiposity, waist

circumference is less commonly used than BMI. Wannamethee et al. (2010) showed that

the association between both WC and BMI and cardio-respiratory fitness is gender

dependent. Moreover, there is less evidence in the literatures about the use of WC as a

single outcome variable. BMI is still used by the Center for Disease Control and

Prevention as a screening tool for its reliability, ease-to-perform and its inexpensive

nature (CDC, 2013). As such, BMI was used in this research as the primary outcome

variable. Changes in WC were analyzed but not as the primary outcome variable.

2.3.1.2 Obesity in the United States

The prevalence of overweight and obesity in United States continues to be high.

According to the Center for Disease Control and Prevention (CDC, 2012), percent of

adults (age 20 years and over) who were obese was 35.9%, and the same group who were

overweight, including obesity was 69.2% (2009 to 2010). Ogden et al. (2012) have

tracked the prevalence of obesity among US adults. According to the report, there was no

change in the prevalence of obesity among adults or children from 2007-2008 to 2009-

2010. Obesity is among the leading modifiable behavior risk for morbidity, mortality and

disability in Americans (Mokdad et al., 2004). Obese individual have a 50 percent higher

risk of dying from cancer than their healthy counterparts (ACS, 2012). Calle et al. (1999)


26

reported that the risk of death from causes such as cardiovascular disease, cancer, or other

diseases increased as BMI increased above the healthiest range of 23.5 to 24.9 in men

and 22.0 to 23.4 in women from a 14-year study of a million person cohorts. A study

using National Cancer Institute (NCI) Surveillance, Epidemiology and End Results

(SEER) data estimated that in 2007, about 34,000 new cases of cancer in men (4%) and

50,500 in women (7%) were due to obesity (NCI, 2013). Figure 2.1 shows the self-

reported obesity among US adults, 25 to 30% on average are obese.

Figure 2.1 Self-reported Obesity among US Adults (BRFSS, 2012)

The prevalence of obesity is now not only in developed countries but includes

emerging economies like China and India that have reported increased prevalence of

obesity. The overweight rate in India increased by 20% between 1998 and 2005 (Sinha,

2010). The prevalence of obesity and overweight is increasing to epidemic proportions at

an alarming rate in China (Gao et al., 2011 and Shang et al., 2012). Swinburn (2011)


27

reported the global obesity pandemic to be the outcome of the changes in global food

system.

2.3.2 Obesity and cancer

2.3.2.1 Evidence of obesity and cancer

Numerous problems are associated with obesity including cardiovascular disease,

cancer, lost physical activity and active life span etc. This research focuses primarily on

obesity as a cause of preventable cancer. Sturm (2002) has stated that obesity roughly has

the same association with chronic health conditions as does twenty years’ aging and that

obesity is associated with 36% increase in inpatient and outpatient spending and a 77%

increase in medications. In an extensive research, more than 900,000 U.S. adults who

were free from cancer at enrollment in 1982 were tracked (Calle et al., 2003). There were

57,145 deaths due to cancer during 16 years of follow-up. This research concluded that

increased body weight was associated with increased death rates for all cancers combined

and for cancers at multiple specific sites. Bianchini et al. (2002) also showed that excess

body weight is directly associated with risk of cancer at several organ sites, including

colon, breast, endometrium, esophagus, and kidney. The AICR (2007) also found

convincing evidence of association between obesity and cancers of the esophagus,

pancreas, colon and rectum, breast, endometrium, and kidney and a probable association

between obesity and gall bladder cancer. Similarly, Guh et al. (2009) confirmed direct

associations between obesity and cancers of the breast, colon and rectum, endometrium,

esophagus, kidney, ovary, and pancreas using a systematic review and meta-analysis.


28

2.3.2.2 Epidemiology

The American Cancer Society has reported that 1 out of every 3 cancer deaths in

the US is linked to excess body fat, poor nutrition and physical inactivity. These are

related and may contribute to cancer risk. Body fat alone contributes to 1 in 5 cancer

death (ACS, 2012). The mechanisms by which obesity induces or promotes tumor-

genesis vary by cancer site. Calle et al. (2003) listed insulin resistance and resultant

chronic level of insulin circulating in the blood relative to glucose, increased bio-

availability of steroid hormones and localized inflammation as the causes for cancer due

to obesity.

Sturm (2002) suggested that despite the consequences of addictive behavior such

as smoking and drinking alcohol is less costly than obesity the former has received more

attention in clinical practice and public health policy. In a study of expected years of life

lost in six preventable cancers (EYLL), Liu et al. (2013) concluded that potential life

years saved by successful prevention would be substantial for lung cancer, breast cancer

and colorectal cancer. According to this report, when considering the annual incidence in

2012, lung cancer would cause the greatest subtotal of EYLL (3,116,000 years) followed

by female breast cancer (1,420,000 years) and colorectal cancer (932,000 years).

2.3.3 Behavior

2.3.3.1 Behavior and cancer

The literature on the potential of behavioral/environmental modification in

preventing cancer cases date back to early 1980s. A classic study by Doll and Peto (1981)

cited by Gotay (2004) concluded that 75% to 80% of cancers in US could have been

avoided and were a result of tobacco and alcohol, physical inactivity, diet and biologic


29

factors and occupational and environmental exposures. Research in this area has

recommended weight control and physical activity through healthy lifestyle, low body

weight maintenance and exercising most days of the week for cancer prevention

(Bianchini et al., 2002). Similarly a systematic review done in 2010 has concluded that

sedentary behavior is ubiquitous in contemporary society and has recommended research

on cancer and sedentary behavior to be a priority (Lynch, 2010). Further, the study

concluded that reducing sedentary behavior may be a viable new cancer control strategy.

In this line, according to Klein et al. (2014) behavioral research can address a wide

variety of key processes and outcomes across the cancer control continuum.

Behavior change requires an understanding of how behaviors are learned and

maintained. Available theories of health behaviors are presented in several publications

(e.g. Anderson, 2004 and Shumaker et al., 2009). Avery (2012) concluded from a

systematic review that interventions improved diet more effectively if they were

informed by behavior theory. Gotay (2004) has briefed that theories of health behavior

are based on the premise that different kinds of variables interact to affect behavior, and

modifying these variables and relationships among them can lead to behavior change.

The factors that affect behavior include individual differences such as knowledge,

attitudes, previous experience and personal preference, the social environment, and the

larger community.

2.3.3.2 Food behavior

Numerous studies have shown that food behavior is associated with obesity and

cancer: Ludwig et al. (2001) showed a positive association of obesity and cancer risk

with soft drink consumption and Epstein et al. (2001) showed an inverse relation with


30

increased fruit and vegetable consumption. Similarly, Nicklas et al. (1993) showed an

inverse relation with eating breakfast. Jeffery and French (1998) examined the

association between TV viewing, fast food eating, and BMI. They concluded that TV

viewing hours and fast food meals were positively associated with increased energy

intake and BMI.

2.3.3.3 Theories of behavior change

The underlying conclusion of research (Avery et al., 2012) related to behavior and

obesity is that health outcomes are dependent on decisions guiding behavior. The bases

for understanding the decision variables are behavior or social science theories or

conceptual models. The theories most generally used in obesity prevention are

(Baranowski et al., 2003):

1. Knowledge-Attitude-Behavior Model

2. Behavioral Learning Theory

3. Health Belief Model

4. Social Cognitive Theory

5. Theory of Reasoned Action or Theory of Planned Behavior

6. Trans-theoretical Model and Stages of Change

7. Social Marketing

Significant misconceptions related to cancer causation, symptoms, and treatment

including expressed feelings of little preventative control of the disease were reported by

Carpenter and Colwell (1995). They concluded that increased knowledge is associated

with increased self-efficacy for cancer screening. Similarly, Weinrich et al. (1998)

documented the importance of providing educational programs to increase participation


31

in prostate cancer screening. Additionally the theory of rational addiction has been used

to justify the lowering of BMI levels of already overweight and obese population by

increasing education level in Miljkovic et al. (2008).

2.3.4 Community-based participatory research (CBPR)

Substantial research has been reported on the effects of community-based

participatory research. Brownson et al. (1996) conducted a study to determine whether a

community-based risk reduction project affected behavioral risk factors for

cardiovascular diseases. They concluded that community-based interventions show

promise in reducing self-reported risk of cardiovascular disease within a relatively brief

period. The success was observed with modest resources.

Chou et al. (1998) reported significant reduction in cigarette use at the initial

follow-up (6 months) and alcohol use at the first 2 follow-ups (up to 1.5 years) in a study

that investigated the secondary prevention effects of a substance abuse primary

prevention program. This study concluded that primary preventions are effective in

reaching and influencing high risk adolescents in a non-stigmatizing manner. Similarly,

Leung et al. (2004) recommended CBPR as a framework that can be applied to gain a

better understanding of the social context in which disease outcomes occur.

Loughlin et al. (1999) reported mixed outcomes of CBPR from a study that

evaluated a four year community-based cardiovascular disease prevention program

among adults aged 18 to 65 years (living in St-Henri, a low-income inner-city

neighborhood in Montreal, Quebec). Though very few community wide program effects

were observed in the study, they reported that several interventions showed promise in

terms of community penetration and impact.


32

Allen et al. (2013) reported CBPR to be a feasible intervention in positively

improving parenting contributors to reduce adolescent substance use. This study was

done to assess the feasibility of a family-skills training intervention developed using a

CBPR framework. This study also explored parental traditional values as a modifier of

preliminary effects.

2.3.4.1 Community interventions measuring BMI as the outcome variable

Economos et al. (2007) has reported the finding of a community intervention to

reduce the BMI z-score in children. A community-based environmental change

intervention was conducted in three culturally diverse urban cities of Massachusetts with

one intervention community (Sommerville) and two control communities with similar

demography. This intervention included many groups and individuals within the

community (including children, parents, teachers, school food service providers, city

departments, policy makers, healthcare providers, before and after school programs,

restaurants, and the media). They concluded that a community-based environmental

change intervention decreased BMI z-scores in children at high risk for obesity.

Similarly, Economos et al. (2009) reported lessons for working with restaurants to

improve health from working with community to improve availability of healthy menu

options in Somerville, Massachusetts.

Taylor et al. (2007) reported the findings of a similar research done to determine

the effectiveness of a two year community-based intervention to prevent excessive

weight gain in five to twelve years olds. They concluded that providing activity

coordinators and basic nutrition education in schools can reduce the rate of excessive

weight gain. Similarly, other studies with BMI as the outcome variable have shown


33

association between BMI and life-style factors. Kamycheva et al. (2003) reported

negative association between physical activity, smoking and BMI and a positive

association between BMI and coffee.

Using BMI as dependent variable in an ordinary least square regression done with

employing the 1984-1999 BRFSS data, (augmented with state level measures pertaining

to the per capita number of fast food and full service restaurants, the prices of a meal in

each type of restaurant, food consumed at home, cigarettes, and alcohol, and clean indoor

air laws), Chou (2004) reported three major findings: a) a large positive effects associated

with the per capita number of restaurants and the importance of trends in this variable in

explaining the stability of obesity between 1960 and 1978 and the increase since 1978, b)

a downward trend in food prices which than account for part of the upward trend in

weight outcomes, and c) a positive cigarette price effect on cigarette consumption.

2.4 Conceptual framework

Prevention depends on individual behavior change embedded in a social context.

The research concern is to develop, implement, and evaluate a program that addresses

primary cancer prevention in small community where the population currently has

limited access to proper nutritional choices, health care, and educational programs on risk

factors for cancer. An added benefit of working in small community is the ability to

control media and overall community context. Further, working in small community

requires fewer actors and much lower intervention costs. Studies such as these can be

cost effective.


34

The issue of access to healthy foods has been central to the work of many

community-based organizations around the country. Researchers have also shown that

only providing health information to people is an insufficient strategy to promote healthy

eating (e.g. Vaandrager and Koelen, 1997). A challenge in cancer prevention is to

achieve long lasting behavior change. A strategy to improve health behavior, including

improved dietary intake and activity level is needed as shown in Figure 2.2. Despite

people’s general awareness of the role of diet in their overall health, it has not led to

improved eating habits. Food purchases and consumption depend not only on the

individual, but also on social, cultural, and environmental factors (Loewenstein, 2000 and

Cohen and Babey, 2012).

Figure 2.2 Norms for Cancer Prevention


35

2.4.1 The mediating variable model

A theory that provides a framework for intervention design and how the

intervention works is the mediating variable model (Baranowski et al., 2003). This model

is a cause and effect sequence between an intervention and an outcome. According to the

perspective of mediating variable model, the independent (demographic) variable

influences the mediator variable. The mediating variable in this research are the

categorical outcome variables: health attitude, cancer knowledge and food behavior. The

change in the mediating variables leads to change in anthropometric measurements like

BMI and WC, common desirable outcome variables. The more strongly the mediating

variable is related to the outcome variable the greater the impact of the intervention

(Baranowski et al., 1997 and Baranowski et al., 1998). Changes in obesity status are

expected to occur as a result of changes in mediating variables that influence food and

health behavior decisions. The mediating variable comes from the theories or models

used to understand behavior. The model is shown in Figure 2.3.

Figure 2.3 Mediating Variable Model

Mediating variable

(Health attitudes, food behaviors, cancer knowledge)

Dependent variable (BMI) Independent variable

(Education, SES, age etc.)


36

2.5 Methods

2.5.1 Data

Two small communities Muleshoe, TX and Dalhart, TX were selected as project

intervention and control sites respectively, based on their similar demographics.

Muleshoe was selected as the intervention site as this small community met the criteria

for implementing community-based research. A required element in community-based

research is an established and ongoing relationship between key community-based

organizations and the research group (Leung et al., 2004). Analysis was done both under

the assumption that there is no systematic difference due to the pre-treatment variable,

and then when this assumption or no significant difference is relaxed. The average

poverty rate in these communities ranged from 9 to 23% with more than one-third of the

populations being Hispanic. The poverty rate in Dalhart was 9-15%. The population of

Muleshoe was about 4,571 with 17-50% of the population estimated to be Hispanic. The

poverty rate of Muleshoe is 17-23% (TTUHSC, 2010). The project sites are shown in

Figure 2.4.

A multi-tiered behavior reinforcing model was implemented in Muleshoe from

June of 2011 to December of 2012, the intervention site. The data for this section were

drawn from the data collected in five independent surveys: two in the control site and

three in the intervention site. First, a baseline (June of 2011) and second, a post-

intervention survey (June of 2012) was conducted at both the control and the intervention

sites. A third, follow-up survey (January, 2013) was conducted only in the intervention

site to test for sustainability of changes guided by the RE-AIM (Reach, Effectiveness,

Adoption, Implementation, and Maintenance) framework (Glasgow et al., 1999).


37

Figure 2.4 Project Intervention and Control Site

There were a grand total of 1,132 respondents in the three surveys. Inclusion

criteria for participation were individuals who were at least 18 years of age, and self-

identified as living in the communities of Muleshoe and Dalhart. They were invited to

participate by mail sent out to a random sample of local population drawn from the local

telephone book. The average show up rate was 22.3%. This random sample was

supplemented by participants invited through flyers and pamphlets distributed in public

areas and printed in local newspapers. The comparison of random mailed participants

(referred as invitees) and the participants recruited through flyers and pamphlets (referred

as volunteers) is shown in Appendix 2.2 through 2.5. Similar recruitment with mail

survey was also done by Crawford et al. (2000). During 2011 survey, there were a total of

382 respondents: 225 from Muleshoe, and 157 from Dalhart. The total participant number

increased to 550 during 2012 survey: 335 from Muleshoe and 215 from Dalhart. Of the

total in Muleshoe and Dalhart, 69 and 39 of them were two year participants respectively.

City of Dalhart (Control)

Hartley and Dallam County

39.4% obesity rate (2011)

6.75% Change (2001 -2009)

City of Muleshoe (Intervention)

Bailey County

41.6% obesity rate (2011)

6.5% Change (2001 -2009)


38

During the 2013 survey, there were a total of 204 respondents, all in Muleshoe.

Of the 204 respondents, 102 respondents had participated either in the 2011 or the 2012

survey. The third survey in the intervention site was used for follow-up effects of the

intervention. All the study protocols were approved by the Texas Tech University

Institutional Review Board. The same sets of activities were conducted during the three

surveys. Anthropometric measurement data were collected and the participants completed

a health assessment survey. Detail of survey respondents and their overlap in between

surveys are shown in Figure 2.5.

Figure 2.5 Survey Respondents


39

2.5.1.1 Anthropometric measurements

Height, weight, and waist circumference along with demographic information

were taken by trained staff. Participants were given a Project Participation Measurement

Information Form for their reference. Height was measured using a stadiometer

(HM200P PortStad, Portable Stadiometer-WMFS) and weight was measured using a

precision scale (UC-321 Pro FIT Precision Scale, 350 lbs capacity). The stadiometer was

assembled with stabilizer pieces against the wall for the most accurate measurement.

Participants were then asked to remove their shoes and place their heels against the base

of the stadiometer where their height was taken in inches, rounded to the nearest 1/8 inch.

Participants were also asked to remove their jackets and any heavy objects in their

pockets prior to measuring their weight using the precision scale.

Measurements were taken in pounds, rounded to one decimal place. The

participant’s WC was measured using a measure tape (QM2000 Measure Mate – Girth

and Linear Measure Tape CM/IN). We asked participants to lift their shirts to expose

their abdomen region. The measure tape was wrapped around the top of their hip bone,

parallel to the ground (CDC, 2011). For our statistical testing, the measurements were

later converted to meters and kilograms. BMI was computed based on recorded height

and weight. BMI was calculated by dividing the participant’s weight (in kilograms) by

their height (in meters) squared (CDC, 2011). BMI was used to classify individuals into

four main weight categories: underweight (<18.5), ideal (18.5-25), overweight (25-30),

and obese (>30).


40

2.5.1.2 The health assessment survey

Self-reported survey questionnaires consisting of two part health assessment

survey were given. The health assessment survey had two parts: 1) Nutrition and Health

Practices Survey, and 2) AMI-HI Fitness Inventory. All the surveys were available in

English and Spanish. A Spanish translator was always available during the surveys. The

first part focused on the participant’s health practices, attitudes and perceptions of cancer

and cancer risk factors and individual dining practices. The demographic questions in the

survey were drafted from the BRFSS 2010 Survey (questions 12.6, 12.7, 12.8, 12.10)

(CDC, BRFSS Questionnaire, 2010). The second part, AIM-HI Fitness Inventory was

created by the American Academy of Family Physicians (AAFP, 2009). This part focused

on the participant’s level of interest in changing food behaviors or their physical activity

levels and dietary intake. The AIM-HI fitness survey was developed and validated by the

American in Motion-Healthy Interventions (AIM-HI) research study, conducted by the

AAFP. All sets of questionnaires were pretested with similar respondents to the intended

survey recipients.

The questionnaire had multiple choice, Likert scale and open ended questions

under the following sub headings:

1. Beliefs regarding cancer - Eight cancer causing factors were identified. Beliefs

regarding overweight and obesity as a cancer causing factor were used as proxy to

changed knowledge.

2. Health practices - Eight questions regarding current health practices with regards to

smoking, snuffing and tanning habits were asked as proxy to cancer prevention behavior.


41

3. Socio-demographics - Marital status, number of people in household, education level,

and income range were included. Respondents’ age, gender, ethnicity and language were

collected in a different worksheet along with anthropometric measurements.

4. Activity level - Several indicator questions like number of TV hours watched, yard

work hours etc. were collected.

5. Food behavior - This included several proxies for food behavior including

consumption of high fat food, number of sugary drink and desserts consumed per day,

number of meals eaten in fast food restaurant, number of home prepared meals, and self-

identification of awareness about different food’s nutritional value, self-weight

perception and interest in finding ways to reduce cancer risk.

6. Emotional health - Questions were designed to understand if poor mental and physical

health and stress or depression obstructed usual activity and if participation in some form

of spiritual or cultural activity provided emotional strength to the respondent.

In addition, respondents had an option to select their current attitude and their

degree of openness to change from a close ended multiple choice question on activity

level, food behavior and emotional health. Beliefs, attitude and nutrition knowledge were

considered as mediating or intermediate variables to study the intervention effectiveness

in addition to other direct change variable such as supermarket sales (Lockwood et al.,

2010). Such variables are referred to as psychosocial measures. Psychosocial measures

are considered to be mediating factors for dietary change (MacKinnon and Dwyer, 1993;

Hansen and McNeal, 1996; Baranowski et al., 1997 and Framson et al., 2009). Self-

reported behavioral outcomes have been selected to use for their ease of interpretation

and to reflect an intervention specified goal. They are the most often used basis for


42

intervention evaluation (Kristal and Ollberding, 2012).The primary weakness of using the

self-reported behavioral outcome is that the respondents exposed to intervention may bias

their reports of behavior to exaggerate true behavior change (Baranowski et al., 1997 and

Caan et al., 2004). Summary statistics of all categorical variables from 1132 respondents

are shown in Appendix 2.1 and the summary statistics of continuous variables are shown

in the results section.

2.5.1.3 Project participation survey

There was a separate questionnaire for project participation in the post-

intervention and follow-up survey. This questionnaire collected data on the attitude of

program participants towards the project activities. After data collection all participants

were given educational materials regarding the risk factor for cancer including

information about obesity and nutrition.

