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Mobile Applications for Weight Management

Theory-Based Content Analysis

      Background

      The use of smartphone applications (apps) to assist with weight management is increasingly prevalent, but the quality of these apps is not well characterized.

      Purpose

      The goal of the study was to evaluate diet/nutrition and anthropometric tracking apps based on incorporation of features consistent with theories of behavior change.

      Methods

      A comparative, descriptive assessment was conducted of the top-rated free apps in the Health and Fitness category available in the iTunes App Store. Health and Fitness apps (N=200) were evaluated using predetermined inclusion/exclusion criteria and categorized based on commonality in functionality, features, and developer description. Four researchers then evaluated the two most popular apps in each category using two instruments: one based on traditional behavioral theory (score range: 0–100) and the other on the Fogg Behavioral Model (score range: 0–6). Data collection and analysis occurred in November 2012.

      Results

      Eligible apps (n=23) were divided into five categories: (1) diet tracking; (2) healthy cooking; (3) weight/anthropometric tracking; (4) grocery decision making; and (5) restaurant decision making. The mean behavioral theory score was 8.1 (SD=4.2); the mean persuasive technology score was 1.9 (SD=1.7). The top-rated app on both scales was Lose It! by Fitnow Inc.

      Conclusions

      All apps received low overall scores for inclusion of behavioral theory-based strategies.
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      References

        • Ogden C.L.
        • Carroll M.D.
        • Kit B.K.
        • Flegal K.M.
        Prevalence of obesity in the U.S., 2009-2010.
        NCHS Data Brief. 2012; 82: 1-8
        • Flegal K.M.
        • Carroll M.D.
        • Kit B.K.
        • Ogden C.L.
        Prevalence of obesity and trends in the distribution of body mass index among U.S. adults, 1999-2010.
        JAMA. 2012; 307: 491-497
        • Neve M.
        • Morgan P.J.
        • Jones P.R.
        • Collins C.E.
        Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis.
        Obes Rev. 2010; 11: 306-321
        • Patrick K.
        • Calfas K.J.
        • Norman G.J.
        • et al.
        Outcomes of a 12-month web-based intervention for overweight and obese men.
        Ann Behav Med. 2011; 42: 391-401
        • Fox S.
        • Duggan M.
        Mobile Health 2012.
        Pew Research Center, Washington DC2012
        • Turner-McGrievy G.
        • Tate D.
        Tweets, apps, and pods: results of the 6-month Mobile Pounds Off Digitally (Mobile POD) randomized weight-loss intervention among adults.
        J Med Internet Res. 2011; 13: e120
        • Stephens J.
        • Allen J.
        Mobile phone interventions to increase physical activity and reduce weight: a systematic review.
        J Cardiovasc Nurs. 2013; 28: 320-329
        • Patrick K.
        • Raab F.
        • Adams M.A.
        • et al.
        A text message-based intervention for weight loss: randomized controlled trial.
        J Med Internet Res. 2009; 11: e1
        • Riley W.T.
        • Rivera D.E.
        • Atienza A.A.
        • Nilsen W.
        • Allison S.M.
        • Mermelstein R.
        Health behavior models in the age of mobile interventions: are our theories up to the task?.
        Transl Behav Med. 2011; 1: 53-71
        • Hebden L.
        • Cook A.
        • van der Ploeg H.P.
        • Allman-Farinelli M.
        Development of smartphone applications for nutrition and physical activity behavior change.
        JMIR Res Protoc. 2012; 1: e9
        • Backinger C.L.
        • Augustson E.M.
        Where there's an app, there's a way?.
        Am J Prev Med. 2011; 40: 390-391
        • Smith A.
        Smartphone ownership—2013 update.
        Pew Research Center, Washington DC2013
      1. Kasbo A, McLaughlin R. Mobile health applications: 2012 study. 2012. verasoni.com/ahha3/mobile-health-applications-2012-study/.

      2. Baker M. Software for shaping up. 2011. online.wsj.com/article/SB10001424052748703961104576148732585957902.html.

        • Carter M.C.
        • Burley V.J.
        • Nykjaer C.
        • Cade J.E.
        Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial.
        J Med Internet Res. 2013; 15: e32
        • Stephens J.
        • Allen J.K.
        • Dennison Himmelfarb C.R.
        "Smart" coaching to promote physical activity, diet change, and cardiovascular health.
        J Cardiovas Nurs. 2011; 26: 282-284
        • Breton E.
        • Fuemmeler B.
        • Abroms L.
        Weight loss—there is an app for that! But does it adhere to evidence-informed practices?.
        Transl Behav Med. 2011; 1: 523-529
        • Abroms L.C.
        • Padmanabhan N.
        • Thaweethai L.
        • Phillips T.
        iPhone apps for smoking cessation: a content analysis.
        Am J Prev Med. 2011; 40: 279-285
        • Cowan L.T.
        • Van Wagenen S.A.
        • Brown B.A.
        • 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; 40: 133-139
        • Neuhausera L.
        • Kreps G.L.
        eHealth communication and behavior change: promise and performance.
        Soc Semiot. 2010; 20: 9-27
        • Wing R.R.
        • Hill J.O.
        Successful weight loss maintenance.
        Annu Rev Nutr. 2001; 21: 323-341
        • Van Dorsten B.
        • Lindley E.M.
        Cognitive and behavioral approaches in the treatment of obesity.
        Med Clin North Am. 2011; 95: 971-988
        • Burke L.E.
        • Wang J.
        • Sevick M.A.
        Self-monitoring in weight loss: a systematic review of the literature.
        J Am Diet Assoc. 2011; 111: 92-102
        • Burke L.E.
        • Conroy M.B.
        • Sereika S.M.
        • et al.
        The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial.
        Obesity (Silver Spring). 2011; 19: 338-344
        • Butryn M.L.
        • Phelan S.
        • Hill J.O.
        • Wing R.R.
        Consistent self-monitoring of weight: a key component of successful weight loss maintenance.
        Obesity. 2007; 15: 3091-3096
        • Steinberg D.M.
        • Tate D.F.
        • Bennett G.G.
        • Ennett S.
        • Samuel-Hodge C.
        • Ward D.S.
        The efficacy of a daily self-weighing weight loss intervention using smart scales and email.
        Obesity. 2013;
        • Kanfer F.
        Self-management methods.
        Pergamon Press, New York NY1991
        • Fogg B.J.
        • Eckles D.
        The behavior chain for online participation: how successful web services structure persuasion.
        in: PERSUASIVE 2007 LNCS. Vol 4744. Springer-Verlag, Berlin2007: 199-209
      3. Fogg B. A behavior model for persuasive design. 2009. www.bjfogg.com/fbm_files/page4_1.pdf.

