Published on in Vol 5, No 7 (2017): July

Controlling Your “App”etite: How Diet and Nutrition-Related Mobile Apps Lead to Behavior Change

Controlling Your “App”etite: How Diet and Nutrition-Related Mobile Apps Lead to Behavior Change

Controlling Your “App”etite: How Diet and Nutrition-Related Mobile Apps Lead to Behavior Change

Original Paper

Department of Health Science, Brigham Young University, Provo, UT, United States

*all authors contributed equally

Corresponding Author:

Joshua H West, MPH, PhD

Department of Health Science

Brigham Young University

2139 LSB

Provo, UT,

United States

Phone: 1 8014223444

Email: josh.west@byu.edu


Background: In recent years, obesity has become a serious public health crisis in the United States. Although the problem of obesity is being addressed through a variety of strategies, the use of mobile apps is a relatively new development that could prove useful in helping people to develop healthy dietary habits. Though such apps might lead to health behavior change, especially when relevant behavior change theory constructs are integrated into them, the mechanisms by which these apps facilitate behavior change are largely unknown.

Objective: The purpose of this study was to identify which behavior change mechanisms are associated with the use of diet- and nutrition-related health apps and whether the use of diet- and nutrition-related apps is associated with health behavior change.

Methods: A cross-sectional survey was administered to a total of 217 participants. Participants responded to questions on demographics, use of diet and nutrition apps in the past 6 months, engagement and likability of apps, and changes in the participant’s dietary behaviors. Regression analysis was used to identify factors associated with reported changes in theory and separately for reported changes in actual behavior, after controlling for potential confounding variables.

Results: The majority of study participants agreed or strongly agreed with statements regarding app use increasing their motivation to eat a healthy diet, improving their self-efficacy, and increasing their desire to set and achieve health diet goals. Additionally, majority of participants strongly agreed that using diet/nutrition apps led to changes in their behavior, namely increases in actual goal setting to eat a healthy diet (58.5%, 127/217), increases in their frequency of eating healthy foods (57.6%, 125/217), and increases in their consistency of eating healthy foods (54.4%, 118/217). Participants also responded favorably to questions related to engagement and likability of diet/nutrition apps. A number of predictors were also positively associated with diet-related behavior change. Theory (P<.001), app engagement (P<.001), app use (P<.003), and education (P<.010) were all positively associated with behavior change.

Conclusions: Study findings indicate that the use of diet/nutrition apps is associated with diet-related behavior change. Hence, diet- and nutrition-related apps that focus on improving motivation, desire, self-efficacy, attitudes, knowledge, and goal setting may be particularly useful. As the number of diet- and nutrition-related apps continues to grow, developers should consider integrating appropriate theoretical constructs for health behavior change into the newly developed mobile apps.

JMIR Mhealth Uhealth 2017;5(7):e95

doi:10.2196/mhealth.7410

Keywords



Currently, 68% of men and 64% of women in the United States are considered overweight or obese [1,2]. Much of this increase can be linked to cheap and unhealthy food production coupled with increased consumption of foods with minimal nutritional value [3]. In addition to obesity, these changes in dietary patterns are associated with many other serious health conditions, including hypertension, stroke, heart disease, elevated cholesterol, and diabetes [4]. Efforts to address the complex dilemma of obesity in the United States typically include the promotion of physical activity and healthy nutritional and dietary habits [5].

With the advent of mobile phone technology, a vast number of health-related mobile apps have been developed and are now being widely used to tackle health problems [6]. Many such apps provide users additional methods for monitoring their health or achieving health-related goals. The proliferation of health-related apps represents a potential resource in addressing obesity [7]. Studies have shown that using health-related apps can successfully lead to health behavior change related to weight loss or weight management [8,9]. Many health care providers see the potential of health-related apps to facilitate weight management and encourage patients to use them [6,10].

