The Relationship Between Having “apps” That Help Track or Manage Health and Other Factors and Quality of Life

Veronica Acosta-Deprez, PhDErlyana Erlyana, MD, PhDTony Sinay, PhDCalifornia State University, Long Beach

Presented at the AACE Conference, October 27-31, 2014, New Orleans, Louisiana

Background There are over 6B phone subscribers or

75% of the world has access to mobile phone (Tomlinson et al., 2013).

The number of mHealth apps has dramatically increased in recent years.

The market revenue reached 2.4B in 2014 and is projected to grow to 26B in 2017 (research2guidance, 2014).

The main target market include chronically ill patients (31%), health and fitness-interested people (28%) and physician (14%) (reasearch2guidance, 2014).

Background By 2018, about 50% of the more than

3.4 billion smartphone and tablet users will download mHealth apps (research2guidance, 2014).

About 31% US population have used mobile phones for health information and apps in 2012 (Pew Internet, 2012).

However mobile apps is not distributed equally across health needs (Martinez-Perez et al, 2013).

E.g. Diabetes and depression have an overwhelming number of apps and research among the top 8 health conditions (Martinez-Perez et. al., 2013).

Apps related to the most prevalent conditions (WHO- Global Burden of Disease, 2004)

Literature:DiabetesAsthmaDepressionHearing lossLow visionOsteoarthritisAnemia Migraine

Commercial:DiabetesDepressionMigraineAsthmaLow visionHearing lossOsteoarthritisAnemia

Apps in Public Health Public health-related apps are growing in

number and popularity (Tucker, 2014). The American Cancer Society developed apps

to prevent cancer: Exercise Counts Calculator, Target Heart Rate Calculator, Calorie Counter, Cigarette Calculator, Smoking Cost Calculator and Mammogram Reminder (Tucker, 2014).

MIT & Children’s Hospital of Boston created Outbreaks Near Me which provides updates about infectious diseases around the world (Tucker, 2014).

The California Poison Control System, which runs that state’s poison control call centers, launched Choose Your Poison in May (Tucker, 2014).

Apps in Public Health (cont.) The EPA & the DHHS have made data available

to app developers, Apps for the Environment in June 2013.

The UNAIDS AIDSinfo app is to understand why and how HIV infection is spread and where treatment, care and support programs are needed.

Using principles of Cognitive Behavior Therapy, apps that integrate CDS (Clinical Decision Support) and predictive analytics deliver helpful reminders, behavior modification suggestions, and strategies for improving user health (Cho et al., 2014)

The (CDC’s) federal Community Health Data Initiative is releasing unprecedented disease prevalence data to encourage creation of applications that make the data more accessible and useable.

Do Apps benefits to consumer? Of 40,000 health apps available, only 16,275 of

these aps were directly linked to patient care and treatment, while others only provide information (questionable benefits).

MyFitnessPal pulled in 40 million users, but the report from the IMS Institute claims that its effectiveness did not meet its popularity.

A study by researchers from the University of Massachusetts Medical School found 25% or fewer lifestyle-based strategies for weight loss – e.g. portion control and identifying reasons behind overeating ( incorporated in 28 of the apps - were likely to be ineffective for weight loss).

Apps & Quality of Life? Quality of life (QOL) is a broad multidimensional

concept that usually includes subjective evaluations of both positive and negative aspects of life (CDC).

There is no study that investigate the significance of apps to improve quality of life directly.

Most studies implied that improve health outcomes were associated with better quality of life.

Health literacy influence: 1) access and utilization of health care, 2) patient-provider relationship, and 3) health outcomes (Paasche-Orlow & Wolf, 2007).

Health literacy is important, but information alone is not enough (Sørensen et al., 2012).

Mixed evidence Free et al. (2013) reported mixed evidence

for the effectiveness of mHealth. The effects of mHealth apps to health behavior

change and disease management remain open to questions (e.g. Which functions and behavior change techniques are effective and were the effectiveness influenced by setting or participant demographics? (Free et al., 2013)

Hundreds of cancer-related apps, however, there is lack of evidence on their utility, effectiveness, and safety (Bender et al., 2013)

Mixed evidence mHealth interventions are effective in

promoting physical activities, however, the generalizability of the findings is unclear (Blackman et al., 2013)

Many mindfulness-related apps, but there is lack of evidence of their usefulness (Plaza et al., 2013).

Purpose of the Studyto explore the association between having “apps”

that help track or manage health and quality of life.

This research is guided by the following questions:1) Does having access to mobile apps that help track or manage health associate with individual quality of life? 2) Does perception of health status associate with individual quality of life?3) Does living with any chronic health problems or conditions, having had faced a serious medical emergency or crisis, having had gone to the emergency room or being hospitalized unexpectedly, or having had experienced any change in physical health associate with individual quality of life?

