psychological architectures of health behaviour change websites

THE PSYCHOLOGICAL ARCHITECTURES OF HEALTH BEHAVIOUR CHANGE WEBSITESDesigning interventions with the CBICMBrian Cugelman, PhD

29 November 2010

Health changing websites: the cutting edge of online behaviour changeToronto, Canada

Presentation partners:

1. ONLINE SOCIAL MARKETING

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SOCIAL MARKETING

“Social marketing is the systematic application of marketing alongside other concepts and techniques to achieve specific behavioural goals, for social or public good.”

(National Social Marketing Centre, 2006)

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EXAMPLES OF CAMPAIGNSQuit smokingExercise more

Drive saferDrink less

Eat healthierEat moreEat less

ONLINE SOCIAL MARKETING

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NEW BREED OF ONLINE HEALTH INTERVENTIONS

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2. THE RESEARCH PROJECT

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RESEARCH QUESTIONS

1. How can online interventions scale to the population level?

2. With such high attrition, what can be done to improve intervention efficacy?

3. How do you design an online health intervention?

4. Which psychological architectures work best?

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CHALLENGES

• Few studies of voluntary behaviours, as most dealt with chronic disease management

• No magic list of psychological design

• Traditional one-way communication models only partially describe online communications, which is increasingly two-way

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SOLUTIONS

• Communication-base Influence Components Model (CBICM)• Psychological architectures• One or two-way communications

• Meta-analysis (what’s meta-analysis?)• Psychology• Adherence

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3. COMMUNICATION-BASED INFLUENCE COMPONENTS MODEL (CBICM)

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THERE IS NO “ONE SIZE FITS ALL” TAXONOMY TO DESCRIBE ONLINE INTERVENTIONS

Stages of change Cialdini

CAPTOLOGY Community-based social marketing

Evidence-based behavioural medicineLearning Theories/Behaviourism

Social Cognitive TheoryTheory of Reasoned Action/Planned Behaviour

And many more....

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ONE-WAY: ONE-TO-ONE, ONE-TO-MANY

(One

-Way

) One

-toOne

Impersonal

Many

Mass Media

(Tw

o-W

ay) O

ne-w

ith Interpersonal Mass Interpersonal

one-with-one

one-to-one

CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

(One

-Way

) One

-toOne

Impersonal

Many

Mass Media

(Tw

o-W

ay) O

ne-w

ith Interpersonal Mass Interpersonal

one-with-one

one-to-one

TWO-WAY: ONE-WITH-ONE

CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

(One

-Way

) One

-toOne

Impersonal

Many

Mass Media

(Tw

o-W

ay) O

ne-w

ith Interpersonal Mass Interpersonal

one-with-one

one-to-one

MASS/INTERPERSONAL DIVIDE

CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

(One

-Way

) One

-toOne

Impersonal

Many

Mass Media

(Tw

o-W

ay) O

ne-w

ith Interpersonal Mass Interpersonal

one-with-one

one-to-one

MASS-INTERPERSONAL COMMUNICATION

CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

COMMUNICATION-BASED INFLUENCE COMPONENTS MODEL (CBICM)

SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

Media ChannelContext

Decode

EncodeDecode

Encode

CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

A framework to describe the psychology of interventions

4. THE STUDY

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THE META-ANALYSIS

• Searched five databases + grey literature

• Obtained 1,271 results• Retrieved 95 full text studies• Selected 31

• Primary analysis: 30 interventions from 29 studies (N=17,524)

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Study name Statistics for each study Std diff in means and 95% CI