2.5.1.4 Missing data

Missing data was dealt by imputation or dropping the observation with missing

values as suggested by (Malhotra, 2007). Imputation was done for observations with

missing demographic information like marital status, income education, number of

people in household, number of home prepared meals, and number of meals in a fast food

restaurant. There were 87 questionnaires with missing data during 2011 survey. During

2012 all questionnaire were checked before collection and respondent were asked to fill if

any response were missing. There were only two questionnaires with missing data in

2012. Two questionnaires in 2012 and one questionnaire from 2011 survey was

completely blank. The blank questionnaires were deleted before imputation. Other


43

missing data were arbitrary. Missing data imputation was done using SAS (2008), V 9.2,

SAS Institute Inc.

2.5.2 Study designs

As explained in Section 2.5.1, there were respondents who participated in one,

two or three surveys. Due to this, one standard statistical design could not be used in

testing the hypothesis. The statistical design had to be associated with the respondent’s

nature of participation. To accomplish accurate statistical comparison the available data

sets allowed the following three evaluation designs: 1) Experimental two group pretest-

posttest design, 2) One shot case study, and 3) One group pretest-posttest design. These

designs were feasible for the evaluation and statistical comparison of findings according

to Doyle and Ward (2001). The respondent pool was sub-categorized to fit in their

respective study design to allow accurate comparison. The designs are explained in short

as follows:


44

1. Experimental: two group pretest-posttest design

This design was used to compare the responses from the intervention and control

site for the sub group of respondents who participated in both the pre- and post-

intervention survey.

2. One-shot case study

This design was used for the sub group of respondents who participated only in

the follow-up survey in the intervention community.

3. One-group pretest-posttest design

This design was used to compare the response of respondents who participated in

both the pre- and post-intervention survey in the intervention community.

Random selection and assigments to

groups

Intervention group

Pretest Intervention Posttest

Random selection and assigments to

groups

Control group


Intervention Posttest



45

The response from the sub group of respondent who do not fit in the study designs

above are the respondents who participated in only one among the pre-, post-, and follow-

up survey. This group was used to measure the community effect. The community effect

group was used to analyze the effect of project activities outside the health and food

information provided during the survey. This group was also used to isolate the natural

effect by comparing the change between the intervention and the control community

within the community effect group. The first year respondents in the study designs

explained above are also included in the community group for the first survey analysis.

2.5.3 Data analysis

This analysis enables the evaluation of the success of the multi-tiered community-

based approach and analyzes its outcome by computing project impact through changes

in awareness and behavior before and after the project as well as by finding the

intervention effect on cancer knowledge and BMI.

2.5.3.1 Project impact and sustainability through group comparisons

Beliefs, attitude and nutrition knowledge are necessary variables to study

intervention effectiveness. These variables have been used to study the intervention

effectiveness and to conduct impact evaluation of community-based behavior change

interventions for obesity prevention studies (e.g. Sacher et al., 2010a; Nguyen et al., 2012

and Rogers et al., 2013). The variables used in this part of data analysis are:

1. Cancer risk awareness

2. Cancer risk behaviors including nutrition awareness

3. Food behavior and other cancer risk behavior


46

The above listed categorical variables were measured in both intervention and

control group during all surveys. The degree to which the group changed their behavior

was compared and analyzed for change and statistical significance. Descriptive statistics,

means and frequency procedures followed by comparison in outcome under each study

design was done. Appropriate test of significance among paired t-test, independent

sample t-test, McNemar’s test and Chi-square test was applied to test the change in the

categorical variable listed above as suitable to each study designs listed in Section 2.5.2.

A paired group comparison was done for paired group within the two group pre-, post-

test design. The p value for categorical variables (variable of interest) for paired group

comparison is from McNemar’s test for proportion for dichotomous response variable. In

cases where the response was not dichotomous variable, the responses were merged to

create dichotomous response.

An important component of the research was to ensure that there is no relapse.

Suitable group comparisons were done to assess the sustainability effect using

comparative analysis from the third year data from the intervention site. A Chi-square test

was done for testing the change in categorical variables in the community group

(community group was described earlier in Section 2.5.2). A Chi-square test for

proportion was done to compare the change between pre- and post-intervention groups

with dichotomous variable used to test sustainability.

Similar methods have been used to find the impact of community-based

interventions in the literature. For example, Goldfinger (2008) and Shaibi et al. (2012)

used a paired t-test for continuous variable for comparing pre-, post-intervention group.

Balcazar (2009a) used a t-test to compare an intervention group with a control group.


47

Crawford et al. (2000) used Chi-square test to examine the change in an independent

sample with categorical variables.

2.5.3.2 The simple intervention effect on cancer knowledge score

The method of finding simple intervention effect is finding the difference between

the change in intervention group minus the change in control (or alternative treatment)

group. This is the best and most comprehensible method (Naresh, 2007 and Kristal and

Ollberding, 2012):

( ) ( )

where, is the variable of interest. Subscript and refer respectively to baseline and

follow-up and subscript and refer to intervention and control respectively. Further,

the statistical test of whether or not the intervention effect is different from zero is based

on the standard error of the intervention effect which is defined in the equation below

(Kristal and Ollberding, 2012),

( ( b f)i

i ( b f)c

c)

where, i is the sample size in the intervention group, and c is the sample size in the

control group.

The simple intervention effect was computed while correcting for confounding

variables. The intervention effect is best estimated after adjustment for characteristics that

are associated with outcome variable to overcome randomization errors and increase

statistical power (Kristal and Ollberding, 2012). According to Kristal and Ollberding

(2012) the best approach is to build a regression model with the change from baseline to

follow-up in the outcome measure as the dependent variable. The covariates are the

baseline value of the outcome variable and other confounding variables. A method also


48

followed by Kristal et al. (2000) is used to find the simple intervention effect with control

for confounding variables. Analysis of the effects of project intervention were based on

adjusted multiple linear regression model. The dependent variable is the change in cancer

knowledge score. The cancer knowledge score was calculated from the cancer knowledge

questions and serves to quantify cancer risk awareness. Cancer knowledge score is the

sum of affirmative responses to the cancer knowledge question set (see Appendix 2.6)

where there were five questions. Hence, the possible range of cancer knowledge score

was 0 to 5. The regression model consists of an indicator variable in the right arm, which

is the intervention effect. The model used is as follows:

i i 1 i 2 i 3 i i 5 i

i 7 i 8 i 9 i 10 i 11 i i

where, the individual intercept term i, is an unobservable effect from factors outside the

scope of survey and project, ij refers to the change in cancer knowledge score from

before intervention to after intervention for individual in time is dummy for

language (1= english, 0=spanish), is marital status, is cancer survivor dummy

(1=if the respondent was a cancer survivor, 0 otherwise), is dummy for overweight

(1=if the respondent is overweight, 0 otherwise), is the independent variable

representing intervention group (1=intervention, 0=control) and is the variable of interest.

Results for this analysis are given both with and without adjustments for the following

covariates: language, marital status, education, income, age, sex, race, cancer survivor,

and weight status. The computation was done using SAS Enterprise Guide 5.1, SAS

Institute Inc.


49

The focus of the project was on changing attitude towards food behavior and the

risk perception towards obesity as a cause of cancer with attitude and perception as

mediating variables. The target variable for overall change by the mediating variables is

BMI, so now we turn to the effect of the intervention on BMI.

2.5.3.3 Effect of intervention on BMI

Body mass index (BMI) was calculated from the data available on height and

weight of respondents from all the survey. The BMI expression explained in Section 2.3,

which is the standard BMI expression for adults, was used to compute BMI from the

available height and weight data. The mean BMI for total respondents during each survey

in control community was compared against mean BMI during each survey in

intervention community. A trend observation was done. BMI comparison was done

separately for male and female respondents.

The overall objective of the project was to improve attitudes and behaviors

towards increased obesity prevention. To find the effect of intervention on BMI, a

multiple linear regression was done. Similar methods have been used to assess the effect

on fruit and vegetable consumption, self-reported, and psychological health of an

introduction of large scale food retailing study in a deprived Scottish community by

Cummins et al. (2005).This analysis enables the evaluation of the success of the multi-

tiered community-based approach by computing the intervention effect on BMI. BMI is

partially determined by a set of covariates, which is defined in the model below:


50

ij i 1 ij 2 ij 3 ij ij ij

where, ij refers to the body mass index for individual in time ; is the

demographic variables such as race, gender, age, age2. is the location dummy

(1=intervention and 0=control), includes experimental treatments such as

intervention and year interaction, and includes food and health behavior

variables such as nutrition awareness, nutrition score, satisfaction, activity score etc.. The

individual intercept term i, is an unobservable effect from factors outside the scope of

survey and project.

The selection of variables and derivation of this model come from the literature

on obesity prevention. Demographic variables, satisfaction, activity score, weight

perception are selected based on the citations reported in Goldfinger et al. (2008). Fast

food consumption and physical activity were used to analyze the effect of three year

community-based study on weight control by Crawford et al. (2000). We added the

variables for nutrition awareness (Nutrition awareness) and nutrition score as suggested

by the mediating variable framework explained in Section 2.4.1. The direct outcome

variables are change in the mediating variables (attitude and behavior) to change BMI,

the dependent variable. The explanatory variables are explained in detail in Table 2.1.

The model was tested for model mis-specification using Ramsey’s Reset test using the

AUTOREG functions in SAS. The model was prone to have problems of

heteroscedasticty due to the cross-section and panel nature of data (Greene, 2003).

Breusch-Pagan test was done to test for heteroscedasticty by using the MODEL function

in SAS. All the analysis was done using SAS Enterprise Guide 5.1, SAS Institute Inc.


51

Table 2.1 Description of explanatory variables

Hispanic Self-identified race (1=Hispanic, 0=White)

Gender Dummy variable for gender (1=male, 0=female)

Age Age of respondent

Age2 Square of variable age

Nutrition awareness

Self-reported food’s nutritional value awareness (1=at least

moderately aware, 0= not at all or somewhat aware)

Fast food Number of meals at fast food restaurant in the last week

Nutrition score Sum of affirmative response for all food habit question

(Appendix 2.6)

Satisfaction Dummy for self-described satisfaction (1=happy or satisfied,

0=otherwise)

Activity score Sum of affirmative response for all activity question (Appendix

2.7)

Intervention site Control or intervention site (1=intervention site, 0=control site)

Year 2012 Dummy for post-intervention year

Post-intervention Interaction dummy for project intervention

Follow-up Interaction dummy for follow-up response in intervention site

Body weight

perception (BWP)

Dummy (1=perceived weight equals actual weight, 0 if not

equal)


52

2.6 Results

2.6.1 Project impact and sustainability through group comparisons

2.6.1.1 Cancer risk awareness

Being aware of the dangers from a particular behavior is essential to changed

behavior. The targeted cancer-risk behavior was primarily obesity, but in addition the

project also focused upon sunburn and tobacco use. The overall knowledge about cancer

risk is shown in Table 2.2 for the intervention and control sites. As shown in Table 2.2,

there was a very high level of knowledge that tobacco use, sunburn and tanning beds are

causes of cancer. There was less knowledge initially that obesity can cause cancer.

Awareness that obesity can cause cancer significantly increased in control and

intervention community during the intervention. The variables were targeted in the

intervention community. However, both intervention and control communities saw

significant improvements in overweight as the cause of cancer. This could be attributed to

the homogenous information provided during the survey to both the control and

intervention community. Awareness on tanning beds as a cause of cancer increased

significantly in the intervention community but decreased significantly in the control

community. Impacts were positive and significant in the intervention community, and

there also was an increasing awareness that sunburn and chewing tobacco are causes of

cancer. There were not comparable positive improvements in awareness outcomes in the

control community for sunburn and chewing tobacco.


53

Table 2.2 Comparison of cancer risk awareness in community group analysis

Cancer risk

awareness

indicators

Intervention Control

Pre-

(N=225)

Post-

(N=199) p value

Pre-

(N=157)

Post-

(N=175) p value

Overweight 38% 51% 0.002***

45% 53% 0.038**

Tanning beds 80% 88% 0.004***

93% 86% 0.009***

Sunburn 92% 93% 0.077* 96% 94% 0.075

*

Chewing tobacco 96% 98% 0.043**

97% 97% 0.147

p values are from Chi-square test

*, **

, and ***

significant at 10, 5 and 1% level of significance respectively

As shown in Table 2.3, a second metric on cancer awareness focuses on

individuals who participated in the data collection both before and after the intervention.

They formed the paired group as discussed earlier. This group also showed improvements

in awareness that being overweight and using tanning beds are risk factors for cancer,

significantly in the intervention community.

Table 2.3 Comparison of cancer risk awareness in paired group

Cancer risk awareness indicators

Intervention (N=68) Control (N=39)

Pre- Post- p value Pre- Post- p value

Overweight 32% 51% 0.003***

49% 56% 0.366

Tanning beds 66% 90% 0.000***

87% 87% 1.000

Sunburn 91% 99% 0.059* 95% 92% 0.564

Chewing tobacco 97% 97% 1.000 92% 90% 0.560

p values are from McNemar’s test

*, **

, and ***



54

The third year community data from the intervention site was used for

sustainability of the intervention effect. The metrics for focused variables of risk

awareness of obesity and tanning beds were assessed (see Table 2.4). The awareness of

obesity risk remained high six months after the intervention finished. However, the

awareness of tanning bed risk declined significantly. This suggests that the awareness of

cancer risk from obesity has increased significantly and sustainably from the intervention.

However, the awareness in the community from obesity risk (53%) is still much lower

than the awareness of risk for other cancer behaviors (sunburn and tobacco). In addition,

knowledge of cancer risk from tanning beds also appears to be lower and less sustainable.

However, from Table 2.3 and 2.4, awareness on tanning beds as cause of cancer

increased from pre-intervention to follow-up. Therefore, the analysis suggest that both

the obesity and tanning bed cancer risk could be usefully targeted in future public

education as these are below desired high levels of awareness.

Table 2.4 Sustainability of cancer risk awareness in the intervention community

Cancer risk awareness indicators

Intervention community

Post-

(N=199)

Follow-up

(N=91) p value

Believe the use of tanning beds can cause cancer 88% 75% 0.023**

Believe being overweight can cause cancer 51% 52% 0.243


*, **

, and ***



55

2.6.1.2 Cancer risk behavior including nutrition awareness

In addition to cancer risk awareness, changes in behavior to reduce or avoid

cancer risk were targeted for obesity and to a lesser extent to sunburn and tobacco use.

Actual obesity, sunburns, and tobacco use are considered to be risk outcomes. Targeted

behaviors for improvement were nutrition awareness for obesity, using sunscreen and

wearing appropriate head covering for sunburn prevention.

Table 2.5 shows the project impact to the community. Nutrition awareness

increased significantly across both communities, wearing cowboy hat increased

significantly only in the intervention community. However, use of sunscreen decreased in

both communities. The decrease was significant in the intervention community. The

reluctance to use of sunscreen has been assessed during the second and third survey in the

intervention community.

Table 2.5 Comparison of project impact indicator variables in the community

Project impact

indicators

Intervention Control

Pre-

(N=225)

Post-

(N=199) p value

Pre-

(N=157)

Post-

(N=175) p value

Nutrition Awareness:

very or moderately

aware

44% 55% 0.082* 31% 49%

0.004***

Using sunscreen: always

or most of the time 25% 23% 0.066

* 30% 26% 0.104

Wear Cowboy hat:

always or most of the

time

18% 29% 0.003***

20% 22% 0.121


*, **

, and ***


Table 2.6 shows the project impact on the paired group. There appears some

increase in cowboy hat use and nutrition awareness however the effect were not


56

significant. This insignificant change in paired group respondents could be better

understood by looking at the demographic differences between respondents in paired

group against those in the community group. The reluctance to sunscreen use as seen

from Table 2.5 could be accounted for the following reasons: 29.86% of the total

respondents reported not to be outside for long to have identified the need to use

sunscreen, 24.26% reported availability of sunscreen as the primary reason for not using

sunscreen, 10% reported time in using sunscreen as an issue and 8% reported costs of

sunscreen as an issue.

Table 2.6 Comparison of project impact indicator variables in the paired group

Project impact indicators

Intervention (N=68) Control (N=39)

Pre- Post- p value Pre- Post- p value

Nutrition Awareness:

very or moderately

aware 53% 63% 0.194 79% 74% 0.366

Wear sunscreen: always

or most of the time 32% 31% 0.782 36% 38% 0.564

Wear Cowboy hat:

always or most of the

time 24% 29% 0.285 21% 21% 1.000

p values are from McNemar’s test

*, **

, and ***


2.6.1.3 Food behavior and other cancer risk behavior

An evaluation of changed in food behavior was done by using the amount/

frequency of fruits and vegetables consumed and changed attitude towards own dietary

habit as key variables. Sunburn was measured by the frequency of getting severely

sunburned because sunburn is the greatest risk factor for getting melanoma skin cancer.

Table 2.7 shows the frequency of response from total respondents, community group and

paired group in control and intervention community during pre-intervention and post-

intervention period. For the intervention community, the percentage in Table 2.7 is


57

calculated in N=225 for total and community group during pre-intervention. And, N= 332

and N=199 for total and community group respectively during post intervention. N= 69

for paired group. For the control community, N=157 for total and community group

during pre-intervention. And N= 214 and N=175 for total and community group

respectively during post intervention. N= 39 for paired group. This measure was

unchanged in both the intervention and control communities. Tobacco use was measured

by the percentage of tobacco smokers and those who use smokeless tobacco. These

measures appeared to be unchanged by the project (Table 2.7).


58

Table 2.7 Food behavior and other cancer risk behavior

Response Intervention Control

Pre- Post- Pre- Post-

I'm eating healthy at this time

Total 34% 34% 38% 31%

Community 34% 26% 38% 29%

Paired group 45% 54% 38% 44%

3-4 or 5 or more servings of fruits and vegetables

Total 44% 42% 45% 43%

Community 44% 40% 45% 41%

Paired group 49% 51% 59% 54%

Smoking

Total 13% 13% 13% 15%

Community 13% 12% 13% 15%

Paired group 1% 15% 10% 15%

Use smokeless tobacco (% doing)

Total 4% 3% 6% 2%

Community 4% 4% 6% 2%

Paired group 3% 1% 3% 3%

Get sunburned when outside 30 minutes (always or most of the time)

Total 17% 16% 11% 18%

Community 17% 16% 11% 18%

Paired group 13% 15% 13% 18%

Use tanning beds

Total 26% 24% 32% 23%

Community 26% 28% 32% 23%

Paired group 16% 19% 26% 21%


59

2.6.2 The simple intervention effect on cancer knowledge score

The cancer knowledge score (CKS) is the sum of affirmative response to cancer

knowledge questions as explained in Section 2.5.3.2. The score ranged from 0 to 5. The

mean score was 3.853 ±0.935 and 4.368±0.731 before and after the intervention

respectively in the intervention community and the score was 4.230±0.872 and

4.179±1.233 before and after the intervention in the control community (see Table 2.9).

The score range was in the intervention community. So the project was successful in

increasing the score from a lower level to a higher level compared to the score in the

control community. The multiple linear regression output for analyzing the simple

intervention effect with control for the confounding variables is shown in Table 2.8. As

shown in Table 2.8 the intervention had significant impact on cancer knowledge score.


60

Table 2.8 Statistical relationship on cancer knowledge score (CKS)

Independent variables Coefficient p value

Intercept 0.490 0.687

Before intervention CKS -0.474***

<.001

English language dummy 0.199 0.474

Marital status -0.106 0.119

Education 0.104 0.286

Income -0.017 0.828

Age 0.006 0.174

Female(dummy) 0.106 0.555

Race 0.080 0.335

Cancer survivor (dummy) 0.035 0.879

Overweight (dummy) 0.243 0.202

Interest in reducing cancer risk (dummy) 0.392 0.634

Intervention (dummy) 0.528***

0.004

R square = 0.372

MSE = 0.793

*, **

, and ***



61

Further, Table 2.9 shows the pre- and post-intervention cancer knowledge score

for the intervention and control community. Table 2.9 also shows the raw and adjusted

intervention effect on the cancer knowledge score calculated from the regression model.

The intervention effect was significant. The score was higher by 0.530 point in the

intervention community. Hence, the approach was successful in increasing knowledge of

cancer risk factors. A multi-tiered community-based participatory approach in obesity

prevention efforts shows promise for changing cancer risk awareness of the community.

Table 2.9 Effect of intervention on cancer knowledge score (N=106)

Cancer knowledge score N Pre-intervention Post-intervention

Intervention (x ± SD) 69 3.853 ± 0.935 4.368 ± 0.731

Control (x ± SD) 38 4.230 ± 0.872 4.179 ± 1.233

Intervention effect (x ± SE)

Raw (p value)

0.040 ± 0.006 (<.001)

Adjusted (p value)

0.530 ± 0.179 (0.004)


62

2.6.3 Effect of intervention on BMI

Obesity was measured by body mass index (BMI). Along with BMI, waist

circumference (WC) was measured for all respondents. The mean BMI and WC for the

respondents from total respondents and community group during the three surveys in

intervention and two surveys in the control community are shown in Table 2.10. As seen

in the table the total respondents form the community group in the pre-intervention

survey. Further, paired group comparison was not done for BMI because BMI was not

the direct intervention outcome variable unlike behavior and attitude. We were interested

in seeing the overall effect rather than BMI change in paired group. Research targeting

BMI has concluded that identification of modifiable influences can lead to long-term

weight loss maintenance (Svetkey et al., 2012).


