Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity


      Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps.


      The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity.


      Top-ranked apps (n=167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London–Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013.


      Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques.


      Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.
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        • Fox S.
        • Duggan M.
        Mobile health 2012.
        Pew Research Center’s Internet & American Life Project, Washington DC2012
        • 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
        • Pagoto S.
        • Bennett G.G.
        How behavioral science can advance digital health.
        Transl Behav Med. 2013; 3: 271-276
        • West J.H.
        • Hall P.C.
        • Hanson C.L.
        • Barnes M.D.
        • 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: e72
        • 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
        • Hong Y.
        • Dahlke D.V.
        • Ory M.
        • et al.
        Designing iCanFit: a mobile-enabled web application to promote physical activity for older cancer survivors.
        JMIR Res Protoc. 2013; 2: e12
        • Rabin C.
        • Bock B.
        Desired features of smartphone applications promoting physical activity.
        Telemed J E Health. 2011; 17: 801-803
        • Van der Weegen S.
        • Verwey R.
        • Spreeuwenberg M.
        • Tange H.
        • van der Weijden T.
        • de Witte L.
        The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design.
        JMIR Mhealth Uhealth. 2013; 1: e8
        • Schoffman D.E.
        • Turner-McGrievy G.
        • Jones S.J.
        • Wilcox S.
        Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: just fun and games?.
        Transl Behav Med. 2013; 3: 320-325
        • Pagoto S.
        • Schneider K.
        • Jojic M.
        • Debiasse M.
        • Mann D.
        Evidence-based strategies in weight-loss mobile apps.
        Am J Prev Med. 2013; 45: 576-582
        • Michie S.
        • Ashford S.
        • Sniehotta F.F.
        • Dombrowski S.U.
        • Bishop A.
        • French D.P.
        A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy.
        Psychol Health. 2011; 11: 1479-1498
        • Bandura A.
        Human agency in social cognitive theory.
        Am Psychol. 1989; 44: 1175-1184
        • Schwarzer R.
        Self-efficacy in the adoption and maintenance of health behaviors: theoretical approaches and a new model.
        in: Schwarzer R. Self-efficacy: thought control of action. Hemisphere, Washington DC1992
        • Sheeran P.
        Intention–behavior relations: a conceptual and empirical review.
        Eur Rev Soc Psychol. 2002; 12: 1-36
        • Webb T.L.
        • Sheeran P.
        Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence.
        Psychol Bull. 2006; 132: 249-268
        • Rhodes R.E.
        • Dickau L.
        Experimental evidence for the intention–behavior relationship in the physical activity domain: a meta-analysis.
        Health Psychol. 2012; 31: 724-727
        • Carraro N.
        • Gaudreau P.
        Spontaneous and experimentally induced action planning and coping planning for physical activity: a meta-analysis.
        Psychol Sport Exerc. 2013; 14: 228-248
        • Bélanger-Gravel A.
        • Godin G.
        • Amireault S.
        A meta-analytic review of the effect of implementation intentions on physical activity.
        Health Psychol Rev. 2013; 7: 23-54
        • Michie S.
        • Richardson M.
        • Johnston M.
        • et al.
        The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions.
        Ann Behav Med. 2013; 46: 81-95
        • USDHHS, U.S. Food and Drug Administration
        Mobile medical applications: guidance for industry and Food and Drug Administration staff.
        USDHHS, U.S. Food and Drug Administration, Washington DC2013
        • 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
        • Nilsen W.
        • Kumar S.
        • Shar A.
        • et al.
        Advancing the science of mHealth.
        J Health Commun. 2012; 17: 5S-10S
        • Hekler E.B.
        • Klasnja P.
        • Traver V.
        • Hendriks M.
        Realizing effective behavioral management of health: the metamorphosis of behavioral science methods.
        IEEE Pulse. 2013; 4: 29-34
        • Spring B.
        • Gotsis M.
        • Paiva A.
        • Spruijt-Metz D.
        Healthy apps: mobile devices for continuous monitoring and intervention.
        IEEE Pulse. 2013; 4: 34-40