Method & Sample Secondary data analysis was conducted using

data from the PEW Internet Health Tracking Survey (2012) - nationwide survey of 3,014 adults living in the United States

n = 2,523 of adults who responded to the question “DO you have/use any software applications or “apps” that help track or manage their health.

Analysis An ordered logistic regression model was conducted

using STATA IC 10 to determine the association between:

Dependent variable: quality of life Independent variables:

◦ having an “apps” to manage their health◦ perceived health status◦ living with any chronic health problems or

conditions◦ faced a serious medical emergency or crisis◦ whether a person has gone to an emergency room

or has been hospitalized unexpectedly ◦ has had experienced any change in physical

health.

Results

13.56%

61.20%

25.23%

Age Groups

<26 years 27    – 64 years>65 years

55.76%

20.88%

23.35%

Marital Status

Married Divorced Single

Results

33.42%

26.41%

40.16%

Education Level

High School or below Some College College Graduates

17.48%

26.68%55.84%

Income Level

Low Medium High

Results

White; 62.95%

Race

White Latino African American Asian or Pacific Islander Mixed race Native American Other

 Quality of Life (Dependent Variable) OR (SE) t 95% CI

Have any software applications or apps to track or manage health .71 (.12) -2.03** .51 – .99

Perceived health status:      

Good 2.59 (.33) 7.55*** 2.02 – 3.31

Fair 7.76 (1.68) 9.45*** 5.07 – 11.87

Poor 13.77 (4.66) 7.74*** 7.09 – 6.76

Living with any chronic health problems or conditions .94 (.11) -0.53  .75 – 1.17

Faced a serious medical emergency or crisis 1.01 (.23)  0.07  .65 – 1.59

Gone to the emergency room or been hospitalized unexpectedly  1.49 (.27)  2.17**  1.03 – 2.14

Experienced any significant change in physical health .92 (.16)  -0.46  .66 – 1.29

Results

Note: *** p<0.01, ** p<0.05, * p<0.1

DiscussionThe results suggested that having apps

was significantly associated with quality of life, consistent with previous studies who reported positive impact of mHealth apps to health outcomes.

Thus, mHealth could be used as a powerful tool to improve individual wellbeing.

However, despite significant increase in numbers of mHealth apps, usability and continuity of mHealth apps use is quite low.

DiscussionApps use was associated with: health

consciousness and mHealth apps efficacy (Cho et al, 2014); and having more chronic medical conditions and engaging in formal volunteering among older adults (Choi & DiNitto, 2013a).

Barriers of apps use include (Choi & DiNitto, 2013b):◦ lack of exposure to internet technology; ◦ lack of financial resources to have access to

technology; ◦ or medical conditions, disabilities, and pain

DiscussionResults are consistent with The MARS

OTC/DTC Study which reported that diet and fitness apps were used by 55.7 million American adults in 2013, up from 43.9 million in 2012 of 20,000 consumers (Comstock, 2014), however:◦ 57% of the respondents claimed having not

have used any type of app in the last 30 days to track health content

◦ 34% of smartphone owners and 31% of tablet owners used their device to look for health-related information,

◦ 25% of smartphone owners and 22% of tablet owners used their device to track their health, diet, or exercise.

DiscussionRuder Finn’s (2013) study showed that less than

one-fifth (16%) of smart phone and tablet users access health apps regularly compared to 59% who used social media apps, and 56% for gaming apps. ◦ For the respondents who used mobile apps, the main

reason they did not access any health-related app in the last 6 months (according to 27% of respondents), was the lack of need to access health related apps.

◦ Those who were 35-44 years old were more likely to use mHealth apps, more than consumers ages 55-64

◦ Men were more likely than women to say they did not have any need to access health related apps.

DiscussionOther potential barriers of mHealth apps

use include:◦ lack of data security (34%) ◦ lack of standards (30%)◦ poor discoverability (29%) (research2guidance,

2014).The lack of regulation for health apps and

doubts of its reliabilityRegulators are four years behind

developersThe flexibility of apps to meet the various

needs of consumers still need more work.

Implications mHealth interventions should be guided by a plausible

theory of behaviour change and should use more than one technique depending on the targeted behavior (Briscou & Aboud, 2012)

To reduce waste and duplication, mHealth need to develop an evidence base, interoperable, follow the existing standards, participatory, promote equity and sustainability, and focus on health not technology (van Heerden et al., 2013)

Smartphone and apps provided an opportunity to collect and deliver health information, and to improve self management and health behavior change but the quality and usability of smart phone apps should be monitored and evaluated (Kratzke & Cox, 2012).

