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Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential

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Abstract

As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of “real-time” objective measurement and “big data” commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.

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References

  • Abroms, L. C., Lee Westmaas, J., Bontemps-Jones, J., Ramani, R., & Mellerson, J. (2013). A content analysis of popular smartphone apps for smoking cessation. American Journal of Preventive Medicine, 45, 732–736. doi:10.1016/j.amepre.2013.07.008

    Article  PubMed  Google Scholar 

  • Almirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014). Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Translational Behavioral Medicine, 4, 260–274. doi:10.1007/s13142-014-0265-0

    Article  PubMed  PubMed Central  Google Scholar 

  • Althoff, T., White, T. W., & Horvitz, E. (2016). Influence of Pokémon go on physical activity: Study and implications. Retrieved from https://arxiv.org/abs/1610.02085

  • Ashour, M., Bekiroglu, K., Yang, C.-H., Lagoa, C., Conroy, D., Smyth, J., et al. (2016). On the mathematical modeling of the effect of treatment on human physical activity (pp. 1084–1091). New York: IEEE. doi:10.1109/CCA.2016.7587951

    Google Scholar 

  • Balatsoukas, P., Kennedy, C. M., Buchan, I., Powell, J., & Ainsworth, J. (2015). The role of social network technologies in online health promotion: A narrative review of theoretical and empirical factors influencing intervention effectiveness. Journal of Medical Internet Research, 17, e141. doi:10.2196/jmir.3662

    Article  PubMed  PubMed Central  Google Scholar 

  • Bandura, A. (1977a). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.

    Article  CAS  PubMed  Google Scholar 

  • Bandura, A. (1977b). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Bandura, A. (1997a). Self-efficacy: The exercise of control. New York: Freeman.

    Google Scholar 

  • Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine (1982), 51, 843–857. doi:10.1016/S0277-9536(00)00065-4

    Article  CAS  Google Scholar 

  • Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda county residents. American Journal of Epidemiology, 109, 186–204.

    CAS  PubMed  Google Scholar 

  • Berli, C., Rauers, A., Luscher, J., Hohl, D. H., Keller, J., & Stadler, G. (2016). Social exchange processes and their association with health regulation and health-related outcomes. Symposium presented at the European Health Psychology Society (EHPS) and the British Psychological Society’s Division of Health Psychology (DHP) Dynamic Systems Modeling Expert Meeting, Aberdeen, Scotland. Retrieved from http://ehps2016.org/files/EHPS2016_Abstracts_Book_08082016.pdf

  • Borrelli, B., Sepinwall, D., Ernst, D., Bellg, A. J., Czajkowski, S., Breger, R., et al. (2005). A new tool to assess treatment fidelity and evaluation of treatment fidelity across 10 years of health behavior research. Journal of Consulting and Clinical Psychology, 73, 852–860. doi:10.1037/0022-006X.73.5.852

    Article  PubMed  Google Scholar 

  • Breton, E. R., Fuemmeler, B. F., & Abroms, L. C. (2011). Weight loss—There is an app for that! But does it adhere to evidence-informed practices? Translational Behavioral Medicine, 1, 523–529. doi:10.1007/s13142-011-0076-5

    Article  PubMed  PubMed Central  Google Scholar 

  • Carson, T. L., Eddings, K. E., Krukowski, R. A., Love, S. J., Harvey-Berino, J. R., & West, D. S. (2013). Examining social influence on participation and outcomes among a network of behavioral weight-loss intervention enrollees. Journal of Obesity, 2013, 1–8. doi:10.1155/2013/480630

    Article  Google Scholar 

  • Centola, D. (2013). Social media and the science of health behavior. Circulation, 127, 2135–2144. doi:10.1161/CIRCULATIONAHA.112.101816

    Article  PubMed  Google Scholar 

  • Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370–379. doi:10.1056/NEJMsa066082

    Article  CAS  PubMed  Google Scholar 

  • Colantonio, S., Coppini, G., Germanese, D., Giorgi, D., Magrini, M., Marraccini, P., Martinelli, M., Morales, M. A., Pascali, M. A., Raccichini, G., Righi, M., & Salvetti, O. (2015). A smart mirror to promote a healthy lifestyle. Biosystems Engineering, 138, 33–43.

