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Implementation of Behavior Change Techniques in Mobile Applications for Physical Activity

Published:January 06, 2015DOI:https://doi.org/10.1016/j.amepre.2014.10.010

      Background

      Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.

      Purpose

      To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.

      Methods

      Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.

      Results

      Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others’ approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.

      Conclusions

      Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.
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      References

        • Klasnja P.
        • Pratt W.
        Healthcare in the pocket: mapping the space of mobile-phone health interventions.
        J Biomed Inform. 2012; 45: 184-198
      1. Smith A. Smartphone ownership—2013 update [Internet]. Washington DC: Pew Research Center’s Internet & American Life Project, 2013:12.pewinternet.org/Reports/2013/Smartphone-Ownership-2013.aspx.

        • Fox S.
        • Duggan M.
        Mobile health 2012 [Internet].
        Pew Research Center’s Internet & American Life Project, Washington DC2012
      2. Citrix. Mobile analytics report [Internet]. Santa Clara CA: Citrix, 2014. http://www.citrix.com/content/dam/citrix/en_us/documents/products-solutions/citrix-mobile-analytics-report-september-2014.pdf?accessmode=direct&_ga=1.42377445.1588170780.1425320050.

        • Mokdad A.H.
        • Marks J.S.
        • Stroup D.F.
        • Gerberding J.L.
        Actual causes of death in the United States, 2000.
        JAMA. 2004; 291: 1238-1245
        • Lee I.-M.
        • Shiroma E.J.
        • Lobelo F.
        • et al.
        Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.
        The Lancet. 2012; 380: 219-229
        • Mohr D.C.
        • Cheung K.
        • Schueller S.M.
        • et al.
        Continuous evaluation of evolving behavioral intervention technologies.
        Am J Prev Med. 2013; 45: 517-523
        • West J.H.
        • Hall P.C.
        • Hanson C.L.
        • et al.
        There’s an app for that: Content analysis of paid health and fitness apps.
        J Med Internet Res. 2012; 14: e72
        • 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.
        • Schneider K.
        • Jojic M.
        • et al.
        Evidence-based strategies in weight-loss mobile apps.
        Am J Prev Med. 2013; 45: 576-582
        • Eyler A.A.
        Are diabetes self-management apps based on evidence?.
        Transl Behav Med. 2013; 3: 233
        • Conroy D.E.
        • Yang C.-H.
        • Maher J.P.
        Behavior change techniques in top-ranked physical activity apps.
        Am J Prev Med. 2014; 46: 649-652
        • 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
        • Landis J.R.
        • Koch G.G.
        The measurement of observer agreement for categorical data.
        Biometrics. 1977; 33: 159-174
        • Azar K.M.J.
        • Lesser L.I.
        • Laing B.Y.
        • et al.
        Mobile applications for weight management: theory-based content analysis.
        Am J Prev Med. 2013; 45: 583-589
        • Maher C.A.
        • Lewis L.K.
        • Ferrar K.
        • et al.
        Are health behavior change interventions that use online social networks effective? A systematic review.
        J Med Internet Res. 2014; 16: e40
        • Christakis N.A.
        • Fowler J.H.
        The spread of obesity in a large social network over 32 years.
        N Engl J Med. 2007; 357: 370-379
        • Pagoto S.L.
        • Schneider K.L.
        • Oleski J.
        • et al.
        The adoption and spread of a core-strengthening exercise through an online social network.
        J Phys Act Health. 2014; 11: 648-653
        • Michie S.
        • Abraham C.
        • Whittington C.
        • et al.
        Effective techniques in healthy eating and physical activity interventions: a meta-regression.
        Health Psychol. 2009; 28: 690-701
        • Williams S.L.
        • French D.P.
        What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour—and are they the same?.
        Health Educ Res. 2011; 26: 308-322