There is also a need to study the unintended consequences such as stress, unwanted distraction from other activities, and infringement upon intimate relationship – digital cyborg and surveillance society (Lupton, 2012).

Conclusion The Department of Health and Human Services (HHS)

writes on its HealthIT.gov site: ◦ “Whether you’re looking to maintain or improve your

health, a large number of web sites, apps, and devices exist to help you track and manage your health and wellness. On your own, you can use such resources to better understand your health and meet your personal health goals. But you may also be able to use the information you collect to help your doctor better understand your concerns and conditions” (About HealthIt.gov)

By reaching patients in real-time, and delivering evidence-based information via a device that they use to manage most aspects of their lives, the provider community has a powerful new tool to stop unhealthy, destructive behaviors before they even occur.

References Bender, J. L., Yue, R. Y. K., To, M. J., Deacken, L., & Jadad, A. R. (2013). A Lot of Action, But

Not in the Right Direction: Systematic Review and Content Analysis of Smartphone Applications for the Prevention, Detection, and Management of Cancer. J Med Internet Res, 15(12), e287. doi: 10.2196/jmir.2661

Blackman, K. C., Zoellner, J., Berrey, L. M., Alexander, R., Fanning, J., Hill, J. L., & Estabrooks, P. A. (2013). Assessing the Internal and External Validity of Mobile Health Physical Activity Promotion Interventions: A Systematic Literature Review Using the RE-AIM Framework. J Med Internet Res, 15(10), e224. doi: 10.2196/jmir.2745

Briscoe, C., & Aboud, F. (2012). Behaviour change communication targeting four health behaviours in developing countries: A review of change techniques. Social Science & Medicine, 75(4), 612-621. doi: http://dx.doi.org/10.1016/j.socscimed.2012.03.016

Cho, J., Park, D., & Lee, H. E. (2014). Cognitive Factors of Using Health Apps: Systematic Analysis of Relationships Among Health Consciousness, Health Information Orientation, eHealth Literacy, and Health App Use Efficacy. Journal of medical Internet research, 16(5).

Choi, N. G., & DiNitto, D. M. (2013a). The Digital Divide Among Low-Income Homebound Older Adults: Internet Use Patterns, eHealth Literacy, and Attitudes Toward Computer/Internet Use. J Med Internet Res, 15(5), e93. doi: 10.2196/jmir.2645

Choi, N. G., & DiNitto, D. M. (2013b). Internet Use Among Older Adults: Association With Health Needs, Psychological Capital, and Social Capital. J Med Internet Res, 15(5), e97. doi: 10.2196/jmir.2333

Fox, S., & Duggan, M. (2012). Mobile health 2012. Pew Research Center's Internet x0026 American Life Project [Internet].

Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., . . . Haines, A. (2013). The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS medicine, 10(1), e1001362.

Heerden, A. v., Tomlinson, M., & Swartz, L. (2012). Point of care in your pocket: a research agenda for the field of m-health. Bulletin of the World Health Organization, 90(5), 393-394.

References Kratzke, C., & Cox, C. (2012). Smartphone Technology and Apps: Rapidly Changing Health

Promotion. International Electronic Journal of Health Education, 15, 72-82.

Lupton, D. (2012). M-health and health promotion: The digital cyborg and surveillance society. Social Theory & Health, 10(3), 229-244.

Martínez-Pérez, B., de la Torre-Díez, I., & López-Coronado, M. (2013). Mobile Health Applications for the Most Prevalent Conditions by the World Health Organization: Review and Analysis. J Med Internet Res, 15(6), e120. doi: 10.2196/jmir.2600

Paasche-Orlow, M. K., & Wolf, M. S. (2007). The causal pathways linking health literacy to health outcomes. American Journal of Health Behavior, 31(Supplement 1), S19-S26.

Plaza, I., Demarzo, M. M. P., Herrera-Mercadal, P., & García-Campayo, J. (2013). Mindfulness-Based Mobile Applications: Literature Review and Analysis of Current Features. JMIR mHealth uHealth, 1(2), e24. doi: 10.2196/mhealth.2733

research2guidance. (2014). mHealth App Developer Economics 2014: The State of Art of mHealth App Publishing.

RuderFinn. mHealth Report 2013. retrieved from http://www.ruderfinn.com/pdf/Ruder%20Finn%20US%20mHealth%20report%20FINAL.pdf .

Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., & Brand, H. (2012). Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health, 12(1), 80.

Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013). Scaling up mHealth: where is the evidence? PLoS medicine, 10(2), e1001382.

Tucker, C. (2011). Public health-related apps growing in number, popularity: Smartphones, tablets used for health. The Nation's Health, 41(8), 1,14-15.