    Article  Google Scholar 

  • Collins, L. M., Baker, T. B., Mermelstein, R. J., Piper, M. E., Jorenby, D. E., Smith, S. S., et al. (2011). The multiphase optimization strategy for engineering effective tobacco use interventions. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 41, 208–226. doi:10.1007/s12160-010-9253-x

    Article  Google Scholar 

  • Collins, L. M., Dziak, J. J., Kugler, K. C., Trail, J. B. (2014). Factorial experiments: efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504

    Article  PubMed  PubMed Central  Google Scholar 

  • Collins, L. M., Kugler, K. C., & Gwadz, M. V. (2016). Optimization of multicomponent behavioral and biobehavioral interventions for the prevention and treatment of HIV/AIDS. AIDS and Behavior, 20, 197–214. doi:10.1007/s10461-015-1145-4

    Article  PubMed Central  Google Scholar 

  • Collins, L. M., Murphy, S. A., Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5), S112–S118.

    Article  PubMed  PubMed Central  Google Scholar 

  • Collins, L. M., Trail, J. B., Kugler, K. C., Baker, T. B., Piper, M. E., & Mermelstein, R. J. (2014b). Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment. Translational Behavioral Medicine, 4, 238–251. doi:10.1007/s13142-013-0239-7

    Article  PubMed  Google Scholar 

  • Conroy, D. E., Dubansky, A., Remillard, J., Murray, R., Pellegrini, C. A., Phillips, S. M., et al. (2016). Using behavior change techniques to guide selections of mobile applications to promote fluid consumption. Urology. doi:10.1016/j.urology.2016.09.015

    PubMed  Google Scholar 

  • Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine, 46, 649–652. doi:10.1016/j.amepre.2014.01.010

    Article  PubMed  Google Scholar 

  • Crane, D., Garnett, C., Brown, J., West, R., & Michie, S. (2015). Behavior change techniques in popular alcohol reduction apps: Content analysis. Journal of Medical Internet Research, 17, e118. doi:10.2196/jmir.4060

    Article  PubMed  PubMed Central  Google Scholar 

  • Davies, E. B., Morriss, R., & Glazebrook, C. (2014). Computer-delivered and web-based interventions to improve depression, anxiety, and psychological well-being of university students: A systematic review and meta-analysis. Journal of Medical Internet Research, 16, e130. doi:10.2196/jmir.3142

    Article  PubMed  PubMed Central  Google Scholar 

  • Davis, R., Campbell, R., Hildon, Z., Hobbs, L., & Michie, S. (2015). Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review. Health Psychology Review, 9, 323–344. doi:10.1080/17437199.2014.941722

    Article  PubMed  Google Scholar 

  • Devlin, A. M., McGee-Lennon, M., O’Donnell, C. A., Bouamrane, M.-M., Agbakoba, R., O’Connor, S., et al. (2016). Delivering digital health and well-being at scale: lessons learned during the implementation of the Dallas program in the United Kingdom. Journal of the American Medical Informatics Association, 23, 48–59. doi:10.1093/jamia/ocv097

    Article  PubMed  Google Scholar 

  • Direito, A., Dale, L. P., Shields, E., Dobson, R., Whittaker, R., & Maddison, R. (2014). Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health, 14, 646. doi:10.1186/1471-2458-14-646

    Article  PubMed  PubMed Central  Google Scholar 

  • Doi, S. A. R., Barendregt, J. J., Khan, S., Thalib, L., & Williams, G. M. (2015a). Advances in the meta-analysis of heterogeneous clinical trials I: The inverse variance heterogeneity model. Contemporary Clinical Trials, 45, 130–138. doi:10.1016/j.cct.2015.05.009

    Article  PubMed  Google Scholar 

  • Doi, S. A. R., Barendregt, J. J., Khan, S., Thalib, L., & Williams, G. M. (2015b). Advances in the meta-analysis of heterogeneous clinical trials II: The quality effects model. Contemporary Clinical Trials, 45, 123–129. doi:10.1016/j.cct.2015.05.010

    Article  PubMed  Google Scholar 

  • Dombrowski, S. U., Sniehotta, F. F., Avenell, A., Johnston, M., MacLennan, G., & Araújo-Soares, V. (2012). Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychology Review, 6, 7–32. doi:10.1080/17437199.2010.513298

    Article  Google Scholar 

  • Estrin, D. (2014). Small data, where n = me. Communications of the ACM, 57(4), 32–34.