An assortment of apps has been developed to help individuals monitor their food consumption through calorie counting or food diary approaches [6,8]. Other apps provide individuals with healthy diet and nutrition facts and information (eg, MyFitnessPal, FitBit, and Lose It!) [8,11]. Studies have found that the use of diet-related apps can lead to improved diet and nutrition health behavior change [9]. Turner-McGrievey et al [9] found that participants using diet apps consumed fewer calories than those using a website or paper journal method for monitoring dietary intake. Similarly, when comparing the use of diet-related apps with website groups and food intake diary groups in a randomized control trial, participants using the apps had higher retention, adherence, and weight loss [12].

Previous research has found that interventions integrating models of behavior change theory may be effective. Inclusion of constructs from established health behavior change theories increases the effectiveness of planning, implementing, and evaluating interventions [13]. Three leading health behavior change theories for nutrition education and intervention are the health belief model (HBM), theory of planned behavior (TPB), and social cognitive theory (SCT) [14]. The HBM has been widely used since the 1950s and includes primary constructs that predict “if” and “why” a person will take action toward detecting, preventing, and controlling unhealthy behaviors. This is achieved through perceived susceptibility and severity, as well as the barriers and benefits associated with engaging in certain behaviors [13]. Constructs that examine individual motivation and attitudes and how they determine the likelihood of performing specific behaviors are the focus of TPB [13]. The SCT identifies the ever-present tension between human agency and social structure [13]. A key advantage of SCT is how it accounts for both individual decision making and environment through the concept of reciprocal determinism, which in turn leads to a deeper sense of self-efficacy through personal experiences, persuasion, and vicarious learning [13]. Provided the complexities associated with nutrition behaviors, a practical approach to behavior change may involve a combination of distinct constructs and elements from each theory effectively forming a polytheoretical approach [14].

Studies have shown that using health apps for diet can successfully lead to positive changes in weight management [12]. Health apps have the potential to decrease some barriers to traditional prescriptions for behavior change, including expense, patient burden, and variable adherence. Specifically, how engaging, convenient, and easy to use the app is can be a mechanism for reducing barriers and increasing adherence. However, none of the leading weight loss apps have been evaluated in a clinical trial, which underscores the need for a descriptive study such as this one to establish associations warranting further investigation [15,16]. Though several theories related to health behavior change are well established and generally accepted, recent content analyses of diet-related apps demonstrate that many diet-related apps have insufficient evidence-informed content or are lacking in theoretical constructs considered important in facilitating behavior change [17-19]. To date, no research has explored the specific mechanisms by which diet-related apps actually generate changes in behavior. The purpose of this study was to explore which behavior change mechanisms are associated with use of diet- and nutrition-related health apps and to examine whether the use of these apps is associated with actual changes in dietary behaviors.


Design and Procedure

This study consisted of a cross-sectional survey to assess the use of diet and nutrition apps in the past 6 months. A Qualtrics survey was distributed via Amazon Mechanical Turk (MTurk) to 239 participants. The survey was open on MTurk twice, once for approximately 2 weeks with a US $1 incentive (89 respondents) and then a second time for a few more weeks with a US $2 incentive (150 respondents). An advantage of Web-based data collection is the ability to access a wide variety of participants that represent a diverse sampling of those using the method of Internet communication [20]. It is projected that data collection through electronic sources will continue to rise because of potential outreach [21]. A typical MTurk sample is representative of the population criterion of interest for this research project.

Sample

A number of inclusion and exclusion criteria were used to define the survey sample. Survey participants had to be at least 18 years of age, live in the United States, and be able to read English. Participants were excluded if they had not used a diet or nutrition app in the last 6 months or if they failed to complete all 16 survey questions. A total of 239 participants responded, of which 217 individuals met all requirements and completed all questions.

Measurement

Participants responded to questions on demographics (eg, age, race, education and income level, state of residence), use of diet/nutrition apps in the past 6 months, engagement and likability of the apps, and changes in the participant’s dietary behaviors. A 5-point Likert-type scale was used to generate response categories for the variables related to behavior change.