        • Fogg B.J.
        Persuasive technology: using computers to change what we think and do (interactive technologies).
        Morgan Kaufmann, San Francisco2003
        • Doshi A.
        • Patrick K.
        • Sallis J.F.
        • Calfas K.
        Evaluation of physical activity web sites for use of behavior change theories.
        Ann Behav Med. 2003; 25: 105-111
      4. Hearn M. Study suggests 70 percent of mobile app users pay “nothing or very little” for apps. 2012.

        • Neuhauser L.
        • Kreps G.L.
        Rethinking communication in the E-health era.
        J Health Psychol. 2003; 8: 7-23
      5. Shaw K, O'Rourke P, Del Mar C, Kenardy J. Psychological interventions for overweight or obesity. Cochrane Database Syst Rev. 2005;(2):CD003818

        • Norris S.L.
        • Zhang X.
        • Avenell A.
        • et al.
        Long-term effectiveness of weight-loss interventions in adults with pre-diabetes: a review.
        Am J Prev Med. 2005; 28: 126-139
      6. Woolston C. Can smartphone apps really help you lose weight? 2011. articles.latimes.com/2011/may/09/health/la-he-skeptic-phone-apps-20110509.

      7. Melnick M. Fitness, weight loss and nutrition apps for your phone. 2012. www.huffingtonpost.com/2012/03/22/fitness-weight-loss-nutrition-apps_n_1371705.html#s803227&title=ThinCam.

      8. Cohen J. The 8 best smart phone apps for weight loss. 2012. www.forbes.com/sites/jennifercohen/2012/08/21/the-8-best-smart-phone-apps-for-weight-loss/.

      9. Alderman L. Losing weight the smartphone way, with a nutritionist in your pocket. 2010. www.nytimes.com/2010/07/17/health/17patient.html.

        • Hollis J.F.
        • Gullion C.M.
        • Stevens V.J.
        • et al.
        Weight loss during the intensive intervention phase of the weight-loss maintenance trial.
        Am J Prev Med. 2008; 35: 118-126
        • Turk M.W.
        • Elci O.U.
        • Wang J.
        • et al.
        Self-monitoring as a mediator of weight loss in the SMART randomized clinical trial.
        Int J Behav Med. 2012;
        • Wang J.
        • Sereika S.M.
        • Chasens E.R.
        • Ewing L.J.
        • Matthews J.T.
        • Burke L.E.
        Effect of adherence to self-monitoring of diet and physical activity on weight loss in a technology-supported behavioral intervention.
        Patient Prefer Adherence. 2012; 6: 221-226
        • Bandini L.G.
        • Schoeller D.A.
        • Cyr H.N.
        • Dietz W.H.
        Validity of reported energy intake in obese and nonobese adolescents.
        Am J Clin Nutr. 1990; 52: 421-425
        • Lichtman S.W.
        • Pisarska K.
        • Berman E.R.
        • et al.
        Discrepancy between self-reported and actual caloric intake and exercise in obese subjects.
        N Engl J Med. 1992; 327: 1893-1898
        • Jakicic J.M.
        • Polley B.A.
        • Wing R.R.
        Accuracy of self-reported exercise and the relationship with weight loss in overweight women.
        Med Sci Sports Exerc. 1998; 30: 634-638
        • Thomas S.L.
        • Hyde J.
        • Karunaratne A.
        • Kausman R.
        • Komesaroff P.A.
        "They all work...when you stick to them": a qualitative investigation of dieting, weight loss, and physical exercise, in obese individuals.
        Nutr J. 2008; 7: 34
        • Burke L.E.
        • Sereika S.M.
        • Music E.
        • Warziski M.
        • Styn M.A.
        • Stone A.
        Using instrumented paper diaries to document self-monitoring patterns in weight loss.
        Contemp Clin Trials. 2008; 29: 182-193
        • Wadden T.A.
        • Berkowitz R.I.
        • Womble L.G.
        • et al.
        Randomized trial of lifestyle modification and pharmacotherapy for obesity.
        N Engl J Med. 2005; 353: 2111-2120