63

Table 2.10 Body mass index (BMI) and waist circumference (WC) outcomes

Intervention community Pre- Post- Follow-up

Mean WC Male Female Male Female Male Female

Total

42.15 39.81

42.04 40.15 40.77 39.69

Community group 42.12 40.09 39.49 38.98

Mean BMI Male Female Male Female Male Female

Total

30.5 31.76

30.01 31.08 27.99 29.52

Community group 30.53 31.43 27.12 30.24

Control community Pre- Post-

Mean WC Male Female Male Female

Total

41.91 37.46

43.31 38.73

Community group 43.21 38.68

Mean BMI Male Female Male Female

Total

29.97 29.97

30.31 28.65

Community group 30.31 28.79

Figure 2.6a and 2.6b shows the mean BMI trend in the total male and female

respondents respectively in the intervention and control community. These measures

show a small trend towards the desired lower levels in the intervention community

against the control for male respondents (Figure 2.6a). A similar plot for female

respondents shows parallel decrease in BMI in both the control and the intervention

community (Figure 2.6b). This suggests that the intervention may be having an effect.

However, the change was insignificant.


64

Figure 2.6a Mean BMI Trend in Total Male Respondents

Figure 2.6b Mean BMI Trend in Total Female Respondents


65

Similarly, Figure 2.7a and 2.7b shows the mean BMI trend in the community

male and female respondents’ group respectively in the intervention and control

community. These measures also show a small trend towards the desired lower levels in

the intervention community against the control for male respondents (Figure 2.7a). A

similar plot for female respondents shows parallel decrease in BMI in both the control

and the intervention community (Figure 2.7b). These findings point to the differential

reactiveness to project intervention by gender. Patterns of weight change is different

between the genders (Kimokoti et al., 2012).


66

Figure 2.7a Mean BMI Trend in Male Community Respondents’ Group

Figure 2.7b Mean BMI Trend in Female Community Respondents’ Group


67

2.6.3.1 Multiple linear regression

A multiple linear regression was done to find the effect of intervention on BMI as

explained in section 2.5.3.3. The variables have been explained in Table 2.1. Table 2.11

shows the summary statistics of the variable used in this regression analysis.

Table 2.11 Summary statistics of dependent and explanatory variables

Variable Mean Std. Dev. Minimum Maximum

BMI 30.41 11.67 13.75 60.39

Hispanic 0.45 0.50 0 1

Gender 0.34 0.47 0 1

Age 52.11 18.57 18 97

Age2 3059.45 1975.96 324 9409

Nutrition awareness 0.56 0.50 0 1

Fast food 1.44 0.06 0 21

Nutrition score 3.31 1.89 0 10

Satisfaction 0.68 0.64 0 4

Activity score 1.08 1.00 0 4

Intervention site 1.32 0.47 1 2

Year 2012 0.48 0.50 0 1

Post-intervention 0.30 0.46 0 1

Follow-up 0.18 0.39 0 1

BWP 0.46 0.50 0 1


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The model was tested for mis-specification and heteroscedasticity by using the

Ramsey RESET test and the Breusch-Pagan test respectively. The Ramsey RESET test

statistic was insignificant. However, the Breusch-Pagan statistic (38.12) was highly

significant (p value = 0.0005) and the null hypothesis of no heteroscedasticty was

rejected. This implied that the standard errors of the parameter estimates could be

incorrect and thus any inference derived from them could be misleading. Therefore, the

model was modified to yield accurate inference as discussed below.

When the variables male, age, age2, nutrition score and fast food were dropped the

new model did not show heteroscedasticity. The Breusch-Pagan test statistic (11.16) was

insignificant (p value = 0.265), and the Ramsey RESET test statistic (0.739) was also

insignificant (p value = 0.390). Therefore the standard errors of the parameter estimates

from the new model are correct and thus the inferences derived from them are not

misleading. However, the variables that were dropped will be included in the model by

following appropriate methods to treat heteroscedasticity. The regression coefficients

from the multiple linear regressions on the new model are shown in Table 2.12 with BMI

as dependent variable.

The coefficient for dummy variable Hispanic was positive and significant,

suggesting that the BMI for Hispanic would be higher by 1.839 compared to the Whites.

Other races were dropped before analysis of this model (6 respondents). Similarly,

dummy variables follow-up and BWP were significant and negative. Negative and

significant coefficient for follow-up suggested if the respondent participated in the

project for longer the BMI was significantly lower. Negative and significant for BWP

suggested individuals who had correct self-weight perception (Table 2.13) would have


69

significantly lower BMI by 2.453. Weight perception can play an important role in food

behavior and health attitude (ter Bogt et al., 2006). Further activity score and nutrition

awareness was also negatively significant. One unit increase in activity score decreased

BMI by 0.665, and nutrition awareness decreased BMI by 1.193.

The variable of interest in this regression to isolate the intervention effect

controlling for other variables was post-intervention variable and follow-up variable. The

post-intervention variable was positively insignificant. But the significant follow-up

variable despite the insignificant intervention site and post-intervention interaction year

variable is suggestive that having increased access to healthy living messages and

exposure to a supermarket environment that promote healthy living can have a negative

relation with respondents’ BMI with longer exposure to the project activities. This last

result might suggest the potential effect if the intervention went beyond one year.

Therefore, negatively significant coefficients for variable follow-up suggest that a longer

term project intervention will have the required effect on BMI. Also the negative

significant relation of BWP with BMI led us to look at BWP more closely.


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Table 2.12 Statistical relationship on BMI

Independent variables Coefficient Std. error p value

Intercept 32.607***

1.213 <.001

Hispanic 1.839***

0.433 <.001

Nutrition awareness -1.193***

0.418 0.004

Satisfaction -0.308 0.326 0.346

Activity score -0.665***

0.207 0.001

Intervention site 0.062 0.749 0.935

Year 2012 -0.959 0.753 0.203

Post-intervention 0.590 0.958 0.538

Follow-up -2.052***

0.662 0.002

BWP -2.453***

0.425 <.001

R square = 0.083

MSE = 44.02

*, **

, and ***



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Table 2.13 shows the perception of weight status versus the actual weight for all

the respondents across all surveys. As shown in Table 2.13, 39% of overweight

respondent perceived their weight to be normal and 65% of obese respondent perceived

their weight to be overweight and 9% obese perceived their weight to be normal. Self-

perception has been explained in relation to cognitive dissonance1 in psychology

literature. While some literature cite cognitive dissonance as a hindrance to obesity

prevention (Stellefson, 2006), others have used it in treating obesity stigma (Ciao and

Latner, 2011).

Table 2.13 Self-perception of body weight (N=1067)

Actual weight

Frequency of perceived weight within each weight category

Underweight Normal Overweight Obese Not-sure Total (N)

Underweight 27% 60% 7% 0% 7% 15

Normal 8% 64% 4% 0% 23% 227

Overweight 1% 39% 32% 0% 28% 347

Obese 2% 9% 65% 11% 13% 478

1 Cognitive dissonance refers to a situation involving conflicting attitudes, beliefs and

behaviors (Festinger, 1962).


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2.7 Conclusions

This paper evaluates the project outcomes of a community-based multi-tiered

behavior change intervention for obesity prevention based on cancer risk awareness and

cancer risk behavior including nutrition awareness. Intervention effects on cancer

knowledge score and the project impact on BMI were calculated. Mean and frequency

comparison of self-reported categorical variables of interest such as cancer risk

awareness, cancer risk behavior including nutrition awareness and food behavior showed

significant improvement against the null hypothesis of no difference before and after the

intervention and separately between the control and intervention community.

The results suggest that the intervention was successful in significantly and

sustainably changing the attitude toward unhealthy weight status as risk for cancer.

However, the awareness in the community from obesity risk was low compared to

awareness for other cancer risk behaviors such as smoking. The analysis suggest that both

the obesity and tanning bed cancer risk could be usefully targeted in future public

education as these are below high levels of awareness.

The paper also reports a significant project impact on cancer knowledge. The

intervention increased the cancer knowledge score by 0.53 points. Further a regression on

BMI showed the required direction of variables in affecting BMI such as activity score,

nutrition awareness etc. If the respondent had participated in the follow-up survey the

BMI would significantly decrease. These findings in the effect on BMI over time with

increased nutrition and cancer risk awareness shows promise using a longer term

intervention. Therefore, increased food nutrition awareness should lead to reduced

obesity over time as suggested by the regression analysis of obesity risk factors on BMI.


73

Overall, this study adds to evidence that shows community-based interventions such as

these have strong potential in bringing long term change in obesity and cancer inducing

behaviors and BMI.


74

Appendix

Appendix 2.1 Frequency of total response

What are your beliefs regarding cancer?

Do you believe the following can

cause cancer? Yes No

Don't

know Missing

Tap water 17% 56% 26% 1%

Tanning bed 84% 5% 11% 1%

Sunburn 92% 3% 5% 1%

Over weight 48% 20% 32% 1%

Alcohol 56% 14% 30% 1%

Chewing tobacco 95% 2% 3% 1%

Smoking tobacco 97% 1% 2% 1%

Caffeine 24% 30% 45% 1%

What are some of your health practices?

Do you currently smoke cigarettes? 15% 85%

1%

Do you currently use smokeless

tobacco products? 3% 96%

1%

Have you or anyone in your family

ever used a tanning bed? 23% 75%

1%

Marital status

Married 64%

Divorced 10%

Widowed 9%

Separated 4%

Never married 12%

Missing 1%

Education

Elementary or some high school 22%

High school graduate 37%

Some college or technical school 22%

College graduate 19%

Missing 1%

Income

Less than $20,000 37%

$20,000 to $35,000 25%

$35,000 to $50,000 13%

$50,000 to $75,000 13%

over $75,000 12%

Missing 1%


75

Self-reporting on nutrition awareness

Not at all 5%

Somewhat 38%

Moderately 36%

Very 19%

Missing 1%

Wear sunscreen when outside for more than 30 minutes

Always 10%

Most of the time 17%

Some of the time 18%

Occasionally 16%

Never 38%

Missing 1%

Wear a cowboy or broad brimmed hat when outside for more than 30 minutes

Always 10%

Most of the time 13%


Occasionally 12%

Never 48%

Missing 1%

When outside for more than 60 minutes how often do you get sunburn?

Always 8%

Most of the time 8%


Occasionally 32%

Never 29%

Missing 1%

Self-weight perception

Underweight 3%

Healthy weight 31%

Over weight 39%

Obese 5%

Not sure 20%

Missing 1%

Interest in finding ways to reduce cancer

Very interested 73%

Somewhat interested 24%

Not interested 2%

Missing 1%


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Appendix 2.2 Comparing invitee’s vs. volunteer’s response

Response item Invitee Volunteer p value

Tell us about yourself

What is your marital status?

Married 60% 67% 0.472

Divorced 16% 10% 0.364

Widowed 12% 7% 0.371

Separated 0% 1% 0.662

Never Married 12% 15% 0.683

What is the highest grade or year of school you completed?

Elementary 8% 12% 0.553

High school graduate 40% 34% 0.570

Some college or technical school 32% 26% 0.518

College graduate 20% 28% 0.406

What is your annual household income from all sources?

Less than $20,000 20% 22% 0.826

$20,000 to $35,000 32% 21% 0.239

$35,000 -$ 50,000 8% 17% 0.238

$50,000 - $75000 16% 18% 0.794

over $75000 24% 21% 0.756

Gender

Male 40% 39% 0.955

Female 60% 61% 0.955

Race

White 63% 80% 0.042

Hispanic 17% 9% 0.189

Black American 3% 1% 0.480

Pacific Islander 0% 0%

India/Alaskan 0% 1% 0.640

Multi-racial 17% 9% 0.189


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Appendix 2.3 Comparing response in intervention site in baseline survey




Married 58% 68% 0.211

Divorced 13% 7% 0.158

Widowed 9% 9% 0.883









Less than $20,000 53% 48% 0.535

$20,000 to $35,000 17% 23% 0.324

$35,000 -$ 50,000 13% 10% 0.502

$50,000 - $75000 13% 12% 0.859

over $75000 4% 6% 0.469

Gender

Male 26% 29% 0.691

Female 74% 71% 0.691

Race

White 30% 24% 0.274

Hispanic 35% 38% 0.555

Black American 0% 0% NA

Pacific Islander 0% 0% NA




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Appendix 2.4 Comparing response from intervention site in post-intervention survey




Married 69% 62% 0.249

Divorced 8% 14% 0.205

Widowed 15% 4% 0.002









Less than $20,000 21% 35% 0.026

$20,000 to $35,000 19% 30% 0.090

$35,000 -$ 50,000 25% 14% 0.027

$50,000 - $75000 10% 14% 0.341

over $75000 25% 7% 0.000

Gender

Male 40% 35% 0.389

Female 60% 65% 0.389

Race

White 79% 40% 0.000

Hispanic 19% 58% 0.000



India/Alaskan 0% 0% NA



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Appendix 2.5 Comparing response from control site in post-intervention survey

Response item Invitees Volunteers p value



Married 39% 78% 0.000

Divorced 14% 7% 0.177

Widowed 18% 1% 0.000









Less than $20,000 39% 23% 0.070

$20,000 to $35,000 29% 36% 0.460

$35,000 -$ 50,000 7% 14% 0.350

$50,000 - $75000 18% 13% 0.478

over $75000 7% 15% 0.275

Gender

Male 29% 36% 0.460

Female 71% 64% 0.460

Race

White 61% 41% 0.048

Hispanic 32% 55% 0.028




Multiracial 7% 3% 0.235


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Appendix 2.6 Cancer knowledge question set

What are your beliefs regarding cancer?

Please select the one choice that best describes you

1. Do you believe the use of tanning beds can cause cancer?

a. Yes b. No c. Don’t know

2. Do you believe getting sunburned can cause cancer?


3. Do you believe being overweight can cause cancer?


4. Do you believe chewing tobacco/using snuff can cause cancer?


5. Do you believe smoking tobacco products can cause cancer?



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Appendix 2.7 Food habit question set

How well do you eat?

I'm eating healthy at this time.

I'm ready to make some changes to eat healthier.

I'm not sure …… but I'm ready to talk about it.

I'm not interested in changing the way I eat at this time.

How many servings of fruits or vegetables do you eat each day?

5 or more

3 to 4

2 or less

How many servings of whole grain do you eat each day?

3 or more

2

1 or less

How many times a week do you eat lean protein like chicken, turkey, fish, tofu or beans?

6 or more

3 to 5

2 or less

How many times a week do you eat high fat foods like fried food, pastries or chips?

1 or less

2 to 3

4 or more

How many times a week do you eat fast food meals or snacks?

1 or less

2 to 3

4 or more

How much margarine, butter or meat fat (lard) do you use?

very little

Some

a lot

How many sugary drinks do you drink each day?

None

1 to 2

3 or more

How many times a week do you eat desserts or other sweets?

3 or less

4 to 6

7 or more

How often do you eat when not hungry, e.g. out of habit or for emotional reasons?

Rarely

Sometimes

Often

All the time


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Appendix 2.8 Physical activity question set

How active are you?

I'm physically active don't need help to be more active.

I'm ready to get more active and would like help.

I'm not sure but I'm ready to talk about it.

I'm not very active and not interested in being more active at this time.

How many hours each day do you spend watching TV or videos or on the computer?

Less than 1

1 to 2

more than 2

How many times a week do you do yard or house work or duties on the job that cause

you to work up a sweat?

4 or more

1 to 3

less than 1

How many times a week do you get out for a brisk walk for 10 minutes or more?

4 or more

1 to 3

less than 1

How many times a week do you participate in sports or an exercise program?

4 or more

1 to 3

less than 1


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CHAPTER III

THE EFFECT ON SUPERMARKET FOOD PURCHASES FROM POINT OF

SALE NUDGES WITH COMMUNITY REINFORCEMENT

Abstract

A project was designed with a supermarket in a small community to provide point of sale

signs or nudges. These signs encouraged consumers to purchase healthier foods in main

supermarket categories accompanied, in the intervention community and store, by overall

healthy choice signs and messages to increase the sale of produce and other healthier

food items. The community education reinforcement program included newspaper

articles, public service announcements on television and in-person healthy eating classes.

Relative to the control store, the program had a statistically significant impact on

increasing purchases within the grain pasta and sauce category among the nudged item’s

categories, and a significant increase in the purchase of green leafy produce category. An

overall increase in the purchase of several nudged item’s categories and produce

categories shows promise in supermarket based community interventions for behavior

change. However, the lack of significant increase in purchases across all categories shows

the challenges of changing food purchase behavior.

Key words: supermarket, nudge, community reinforcement, obesity, food choice


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3.1 Introduction

The Dietary Guidelines for Americans 2010 recommend stronger environmental

strategies for improving the population’s eating practices, including interventions to

influence food purchasing behaviors in stores (USDA-USDHH, 2010). A number of

research projects have focused on the importance of supermarket accessibility in relation

to obesity and obesity related health problems (Spence et al., 2009). However, very few

research has focused on using supermarkets to promote health despite the fact that

supermarkets play an important role in food purchasing (Glanz and Mullis, 1988). An

average consumer makes on an average 2.2 trips per week to the supermarket (FMI,

2011). Supermarket can play a vital role in community-based participatory obesity

prevention efforts. A key recommendation is to use the supermarket in healthy diet

promotion by linking supermarket profits with healthy diets and by making healthy foods

more appealing and available (RWJF, 2011).

The use of supermarkets as a potential site for effective consumer education was

researched substantially in the late 1980s and early 1990s (e.g. Ernst et al., 1986 and

Scott et al., 1991). Light et al. (1989) reported on a feasibility test of the supermarket as a

site for consumer nutrition education program “Eat for Health.” This was a joint research

study by the National Cancer Institute (NCI) and Giant Food Inc., a regional supermarket

chain in the Washington-Baltimore area. Despite the effectiveness of supermarket based

programs like “Eat for Health” (e.g. Scott et al., 1991 and Song et al., 2009) there is little

use of supermarket interventions to prevent and reverse the trends in obesity despite the

potential. Also, a systematic review by Escaron et al. (2013) reported overall

effectiveness from grocery interventions while showing a need for more rigorous testing


85

of interventions. Additionally, no other supermarket intervention that specifically

employs point of sales signs (referred hereafter as shelf talker) can be found after Light

et al. (1989) and Patterson et al. (1992).

While obesity is reaching epidemic levels, several studies on community-based

models have shown success in reversing the obesity trend and in preventing disease (e.g.

Brownson et al., 1996; Chou et al., 1998 and Allen et al., 2013). Other studies like Baker

and Brownson (1998), Merzel and D’Afflitti (2003), and Corda et al. (2010) emphasized

community-based programs which encompass multiple interventions as the model with

best potential to achieve behavior change that will reduce person’s health risk. This

research trend has led to advocacy for community-based efforts by such organization as

the American Cancer Society (ACS, 2012).

One approach less used in grocery stores, also suggested by Gittelsohn and Lee

(2013), is to coordinate the grocery store interventions with community reinforcement to

encourage awareness and to hopefully shift purchase to healthier products. The design of

the current program was to develop a community-based cancer risk reduction program. It

was intended to develop an effective feasible program that could be adopted relatively

easily by small communities but could also be applicable in a broader context. We have

described the model in McCool et al. (2013). The project was implemented in a major

supermarket in a small community. The store and the community was a good setting to

implement the intervention due to high rates of obesity in the community and because the

community was small the message over the media regarding healthy eating habit could be

relatively easily and inexpensively controlled.


86

The project model focused on incorporating obesity risk education in the local

supermarket. The supermarket focus was designed with an objective to encourage the

development of a community environment supportive of lifestyles that could reduce

residents risk for obesity and cancer. Since the local supermarket is the largest source of

daily food needs it can be an excellent source of information and reinforcement for

healthy behavior. The supermarket was used for following purposes:

to conduct healthy food demonstrations,

to put shelf talkers on comparatively healthy food product,

as an active site of information flow by using posters regarding healthy

eating were placed in the store, and

to access food purchase data to evaluate the effect of the project on food

purchase behavior.

The goal of this essay is to conduct an outcome evaluation by measuring the

achievement of program objectives. The specific objectives of the analysis are to see if

the point of sale nudges and supermarket health promotion intervention could increase

the consumption of relatively healthier items and fresh fruits and vegetables. The focus is

on quantitative outcomes and overall on whether the supermarket intervention was

effective in changing food purchase behavior. The objective was addressed by using

supermarket sales data. The use of supermarket sales data have been recommended also

by Tin et al. (2007) from a review of 22 studies that used supermarket sales data to

supplement food and nutrition monitoring methods.


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3.2.1 Risk factors for obesity

The causes of obesity are complex. There is also a disparity in obesity incidence

rates among population groups. According to the American Cancer Society disparities

predominantly arise from inequities in work, wealth, income, education, housing, and

overall standard of living, as well as social barriers to high-quality cancer prevention,

early detection, and treatment services (ACS, 2012). There appears to be little difference

in the cancer incidence and mortality rates of rural and urban populations in the United

States. But, evidence suggests that cancer tends to be diagnosed at a more advanced stage

among rural populations (Monroe et al., 1992; Gosschalk and Carozza, 2003; Philips et

al., 2011 and Gong et al., 2012). The American Cancer Society also suggested that

patients from ethnic minorities are substantially more likely to be diagnosed with cancer

at a later stage when treatment can be more extensive and more costly (ACS, 2012).