The study used three health behavior theories to formulate the Likert-type scale survey questions focusing on mechanisms of behavior change and actual diet/nutrition-related behaviors. Questions based on SCT included those that measured outcome expectations, self-efficacy, subjective norms and knowledge, whereas questions based on TPB measured behavioral beliefs, intentions, attitudes, desires, normative beliefs, and goal setting. Finally, one question based on HBM measured perceived benefits. A composite total theory variable was constructed to provide a global estimate of changes in theory-related constructs. A polytheoretical measure was determined to be in line with the viewpoint that behaviors relating to diet/nutrition are too complex for any one single theory [14]. The Cronbach alpha coefficient for this composite variable was .941 (behavior=.842; engagement=.875). This variable was not normally distributed. Hence, a square root transformation was used.

Statistical Analysis

Stata version 14 (StataCorp) was used to calculate all statistics. Descriptive statistics were calculated for each of the demographic, theory, engagement, and behavior variables. Multiple regression analysis was used to identify factors associated with reported changes in theory and separately for reported changes in actual behavior, after controlling for potential confounding variables.


The majority of study participants were white (83.9%, 182/217) and between the ages of 26 and 34 (44.2%, 96/217; Table 1). Most participants (87.5%, 190/217) had at least some college education. Just over half of the participants (55.8%, 121/217) were female, and 41.5% (90/217) were from the Southern United States.

Most (59.9%, 130/217) strongly agreed with the statement that using diet/nutrition apps increased their motivation to eat a healthy diet, whereas an additional 36.8% (80/217) agreed with the same statement (Table 2). Many respondents indicated that the apps improved self-efficacy: 43.8% (95/217) strongly agreed and 42.4% (92/217) agreed with the statement that using diet/nutrition apps increased their ability to eat a healthy diet, and 43.3% (94/217) strongly agreed and 48.4% (105/217) agreed with the statement that using diet/nutrition apps increased their confidence that they can eat a healthy diet. More than half (59.0%, 128/217) of the participants strongly agreed that using diet/nutrition apps increased their desire to set goals to eat a healthy diet, whereas 51.1% (111/217) strongly agreed that using diet/nutrition apps increased their ability to achieve their healthy diet goals.

Table 1. Summary of participant demographics.
DemographicsFrequency, n (%)
(N=217)
Age in years

18-2516 (7.4)

26-3496 (44.2)

35-5490 (41.5)

55-6411 (5.1)

65 or over4 (1.8)
Race

American Indian2 (0.9)

Asian15 (6.9)

Black/African American17 (7.8)

Native Hawaiian/Other Pacific Islander1 (0.5)

White182 (83.9)
Ethnicity

Hispanic/Latino13 (6.0)

Non-Hispanic/Non-Latino204 (94.0)
Gender

Male96 (44.2)

Female121 (55.8)
Education level

Less than high school1 (0.5)

Diploma/GED26 (12.0)

Some college56 (25.8)

2-year degree23 (10.6)

4-year degree91 (41.9)

Master’s degree18 (8.3)

Professional degree (MD, JD)2 (0.9)
Region of residence in USA

West49 (22.6)

South90 (41.5)

Midwest36 (16.6)

Northeast42 (19.4)
Household income, in US dollars (2016)

Less than 30,00050 (23.0)

30,000-39,99930 (13.8)

40,000-49,99928 (12.9)

50,000-59,99929 (13.4)

60,000-69,99922 (10.1)

70,000-79,99914 (6.5)

80,000-89,99914 (6.5)

90,000-99,9999 (4.2)

100,000 or more21 (9.7)
Table 2. Summary of participant reponses to theory questions.
QuestionaResponse (N=217), n (%)