Std diff Standard Lower Upper in means error limit limit Z-Value p-Value

Bersamin et al. (2007) 0.470 0.174 0.130 0.810 2.707 0.007Bewick et al. (2008) 0.123 0.113 -0.099 0.345 1.084 0.278Bruning Brown et al. (2004) a 0.294 0.172 -0.043 0.632 1.708 0.088Bruning Brown et al. (2004) b 0.637 0.264 0.120 1.154 2.413 0.016Celio et al. (2000) 0.494 0.298 -0.090 1.077 1.657 0.097Chiauzzi et al. (2005) 0.145 0.137 -0.122 0.413 1.064 0.288Dunton et al. (2008) 0.196 0.177 -0.152 0.543 1.103 0.270Gueguen et al. (2001) 0.303 0.325 -0.334 0.939 0.931 0.352Hunter et al. (2008) 0.178 0.095 -0.008 0.364 1.870 0.061Jacobi et al. (2007) 0.478 0.206 0.074 0.881 2.319 0.020Kim et al. (2006) -0.435 0.288 -1.000 0.130 -1.509 0.131Kosma et al. (2005) 0.361 0.239 -0.107 0.829 1.512 0.131Kypri et al. (2004) 0.400 0.222 -0.035 0.835 1.802 0.072Kypri et al. (2005) 0.206 0.549 -0.869 1.282 0.376 0.707Lenert et al. (2004) 0.201 0.567 -0.910 1.311 0.354 0.723Marshall et al. (2003) -0.068 0.125 -0.314 0.177 -0.547 0.585McConnon et al. (2007) -0.092 0.178 -0.440 0.256 -0.519 0.604McKay et al. (2001) 0.116 0.243 -0.360 0.592 0.477 0.634Moore et al. (2005) -0.008 0.200 -0.401 0.384 -0.042 0.966Napolitano et al. (2003) 0.527 0.287 -0.036 1.090 1.833 0.067Oenema et al. (2005) 0.169 0.102 -0.032 0.369 1.648 0.099Petersen et al. (2008) 0.014 0.031 -0.047 0.074 0.444 0.657Roberto et al. (2007) 0.162 0.434 -0.688 1.012 0.374 0.709Severson et al. (2008) 0.189 0.106 -0.019 0.397 1.778 0.075Strecher et al. (2005) 0.116 0.077 -0.035 0.266 1.505 0.132Strom et al. (2000) 0.476 0.304 -0.120 1.072 1.565 0.118Swartz et al. (2006) 0.327 0.419 -0.494 1.148 0.781 0.435Tate et al. (2001) 0.194 0.223 -0.244 0.632 0.869 0.385Verheijden et al. (2004) -0.002 0.176 -0.346 0.342 -0.012 0.991Winett et al. (2007) 0.507 0.082 0.346 0.667 6.201 0.000

0.194 0.042 0.111 0.278 4.582 0.000

-2.00 -1.00 0.00 1.00 2.00

Favours Control Favours Intervention

Meta Analysis

Meta Analysis

EFFECT SIZES

-0.4-0.3-0.2-0.10.00.10.20.30.4

Survey Only (Waitlistor Placebo)

Website Print (Major)

Overall: d=.194, p=.000, k=30

d

EFFECT SIZE BY INTERVENTION DURATION

-0.4-0.3-0.2-0.10.00.10.20.30.40.50.60.7

One-time From 2 days to 1month

Beyond 1 to 4months

Beyond 4 to 7months

Beyond 7 to 13months

d

DOSE: ADHERENCE OVER TIME

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DOSE: THREE VARIABLES

COR r=.37, p<.000, k=5

InterventionAdherence

OutcomeEffect Size

StudyAdherence MR r=.481, p=.006, k=28

MR r=.455, p=.109, k=13COR r=.240, p<.000, k=9

COR: Correlation effect sizeMR: Meta-regression estimate

RELATIVE INFLUENCE COMPONENTS AND OUTCOMES

876543210

Relative Behavioural Determinants (sum)

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

-0.5

Effe

ct S

ize

(d)

Print (Major)Website

Survey Only (Waitlist or Placebo)

ControlMediaSimple

CBICM SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

Media ChannelContext

Decode

EncodeDecode

Encode

INTERVENTION MESSAGE

SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

Media ChannelContext

Decode

EncodeDecode

Encode

AUDIENCE INTERPRETER

SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

Media ChannelContext

Decode

EncodeDecode

Encode

LOOKING FORWARD

• CBICM to help build future systems (and social media engagement)

• Mass-interpersonal public health campaigns

• State eHealth

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STUDY CREDITSFirst comprehensive meta-analysis on the

psychological design of online interventions

• CBICM in 2009: CUGELMAN, B., THELWALL, M., & DAWES, P. (2009) Communication-based influence components model. Paper presented at the Persuasive 2009, Claremont.

• 1st published Jan 2010 : CUGELMAN, B. (2010) Online social marketing: Website factors in behavioural change. University of Wolverhampton, Wolverhampton.

• 2nd extended publication in 2011: CUGELMAN, B., THELWALL, M., & DAWES, P. (2011, forthcoming) Online interventions for social marketing health behaviour change campaigns: A meta-analysis of psychological architectures and adherence factors. Journal of Medical Internet Research. (Get a pre-publication copy at www.cugelman.com) 35

THANK YOUBrian Cugelman, PhD

Phone: +1 (416) 921-2055brian@alterspark.com

Get in touch

www.AlterSpark.com

@AlterSpark alterspark alterspark alterspark

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