Access to food is another factor in being able to select healthy foods. Kumanyika

et al. (2008) and Wang and Beydoun (2007) have pointed to income limitations as well as

the accessibility of chain supermarkets to limit a persons’ ability to acquire foods of

higher nutrition value. The relative costs of foods with high and low nutrient density and

caloric values may be another factor contributing to the unhealthy choice. Several

research studies (Drewnowski, 2003; Drewnowski and Specter, 2004; Drewnowski and

Darmon, 2005; Ard et al., 2007 and Maillot et al., 2007) suggest that the current market

prices encourage unhealthy eating. Products with high fat and sugar and low in other

nutrients are relatively less expensive than foods such as fruits and vegetables and whole

grain products.


88

3.2.2 Supermarket interventions

A recent report published by the Robert Wood Johnson Foundation (RWJF, 2011)

has emphasized harnessing the power of supermarkets to help reverse childhood obesity.

The report was developed on the following two guidelines:

1. “Marketing strategies to encourage healthy eating must improve the bottom

line, or at least be revenue neutral for food retailers and manufacturers, and

2. Public health researchers and food retailers and manufacturers should work

together to study how grocery store environments and marketing strategies

affect shoppers’ purchases and preferences.”

Light et al. (1989) reported a feasibility test of supermarket as a site for consumer

nutrition education program “Eat for Health”. The reported supermarket nutrition

intervention was built on previously successful collaborative experiences between

Federal agencies and supermarket and on the experience of other researchers who had

conducted point-of-purchase studies. The added components of the “Eat for health”

program were the scope of the project, its length, its extensive advertising, and the scale

and depth of the evaluation. The details of the method and analysis of the impact of the

NCI-Giant Food Eat for Health Study can also be found in (Patterson et al., 1992). Other

studies of health promotion through supermarket intervention have followed though no

publicly reported efforts have used point of sale signs to promote healthy food habit since

Light et al. (1989).

Escaron et al. (2013) excellently summarized the studies published from 1978

through 2012 on supermarket and grocery store interventions (N=33) to promote

healthful eating. This paper identified 58 articles and characterized 38 supermarket


89

interventions. Some other studies that were not included in Escaron et al. (2013) also

studied supermarket intervention. Two cross-sectional time series models, estimated with

a variance components procedure, showed the effectiveness of coupons, assuming habit

and non-habit persistence along with consumer demographics (Ward and Davis, 1978).

Similarly, Cummins et al. (2005) reported evidence for an improvement in psychological

health of those directly engaged with the intervention although a net intervention effect

on fruit and vegetable consumption was not found.

3.2.3 Use of scanner data

An evaluation of the feasibility and applicability of supermarket sales data in

population food and nutrition monitoring has been done by Tin et al. (2007). In this

study, eighteen studies that collected supermarket sales data for various population food

and nutrition monitoring purposes and four feasibility studies were reviewed. The

findings supported the feasibility of using supermarket sales data to monitor population

food purchasing patterns. Further, the study showed that it is possible to use various kinds

of sales data (directly collected check out scanner data, commercially available data sets,

and grocery receipts) in population nutrition monitoring.

Using sales data have some advantage over traditional survey methods. Since

sales data are objective, they are relatively free form recall bias or deliberate

misinformation commonly encountered in traditional surveys. Use of supermarket sales

data was recommended as an indirect measure of intervention effectiveness (Kristal and

Ollberding, 2012). Similarly, Andreyeva and Luedicke (2013) used scanner sales data

obtained from supermarket chain to assess how the Women Infant Children revisions

affected purchases of bread and rice among WIC-Participating households in Connecticut


90

and Massachusetts. In that study the main outcome variables were total weight of bread,

rice, and tortillas (in ounces) purchased by a household in a given month.

3.2.4 Influence of personal characteristics in food choice decisions

A trivariate probit model estimation of the effect of respondent’s personal

characteristics on decision to participate in attaining healthy weight was done by Chen

and Huffman (2009). The personal characteristics included were education, reading food

labels (signaling concern for good health), adjusted family income, opportunity cost of

time, occupation, marital status, race and ethnicity. This study concluded that men and

women who read more food labels are more likely to participate in moderate or vigorous

physical exercise and women are less likely to be obese. In a previous study, Bender and

Derby (1992) found that US label readers are more likely to be female, older, more

educated and on restriction diets.

Research on the use of nutrition facts label in Honolulu reported that one half of

the shoppers used nutrition facts label and an additional 18% reported using labels

sometimes. The frequency of use did not differ by age, but Caucasians reported using

labels more often than all of the others ethnic groups (Dooley et al., 1998). In a survey

conducted by the Food Marketing Institute (FMI), 31% of the food shoppers interviewed

either said they did not know what Percent Daily Values meant or defined it incorrectly

(FMI, 2013).


91


The dictionary definition of persuasion is to cause someone to believe or

convince. Eysenck and Keane (2005) identified persuasion as an information processing

activity, in which thoughts are actively manipulated to create new beliefs and attitudes.

Heath and Fairchild (2007) defined persuasion as any activity which changes the attitudes

of the recipient. Two different types of persuasion are defined by Heath (2006): rational

and emotional. Performance claims, promotions, offers and the like employ both rational

and emotional persuasion. Moreover repeated positive experience from consuming a

product leads to strong brand loyalty. Promotions, convenience, advertising etc. to some

extent have control over individual’s choice.

Figure 3.1 Conceptual Framework for the Supermarket Intervention


92

Figure 3.1 above shows the conceptual framework for the supermarket

intervention. A traditional view considered human behavior to be largely a rational

choice but the rational choice view is narrow (Cawley, 2004). Non-economic factors also

affect decision explained by behavioral economics. Behavioral economics assumes that

people depart from rationality in systematic ways (Kahneman, 2003). The role of

psychology in decision making have been explained in Damasio (1994) as cited in

Loewenstein (2000).

Success has been reported in applying behavior economics to increase healthy

food choice and consumption. A growing body of research suggests that people respond

to contextual cues without conscious thought or decision making, perception also affects

decision. Distraction as an external cue is reported to have major effect on the food

selected, the amount consumed and the eater’s perception (Just et al., 2007). Self-

attribution: when people feel that they have made their own decisions, has been recorded

to provide greater satisfaction to the consumer (Just and Wansink, 2009). Choice

architecture (where in choices are affected without letting the decision makers know that

their decision have been influenced) has been shown to work (Just et al., 2007; Just and

Wansink, 2009; Hanks et al., 2012a; Wansink et al., 2012 a, b and Van Kleef et al.,

2013). The ability to act automatically (e.g. if people touch something very hot, they will

withdraw their hands before they have time to make conscious decision to do so) is a

protective measure. However, it extends to eating and food choices (Cohen and Babey,

2012).


93

Consumer behavior in food choice decisions can be usefully characterized in two

ways: a) prescriptive that can be persuaded, and b) rapid decision based on heuristic

devices (appearance, shapes, sizes, logos, brands etc.). The most important factor in a

supermarket intervention success is how it influences the consumer. Persuasion and

reinforcement mechanism during post facto choice behavior was identified also by Sheth

(1974). Both the prescriptive and rapid decision approaches are based on behavior. There

are specific behaviors initiated automatically by contextual cues that were previously

congruent with the performance of behaviors (Orbell and Verplanken, 2010).

This research applies both prescriptive and rapid food choice decision theories to

change the behavior of consumers towards healthier food choice decision. The shelf

talkers that were placed near relatively healthier items is proposed to help rapid decision

makers by altering the heuristic device by providing healthy living visual cue. Providing

shelf talkers has been listed under contextual influences in Cohen and Babey (2012). On

the other hand, the overall multi-tiered health promotion approach supports prescriptive

decision making based on persuasion.

Further, research on behavioral change in many different areas (e.g. smoking

cessation, nutrition, and exercise) has focused on the multiple aspects involved in

promoting behavioral change (e.g., an individual’s readiness to change, economic factors,

barriers to change). Drawing on literature across disciplines (economics, business,

psychology, and marketing) it is clear that behavioral change is influenced by a multitude

of factors (Fisher et al., 2002 and Rose et al., 2009). Likewise, reinforcements for

behavior change should come from many different aspects of the environment to be most

effective. Economos et al. (2007) demonstrated how the community context can be


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modified and reinforcements can be offered to prevent weight gain. Embedding

reinforcements within multiple levels of a person’s environment provides a

comprehensive effort towards addressing obesity risk factors.

3.4 Methods

3.4.1 Research setting

A small community of Muleshoe, TX was the setting to implement the project.

The supermarket intervention was a part of a multi-tiered cancer prevention model with a

substantial focus on obesity reduction. The activities in the supermarket were

implemented in partnership with the management team of a local supermarket located in

the intervention community. The selected supermarket was United Supermarket LLC.

This supermarket is the largest supermarket of its size and sale volume in the city

followed by small chain stores. The store intervention can be categorized into three types:

1. Point of sale signs or nudges to encourage consumers to purchase healthier

foods in main supermarket categories,

2. An overall emphasis and communication to promote purchase and

consumption of fresh fruits and vegetables (hereafter referred as produce) and

other identified healthier food items, and

3. Community efforts to reduce obesity. The point of sale nudges and healthy

eating messages were reinforced by a community education program

including newspaper articles, food demonstrations, and public service

announcements on television and in-person healthy eating classes.

The supermarket intervention to promote purchase and consumption of produce

were carried out from January of 2012 to December of 2012 (hereafter referred as the


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trial period). Products were selected by their relatively higher NuVal score within each

category targeted. The NuVal system places a single numeric score 1-100 based upon the

nutritional characteristics of the product and allows us to compare foods using a single

metric to establish the relative healthfulness (Katz et al., 2010). The shelf talker nudges

were checked once every month for placement accuracy. A total of 402 items were

identified for placing shelf talkers. The data for this paper is the sales data of the 402

product with shelf talkers from the intervention store and the sales data of same products

from the control store.

3.4.2 Data

The data set for this research was different for the produce and the identified

healthier food items. Each data set has been explained separately under produce and point

of sales nudges sections below. Further, the demographic data used to analyze the

awareness and attitude towards healthy food promotion in supermarket are described

under the demographics and project activities data in the sections below.

3.4.2.1 Produce

The data set includes monthly sales data (measured in units sold per product or in

ounces per produce item) and their price, from January 2011 to December 2011 (hereafter

referred to as the pre-trial period) and the trial period from the intervention and control

store. There were a total of 2240 produce items being sold over the period of 24 months.

However, many of the produce items were either discontinued or seasonal sales. The data

set was cleaned to include items that were consistently sold over the 24 months (12

month of pre-intervention and 12 month of post-intervention) period with Universal

Product Codes (UPC). Thus the analysis is done in following two parts:


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1. Continued items: Includes sales data for only 10 categories of produce: apples,

avocados, bananas, beans, bell-pepper, berries, carrots, citrus, cucumber, and eggplant

that were sold across the 24 months,

2. Continued and discontinued items: The dataset with or without continued sale

of a unique UPC coded item categorized into the following fourteen categories: apples,

avocados, bananas, beans, broccoli, citrus, corn, cucumber, grapes, green-leafy, other

fruits, other vegetables, potatoes and salad. However, the bean and potato categories were

dropped from the analysis. The bean category were dropped because it contained many

dried bean items as well and the project promotion was on increasing sales of fresh fruits

and vegetables and not other forms of vegetables including dry beans. Similarly potatoes

was also dropped because there is an increasing debate among nutritionist that potato is

biologically a vegetable but unlike other vegetable it is a starchy food like grains.

3.4.2.2 Point of sale nudges

The data for this paper is the monthly sales data of the 402 products with shelf

talkers, from the intervened store and the control store. The control store was in a

location with demographics similar to intervention store location. The data includes

monthly sales data (measured in units sold per item) from January 2011 to December

2012 from both stores. The data were obtained from computerized cash registers, which

record all individual purchases by UPC. None of the selected items were on promotion or

discounted in price differently at any time during the analysis period. A similar

comparison of sales data with two stores in matching geographic region and demography

was accomplished by Sacks et al. (2010). The 402 selected items were categorized into

12 categories based on the Food Marketing Institute Product Category List, published in


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2012 (FMI, 2012). The 12 categories with the number of items in each category in

parenthesis are: Bakery and Bread (27), Baking (24), Beverages (29), Soda (4), Breakfast

(48), Can foods (70), Dairy and Cheese (34), Frozen (41), Grain Pasta and Sauce (10),

Meat and Seafood (39), Pantry (41), Snacks (35). Monthly sales data has been reported to

have the same variation compared to weekly data by Narhinen et al. (1998).

3.4.2.3 Community awareness and project activities data

Two small communities: Muleshoe and Dalhart in the state of Texas were

selected, as project intervention and control sites respectively, based on their similar

demographics. Muleshoe was selected as the intervention site as this small community

met the criteria for implementing community-based research. The average poverty rate in

these communities ranged from 9% to 23% and more than one third of the populations

are Hispanic. The poverty rate of Dalhart is 9-15%. The population of Muleshoe is about

4,571 with 17-50% of the population estimated to be Hispanic. The poverty rate of

Muleshoe is 17-23% (TTUHSC, 2010). The data for this section were drawn from the

data collected in two independent surveys among a total of three surveys undertaken

during the project period. The demographic data used in this chapter were from the post-

intervention survey (June of 2012) that was conducted at both control and intervention

site and from a follow-up (January of 2013) survey that was conducted only in the

intervention site. The project activity data used in the analysis is from the intervention

site from the post-intervention and follow-up survey.

There were a total of 550 respondents during 2012 survey: 335 from Muleshoe

(Intervention) and 215 from Dalhart (Control). Of the total respondents in Muleshoe and

Dalhart, 69 and 39 of them had participated in the baseline pre-intervention survey.


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Inclusion criteria for participation were for individuals who were at least 18 years of age,

and self-identified as living in the communities of Muleshoe and Dalhart. They were

invited to participate by mail sent out to a random sample of local population drawn from

the local telephone book. The average show up rate was 22.3%. This random sample was

supplemented by participants invited through flyers and pamphlets distributed in public

areas and printed in local newspapers. Similar recruitment with mail survey was also

done by Crawford et al. (2000). The demography of mail recruited respondents and the

respondents recruited through flyers and pamphlets were not significantly different

(Appendix 3.1 and 3.2) in both the comparison and intervention site. Appendix 3.3 also

shows that the response on other indicator variables used in this analysis was not

significantly different between the respondents recruited through flyers and pamphlets in

the intervention site during the post-intervention survey.

3.4.3 Data analysis

The data analysis was done separately for the two data sets: 1) produce data, and

2) point of sale nudges data. Similarly, the data set included household characteristics of

respondent who shopped from this supermarket. However the sales data was not

available at individual level. Hence, the analysis is limited to comparing the change in

sales by category in the intervention and control supermarket during the pre-trial and trial

period. Similar tests to compare change in sales in units sold per item has been done to

analyze supermarket intervention (Sacks et al., 2011). Further, the demographic data

available from the questionnaire survey was used to compare the attitude toward project

activities conducted in the supermarket and to find attitudes towards in-store healthy food

choice messages and nudges by demographics.


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The analysis of this data set however was limited due to lack of category sales

data to compute proportional sale as done by Narhinen et al. (1998). Since the data

available from the supermarket was a complete set of sales data in the produce aisle,

computation of category sales data would have been possible. However, the lack of

standard categories within the produce section would make the result inconsistent. Hence

the category sales were not computed. Further, the interest of the research was to increase

the sales of fresh fruits and vegetables overall and not a particular item with a fruit or a

vegetable category. The interest in putting shelf talkers on healthier items was to

increase proportional sales of the nudged item within category. However the lack of

category sales data limited our analysis to looking at the sales difference with control

store during the pre-trial and trial period.

All the analysis was done using SAS Enterprise Guide 5.1, SAS Institute Inc. The

sales data in units sold per month was standardized to compute the units sold per unit

dollar before analysis to account for the price differential between the pre- and post-

intervention period, the price across all months was same in both the comparison and

intervention stores.

3.4.3.1 Produce data analysis

Paired comparison for pre- and post-intervention sales in intervention community

The difference between the monthly sales data on produce purchases during the

pre-trial and trial period was tested using paired t-tests for the data set of items that were

sold each month from January 2011 to December 2012 in the intervention store.


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Change in produce sales an overall comparison

In the paired comparison if a brand of oranges in the citrus category with a specific

UPC code would have been continued to be sold but if it was also substituted (not

replaced) by a new brand of oranges with a different UPC code within the citrus category.

In cleaning the data for this analysis the item with the different UPC code that was started

to be sold later in the year would be deleted (as a discontinued item) and hence the

deleted item would cause the total sale of citrus to decrease by including only the item

with the specific UPC code in the analysis. This caused an unusual fluctuation in sales

data. For this reason the analysis was done for all items in the category following the

paired comparison. The sales data on produce purchases with or without continued sale of

a particular item was used to test the mean difference of sales against a null hypothesis of

zero difference by using independent t-test for the dataset.

To undertake an overall comparison, all the items available in the produce data set

were categorized as explained in 3.4.2.1 (second part). The overall change in the sales of

produce was computed by double difference method. The method of finding simple

intervention effect is finding the difference between the changes in intervention mean

minus the change in comparison mean. This is a typical way to measure the change and is

a comprehensible method (Naresh, 2007 and Kristal and Ollberding, 2012):

( ) ( )

where, is the variable of interest. Subscript and refer to pre-trial and trial

respectively, and subscript and are for intervention and comparison.

Further, the percentage change in sum of units sold per month for each category

was computed for both intervention and control store. The overall percentage change in


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produce sale as found from the formula above was computed from the pre-trial sale in the

intervention community. The mean sales during the pre-trial and trial period were

compared using independent t-test, separately for the intervention and control site.

3.4.3.2 Point of sale nudges data analysis

The change in sales data in units sold per month was summed across each

category of identified food items from pre-trial to trial period. The percentage change in

the sum of units sold per month for each category was computed for both intervention

and control store. However, the change in mean sales in units sold per month from the

pre-trial to trial period was tested using independent t-test separately for the intervention

and control store. Unlike for the produce data set, no paired tests were done for the point

of sale nudges data.

The overall percentage change in produce sale was computed from the pre-trial

sale in the intervention community. The overall effect of the project intervention in sales

of point of sale nudges item was computed by double difference method. A method of

finding simple intervention effect by finding the difference between the changes in

intervention mean minus the change in comparison mean as explained in 3.4.3.1 was

used.

3.4.3.3 Community awareness and use of project activities

Intervention awareness and attitude towards intervention (shelf talkers and NuVal

scores) of the respondents who self-reported regular shopping in the supermarket was

also analyzed by demographics.


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3.5 Results

3.5.1 Produce sales

3.5.1.1 Paired comparison in intervention community

The mean units of produce sold per month during the pre-trial and trial period in

the intervention community, the change in unit sales per month and the corresponding p

value from a paired t-test between sales during the pre-trial and trial period is shown in

Table 3.1. A paired t-test was used because the comparison was done for the produce

items that were consistently sold over the 24 months (12 month of pre-intervention and

12 month of post-intervention period followed by UPC. Further if data for any month was

missing in one of the year the data for concurrent month was deleted from the other year

to make a paired observation. As shown in Table 3.1, the sales of bananas, carrots,

cucumber and eggplant increased significantly, while there was a significant decrease in

sales of citrus. For the reasons explained in Section 3.4.3.1, the paired comparison in

Table 3.1 shows large fluctuations in the sale of common fruits like citrus.


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Table 3.1 Sales of produce during pre-trial and trial periods

Category

Pre-trial Trial

Mean SD Mean SD Change p value

Apples 288.39 781.33 337.68 860.25 49.29 0.166

Avocados 4395.39 4039.28 4143.43 3392.26 -251.96 0.567

Bananas 7702.25 7921.46 8056.30 8280.44 354.05 0.028**

Beans 13.97 22.24 21.97 44.78 8.00 0.181

Bell pepper 345.42 590.39 358.78 581.00 13.35 0.502

Berries 9.51 33.24 15.42 41.40 5.91 0.178

Carrots 66.40 113.09 73.64 131.51 7.23 0.055*

Citrus 4072.78 8411.63 3014.71 6452.85 -1058.07 0.014**

Cucumber 54.93 93.80 134.98 202.26 80.05 0.037**

Eggplant 11.82 2.87 17.15 6.08 5.33 0.026**

p value from paired t-test, * and

** significant at 10 and 5% level of significance

respectively


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3.5.1.2 Change in produce sales and overall comparison

The change in produce sales was computed by finding the change in sum of

produce sales in units sold per month in pre-trial period and trial period in both the

control and intervention store. The percentage change in the sum of produce sales in units

sold per month and the overall change found by double difference method as explained in

Section 3.4.3.1 is shown in Table 3.2. The table reports the percentage change in sales

(monthly units sold summed across each category) by category for all the fresh produce

items sold in the supermarket. The change % for the intervention and control store is the

change from pre-trial period. As shown in Table 3.2, the sale of avocados, citrus, and

grapes decreased in both the intervention and control store. In addition, in the control

store the sale of apples, broccoli, corn, and salad also decreased in the trial period from

pre-trial period.