Strongly disagreeDisagreeNeutralAgreeStrongly agree
Increased my belief that poor diet/nutrition leads to diseaseb,c4 (1.8)35 (16.1)45 (20.7)94 (43.3)39 (18.0)
Increased my belief that eating a healthy diet can prevent diseaseb,c4 (1.8)22 (10.1)34 (15.7)93 (42.9)64 (29.5)
Increased my belief that diseases related to poor diet/nutrition are harmfulb,c4 (1.8)22 (10.1)40 (18.4)76 (35.0)75 (34.6)
Increase my belief that eating a healthy diet is important in preventing diseaseb,c4 (1.8)14 (6.5)31 (14.3)91 (41.9)77 (35.5)
Increased my motivation to eat a healthy dietb1 (0.5)6 (2.8)0 (0.0)80 (36.8)130 (59.9)
Increased my ability to eat a healthy dietb4 (1.8)8 (3.7)18 (8.3)92 (42.4)95 (43.8)
Increased my confidence that I can eat a healthy dietb1 (.5)6 (2.8)11 (5.0)105 (48.4)94 (43.3)
Increased my desire to eat a healthy dietc0 (0.0)2 (0.9)12 (5.6)94 (43.3)109 (50.2)
Increased my intentions to eat a healthy dietc1 (0.5)1 (0.5)5 (2.3)88 (40.5)122 (56.2)
Increased my attitudes about the importance of eating a healthy diet in preventing diseasec3 (1.4)15 (6.9)21 (9.7)97 (44.7)81 (37.3)
Increased my belief that people important to me want me to eat a healthy dietc9 (4.2)33 (15.2)52 (24.0)66 (30.4)57 (26.2)
Increased my perception that many other people are eating a healthy dietb5 (2.3)41 (18.9)46 (21.2)69 (31.8)56 (25.8)
Increased my knowledge of the diseases that are caused by poor diet/ nutritionb14 (6.5)44 (20.3)42 (19.3)73 (33.6)44 (20.3)
Increased my knowledge of the ways in which I can eat a healthy dietb1 (.5)7 (3.2)14 (6.4)98 (45.2)97 (44.7)
Increased my awareness of the benefits of eating a healthy dietd1 (0.5)12 (5.5)30 (13.8)95 (43.8)79 (36.4)
Increased my desire to be healthyc1 (0.5)5 (2.3)8 (3.7)78 (35.9)125 (57.6)
Increased the social support I have received for eating a healthy dietb11 (5.0)41 (18.9)39 (18.0)78 (35.9)48 (22.1)
Increased the positive feedback I have received for eating a healthy dietb12 (5.5)20 (9.2)41 (18.9)89 (41.0)55 (25.4)
Increased my desire to set goals to eat a healthy dietc0 (0.0)0 (0.0)5 (2.3)84 (38.7)128 (59.0)
Increased my ability to achieve my healthy diet goalsb0 (0.0)1 (0.5)11 (5.1)94 (43.3)111 (51.1)

aAll theory questions in the survey were preceded by this statement: Now think about the diet/nutrition app(s) that you have used in the past 6 months. Using the app(s) has…

bQuestions were derived from the social cognitive theory.

cQuestions were derived from the theory of planned behavior.

dQuestions were derived from the health belief model.

The majority of participants strongly agreed that using diet/nutrition apps led to changes in their behavior, namely increases in actual goal setting to eat a healthy diet (58.5%, 127/217), increases in their frequency of eating healthy foods (57.6%, 125/217), and increases in their consistency of eating healthy foods (54.4%, 118/217; Table 3).

Participants strongly agreed that the diet/nutrition apps they used were easy to use (62.9%, 156/217) and helpful (60.5%, 150/217; Table 4). Many also strongly agreed that they liked the apps (54.4%, 135/217) and enjoyed using the apps (44.8%, 111/217). About half of the participants (54.0%, 134/217) strongly agreed that they would recommend the apps to others. This scale, measuring engagement, received a high Cronbach alpha (.875).

Two of the predictors were significantly associated with theory (Table 5). Specifically, app engagement (P<.001) and app price (P<.01) were associated with theory.

Several predictors were also positively associated with diet-related behavior change (Table 6). Theory (P<.001), app engagement (P<.001), app use (P<.003), and education (P<.01) were all positively associated with behavior change.

Figure 1 summarizes significant relationships between theory, behavior, and other predictors. Arrows indicate the hypothetical direction of the relationships and asterisks indicate the level of statistical significance. In the model, the price of the app and the level of participant engagement affect theory, which in turn drives behavior change. Engagement also affects behavior directly. Finally, the level of participant education and the frequency of app use have a direct impact on behavior change.