Table 3.2 also reports the overall change in units sold (%). The overall change is

derived from change in intervention and control store. There was an overall growth in the

sales of apples, bananas, broccoli, corn, green leafy, other fruits, and salad. The results

also showed there was less decline in the sales within category avocado, citrus, and

grapes. The overall decrease in sales was observed in categories cucumbers and other

vegetables. The decrease in the sales of cucumbers was very high (21%) and could not be

explained with the available data set.

Further, as shown in Table 3.2 the total number of units sold from pre-trial period

to the trial period decreased in both stores. The total number of units sold decreased by

12% in the intervention store and by 16% in the control store. Using the double

difference method the overall change in the monthly units sold was an increase of 4% in


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the sales during the trial period in the intervention store. The overall sales increase was

highest for the categories apples, broccoli and green leafy vegetables.

Table 3.2 Change in units sold from pre-trial period to trial period and overall change

Produce category

Change in units sold (%)

Intervention Control Overall

Apples 6 -13 19

Avocados -6 -9 3

Bananas 5 2 3

Broccoli 9 -5 14

Citrus -19 -25 6

Corn 7 -2 9

Cucumber 0 21 -21

Grapes -34 -36 3

Green leafy 62 39 23

Other fruits 7 1 6

Other vegetables 0 3 -3

Salad 2 -3 5

Total -12 -16 4


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Independent t-test was used to compare the sales during the pre-trial period with

the sales during the trial period in both intervention and control store. Table 3.3 shows

the statistics from independent t-test for mean difference from pre-trial to trial period

with a null hypothesis of no difference in sales in units sold per month between the pre-

trial period and trial period separately in the intervention and control store. As shown in

Table 3.3, mean sales in units sold per month was significant only for green leafy

produce (at 10% level of significance in the control store and 5% level of significance in

the intervention store).

The analysis reported in Tables 3.2 and 3.3 supports the inference that the project

had a positive impact on purchase of fresh produce. The change in produce purchase in

green leafy produce category was significant and the overall effect was positive.


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Table 3.3 Change from pre-trial to trial period for produce (mean no. of units sold)

Produce

category

Control Intervention Mean change

Mean (SD) Mean (SD)

Pre-trial Trial Pre-trial Trial Control Intervention

Apples 173.30 150.55 103.17 109.44 -22.75 6.27

(995.86) (827.37) (569.42) (575.57)

Avocados 537.76 491.70 462.74 436.18 -46.06 -26.56

(2038.89) (1638.06) (1865.65) (1670.32)

Bananas 1194.61 1213.24 770.26 805.70 18.63 35.44

(5219.32) (5263.09) (3376.41) (3530.45)

Broccoli 17.82 16.62 9.27 9.99 -1.20 0.72

(78.43) (71.51) (42.56) (44.84)

Citrus 1239.49 929.60 1126.16 907.45 -309.89 -218.71

(6440.96) (4249.20) (5604.14) (4364.26)

Corn 299.20 293.04 200.50 215.27 -6.16 14.77

(1611.94) (1462.32) (1047.24) (1090.05)

Cucumber 248.05 298.95 217.01 217.02 50.90 0.01

(875.28) (1042.21) (830.63) (712.06)

Grapes 124.85 80.50 67.52 44.85 -44.35 -22.67

(519.57) (256.33) (242.98) (140.45)

Green 2.28 3.14 2.19 3.53 0.86

* 1.34

**

(7.04) (8.17) (8.89) (11.78)

Fruits 37.72 37.82 26.36 28.07 0.10 1.71

(277.87) (268.70) (189.74) (192.28)

Vege. 57.55 58.94 36.77 36.73 1.39 -0.04

(491.31) (356.57) (337.67) (242.44)

Salad 41.98 40.95 31.77 31.47 -1.03 -0.30

(324.91) (313.90) (298.97) (289.50)

p values from t-test, * and

** significant at 10 and 5% level of significance respectively


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3.5.2 Point of sale nudges

3.5.2.1 Change in sales and overall comparison

The sum of sales in units sold per month was higher in the control store than in

the intervention store for all categories during both the pre-trial and trial periods. The

change in sales of nudged item (items with shelf talkers) was computed by finding the

change in sum of sales of nudged item in units sold per month from pre-trial period to

trial period in both comparison and intervention stores. The overall change was found by

using the double difference method as explained in Section 3.4.3.1. The results are shown

in Table 3.4. The table reports the percentage change in sales (monthly units sold

summed across each category) by category for all nudged item and the change %. The

change % is the change from pre-trial period. The overall change in units sold (%) is the

overall change from the units sold in the pre-trial period.

As shown in Table 3.4, the total units sold of the nudged items decreased from the

pre-trial period to the trial period in both stores. The total units sold of nudged items

decreased by 4% and 5% respectively in the intervention and control store. The project

expectation was for these items’ sale to be increased within their respective category. Due

to lack of category sales data it could not be clarified if the drop in sales was throughout

the category or in the nudged items only. However, the availability of sales data and

hence the comparison of sales with the control store provides some idea on the effect of

intervention.

The change in monthly sales from pre-trial to trial period was not homogenous

across the categories. The sale of nudged items in bakery and bread, can food, dairy,

drinks excluding soda, soda, and meat and seafood category decreased in both the


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intervention and control store. The sale of nudged items in breakfast category decreased

only in the control store. There was an overall growth in the sales of nudged items in

categories breakfast, frozen, and grain and pasta. The results also showed there was less

decline in the sales of nudged items in categories can food, dairy, drinks excluding soda,

meat and seafood, and soda. The overall decrease in sales was observed in the sales of

nudged items in categories bakery and bread, baking, snacks and a higher decline in the

intervention store in the category pantry. From this comparison of units sold, the category

grain pasta and sauce followed by the categories of (a) canned food, (b) frozen, and (c)

meat and seafood shows a potential positive nudging effect.


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Table 3.4 Total percentage change (units sold) from the pre-trial to trial period

Point of sale shelf talker category Change in units sold (%)

Intervention Control Overall

Bakery and bread -14 -4 -10

Baking 25 29 -4

Breakfast 2 0 2

Can food -3 -8 11

Dairy -10 -14 4

Drinks excluding soda -4 -5 1

Frozen 13 1 12

Grain and Pasta 44 15 29

Meat and seafood -4 -10 6

Pantry -11 -6 -5

Snacks 4 14 -10

Soda -1 0 -1

Total -4 -5 1


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An independent t-test was used to compare the sales of nudged items during the

pre-trial period with the sales of nudged items during the trial period, separately for the

control and intervention stores. Table 3.5 shows the statistics from independent t-test for

mean difference from pre-trial to trial period with a null hypothesis of no difference in

sales in units sold per month between the pre-trial period and trial period separately in the

intervention and control store. As shown in Table 3.5, mean sales in units sold per month

was significantly higher in the intervention store only for nudged items in category grain

and pasta (at 5% level of significance). From this t-test the category grain pasta and sauce

shows a potential positive nudging effect.


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Table 3.5 Change from pre-trial to trial period (mean units sold per month)

Category Control Intervention

Mean (SD) Mean (SD) Mean change

Pre-trial Trial Pre-trial Trial Control Intervention

Bakery and bread 54.26 51.87 42.57 36.84

-2.39 -5.73 (86.56) (88.84) (65.61) (57.79)

Baking 9.14 11.80 6.43 8.04

2.66 1.61 (17.42) (22.85) (12.72) (16.41)

Breakfast

5.69 5.67 3.90 3.99 -0.02 0.09

(7.41) (7.33) (4.80) (4.61)

Can food

48.47 44.80 27.06 26.26 -3.67 -0.80

(131.38) (119.34) (71.63) (66.45)

Dairy

34.39 29.63 29.61 26.52 -4.76 -3.09

(108.91) (83.54) (93.42) (84.22)

Drinks excluding soda

16.39 15.51 13.21 12.63 -0.88 -0.58

(44.47) (38.24) (45.45) (43.16)

Frozen

9.18 9.31 5.42 6.11 0.13 0.69

(10.28) (11) (6.38) (7.06)

Grain and Pasta

13.94 16.07 8.61 12.42 2.13 3.81

**

(12.45) (16.84) (7.74) (17.40)

Meat and seafood

8.11 7.27 5.72 5.46 -0.84 -0.26

(12.61) (11.91) (8.98) (7.68)

Pantry

16.89 15.91 10.56 9.41 -0.98 -1.15

(24.18) (25.37) (15.30) (17.07)

Snacks

9.23 10.51 8.50 8.88 1.28 0.38

(14.24) (15.62) (12.19) (12.34)

Soda 11.95 11.91 7.48 7.44

-0.04 -0.04 (7.71) (7.59) (5.19) (5.86)

p values from t-test, * and

** significant at 10 and 5% level of significance respectively

The results are suggestive that the effect of a healthy nudge intervention will be

greatest when it focuses upon products with already existing product promotion

information. Specifically, it is likely the case that whole grain pastas and healthier sauces

were successfully encouraged because of the existing programs to encourage

consumption of whole grains (e.g. http://choosemyplate.gov/food-groups/grains-

why.html). The increased consumption of whole grains has been a common

encouragement by dietitians for a number of years.


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3.5.3 Community awareness and use of project activities

3.5.3.1 Community’s perception on project activities

In doing the analysis to find the effect of supermarket intervention in healthy food

choice decision the current analysis was restricted by the unavailability of data to analyze

the direct effect of intervention at an individual level. However, an important indicator of

success and usefulness of the project is whether community members recognized and

found project activities to be helpful. With this end in mind an analysis of respondent’s

attitude towards project activities was done by using the data available from the

questionnaire survey done during the trial period and the follow-up survey as described in

Section 3.4.2.3. From the survey during the trial period, 85% of the respondents in the

intervention site had seen project health messages in the project period. This shows the

overall awareness of health messages in the community to be 85%.

The results of respondents’ attitude towards each project activities during the trial

period are shown in Table 3.5. As shown in the table, community members were most

aware of the shelf talkers in the supermarket (38%) followed by information from

community education classes (25%) and nutrition education classes (5%). Fifty-four

percent of the respondents reported to have tasted the foods during the food tastings

organized by the project in the supermarket. Project activities were viewed very

positively by community members. Of the respondents’ aware of information and

programs provided to 4-H, boy and girl scouts, youth and other groups on cancer

prevention 97% reported it to be helpful. Of the respondents who had seen blue or green

signs: shelf talkers in the supermarket, 80% reported it to be helpful. Similarly, 85% of


114

the respondents’ who had considered NuVal score before purchasing food item reported

the score system to be helpful.

Table 3.6 Community views of the project during the trial period

Value use of information provided (N=276) yes proportion

1. Aware of information and programs provided to 4-H, boy and girl

scouts, youth and other groups on cancer prevention

70 25%

If yes, agree (strongly agree or agree) these programs have helped

learn more about cancer risk reduction and healthy living

68 97%

2. Participate in monthly education classes at County Electric Co-op 14 5%

If yes, agree (strongly agree or agree) participation has helped to

learn more about cancer

14 100%

3. Taste foods at the food tasting at the Supermarket 148 54%

Tried the recipes received at food tasting in the Supermarket 53 36%

4. Seen Cut your risk boost your health (shelf talkers) 106 38%

If yes, agree (strongly agree or agree) that the signs were helpful 85 80%

5. Consider NuVal score before purchasing food item 39 14%

If yes, higher NuVal score should be selected 28 62%

If yes, NuVal score is helpful (very helpful or helpful) 33 85%


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The results of respondents’ attitude towards each project activities during the

post-trial period from the follow-up survey are shown in Table 3.7. As shown in the table,

community members were most aware of the shelf talkers in the supermarket (48%)

followed by information from community education classes (28%) and nutrition

education classes (5%). Sixty percent of the respondents reported to have tasted the foods

during the food tastings organized by the project in the supermarket. Of the respondents’

aware of information and programs provided to 4-H, boy and girl scouts, youth and other

groups on cancer prevention 54% reported it to be helpful. Of the respondents who had

seen blue or green signs: shelf talkers in the supermarket, 67% reported it to be helpful.

Similarly, 5 % of the respondents’ who had considered NuVal score before purchasing

food item reported the score system to be helpful. Hence all the project activities were

viewed positively by the community members, less positively during the follow-up

period compared to during the trial period.


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Table 3.7 Community views of the project from the follow-up survey

Value use of information provided (N=200) yes proportion

1. Aware of information and programs provided to 4-H, boy and girl

scouts, youth and other groups on cancer prevention

57 28%

If yes, agree (strongly agree or agree) these programs have helped

learn more about cancer risk reduction and healthy living

31 54%

2. Participate in monthly education classes at County Electric Co-op 11 5%

If yes, agree (strongly agree or agree) participation has helped to

learn more about cancer

8 73%

3. Taste foods at the food tasting at United Supermarket 120 60%

Tried the recipes received at food tasting in the Supermarket 64 53%

4. Seen Cut your risk boost your health (shelf talkers) 96 48%

If yes, agree (strongly agree or agree) that the signs were helpful 64 67%

5. Consider NuVal score before purchasing food item 41 20%

If yes, higher NuVal score should be selected 16 39%

If yes, NuVal score is helpful (very helpful or helpful) 22 54%

3.5.3.2 Awareness by demographics

From the available data set it was also possible to analyze the intervention

awareness and attitude towards intervention (shelf talkers and NuVal scores) of the

respondents who self-reported regular shopping in the supermarket was also analyzed by

demographics. This section explains the intervention awareness and attitude towards

intervention by several demographic variables (education, income, race, age, gender, and

language).


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Education

Table 3.8a shows the awareness and use of NuVal score by level of education.

The highest proportion of respondents who considered NuVal score system during

purchase was high school graduate, 38% and 50% respectively in intervention and

control community. The highest proportion of respondent who used NuVal score system

during purchase and considered it helpful was also high school graduates, 29% and 38%

respectively in intervention and control community.

Table 3.8a Use of NuVal score system by education level

NuVal score

Consider during purchase (%) Consider helpful (%)

Intervention Control Intervention Control

Elementary 12 7 24 7

High school graduate 38 50 29 38

Some college or technical school 25 27 22 34

College graduate 25 17 24 21

Table 3.8b shows the awareness and use of healthy eating signs placed by the

project by the respondents’ level of education in the intervention site. The highest

proportion of respondents who considered healthy eating signs during purchase was high

school graduate (39%). The highest proportion of respondent who used healthy eating

signs during purchase and considered it helpful was also high school graduates (39%).


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Table 3.8b Use of in-store signs by education level in the intervention community

Store signs Consider during purchase (%) Consider helpful (%)

Elementary 21% 24%

High school graduate 39% 39%

Some college or technical school 21% 21%

College graduate 19% 16%

Income

Table 3.9a shows the awareness and use of NuVal score system by income. The

highest proportion of respondents who considered NuVal score system during purchase

was in the income range of $20,000 to $35,000, 23% and 33% respectively in

intervention and control community. The highest proportion of respondent who used

NuVal score system during purchase and considered it helpful were in the income range

less than $20,000 in intervention cite (34%) and in the range of $20,000 to $35,000

(38%) in the control community.

Table 3.9a Use of NuVal score system by income groups

NuVal score



Less than $20,000 31% 27% 34% 24%

$20,000 - $35,000 23% 33% 21% 38%

$35,000 -$ 50,000 19% 10% 19% 3%

$50,000 - $75000 10% 20% 9% 17%

over $75000 17% 10% 17% 17%


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Table 3.9b shows the awareness and use of healthy eating signs placed by the

project by the respondents’ level of income in the intervention community. The highest

proportion of respondents who considered healthy eating signs during purchase in the

intervention community were in the income level less than $20,000 (42%). The highest

proportion of respondent who used healthy eating signs during purchase and considered it

helpful was also in the income level less than $20,000 (45%).

Table 3.9b Use of in-store signs by income groups in the intervention community


Less than $20,000 42% 45%

$20,000 - $35,000 20% 22%

$35,000 -$ 50,000 14% 14%

$50,000 - $75000 13% 10%

over $75000 10% 9%

Gender

Table 3.10a shows the awareness and use of NuVal score system by gender. The


were female, 75% and 63% respectively in intervention and control community. The

highest proportion of respondent who used NuVal score system during purchase and

considered it helpful were also female, 76% and 59% in the intervention and control

community respectively.


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Table 3.10a Use of NuVal score system by gender

NuVal Score



Male 25% 37% 24% 41%

Female 75% 63% 76% 59%

Table 3.10b shows the awareness and use of healthy eating signs placed by gender

in the intervention community. The highest proportion of respondents who considered

healthy eating signs during purchase in the intervention community were female (70%).

The highest proportions of respondent who used healthy eating signs during purchase and

considered it helpful were also female (73%).

Table 3.10b Use of in-store signs by gender in the intervention community


Male 30% 27%

Female 70% 73%

Race

Table 3.11a shows the awareness and use of NuVal score system by race. The


were white, 56% and 50% respectively in intervention and control community. The

highest proportion of respondent who used NuVal score system during purchase and

considered it helpful were also white, 52% and 48% in the intervention and control

community respectively.


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Table 3.11a Use of NuVal score system by race

NuVal score



White 56% 50% 52% 48%

Hispanic 44% 43% 48% 45%

Other 0% 7% 0% 7%

Table 3.11b shows the awareness and use of healthy eating signs placed by race in

the intervention community. The highest proportion of respondents who considered

healthy eating signs during purchase in the intervention community were white (53%).

The highest proportions of respondent who used healthy eating signs during purchase and

considered it helpful was very close, 49% Whites and 50% Hispanics.

Table 3.11b Use of in-store signs by race in the intervention community


White 53% 49%

Hispanic 45% 50%

Other 1% 2%


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Language

Table 3.12a shows the awareness and use of NuVal score system by language.


purchase spoke English language, 92% and 80% respectively in intervention and control

community. The highest proportion of respondent who used NuVal score system during

purchase and considered it helpful also spoke English, 86% and 83% in the intervention

and control community respectively.

Table 3.12a Use of NuVal score system by language

NuVal score



English 92% 80% 86% 83%

Spanish 8% 20% 14% 17%

Table 3.12b shows the awareness and use of healthy eating signs placed by

language in the intervention community. The highest proportion of respondents who

considered healthy eating signs during purchase in the intervention community spoke

English (93%). The highest proportions of respondent who used healthy eating signs

during purchase and considered it helpful were also spoke English (92%).

Table 3.12b Use of in-store healthy eating signs by race


English 93% 92%

Spanish 7% 8%


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Age

Table 3.13a shows the awareness and use of NuVal score system by age group.


purchase were aged more than 30, 92% and 86% respectively in intervention and control

community. The highest proportion of respondent who used NuVal score system during

purchase and considered it helpful were also in the age group more than 30, 86% and

72% in the intervention and control community respectively.

Table 3.13a Use of NuVal score system by age group

NuVal score



Less than or equal to 30 8% 23% 14% 28%

More than 30 92% 77% 86% 72%

Table 3.13b shows the awareness and use of healthy eating signs placed by age in

the intervention community. The highest proportion of respondents who considered

healthy eating signs during purchase in the intervention community was more than 30

years old (86%). The highest proportions of respondent who used healthy eating signs

during purchase and considered it helpful were also in the age group more than 30 years

old (85%).

Table 3.13b Use of in-store signs by age group


Less than or equal to 30 14% 15%

More than 30 86% 85%


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3.6 Conclusions

Community-based models that incorporate the supermarket in program activities

are rare despite its potential effectiveness. A community-based cancer risk reduction

program was implemented with a major supermarket in a small community. The small

community setting allowed control over the media and a supermarket based intervention

provided a key source of information and reinforcement for healthy behavior. In the

project model, the supermarket was used a) to conduct healthy food demonstration, b) to

put shelf talkers with healthy eating message in comparatively healthy food products, c)

to provide healthy eating information, and d) to access food purchase data to evaluate the

effect of the project on food purchase behavior. This research paper reports the project

effect on food purchase behavior based on monthly sales in units sold per month for

produce items and healthier items identified with shelf talkers.

The overall change analysis that employed a double difference method and

comparative analysis suggest that the project had a positive impact on purchase of fresh

produce. The overall increase (or less decrease) was positive. On looking at the change

by category the change in produce purchase in green leafy produce category was

significant. Similarly, the overall change was positive in the sales of healthier items

identified with shelf talkers. On looking at the change by categories the change was

positive and significant for the category grain pasta and sauce. Thus the findings suggest

that supermarket interventions have potential in changing food behavior. However, the

lack of positive change across all categories also suggests the challenges associated with

changing food behavior. Another analysis of attitude towards project activities showed


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that that the general awareness was 85% and majority either strongly agreed or agreed

that the project activities were helpful.

The analysis reported in this paper was restricted by the lack of household level

data on food and produce items’ purchase. The data also lacked category level sales to

compute change in the market share of identified items with shelf talkers. However, the

demographic data available from the associated survey undertaken by the project was

used to analyze the supermarket intervention awareness and attitude towards awareness

by demographics. The results suggest that high school graduate in less than $20,000

income per year, English language speaking female over the age of 30 were most aware

of the interventions and had positive attitude towards the interventions. The data also did

not allow isolation of the shelf talkers effect from the community-based healthy message

reinforcement effect.