Table 3. Summary of participant responses to behavior change questions.
QuestionaResponse (N=217), n (%)

Strongly disagreeDisagreeNeutralAgreeStrongly agree
Increased my actual goal setting to eat a healthy diet1 (0.5)0 (0.0)11 (5.1)78 (35.9)127 (58.5)
Increased my frequency of eating healthy foods0 (0.0)2 (0.9)8 (3.7)82 (37.8)125 (57.6)
Increased my consistency in eating healthy foods0 (0.0)1 (0.5)8 (3.7)90 (41.4)118 (54.4)

aAll theory questions in the survey were preceded by this statement: Now think about the diet/nutrition app(s) that you have used in the past 6 months. Using the app(s) has…

Table 4. Summary of participant responses to engagement questions.
QuestionaResponse (N=217), n (%)

Strongly disagreeDisagreeNeutralAgreeStrongly agree
The app(s) was helpful2 (0.8)3 (1.2)4 (1.6)89 (35.9)150 (60.5)
The app(s) was easy to use1 (0.4)2 (0.8)6 (2.4)83 (33.5)156 (62.9)
I enjoyed using the app(s)2 (0.8)4 (1.6)26 (10.5)105 (42.3)111 (44.8)
I liked the app(s)1 (0.4)3 (1.2)10 (4.0)99 (39.9)135 (54.4)
I would recommend the app(s) to others1 (0.4)3 (1.2)19 (7.7)91 (36.7)134 (54.0)

aAll engagement questions in the survey were preceded by this statement: Considering the diet/nutrition app(s) that you have used in the past 6 months…

Table 5. Ordinary least squares regression results for determinants of theory.
Determinants of theoryCoefficient
(Standard error)
tP>| t |95% CI
App engagement1.06 (0.17)6.42<.0010.74-1.39
Price of app0.50 (0.20)2.50.010.11-0.90
Frequency of app use0.11 (0.17)0.64.52−0.23 to 0.45
Gender0.16 (0.17)0.98.33−0.17 to 0.50
Age0.05 (0.11)0.49.63−0.16 to 0.26
Income−0.66.51−0.17 to 0.09
Education−1.36.18−0.11 to 0.02
Constant1.33 (0.61)2.20.030.14-2.53
Table 6. Ordinary least squares regression results for determinants of behavior change.
Independent VariablesCoefficient
(Standard error)
tP>| t |95% CI
Theory0.10 (0.02)4.87<.0010.06-0.14
App engagement0.39 (0.05)7.56<.0010.29-0.50
Price of app−0.06 (0.06)−1.02.31−0.18 to 0.06
Frequency of app use0.15 (0.05)3.02.0030.05-0.25
Gender0.05 (0.05)0.96.34−0.49 to 0.14
Age−0.00 (0.03)−0.14.89−0.06 to 0.06
Income0.01 (0.01)0.88.38−0.01 to 0.03
Education0.05 (0.02)2.60.010.01-0.09
Constant0.20 (0.18)1.14.26−0.15 to 0.55
Figure 1. Factors influencing behavior change. Figure 1 illustrates the relationship between mobile application attributes, theoreticaldeterminants of behavior and behavior. Arrows indicate the hypothetical direction of therelationships and stars indicate the statistical significance.
View this figure

Principal Findings

The purpose of this study was to explore behavior change mechanisms associated with the use of diet- and nutrition-related health apps and to examine if the use of these apps is associated with actual changes in dietary behaviors. The results of this study demonstrate that the use of diet/nutrition apps is associated with diet-related behavior change. In addition, this study showed that behavior change theory was positively associated with actual behavior change related to the use of diet/nutrition apps.

Participants in this study reported increased motivation, desire, and ability to improve their dietary intake with app use. Likewise, participants indicated an increase in their ability to establish and achieve dietary goals. Taken together such increases indicate that diet- and nutrition-related apps improve self-efficacy, or strengthen one’s belief that they can engage in healthy dietary behaviors. Self-efficacy is a key component of SCT and is widely considered to be a powerful predictor of health behavior and appears to be a key mechanism by which health apps facilitate behavior change [8,22,23].