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Appendix

Appendix 3.1 Response comparison in intervention site from post-intervention survey



Married 69% 62% 0.249

Divorced 8% 14% 0.205

Widowed 15% 4% 0.002









Less than $20,000 21% 35% 0.026

$20,000 to $35,000 19% 30% 0.090

$35,000 -$ 50,000 25% 14% 0.027

$50,000 - $75000 10% 14% 0.341

over $75000 25% 7% 0.000

Gender

Male 40% 35% 0.389

Female 60% 65% 0.389

Race

White 79% 40% 0.000

Hispanic 19% 58% 0.000

p values from independent t-test


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Appendix 3.2 Response comparison from control site during post-intervention survey

Response item Invitees Volunteers p value


Married 39% 78% 0.000

Divorced 14% 7% 0.177

Widowed 18% 1% 0.000









Less than $20,000 39% 23% 0.070

$20,000 to $35,000 29% 36% 0.460

$35,000 -$ 50,000 7% 14% 0.350

$50,000 - $75000 18% 13% 0.478

over $75000 7% 15% 0.275

Gender

Male 29% 36% 0.460

Female 71% 64% 0.460

Race

White 61% 41% 0.048

Hispanic 32% 55% 0.028

p value from independent t-test


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Appendix 3.3 Comparing response in intervention site during post-intervention survey

Aware of information and programs provided to 4-H, boy and girl scouts, youth and

other groups on cancer prevention.

Yes 21% 26% 0.448

No 79% 74% 0.448

If yes, these programs have helped you or your families learn more about cancer

prevention and healthy living.

Strongly agree 13% 30% 0.205

Agree 73% 62% 0.412

Neutral 13% 9% 0.582

Disagree 0% 0% NA

Strongly disagree 0% 0% NA

Participate in monthly education classes at Bailey County Electric Co-op

Yes 7% 3% 0.173

No 93% 97% 0.173

If yes, participation has helped to learn more about

cancer


Agree 60% 67% 0.819

Neutral 0% 0%

Disagree 0% 0%

Strongly disagree 0% 0%

Do you shop at united supermarket?

Yes 94% 89% 0.211

No 6% 10% 0.254

Don’t know 0% 1% 0.537

Have you participated in food tastings?

Yes 52% 51% 0.851

No 48% 48% 0.972

Don’t know 0% 1% 0.382

Have you tried the recipes?

Yes 52% 51% 0.913

No 48% 49% 0.913

Have you seen the signs “cut your risk boost your health” in United Supermarket?

Yes 30% 40% 0.137

No 30% 32% 0.760

Have seen 24% 20% 0.526

Don’t know 17% 9% 0.054

If yes, have you found them helpful in making healthy food choice


Agree 67% 53% 0.255

Neutral 24% 19% 0.621

Disagree 0% 0% NA

Strongly disagree 0% 0% NA


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Do you consider a food item’s NuVal score before purchasing an item?

Yes 18% 14% 0.443

No 37% 44% 0.293

Don’t know 45% 42% 0.626

Do you select items with higher or lower Vu-Val score to select the best nutritional

value?

Higher 37% 42% 0.507

Lower 39% 30% 0.218

Don’t consider NuVal 16% 20% 0.456

Don’t know 9% 8% 0.886

Do you find NuVal score helpful in making healthy food choices?


Agree 42% 32% 0.404

Neutral 47% 46% 0.918

Disagree 0% 2% 0.581

Strongly disagree 0% 3% 0.432

p value from independent t-test


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CHAPTER IV

COMMUNITY-BASED BEHAVIOR CHANGE INTERVENTION FOR OBESITY

PREVENTION: A SYSTEMATIC REVIEW AND META-ANALYSIS

Abstract

This chapter reports the findings of a systematic review of research on community-based

behavior change interventions implemented for obesity prevention. Obesity is a pressing

international public health problem where community-based improvement efforts are

being used to improve obesity outcomes by improving energy balance related behavior

(EBRBs). An assimilation of the literature on impact evaluation of community-based

behavior change interventions designed to address obesity shows potential in guiding the

design, implementation, and evaluation of future community-based behavior change

interventions. Both the narrative synthesis and meta-analysis revealed that community-

based behavior change intervention have a strong impact in decreasing obesity and

energy balance related behaviors but this impact varies substantially. The overall fixed

and random effect of such interventions showed significant but modest decrease in BMI.

Further, this research leads to a much needed consideration during the design of effective

community-based behavior change interventions. The findings of this review suggest that

the designers of community-based interventions should carefully consider between large-

scale, less-intense vs. small-scale more-intense interventions for weight loss and obesity

prevention.

Key words: community-based, behavior, obesity prevention, body mass index (BMI),

energy balance, systematic review, meta-analysis


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4.1 Introduction

Obesity was often considered to be primarily an individual’s problem and some

success in the areas of diet and physical activity on an individual basis was observed (e.g.

Glanz et al., 1998). However, attaining obesity prevention or weight loss without relapse

has posed challenge in obesity prevention and at a societal level has been insufficient. It

is more likely that individual behavior change will be sustained if it is supported by

environment that reinforces healthy choices (Economos and Irish-Hauser, 2007). An

individual’s behavior is framed through individual differences (knowledge and attitude,

previous experiences, and personal preferences), the social environment, and the larger

community (Gotay, 2004). The increasing prevalence and increasing obesity trends

across communities led researchers to target obesity as a societal problem rather than

solely individual’s problem. A rationale for community-based interventions has also been

developed by Economos and Irish-Hauser (2007).

Behavior change has been a key strategy, as the obesity prevention paradigm

shifts from individual based intervention to community-based interventions. The behavior

change is geared towards attaining a decrease in body weight by community-based

interventions for obesity prevention. A generic conceptual framework for understanding

how interventions may promote behavior change has been provided in Baranowski et al.

(2009). Behavior change in obesity prevention can be based upon the concept of energy

balance. Energy balance is achieved when calories being consumed are equal to calories

expended. The World Health Organization (WHO) defined the fundamental cause of

obesity and overweight to be an energy imbalance between calories consumed and


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calories expended (WHO, 2013 and Baranowski et al., 2003). Further, WHO (2013)

pointed out that globally there has been:

an increased intake of energy dense foods that are high in fat, and

a decrease in physical activity due to the increasingly sedentary nature of many

forms of work, changing modes of transportation, and increasing urbanization.

According to US Department of Health and Human Services, a person gains weight when

he/she consumes more calories from food than the body uses through its normal functions

i.e. the basal metabolic rate (BMR) and physical activity (Miljkovic et al., 2008).

In this chapter, obesity prevention is studied with a focus on community-based

behavior change interventions. A key to obesity prevention is behavior change. However,

informing people of the health risks associated with their behavior is not sufficient

(Jeffery, 1995). Multiple behavior interventions can have significant effect in behavior

change towards obesity prevention ( Johnson et al., 2008). A behavioral science research

in weight gain has recommended four key topics related to obesity prevention and

physical activity promotion from a systematic review (Wing et al., 2001). The topics are:

1) environmental factors, 2) adoption and maintenance of healthful eating, physical

activity and weight, 3) etiology of eating and physical activity, and 4) multiple behavior

change interventions.

Related systematic review and meta-analysis in the area of community-based

behavior change interventions is Seo and Sa (2008). But Seo and Sa reviewed psycho-

behavioral obesity interventions among a limited group (US multiethnic and minority

adults). Similar review to find the potential to prevent or delay type 2 diabetes with

intensive lifestyle interventions was done by Satterfield et al. (2003), however the focus


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was on diabetes prevention. Another search of community-based intervention and

subsequent systematic review was done for community-based intervention to reduce

overweight and obesity by Gao et al. (2008) but the interventions were limited to China.

These are the only literature of the kind reported in this paper found in the obesity

prevention literature. The existent literature in the area of obesity prevention has not

looked specifically at community-based behavior change interventions.

A major question still exists on how to make community-based interventions

more effective, efficient and replicable. This question can partly be answered by

assimilating the intervention effect of community-based interventions in modifying

behavior and changing weight status towards obesity prevention. To accomplish this goal

a systematic review and a meta-analysis was undertaken. The systematic review method

has been cited as the best form of synthesizing evidence collected in the literature

(Wright et al., 2007). There are systematic reviews on what works best in an energy

balance related behavioral intervention. For example Stralen et al. (2011) has concluded

that self-efficacy and intention are relevant in physical activity interventions.

The current research provides a significant contribution to obesity prevention

literature by synthesizing the impact of community-based obesity prevention research

from different disciplines. This research will contribute in developing a trans-

disciplinary, translational obesity prevention program. The development of such program

has also been stressed by Kremers et al. (2006) and (Schmitz and Jeffery, 2000). A shift

from the traditional uni-disciplinary research to translational, trans-disciplinary research

has potential to accelerate both discovery and its translation to practice, and eventually

policy (Nebelling, 2012). This review intends to help recommend an effective


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community-based intervention program, the necessity of which was also pointed by

Kristal and Ollberding (2012), by looking at the evidence from previous community-

based behavior change interventions.


Looking at obesity from an energy balance perspective requires looking at two

components energy intake and energy expenditure. Energy intake can be influenced or

determined by socioeconomic to psychological factors. Evidence of diverse determinants

of energy intake is provided below.

Drewnowski (2003) discussed diet structure with Engel’s law “wealth and poverty

have profound effects on diet structure, nutrition and health.” According to Engel’s law

the proportion of income spent on food diminishes as income increases. Drewnowski

(2003) suggested that diet structure changes as well. Income and the macronutrient

composition of the diet are linked at the aggregate and most likely the individual level.

Furthermore, people in the higher income nations consume more added sugars and fats

than do people in lower income nations.

The availability and accessibility of fast foods, snacks and soft drinks, food

advertising and food marketing have been addressed by environmental studies (e.g. Kant

and Schatzkin, 1994; Harnack et al., 1999; French et al., 2001; Ludwig et al., 2001; Zizza

et al., 2001 and Swinburn et al., 2004 ). Cohen and Babey (2012) reviewed evidence that

dietary behaviors are in large part the consequence of automatic response to contextual

food cue. Restaurants and grocery stores are the primary setting from which people

obtain food. These settings are often designed to maximize sales of food by strategically


135

placing and promoting items to encourage impulse purchases of high margin food

products.

A study that investigated the association between walkability and obesity

stratified by neighborhood race and socioeconomic status (SES) among adults residing in

Baltimore City by Casagrande et al. (2010) concluded that among some neighborhoods,

high walkability was associated with less obesity. Other studies like Jp (2011) reported

that factors governing energy intakes influence the energy balance far more powerfully

than factors determining resting energy expenditure. Pagliassotti (1997) reported that an

imbalance between energy intake and energy expenditure can explain approximately 80%

of the variance in body weight gain in a dietary model of obesity.

Thus prevention of obesity will require the study of how behavioral and

environmental factors have interacted to produce positive energy balance and weight

gain. Reversing the epidemic of obesity will require modifying some combination of

these factors to help the population achieve energy balance and a healthy body weight.

While body weight is strongly influenced by biological and behavioral factors, changes in

the environment promoting positive energy balance have been most responsible for the

obesity epidemic.

The best strategy for reversing the obesity epidemic is to focus on preventing

positive energy balance in the population through small changes in diet and physical

activity that take advantage of our biological systems for regulating energy balance. By

simultaneously addressing the environment to make it easier to make better food and

physical activity choices (Hill, 2006). A similar systematic review that included all the

non-drug controlled interventions in Mainland, China which used anthropometric


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outcome measures was done by (Gao, 2008). This review concluded that comprehensive

interventions with at least physical activity, dietary intervention and health education may

be effective in reducing obesity in China. Another similar search was done in MEDLINE

in 2002 (Fogelholm and Lahti-Koski, 2002). This research concluded that interventions

may need a stronger emphasis on changes in the local physical and social environment to

achieve improvements in energy expenditure due to physical activity


The energy balance as defined by the National, Heart Lung and Blood Institute is

the balance of calories consumed through eating and drinking compared to calories

burned through physical activity (NIH-NHLBI, 2013). What we eat and drink is energy-

in and what we burn is energy-out. Three conditions may occur:

1. Energy-in equal energy-out

2. More energy-in than energy-out

3. More energy-out that energy-in

Obesity is a function of an individual’s energy balance over a number of time

periods. Energy balance in a given time period is the difference between calories

consumed and expended in that period. In addition to this cumulative energy balance,

age, gender, race, ethnicity and genetic factors unique to an individual help determine

weight outcomes by influencing the process by which energy balances are translated into

changes in body mass. According to Stralen et al. (2011) psychosocial variables affect an

individual’s energy balance and are called the energy balance-related behavior (EBRBs).

The EBRBs are the set of variables that cause obesity by affecting the choice decision on

how much energy to consume, how much and by what means to expend the energy.


137

There are research conducted in different discipline regarding the effect of EBRBs on

obesity such as in nutrition and physical activity (Dionne et al., 1997; Seale and

Rumpler, 1997; Murgatroyd et al., 1999 and Mobasheri et al., 2005) and psychology

(Williamson and Stewart, 2005 and Sluis et al., 2010 ). However, a single cause is not

independent of other causes, obesity is an outcome of complex set of behavior and

environment (Fiseković, 2005).

This systematic review is based on an analytic framework where obesity is the

cumulative function of individual’s energy imbalance over time. This analytical

framework was developed by Chou et al. (2004). The framework is explained here.

Obesity is a function of an individual’s energy balance over a number of time

periods (which are ages). If is the energy balance in period j, then it can be defined as,

(1)

where, are calories consumed in period j and are calories expended in all activities

(basal metabolic rate and physical activity) in period j.

Weight status (O) is a cumulative function of the individual’s energy balance over

a number of periods:

( ) (2)

where, is a vector of variables that is specific to an individual and is related to his or

her genetic predisposition towards obesity. Chou et al. (2004) have listed age, gender,

race, and ethnicity along with the genetic predisposition to be included in the vector .

Equation 2 thus suggests that according to the energy imbalance model of obesity it is

necessary to explain the determinants of calories consumed and calories expended.

Obesity is not an outcome variable by itself. Obesity is an outcome of decisions made to


138

fulfill other goals. However, Chou et al. (2004) does not include other factors that

influence food choice decisions and underlying behaviors that causes obesity. In this line,

the following factors, in addition to the elements listed for vector can be considered as

factors that influences energy intake and energy expenditure:

Energy intake

Food demand (price, expenditure/income, advertising, demo, etc.)

Advertising

Food environment (including access)

Genes/Demography

Nutrition knowledge

Psychological or emotional reasons for intake (e.g. stress)

Quality of Life (vacations, aspiration-professional/cultural/educational,

Energy expenditure

Exercise

Work (nature of employment)

Lifestyle

Neighborhood environment (including access)

Genes/Demo

Nutrition knowledge

Psychological or emotional reasons for metabolism or burning (e.g. stress)


139

An extension to equation 2, also developed by Chou et al. (2004) is as follows:

( ) (3)

where, is calorie consumed, is active leisure (either the time allocated to this activity

or the total energy expended by an average person in performing each of a variety of

leisure activities), is a similar concept pertaining to household chores, is energy

expended by an average person in the occupation performed by the individual at issue,

is cigarette smoking, is age, is gender, and summarizes racial and ethnic

background.

In equation 3, are independent variables. The other variables:

are the EBRBs in general or the variables affected by other EBRBs

(namely health attitude, cancer knowledge and food behaviors). The later set of EBRBs

(health attitude, cancer knowledge and food behaviors) also affects obesity directly or

indirectly by affecting the variables included in equation 3. Thus, equation 3 might not be

complete. However, the goal of this systematic review is not to develop an extension to

equation 3 or to develop a new obesity model. The objective of this review is to

assimilate the empirical evidence on the effect of community-based behavior change

intervention designed to affect obesity by changing the EBRBs. The goal is to synthesize

such research from different discipline towards recommending a translational

community-based behavior change approach in obesity prevention program.


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4.4 Methods

The systematic review technique is fairly new in applied economics. Google

scholar search for meta-analysis and systematic review in the title of articles published in

the American Journal of Applied Economics had zero hits. The same search in the

American Journal of Public Health resulted in 19 hits for search term “systematic review”

and four hits for search term “meta-analysis” in the topic. Hence the method followed in

this analysis follows from methods applied in public health. The methods have been cited

from relevant publications in obesity and public health related journals like the Obesity

Reviews and the American Journal of Public Health.

4.4.1 Search strategy

A systematic review of published literature from earliest available to December of

2013 was done from January to March of 2014. The articles were planned to be identified

through searches in the following databases: MEDLINE (through PubMed), CENTRAL

(The Cochrane Library), Web of Science, EMBASE, and Google Scholar. However, a

similar systematic review was done in MEDLINE in 2002 (Fogelholm and Lahti-Koski,

2002), hence the search in MEDLINE was dropped. The search was limited to Google

Scholar and Web of Science due to time limitations.

A preliminary search with BMI and obesity in the topic and anywhere in the

article resulted in 467,000 hits in Google Scholar and 29,734 articles in the Web of

Science alone. Hence, in lieu of the search terms anywhere in the article the search was

objectively narrowed to include search terms ‘BMI’ ‘obesity’ ‘community’ and

‘behavior’ in the title and anywhere in the article. The objective was to review the effect

of community-based behavior change intervention on obesity behavior (EBRBs) and


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BMI however the search was done for ‘community’ instead of ‘community-based’ to

avoid missing articles that refer to their intervention as a community intervention instead

of community-based intervention. To ensure inclusion of all community-based

interventions, a second search in the data bases was done for the search terms ‘BMI’

‘obesity’ ‘community-based’ and ‘behavior.’ The search strategy was adapted according

to the requirements of individual databases.

4.4.2 Language and data restrictions

No language restrictions were applied during the search. The publication year

restrictions include publications from earliest records available to the December of 2013.

However, most of the articles on the research topic were found to be published after the

year 2000 (Appendix 4.1).

4.4.3 Selection criteria

Inclusion criteria were developed to address the problem of heterogeneity in

intervention type and outcome measures as suggested by Mulrow et al. (1997). The

research question was specific. The inclusion criteria ensure that the research was

conducted in a consistent manner and the methods applied was on a community-based

intervention and the outcome variable was behavior related to obesity and BMI. The

applied selection criteria for the study were: 1) it must have a predetermined focus on

changing variables related to obesity behavior –the EBRBs, 2) it must be carried out in a

community setting, or report findings of research being carried out in community setting,

and 3) the intervention must be a community-based intervention. The set of EBRBs

developed during the systematic review were self-efficacy, nutrition knowledge, stress,


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and lifestyle behavior. Articles reporting clinical trials with or without animals will not be

included. All types of publication available in the databases were included.

The exclusion criteria are behavior change through clinical trials, research

conducted outside a community setting, research that modifies food options and/or food

prices. The researchers are specifically interested in the success of translational

community-based research for behavior change towards obesity prevention without

confounding effect from other factors such as change in food price. Observational study

that did not have an intervention component such as Peracchio et al. (2012) were not

included. This review only includes intervention evaluation studies because the

researchers are specifically interested in the success of translational community-based

research for behavior change towards obesity prevention.

4.4.4 Data extraction and synthesis

Following the systematic search, a screening of titles and abstracts was done to

identify their potential inclusion in the review. The following data were extracted into

tables in excel. In the first screening authors, publication year, research objectives,

methodology and major results were extracted in a excel file. The extracted data file was

checked for completeness and accuracy and a final data file was made. A method of

narrative synthesis adapted and developed by Popay et al. (2007) was followed for this

review. This method has been followed by several researchers in the context of behavior

change including McMahon and Fleury (2012), Everson-Hock et al. (2013), Chisholm et

al. (2012), Gordon et al. (2011), and Skov et al. (2013). The findings that made it through

the final selection were grouped and narrative synthesis was applied to each group. This

systematic review process is shown in Figure 4.1.


143

4.4.5 Validation of systematic review

An additional separate search with the search terms ‘community-based’

‘systematic review’ and ‘behavior change’ in the title was done in Google Scholar and

Web of Science to validate this search.


144

Figure 4.1 The Systematic Review Process Used


145

4.4.6 Meta-analysis

The selected papers were checked for data on number of participants, change in

weight status and BMI to conduct meta-analysis. The comprehensive meta-analysis

software following Gourlan et al. (2011) was used to run both the fixed and random

effects model. Under the fixed effect model it was assumed that there was one true effect

size which underlies all the studies in the analysis and the differences in observed effects

are due to sampling error. Under the random effects model the variance in true effect was

allowed to vary from one study to another (Borenstein et al., 2009). For example the

effect size might be higher or lower in studies where the participants are older or more

educated etc. A forest plot of standard difference in means was also computed. The

methods are closely followed from (Gonzalez-Suarez, 2009), a meta-analysis of school

based interventions in childhood obesity prevention.

4.5 Results

The search in Google Scholar and Web of Science resulted in 21,000 and 312 hits

respectively. From these hits, a careful selection with following the inclusion and

exclusion criteria resulted in 46 hits. Of these 46 hits, the analysis was done for 24

articles. The other 22 articles were not included for the reasons shown in Figure 4.1. A

summary of the selected articles is shown in Appendix 4.1.

In the initial screening of articles based on the inclusion and exclusion criteria

school based interventions were also included. However, the search revealed numerous

systematic reviews already been done for school-based intervention evaluation research

(e.g. Peterson and Fox, 2007 and Bluford et al., 2007). A systematic review on economic

incentives and nutritional behavior of children in the school setting has been done by


146

Jensen et al. (2011). A review by Gittelsohn and Kumar (2007) concluded based on a

review of school-based obesity prevention interventions that the school-based obesity

prevention researchers needs improvement in its theoretical framework and the attention

should be changed towards larger randomized clinical trials. Further, environmental- and

community-based approaches should be more promising. Other reviews in childhood

obesity prevention through school based interventions are: DeMattia and Lee Denney

(2008), Kropski et al. (2008), Wofford (2008), Cook-Cottone et al. (2009), Hesketh and

Campbell (2010), Safron et al. (2011), Bleich et al. (2013), Showell et al. (2013), and

Thapa and Lyford (2014). For the reasons of abundant literature available on learning

from school-based obesity prevention program, school-based interventions were later not

included in this systematic review but could be included in the future for comparison of

school-based interventions with community-based interventions.