Survey results also indicate that app use helped to create attitudes supportive of improved dietary behaviors, as well as behavioral intentions to eat a healthy diet. The TPB postulates that behavioral attitudes and beliefs coupled with subjective norms and self-efficacy predict behavioral intentions [13]. Results from this study indicate that attitudes related to the importance of eating healthy and the subsequent behavioral intentions of doing so are mechanisms for behavior change when using diet- and nutrition-related apps. This dovetail between self-efficacy and enhanced autonomy has been observed in other studies where behavior change was achieved through the use of mobile health informatics tools that are patient-centered and increase self-management skills [11,24].

Finally, participants reported an increase in knowledge of the ways in which they can eat a healthy diet and an awareness of the benefits of improving dietary habits. Though general knowledge alone is often an insufficient change agent [25], this study demonstrates that knowledge specifically related to ways in which one can improve dietary behavior and the benefits of making such improvements are mechanisms for change when using diet- and nutrition-related apps.

Limitations

Several limitations should be considered when interpreting the findings of this study. First, this study has limited racial and ethnic diversity among participants. Respondents in this study were primarily white with similar levels of age, education, and economic status. This limitation is likely a reflection of the demographic using MTurk’s Web-based surveying system [26]. Additional research is needed with a more diverse sample to make generalizations related to this study’s key findings.

Second, this study included only limited data on participants due to the need to balance resource constraints with various research questions of interests. The study would be strengthened by collecting additional participant data and information. For example, collecting information regarding respondents’ height and weight to examine relationships between health outcomes such as body mass index and diabetes and app use may have been helpful. Third, a pre- and posttest design evaluating participants’ dietary and nutritional behaviors before and after downloading the app may reveal additional insight into mechanisms for behavior change related to app use. Future studies may benefit from qualitative research designs targeting motivations of diet/nutrition app use over time. Fourth, understanding participants’ motivations for downloading and using diet- and nutrition-related apps may also have been useful, as would have determining whether or not those apps met participants’ expectations. Despite these limitations, this study represents an initial effort to understand the mechanisms by which diet- and nutrition-related apps lead to behavior change, which can guide both future app development and research design.

Conclusions

Diet- and nutrition-related mobile apps show promise as tools to successfully facilitate positive health behavior change. The results of this study confirm that the use of diet/nutrition apps is associated with diet-related behavior change. Furthermore, apps that focus on improving motivation, desire, self-efficacy, attitudes, knowledge, and goal setting may be particularly useful. To ensure that mobile apps are effective health behavior change agents, theories and their respective constructs known to facilitate health behavior change, such as those of SCT, TPB, and HBM, should continue to be integrated into health app design and implementation. Moving forward, developers of diet/nutrition apps may consider design configurations that emphasize the provision of knowledge to shape attitudes and beliefs, followed by attempts to influence actual skill development in app users. Elements of gamification or other such paradigms may be useful to maintain user motivation and the desire to be persistent in making weight loss efforts.

Acknowledgments

This study was funded by a teaching enhancement grant from Brigham Young University.

Conflicts of Interest

None declared.