Some school based interventions that were included in the initial search based on

the inclusion and exclusion criteria but were later not discussed in this review are:

Gortmaker et al. (1999), Caballero et al. (2003), Neumark-Sztainer et al. (2003), Kain et

al. (2004), Wang et al. (2006), Taylor et al. (2007), Foster et al. (2008), Hughes et al.

(2008), Sanigorski et al. (2008), Gentile et al. (2009), Hoelscher et al. (2010), Chomitz et

al. (2010), Hollar et al. (2010), Crespo et al. (2012), and Moore et al. (2013). Further,

articles explaining the design of community-based research such as Broussard et al.

(1995) and Beckman et al. (2006) and study protocols of community-based research such

as de Silva-Sanigorski et al. (2010), Eisenmann et al. (2008), Shrewsbury et al. (2009),

and Taylor et al. (2013) are not included in the narrative synthesis and meta-analysis.


147

4.5.1 Trend in community-based intervention evaluation research

Based on the findings of this systematic review, the trend in community-based

behavior change intervention evaluation research reports follows an increasing trend as

shown in Figure 4.2. The period of 2005 to 2009 saw an increase in the number of

community-based research including in the US. Most of the articles on the research topic

were found to be published after the year 2000.

Figure 4.2 Trend in Community-based Evaluation Research

Further validation search of Google Scholar and Web of Science with

‘community-based’ ‘systematic review’ and “behavior change” in the title resulted in

zero hits.

0

2

4

6

8

10

12

1990 to

1994

1995 to

1999

2000 to

2004

2005 to

2009

2010 to

2013

No. of articles in and

outside US

No. of articles in US


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4.5.2 Narrative synthesis

This narrative synthesis reports the effectiveness of community-based behavior

change interventions on weight status and energy balance related behaviors. The

reporting is done under two groups: 1) Small scale relatively more intense interventions,

2) Large scale relatively less intense interventions.

4.5.2.1 Small scale relatively more intense interventions

Promotoras (community health workers) program

This review included three promotoras based studies: Balcazar et al. (2009a),

Shaibi et al. (2012), and Schwartz et al. (2013). Promotoras based programs have

emerged as promising models to address reduction of risk factors in Mexican Americans

as cited in Balcazar et al. (2009a). Both the promotoras based community-based

interventions evaluation report that the effect was significant. Balcazar et al. (2009)

reported statistically significant difference between perceived benefits and two heart

healthy behaviors (salt and sodium, and cholesterol and fat), and Schwartz et al. (2013)

reported significant decrease in weight status from pre- to post- treatment. Similarly,

Shaibi et al. (2012) reported significant decrease in BMI z-score, BMI percentile, and

waist circumference. They also reported increase in cardiorespiratory fitness and

decreases in physical inactivity and dietary fat consumption. Balcazar et al. (2009) and

Shaibi et al. (2012) were evaluation of a pilot program and both of these including

Schwartz et al. (2013) were targeted for Latino adults.


149

Family-based

Four articles included in this analysis were family-based intervention in a

community-based design: Robertson et al. (2008), Sacher et al. (2010), Smith et al.

(2010), and Bean et al. (2012). Statistically significant decrease in BMI is reported in

Sacher et al. (2010) and Smith et al. (2010). No statistically significant decrease in BMI

is reported in Robertson et al. (2008). However, statistically significant improvements

were observed in children’s quality of life and lifestyle, child parent relationship and

parents’ mental health without significant increase in fruit and vegetable consumption,

participation in moderate/vigorous exercise, and children’s self-esteem. Further Sacher et

al. (2010) also observed significant between group differences in cardiovascular fitness,

physical activity, sedentary behaviors and self-esteem. Similar observation is reported by

Smith et al. (2013). The target population in these interventions was children. Another

similar evaluation of a parent intervention for overweight children is Bean et al. (2012).

This study with (N= 41 families) found no significant treatment effect with few

improvements in dietary habit.

The analysis this far reveals that with focused population in small group

participating in an intensive community-based intervention with relatively less number of

direct beneficiaries (Appendix 4.1) have reported significant decrease in weight or BMI.

Number of participants (N) was 116 in Sacher et al. (2010), N was 440 in Smith et al.

(2010), and N was 15 in Shaibi et al. (2012). Significant decrease in weight or BMI was

not reported in Robertson et al. (2008), N was 27. Though significant decrease in BMI or

weight status was not observed across all studies synthesized this far, significant effect on

behavior change was observed in all reports synthesized this far.


150

Faith- and culture-based

Five articles included in this analysis were faith- and culture-based interventions:

Heath et al. (1991), McNabb et al. (1997), Yancey et al. (2006), Goldfinger et al. (2008),

and Kim et al. (2008). The numbers of intervened participants were 30, 15, 366, 26, and

36 adults respectively. All reported significant decrease in weight and BMI except

Yancey et al. (2006). The populations were African American in McNabb et al. (1997),

Yancey et al. (2006), Goldfinger et al. (2008), and Kim et al. (2008), and Native

American in Heath et al. (1991). The results suggest that peer-led weight loss and

behavior change interventions in small groups in a community-based setting can show

impact in hard to reach populations. African Americans bear the highest burden of

obesity and yet are less likely to benefit from weight loss programs compared to Whites

(Kumanyika, 2002). However the effect appears to not be significant when the number of

beneficiaries was high (N=366) in Yancey et al. (2006).

Other small-scale, relatively intense interventions research reports that have

reported significant weight loss and were included in this analysis are Graffagnino et al.

(2006) and Folta et al. (2009). A modest yet significant reduction in BMI is also reported

in Nguyen et al. (2012). However, the research findings from the same study as reported

by Nguyen et al. (2012) had reported non-significant effect in BMI or BMI score

(O’connor et al., 2008). A significant treatment effect was also reported by Rejeski et al.

(2011) based on a lifestyle intervention in a community setting for elderly cardiovascular

disease survivor.


151

4.5.2.2 Large scale relatively less intense interventions

Seven articles included in this analysis were less intense large scale community-

based behavior change interventions: (Jeffery, 1995), Crawford et al. (2000), McGuire et

al. (2001), Giachello et al. (2003), Jenum et al. (2006), Mohatt et al. (2007), Fotu et al.

(2011), and Rogers et al. (2013). The direct beneficiaries of this study ranged from larger

population sample group to communities. The number of respondents ranged from 394

respondents in Giachello et al. (2003) to 12 communities in Rogers et al. (2013) and 22

villages in Fotu et al. (2011). The longest project duration reported was by Jeffery (1995).

The reports of the findings from the Minnesota Heart Health Program (MHHP), a

13 year research and demonstration project was reported by Jeffery (1995) in this no

significant changes in weight were observed in the MHHP program. Similar non-

significant change in weight status was reported in Crawford et al. (2000). Both of these

study reports that compared to more intensive interventions weight changes were modest

but participation was large in these less intense large scale interventions. Similarly, Fotu

et al. (2011) concluded that community-based interventions in high obesity prevalence

area may require more intense or longer interventions.

Positive relationship between the degree of dietary restraint and lower weight was

shown by McGuire et al. (2001). Further this study showed methods developed to

increase behavioral and cognitive strategies have potential to control weight. Significant

increase in physical activity in the intervention group compared to control group was

reported by Jenum et al. (2006). This study concluded that theory driven low cost

population based intervention program could increase physical activity and reduce weight

gain. Similarly, Mohatt et al. (2007) also concluded that primary and secondary


152

prevention efforts that focus on increasing the intrinsic strengths of indigenous

knowledge can bring positive public health transformations based on a community-based

participatory research in Alaska. Significant increase in fruits and vegetable consumption

and significant sugary drink consumption limitation by children was reported by Rogers

et al. (2013). This study suggests that a multi-setting, community-based intervention with

a consistent message can positively impact behaviors that lead to childhood obesity.

4.5.3 Meta-analysis

The descriptive data was available from 15 articles as shown in Table 4.1, the

length of treatment ranged from 8 week to 1 year. Maximum length of follow-up was

three years. However, only ten articles from the 25 articles selected for narrative

synthesis including the 15 articles with descriptive data had the complete information

needed for a meta-analysis (Figure 4.3). Further, McGuire et al. (2000) had three

independent treatment groups. Hence they were treated separately during the meta-

analysis.


153

Table 4.1 Descriptive data of community-based behavior change evaluation reports

Study

Total

(N) Sex Age

Follow-

up (N)

Length of

treatment

Length of

follow-up

Heath et al. (1991) 406 M/F 54±15 406 37 wks 50 wks

Jeffery (1995) 280 M/ F 47 169 8 wks 1yr

McNabb et al. (1997) 39 F 56 39 14 wks NA

McGuire et al. (2000) 1226 M/ F 32.3±6.8 1044 NA NA

Crawford et al. (2000) 1226 M/ F 20 to 45 854 1 yr 3 yrs

Graffagnino et al. (2005) 418 M/ F 51.8±11.1 198 6 mnths 3 yrs

Yancey et al. (2006) 366 F NA 187 1 yr NA

Goldfiner et al. (2008) 26 M/ F 68 21 10 wks 1 yr

Folta et al. (2009) 110 F 57 55 12 wks NA

Sacher et al. (2010) 116 M/ F 10 37 12 wks 1 yr

Fotu et al. (2011) 1712 M/ F 14.5 815 NA 2.4 yrs

Nguyen et al. (2012) 151 M/ F 13 to 16 60 1 yr NA

Shaibi et al. (2012) 15 M/ F 15±0.9 15 12 wks NA

Schwartz et al. (2013) 477 M/ F 40 450 12 wks NA

Smith et al. (2013) 440 M/ F 6.1 (0.8) 274 10 wks NA

± CI, (SD), NA-not available, NS-not significant, M-male, F-female


154

Table 4.2 shows the reported baseline and post-intervention body weight with

confidence interval (CI) or standard deviation (SD), the change in body weight, and

respective p values. The minimum change in body weight was .001lbs (insignificant) in

Shaibi et al. (2012) and the maximum change in body weight was a decrease by 10 lbs

(significant) in McNabb et al. (1997). Body weight increased post-intervention only in

Nguyen et al. (2012).


155

Table 4.2 Body weight comparison from selected interventions

Study Unit Baseline Post-test Change p value

Heath et al. (1991) kg 80.00±13.00 NA -4.09±4.9 <0.05

Jeffery (1995)

Eat smart for your heart-2 kg 76.9 NA -0.5 NA

Eat smart for your heart-2 kg 79.7 NA -0.9 NA

BYFF kg 79.7 NA -0.9 NA

Wise weighs kg 75.5 NA -2.5 NA

McNabb et al. (1997) lbs 199.00±29.06 189±27.69 -10 ±10.28 <0.0001

McGuire et al. (2000)

-Restraint lbs 167.12±38.44 NA -0.74 0.001

-Flexible restraint lbs 167.12±38.45 NA -1.37 0.001

-Rigid restraint lbs 167.12±38.46 NA -1.15 0.001

Crawford et al. (2000) NA NA NA NA NS

Graffagnino et al. (2005) lbs 106 (28.8) 100.1 (27) -6 (8) <0.0001

Yancey et al. (2006) lbs 81.47 83.38 1.31 0.002

Goldfinger et al. (2008) lbs 194.3 185 -9.3 0.001

Folta et al. (2009) lbs 89.5 (18.8) 87.7 (19.6) -1.7(2.4) 0.010

Sacher et al. (2010) NA NA NA NA NA

Fotu et al. (2011) NA NA NA NA NA

Nguyen et al. (2012)

Loozit only kg 82.4 (12.4) 85.9 (13.4) 1.9 ± 4.7 NS

Loozit and ATC kg 84.2 (16.3) 88.1 (17.7) 1.9 ± 4.7 NS

Shaibi et al. (2012) kg 90.60 ± 6.80 89.9 ± 7.2 -0.001 0.440

Schwartz et al. (2013) lbs 179.76 176.86 -2.9 <.0001

Smith et al. (2013) NA NA NA NA NA

± CI and (SD) when available, NA-not available, NS-not significant


156

Table 4.3 shows the reported baseline and post-intervention BMI with CI or SD,

the change in BMI, and p value. The minimum change in BMI was 0.1, insignificant

decrease in Sacher et al. (2010) and insignificant increase in Nguyen et al. (2012). The

maximum change in BMI was a decrease by 1.5 (significant) in Heath et al. (1991). The

BMI increased post-intervention only in Yancey et al. (2006), Fotu et al. (2009) and

Nguyen et al. (2012).


157

Table 4.3 BMI comparison in selected interventions

Study Baseline Post-test Change p value

Heath et al. (1991) 31 29.50 -1.50±2.20 <0.05

McNabb et al. (1997) 33.90±5.41 32.50±5.41 -1.40±1.61 <0.02

McGuire et al. (2000)

Restraint 27.02±5.74 NA -0.13 0.001

Flexible restraint 27.02±5.75 NA -0.26 0.001

Rigid restraint 27.02±5.76 NA -0.21 0.001

Crawford et al. (2000) 27.70 (4.70) NA NA NS

Graffagnino et al. (2005) 37.50±8.80 NA NA NA

Yancey et al. (2006) 29.72 (6.36) 30.48 0.76 <0.0001

Goldfiner et al. (2008) 32.70 30.90 -1.80 0.001

Folta et al. (2009) 33.20 (5.70) 32.60 (6) -0.60(0.90) 0.01

Sacher et al. (2010) NA NA -0.10 0.70

Fotu et al. (2011) 22.90 (4.10) 25.20 (4.20) 2.30 NA

Nguyen et al. (2012)

Loozit only 30.80 (3.50) 30.80 (3.80) 0.10 (-0.30 to 0.40) NA

Loozit and ATC 30.80 (4.20) 31.40 (4.80) 0.10 (-0.30 to 0.40) NA

Shaibi et al. (2012) 32.50 ±1.60 32.00±1.70 -1.50 0.06

Schwartz et al. (2013) 31.71 31.19 -0.52 <0.0001

Smith et al. (2013) 22.50 (3.60) 22.10 (3.70) -0.50 (-0.60 to -0.40) <0.0001

± CI, (SD), NA-not available, NS-not significant


158

All the literature that reported the standard difference in mean, sample size and

paired p value were selected. The total number of individuals covered in this analysis was

5673. The follow-up data for intervention group was available for 4616 individuals. The

data used for the meta-analysis is shown in Table 4.4 and the output is shown in Figure

4.3.


159

Table 4.4 Data for meta-analysis

Study Diff. in means Sample size

Paired groups

p value Tail Effect direction

Heath et al. (1991) 1.500 406 0.050 2 Negative

McNabb et al. (1997) 1.400 39 0.020 2 Negative

McGuire et al. (2000a) 0.130 1044 0.001 2 Negative

McGuire et al. (2000b) 0.260 1044 0.001 2 Negative

McGuire et al. (2000c) 0.211 1044 0.001 2 Negative

Yancey et al. (2006) 0.760 187 0.001 2 Positive

Goldfiner et al. (2008) 1.800 21 0.001 2 Negative

Folta et al. (2009) 0.600 55 0.010 2 Negative

Sacher et al. (2010) 0.100 37 0.700 2 Negative

Shaibi et al. (2012) 1.500 15 0.060 2 Negative

Schwartz et al. (2013) 0.520 450 0.001 2 Negative

Smith et al. (2013) 0.500 274 0.001 2 Negative

Figure 4.3 shows the forest plot of the standard difference in mean, the dots and

95% CI. The meta-analysis shows that the intervention had modest yet significant

decrease in BMI in both fixed and random effects model. The fixed effect model standard

difference in mean BMI is -0.109, 95% CI = -0.139, -0.080 and random effects model

standard difference in mean BMI is -0.134, 95% CI = -0.216, -0.052. This meta-analysis

provided evidence that community-based behavior change interventions are favorable for

obesity prevention. Similar meta-analysis for school-based obesity prevention

intervention by Gonzalez-suarez et al. (2009) found convincing evidence that school-


160

based interventions are effective. The odds of participants being overweight or obese in

the school based intervention programs compared with the control were significantly

protective. Gonzalez-suarez et al. (2009) further found that interventions conducted for

more than one year had a higher odds ratio of decreasing the prevalence of obesity.


161

Model Study

Statistics for each study Std. diff. in means and 95% CI

Std.

diff in

means

Std.

error Variance

Lower

limit

Upper

limit

Z

value p value -1.00 -0.50 0.00 0.50 1.00

Heath et al. -0.098 0.050 0.002 -0.195 -0.000 -1.961 0.050

McNabb et al. -0.389 0.166 0.028 -0.714 -0.063 -2.342 0.019

McGurie et al. -0.102 0.031 0.001 -0.163 -0.041 -3.291 0.001

McGurie et al. -0.102 0.031 0.001 -0.163 -0.041 -3.291 0.001

McGurie et al. -0.102 0.031 0.001 -0.163 -0.041 -3.291 0.001

Yancey et al. 0.332 0.075 0.006 0.185 0.479 4.422 0.000

Goldfinger et al. -0.840 0.254 0.064 -1.337 -0.343 -3.310 0.001

Folta et al. -0.360 0.139 0.019 -0.633 -0.087 -2.587 0.010

Sacher et al. -0.064 0.165 0.027 -0.386 0.259 -0.388 0.698

Shaibi et al. -0.528 0.276 0.076 -1.069 0.012 -1.917 0.055

Schwartz et al. -0.185 0.048 0.002 -0.278 -0.092 -3.893 0.000

Smith et al. -0.239 0.061 0.004 -0.359 -0.118 -3.894 0.000

Fixed -0.109 0.015 0.000 -0.139 -0.080 -7.394 0.000

Random -0.134 0.042 0.002 -0.216 -0.052 -3.216 0.001

Figure 4.3 Forest Plot of the Standard Difference in Mean BMI


162

4.5.4 Large-scale, less-intense or small-scale, more-intense interventions?

Impacts of small scale relatively intense intervention suggest that significant

weight loss can be achieved. The relapse and follow-up effect of such interventions

included in this synthesis and meta-analysis have not been reported from small scale

interventions. According to Sacher et al. (2010) the limitation of these types of studies

was a short follow-up time which leaves the long term intervention impact unclear. The

significant impact in the initial months after intervention may relapse (Yancey et al.

(2006). The impacts of large scale less intense community-based interventions suggest

that a consistent decrease in BMI from pre- to post- intervention was not observed.

However, there were positive changes in attitudes and behavior. For eg. Rogers et al.

(2013) reported significant increase in fruits and vegetable consumption. Additionally, a

review published by Jeffery (1993) concluded that multiple health behavior intervention

is effective in behavior change.

A community originated community-based intervention that was not included due

to misfit in the narrative synthesis is the Whetstone et al. (2011) study of North Carolina.

This study assesses the change in children’s health behaviors and weight status after

participation in a community originated intervention. This study was unique and needs

consideration because the intervention was community originated. The conclusion of this

research was that the community originated interventions to raise awareness about food

choices and to change policies and environments may improve BMI z-scores. Other

similar hits that were not included are Samuels et al. (2010) and Cheadle et al. (2010)

which describes the approaches used to measure the extent and impact of environmental

changes in three community level obesity prevention initiatives in California. The effort


163

is to provide empirical evidence from intervention studies. These reports conclude that

the Healthy Eating, Active Communities (HEAC) programs successfully changed

children’s food and physical activity environments.

The current systematic review and meta-analysis suggest that change in BMI and

wegiht status through community-based behavior change intervention has promise.

However, as shown in Chapter one of this dissertation cost-effectiveness analysis should

be considered in deciding between low per unit cost long term trials or focused short term

interventions. This research has potential to bring a needed discussion in the area of

obesity prevention through community-based intervention in designing large scale less

intense intervention or small scale more intense interventions.

4.6 Conclusions

A systematic review and meta-analysis of community-based intervention was

done with an objective to synthesize research on community-based behavior change

interventions implemented for obesity prevention. The search was done in Google

Scholar and Web of Science. The analysis accomplished both a narrative analysis and

meta-analysis. Narrative analysis was done for all the articles that fulfilled the selection

criteria including the articles included in the meta-analysis. A meta-analysis was done for

articles that had the needed information to conduct the meta-analysis. The narrative

synthesis and meta-analysis reveal that community-based behavior change interventions

have promise to change behavior towards obesity prevention and to change BMI. The

overall fixed and random effect of such intervention show a modest yet significant

decrease in BMI. Based on the findings of this research, suffcient research and focus

whould be given by designers of such intervention. This research is suggestive that


164

designers should carefully consider among large scale less intense and small scale more

intense interventions. The current research provides a significant contribution to obesity

prevention literature by synthesizing the impact of community-based obesity prevention

research from different disciplines and creating a need in this area to focus on cost-

effectiveness of behavior change interventions.