References

  1. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014 Feb 26;311(8):806-814. [CrossRef] [Medline]
  2. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA 2010 Jan 20;303(3):235-241. [CrossRef] [Medline]
  3. O'Kane G. What is the real cost of our food? implications for the environment, society and public health nutrition. Public Health Nutr 2012 Feb;15(2):268-276. [CrossRef] [Medline]
  4. Ebbeling CB, Pawlak DB, Ludwig DS. Childhood obesity: public-health crisis, common sense cure. Lancet 2002 Aug 10;360(9331):473-482. [CrossRef] [Medline]
  5. Mirmiran P, Mirbolooki M, Azizi F. Familial clustering of obesity and the role of nutrition: Tehran lipid and glucose study. Int J Obes Relat Metab Disord 2002 Dec;26(12):1617-1622. [CrossRef] [Medline]
  6. Bert F, Giacometti M, Gualano MR, Siliquini R. Smartphones and health promotion: a review of the evidence. J Med Syst 2014 Jan;38(1):9995. [CrossRef] [Medline]
  7. Hebden L, Cook A, van der Ploeg H, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc 2012;1(2):e9 [FREE Full text] [CrossRef] [Medline]
  8. West JH, Hall PC, Arredondo V, Berrett B, Guerra B, Farrell J. Health behavior theories in diet apps. J Consum Health Internet 2013 Jan;17(1):10-24. [CrossRef]
  9. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc 2013 May 1;20(3):513-518 [FREE Full text] [CrossRef] [Medline]
  10. Handel MJ. mHealth (mobile health)-using apps for health and wellness. Explore (NY) 2011;7(4):256-261. [CrossRef] [Medline]
  11. Hasman L. An introduction to consumer health apps for the iPhone. J Consum Health Internet 2011 Oct;15(4):322-329. [CrossRef]
  12. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res 2013;15(4):e32 [FREE Full text] [CrossRef] [Medline]
  13. Glanz K, Rimer B, Viswanath K. Health Behavior Theory, Research, and Practice. San Francisco, CA: Jossey-Bass; 2015.
  14. Achterberg C, Miller C. Is one theory better than another in nutrition education? a viewpoint: more is better. J Nutr Educ Behav 2004;36(1):40-42. [Medline]
  15. Pagoto S, Bennett GG. How behavioral science can advance digital health. Transl Behav Med 2013 Sep;3(3):271-276 [FREE Full text] [CrossRef] [Medline]
  16. Acharya SD, Elci OU, Sereika SM, Styn MA, Burke LE. Using a personal digital assistant for self-monitoring influences diet quality in comparison to a standard paper record among overweight/obese adults. J Am Diet Assoc 2011 Apr;111(4):583-588 [FREE Full text] [CrossRef] [Medline]
  17. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! but does it adhere to evidence-informed practices? Transl Behav Med 2011 Dec;1(4):523-529 [FREE Full text] [CrossRef] [Medline]
  18. West JH, Hall PC, Hanson CL, Barnes MD, Giraud-Carrier C, Barrett J. There's an app for that: content analysis of paid health and fitness apps. J Med Internet Res 2012;14(3):e72 [FREE Full text] [CrossRef] [Medline]
  19. Brunstein A, Brunstein J, Mansar SL. Integrating health theories in health and fitness applications for sustained behavior change: current state of the art. CE 2012;03(08):1-5. [CrossRef]
  20. Wright K. Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. J Comput Mediat Commun 2005;10(3):00. [CrossRef]
  21. Evans JR, Mathur A. The value of online surveys. Internet Res 2005 Apr;15(2):195-219. [CrossRef]
  22. Azar KM, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med 2013 Nov;45(5):583-589. [CrossRef] [Medline]
  23. Cowan LT, Van Wagenen SA, Brown BA, Hedin RJ, Seino-Stephan Y, Hall PC, et al. Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory. Health Educ Behav 2013 Apr;40(2):133-139. [CrossRef] [Medline]
  24. Crane D, Garnett C, Brown J, West R, Michie S. Behavior change techniques in popular alcohol reduction apps: content analysis. J Med Internet Res 2015;17(5):e118 [FREE Full text] [CrossRef] [Medline]
  25. Rimal RN. Closing the knowledge-behavior gap in health promotion: the mediating role of self-efficacy. Health Commun 2000;12(3):219-237. [CrossRef] [Medline]
  26. Ross J, Irani L, Silberman M, Zaldivar A, & TB. Who are the crowdworkers? shifting demographics in mechanical turk. In: Extended Abstracts on Human Factors in Computing Systems. 2010 Presented at: CHI EA '10; April 10-15, 2010; Atlanta, GA p. 2863-2872. [CrossRef]


HBM: health belief model
SCT: social cognitive theory
TPB: theory of planned behavior


Edited by G Eysenbach; submitted 26.01.17; peer-reviewed by S Litzkendorf, J Bian, B Cao, R Biviji; comments to author 16.03.17; revised version received 05.05.17; accepted 06.05.17; published 10.07.17

Copyright

©Joshua H West, Lindsay M Belvedere, Rebecca Andreasen, Christine Frandsen, P Cougar Hall, Benjamin T Crookston. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 10.07.2017.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.