This review could not include many research findings ( e.g. Epstein et al., 1984;

Johnson et al., 1997; Delahanty et al., 2002 and Klesges et al., 2008) though it was very

close to community-based intervention due to lack of identification as a community-

based intervention. Similarly other such un-included research are Pratt et al. (2007)

because it is a review report of design characteristics of worksite environmental

interventions. A future research in the area to be observed is the Growing Right Onto

Wellness (GROW) program. Po’e et al. (2013) has reported the concept of GROW

community-based research. Another important literature on family, community and

clinic collaboration to treat overweight and obese children to see is Robinson et al.

(2013). In future, a more comprehensive research that will conduct review, synthesis and

meta-analysis for effectiveness of community-based intervention by the socioeconomic

status of participants would also be beneficial to the literature as well.


165

Appendix

Appendix 4.1

A Randomized Community Intervention to Improve Hypertension Control among

Mexican Americans: Using the Promotoras de Salud Community Outreach Model

(Balcazar et al., 2009)

Setting, location Mexican Americans of the El Paso, Texas

Participants, activities and

duration

Diagnosed with hypertension (N: I=58, C=40)

Mean age 54.9

9 week promotora intervention

Design Randomized feasibility pilot intervention

Theory/model Promotoras de salud community outreach model

Main EBRBs measured

Clinical measures of blood pressure, BMI, Waist

circumference

Self-reported behaviors associated with high blood

pressure

Attitudes and beliefs around blood pressure

Effectiveness

Statistically significant difference between perceived

benefits and two heart healthy behaviors (salt and sodium,

and cholesterol and fat)

Dietary intake in a randomized-controlled pilot of NOURISH: A parent intervention

for overweight children (Bean et al., 2012) *

Setting, location Virginia


duration

Parents of overweight and obese children (N: I=46,

C=50)

2008 to 2009

Design Randomized controlled pilot

Theory Social cognitive theory

Main EBRBs measured Dietary assessment of parents and children conducted at

baseline, post-test, and 6-month follow-up

Effectiveness

Among parents who self-select into a childhood obesity

program, minimal intervention can elicit short-term dietary

changes comparable to those of a structured intervention.

Can anyone successfully control their weight? Findings of a three year community-based

study

of men and women (Crawford et al., 2000)

Setting, location Theory


duration

Aged 20-45 (N=854 of 1226 enrolled), non-pregnant

3 yrs

Design Paired group design, randomized control

Theory NA

Main EBRBs measured Body weight, weight control behavior, dietary intake, fast

food consumption, physical activity, television viewing

Effectiveness Without much greater efforts to promote and support

weight control, most people will be unable to avoid weight


166

gain and very few will manage to lose weight, and as a

consequence the prevalence of obesity will continue to

rise.

The StrongWomen–Healthy Hearts Program: Reducing Cardiovascular Disease Risk

Factors in Rural Sedentary, Overweight, and Obese Midlife and Older Women (Folta et

al., 2009)

Setting, location 8 counties in Arkansas and Kansas


duration

Sedentary midlife and older women

12 week twice-weekly heart health program

55 respondents followed for 12 weeks

Design Randomized (by community) controlled trial


Main EBRBs measured Weight, waist circumference, diet, physical activity and

self-efficacy

Effectiveness

Our results suggest that a community-based program can

improve self-efficacy, increase physical activity, and

decrease energy intake, resulting in decreased waist

circumference and body weight among at-risk women.

Outcome results for the Ma’alahi Youth Project, a Tongan community-based obesity

prevention programme for adolescents (Fotu et al., 2011)

Setting, location Tonga


duration

Three year

22 villages in three districts

Design Quasi-experimental

Theory NA

Main EBRBs measured

Social marketing and capacity building

Education activities that promote physical activity and

local fruits and vegetables

Effectiveness

No impact on the large increase in prevalence of

overweight and obesity among Tongan adolescents.

Community-based interventions in such populations with

high obesity prevalence may require more intensive or

longer interventions, as well as specific strategies targeting

the substantial socio-cultural barriers to achieving a

healthy weight.

Project HEAL: Peer Education Leads to Weight Loss in Harlem (Goldfinger et al., 2008)

Setting, location Minority Harlem community, New York city


duration

April to June 2006

Two trained peer leaders led the 8 sessions at the

church.

Ten weeks after enrollment, at the eighth and final

session of the course, and at 22 and 32 weeks and 1 year

after enrollment, trained research assistants, who were

blinded to patients’ baseline weights, surveyed and

weighed participants.


167

Design NA

Theory NA

Main EBRBs measured Pre and post course weights, self-reported behaviors and

quality of life.

Effectiveness

A peer-led, community-based course can lead to weight

loss and behavior change. Significant decrease in weight

and BMI. The minority communities most affected by

obesity and diabetes may benefit from this low-cost,

culturally appropriate intervention.

Effect of a Community-Based Weight Management Program on Weight Loss and

Cardiovascular Disease Risk Factors (Graffagnino et al., 2006)

Setting, location Community medical wellness center


duration

2001 to 2004

N= 198 (142 women, 56 men)

Design Retrospective outcomes analysis

Theory NA

Main EBRBs measured Anthropometric measures, program participation

Effectiveness

There was a significant correlation between percentage

weight loss and number of weekly counseling sessions

attended and number of visits to the wellness center for

exercise.

The relatively high drop-out rate associated with this

program suggests the need to identify strategies and

techniques to enhance adherence and completion of

programs.

Community-based exercise and weight control: diabetes risk reduction and glycemic

control in Zuni Indians (Heath et al., 1991)

Setting, location Southwest New Mexico


duration

Zuni Indians, 2 years

Two 1-hr aerobics exercise session per week. It has grown

to

> 48 sessions 5 days/week several times daily

Design NA

Theory NA

Main EBRBs measured Weight, BMI, fasting blood glucose level

Effectiveness

Demonstrates the effectiveness of a community-based

exercise program in facilitating weight loss and improved

metabolic control in a Native American Indian group.

Community Programs for Obesity Prevention: The Minnesota Heart Health Program

(Jeffery, 1995)

Setting, location Minnesota


duration

Adult education classes

Worksite weight control program

Weight loss by home correspondence

Weight gain prevention program


168

6 communities

Design NA

Theory NA

Main EBRBs measured Weight

Effectiveness

No significantly positive effect on weight status, ceiling

effect. Too many things at once must have distracted from

attaining a single goal. Clearly informing people is not

sufficient.

Promoting Physical Activity in a Low-Income Multiethnic District: Effects of a

Community Intervention Study to Reduce Risk Factors for Type 2 Diabetes and

Cardiovascular Disease (Jenum et al., 2006)

Setting, location Oslo, Norway


duration

N: Baseline= 2950 30-65 years old, Follow-up= 1776)

Promotion of physical activity

Design Pseudo experimental cohort design

Theory Social cognitive theory and social ecological models

Main EBRBs measured BMI, Non fasting blood sample

Effectiveness

A net increase in physical activity of 9%, corresponding to

25% relative reduction in the proportion of inactive people

in the intervention district compared with a 5% reduction

in the control district.

The WORD (Wholeness, Oneness, Righteousness, Deliverance): A Faith-Based Weight-

Loss

Program Utilizing a Community-Based Participatory Research Approach (Kim et al.,

2008)

Setting, location North Carolina


duration

N=73, I= 36, C=37

Weekly small groups led by trained community members

Healthy nutrition, physical activity, and faith’s connection

with health

Design Two-group, quasi –experimental delayed intervention

design

Theory Social support theory and stages of change trans-

theoretical model

Main EBRBs measured Anthropometric measurement, health-behavior variables

Effectiveness

The mean weight loss of the treatment group was 3.60 ±

0.64 lbs. compared to the 0.59 ± 0.59-lb loss of the control

group

The relationship between restraint and weight and weight-related behaviors among

individuals in a community weight gain prevention trial (McGuire, 2001)

Setting, location

Minneapolis/St Paul, Minnesota metropolitan area through

newspaper advertisements, mailings to employees at the

University of Minnesota, and direct telephone recruitment


duration

N=1226

3 year weight gain prevention study


169

Participants of Pound of Prevention (POP) study

Low-intensity intervention for preventing age-related

weight gain.

Design NA

Theory NA

Main EBRBs measured Weight, weight contributing behaviors, physical activity

Effectiveness

Developing methods to increase behavioral and cognitive

strategies to control weight may help to prevent weight

gain in clinical and community samples

The PATHWAYS Church-Based Weight Loss Program for Urban African-American

Women at Risk for Diabetes (McNabb et al., 1997)

Setting, location NA


duration

Urban Africa-American women

N= 39, I=15, C=18

Design NA

Theory NA

Main EBRBs measured Weight, behaviors

Effectiveness

Apart from reaching a wider audience, administering the

program in a community-based setting did not lead to any

observable differences in outcomes compared with those

obtained in the clinic administration.

The Center for Alaska Native Health Research Study: A Community-based Participatory

Research Study of Obesity and Chronic Disease-related Protective and Risk Factors

(Mohatt et al., 2007)

Setting, location Southwestern Alaska


duration

N=753 of 922 in baseline Alaska Native

Design NA

Theory NA

Main EBRBs measured Anthropometric measurements

Effectiveness

The results strongly indicate that solution focused research

utilizing primary and secondary prevention strategies may

provide evidence to prevent further incidence of chronic

disease.

Loozit study

Twelve-Month Outcomes of the Loozit Randomized Controlled Trial. A Community-

Based Healthy Lifestyle Program for Overweight and Obese Adolescents (B. Nguyen et

al., 2012)

Setting, location Community health center and children’s hospital in

Sydney, Australia.


duration

A total of 151 overweight or obese 13- to 16-year-olds.

Design 2-arm randomized controlled trial

Theory NA

Main EBRBs measured BMI, waist to height ratio


170

Effectiveness

The Loozit randomized controlled trial produced a

significant but modest reduction in body mass index z

score and improved psychosocial outcomes at 12 months.

Evaluation of a community-based weight management program for overweight and obese

adolescents: The Loozit study (O’Connor et al., 2008)



duration

Group-based intervention over five months

Design Short term effectiveness trial

Theory Cognitive behavioral approach

Main EBRBs measured

Self-efficacy, motivation, perseverance

Anthropometry, metabolic profile, self-report

questionnaire

Effectiveness

The Loozit study demonstrates for the first time that it is

feasible to perform measurement assessments and retain

overweight and obese adolescents to a weight management

program based in a community setting.

Translating Weight Loss and Physical Activity Programs Into the Community to Preserve

Mobility in Older, Obese Adults in Poor Cardiovascular Health (Rejeski et al., 2011)



duration

Ambulatory, overweight or obese, community-dwelling

older adults who either had CVD or cardio-metabolic

dysfunction and evidence of self-reported limitations in

mobility

Design Randomized controlled trial


Main EBRBs measured Time to complete a 400-m walk in seconds

Effectiveness

Existing community infrastructures can be effective in

delivering lifestyle interventions to enhance mobility in

older adults in poor cardiovascular health with deficits in

mobility

Pilot of "Families for Health'': community-based family intervention for obesity

(Robertson et al., 2008)

Setting, location Coventry, England


duration

27 overweight or obese children aged 7-13 years (18 girls,

9 boys) and their parents, from 21 families.

“Families for Health” is a 12-week program with parallel

groups for parents and children, addressing parenting,

lifestyle change and social and emotional development.

Design NA

Theory NA

Main EBRBs measured BMI z-score

Effectiveness

BMI z score was reduced by 20.18 (95% CI-20.30 to-0.05)

at 3 months and-20.21 (-0.35 to-0.07) at 9 months.

Statistically significant improvements were observed in

children's quality of life and lifestyle (reduced sedentary


171

behavior, increased steps and reduced exposure to

unhealthy foods), child-parent relationships and parents'

mental health. Fruit and vegetable consumption,

participation in moderate/ vigorous exercise and children's

self-esteem did not change significantly. Topics on

parenting skills, activity and food were rated as helpful and

used with confidence by most parents.

Impact of Let’s Go! 5-2-1-0: A Community-Based, Multi-setting Childhood Obesity

Prevention Program (Rogers et al., 2013)

Setting, location Maine


duration

12 communities

Repeated random telephone

Surveys with 800 parents of children to measure

awareness of messages and child behaviors. Surveys

were conducted in schools, child care programs, and

afterschool programs to track changes in policies and

environments.

Design NA

Theory Social ecological theory

Main EBRBs measured Awareness knowledge and behavior change

Effectiveness

A multi-setting, community-based intervention with a

consistent message can positively impact behaviors that

lead to childhood obesity

MEND program

Randomized Controlled Trial of the MEND Program: A Family-based Community

Intervention for Childhood Obesity (Sacher et al., 2010)



duration

N=11 obese children (BMI ≥ 98th percentile

Randomly assigned to intervention or waiting list control

(6-month delayed intervention).

Parents and children attended eighteen 2-h group

educational and physical activity sessions held twice

weekly in sports centers and schools, followed by a 12-

week free family swimming pass.

Design Randomized control design

Theory NA

Main EBRBs measured

Waist circumference, BMI, body composition, physical

activity level, sedentary activities, cardiovascular fitness,

and self-esteem were assessed at baseline and at 6 months

Effectiveness

Participants in the intervention group had a reduced waist

circumference z-score (−0.37; P < 0.0001) and BMI z-

score (−0.2 ; P < 0.0001) at months when compared to

the controls. Significant between-group differences were

also observed in cardiovascular fitness, physical activity,

sedentary behaviors, and self-esteem.

Assessing the short-term outcomes of a community-based intervention for overweight


172

and obese children:The MEND 5-7 program (Smith et al., 2013)

Setting, location Community venues at 37 locations across the UK.


duration

440 overweight or obese children (42% boys; mean age 6.1

years; body mass index (BMI) z-score 2.86) and their

parents/carers participated in the intervention.

MEND 5-7 is a 10-week, family-based, child weight-

management intervention consisting of weekly group

sessions. It includes positive parenting, active play,

nutrition education and behavior change strategies.

Design Repeated measures.

Theory NA

Main EBRBs measured NA

Effectiveness

Participation in the MEND 5-7 program was associated

with beneficial changes in physical, behavioral and

psychological outcomes for children with complete sets of

measurement data, when implemented in UK community

settings under service level conditions.

Family-Based Risk Reduction of Obesity and Metabolic Syndrome: An Overview and

Outcomes of the Idaho Partnership for Hispanic Health (Schwartz et al., 2013)

Setting, location Rural Idaho


duration

Compañeros en Salud (CeS) is a promotora- led wellness

program

Improve nutrition and physical activity behaviors as well

as increase community support and infrastructure for

healthy living.

Design NA

Theory NA

Main EBRBs measured weight, BMI, metabolic syndrome risk, A1c, glucose,

blood pressure, and cholesterol

Effectiveness

CeS model as a promising best practice for effecting

individual and family- level physiologic and behavioral

outcomes for obesity prevention.

Effects of a Culturally Grounded Community-Based Diabetes Prevention Program for

Obese Latino Adolescents (Shaibi et al., 2012)



duration

Fifteen obese Latino adolescents (body mass index

percentile = 96.3 ± 1.1, age = 15.0 ± 0.9 years)

completed a 12-week intervention that included weekly

lifestyle education classes delivered by bilingual/

bicultural promotoras and three, 60-minute physical

activity sessions per week.

Design NA


Main EBRBs measured

Anthropometrics (height, weight, BMI, and WC)

Cardiorespiratory fitness, physical activity/ inactivity,

nutrition behaviors, and insulin sensitivity and glucose


173

tolerance by a 2-hour oral glucose tolerance test.

Effectiveness

The intervention resulted in significant decreases in BMI z

score, BMI percentile, and waist circumference; increases

in cardiorespiratory fitness; and decreases in physical

inactivity and dietary fat consumption.

Challenges in Improving Fitness: Results of a Community-Based, Randomized,

Controlled Lifestyle Change Intervention (Yancey et al., 2006)



duration

366 healthy, obese African American women

8-week culturally targeted nutrition and physical activity

intervention on body composition.

Data were collected at baseline, 2, 6, and 12 months

Design randomized, attention-controlled, two-group trial

Theory NA

Main EBRBs measured NA

Effectiveness

The intervention produced modest short-term

improvements in body composition, but the economic

incentive of a free 1-year gym membership provided to all

participants was a more potent intervention than the

education and social support intervention tested. However,

longer-term fitness enhancement remains elusive and

demands research and policy attention.


174

CHAPTER V

SUMMARY AND CONCLUSIONS

Obesity is pervasive and has become a major public health concern. It was

traditionally addressed as an individual’s concern. However, with the increasing rates of

obesity there is a strong effort to address obesity through community-based approaches to

achieve effects on a societal level. Community-based interventions for obesity prevention

commonly have targeted reduction in body weight usually measured by an individual’s

body mass index (BMI). However, changing body weight without relapse has been a

challenge in obesity prevention. With this, the need to identify successful strategies to

design effective and efficient community-based interventions is increasing.

This dissertation develops a conceptual framework for addressing obesity as a

market failure of unhealthy foods in the first chapter. Obesity is largely an outcome of

increased demand and supply of unhealthy foods. However, the current market price of

unhealthy foods does not include the societal cost of unhealthy food i.e. the cost of

obesity. Hence obesity is conceptualized as a market failure of unhealthy foods. A

negative externality in the production and consumption of unhealthy foods those are high

in fat, sugar, and salt. The framework looks at applying behavioral tools to decrease the

demand of unhealthy foods that will lead to decrease in the social cost of unhealthy foods

and will in the long run decrease the externality effect i.e. the cost of obesity. Further, this

framework develops the argument for applying low cost large scale methods such as

community-based behavior change interventions to increase the demand of healthy foods

while decreasing the demand of unhealthy foods. This market failure framework for the

obesity problem leads to the potential of behavioral interventions for obesity prevention.


175

Behavioral interventions have a potential to complement the ongoing efforts in

changing the supply and demand of products that are of concern to public health, for

example taxation to change the demand of addictive products such as cigarettes and

alcohols or perhaps even unhealthy foods. Obesity prevention can be achieved through

behavior change by complementary incorporation of behavioral economics with

traditional economics. This could lead to better addressing the obesity epidemic by

designing an integrated obesity prevention effort that incorporates all the aspects of

obesity.

This dissertation further looks at the impact of a community-based multi-tiered

behavior change intervention on cancer risk awareness and BMI, and the effect of healthy

eating signs in supermarket with community reinforcement on supermarket sales in the

second and third chapters respectively. The concluding research (in the fourth chapter) of

this dissertation is a systematic review and meta-analysis to look at the impact of the

available community-based behavior change interventions on behavior change towards

obesity prevention and weight loss (decrease in BMI).

The second chapter evaluates the project outcomes of a community-based multi-

tiered behavior change intervention by comparing food behavior and attitude before and

after the project and by analyzing the intervention effect on cancer knowledge and BMI.

The methods of analysis were adapted for this purpose from common health promotion

impact evaluation methods. The analysis used data available from five questionnaire

surveys, three in the intervention community and two in the control community.

The results from second chapter suggest that the intervention was successful in

significantly and sustainably changing health attitude and cancer knowledge. The results


176

also suggested that long term interventions are more promising than short term

interventions in changing BMI. While attitude and knowledge could be changed in short

term sustainable weight change requires longer interventions. Community-based

interventions such as these have strong potential in bringing long term change in obesity

through targeting the behaviors associated with obesity rather than by targeting decrease

in body weight and will have sustainable impact.

Community-based models that incorporate supermarket in program activities are

rare despite its potential effectiveness. The third chapter reports the findings of a

community-based program specifically the project effect on food purchase behavior. The

analysis is based on monthly sales (in units sold per month) data available from the

participating supermarket. The data were available separately for the produce items and

healthier items identified with shelf talkers. The method was based on the concept of

modifying both rapid and prescriptive food choice decision, the former targeted to be

affected by shelf talkers and the later targeted to be affected by the overall promotion of

healthful eating for obesity prevention.

The overall analysis in the third chapter suggests that the project had a positive

impact on purchase of fresh produce, mainly on green leafy vegetables. The effect was

also significant in the sales of whole grain pasta and sauce. The findings suggest that

supermarket interventions have potential in changing food behavior. However, the lack of

positive change across all categories also suggests the challenges associated with

changing food behavior.

The fourth chapter reports the findings of a systematic review and meta-analysis

of community-based interventions. This paper is a synthesis of research on community-


177

based behavior change interventions implemented for obesity prevention. A method of

systematic review followed by narrative synthesis and meta-analysis was done. Both the

narrative synthesis and meta-analysis reveal that community-based behavior change

interventions have promise to change behavior towards obesity prevention and to some

extent to change (decrease) BMI. Additionally, the difference in project impact between

large-scale, less-intense and small-scale more-intense interventions suggests that

designers of community-based obesity prevention interventions should carefully consider

between large-scale, less-intense and small-scale more-intense interventions. There

appears to be trade off in outcomes and cost-effectiveness between large-scale, less-

intense and small-scale more-intense interventions.

Overall, addressing obesity as a market failure to design behavioral intervention

effort to change (decrease) demand of unhelathy food provides a useful framework to

study obesity prevention in the realm of market structure. Further, behavior change

interventions through community-based interventions showed positive results with a

supermarket component. Supermarkets as the center of such intervention has promise in

changing food choice decisions in communities.

This dissertation however had some limitations. The cost effectiveness of the

intervention was not analysed though the necessity to develop cost effective methods

have been discussed in the first chapter. Further, in analyzing the effect of intervention in

supermarket sales the effect of shelf talker signs could not be isolated from the overall

community and supermarket based healthy promotion messages.


178

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