    Article  Google Scholar 

  • Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. Retrieved January 20, 2016, from http://dl.acm.org/citation.cfm?id=1979614

  • Greenwald, A. G. (2012). There is nothing so theoretical as a good method. Perspectives on Psychological Science, 7, 99–108. doi:10.1177/1745691611434210

    Article  PubMed  Google Scholar 

  • Hales, S. B., Davidson, C., & Turner-McGrievy, G. M. (2014). Varying social media post types differentially impacts engagement in a behavioral weight loss intervention. Translational Behavioral Medicine, 4, 355–362. doi:10.1007/s13142-014-0274-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Heckler, E., Klasnja, P., Traver, V., & Hendriks, M. (2013). IEEE Xplore abstractRealizing effective behavioral management of health: The metamorphosis of behavioral science methods. Retrieved January 20, 2016, from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6603401

  • Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., et al. (2016a). Agile science: Creating useful products for behavior change in the real world. Translational Behavioral Medicine, 6, 317–328. doi:10.1007/s13142-016-0395-7

    Article  PubMed  PubMed Central  Google Scholar 

  • Hekler, E. B., Michie, S. F., Rivera, D. E., Collins, L. M., Pavel, M., Jimison, H., Garnett, C., Parral, S., Spruijt- Metz, D. (2016b). Advancing models and theories for digital behavior change interventions. American Journal of Preventive Medicine, 51, pp. 825–832, doi:10.1016/j.amepre.2016.06.013

    Article  PubMed  Google Scholar 

  • Hermens, H., op den Akker, H., Tabak, M., Wijsman, J., & Vollenbroek, M. (2014). Personalized coaching systems to support healthy behavior in people with chronic conditions. Journal of Electromyography and Kinesiology, 24(6), 815–826. doi:10.1016/j.jelekin.2014.10.003

    Article  CAS  PubMed  Google Scholar 

  • Hirschberg, D. L., Betts, K., Emanuel, P., & Caples, M. (2014). Assessment of wearable sensor technologies for biosurveillance (Department of Defense No. ECBC-TR-1275).

  • Huang, G. C., Unger, J. B., Soto, D., Fujimoto, K., Pentz, M. A., Jordan-Marsh, M., et al. (2014). Peer influences: The impact of online and offline friendship networks on adolescent smoking and alcohol use. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 54, 508–514. doi:10.1016/j.jadohealth.2013.07.001

    Article  Google Scholar 

  • Hunter, R. F., McAneney, H., Davis, M., Tully, M. A., Valente, T. W., & Kee, F. (2015). “Hidden” social networks in behavior change interventions. American Journal of Public Health, 105, 513–516. doi:10.2105/AJPH.2014.302399

    Article  PubMed  Google Scholar 

  • Jiang, L. C., Bazarova, N. N., & Hancock, J. T. (2011). The disclosure-intimacy link in computer-mediated communication: An attributional extension of the hyperpersonal model. Human Communication Research, 37, 58–77. doi:10.1111/j.1468-2958.2010.01393.x

    Article  Google Scholar 

  • Kan-Leung, C., Inon, Z., Dana, N., & Jennifer, G. (2014). Predicting agents’ behavior by measuring their social preferences. Frontiers in Artificial Intelligence and Applications. doi:10.3233/978-1-61499-419-0-985

    Google Scholar 

  • Kok, G., Gottlieb, N. H., Peters, G.-J. Y., Mullen, P. D., Parcel, G. S., Ruiter, R. A. C., et al. (2016). A taxonomy of behaviour change methods: An intervention mapping approach. Health Psychology Review, 10, 297–312. doi:10.1080/17437199.2015.1077155

    Article  PubMed  Google Scholar 

  • Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., & Hedeker, D. (2013). Mobile health technology evaluation: the mHealth evidence workshop. American Journal of Preventive Medicine, 45(2), 228–236

    Article  PubMed  PubMed Central  Google Scholar 

  • Lagoa, C. M., Bekiroglu, K., Lanza, S. T., & Murphy, S. A. (2014). Designing adaptive intensive interventions using methods from engineering. Journal of Consulting and Clinical Psychology, 82, 868–878. doi:10.1037/a0037736

    Article  PubMed  PubMed Central  Google Scholar 

  • Latkin, C. A., & Knowlton, A. R. (2015). Social network assessments and interventions for health behavior change: A critical review. Behavioral Medicine, 41, 90–97. doi:10.1080/08964289.2015.1034645

    Article  PubMed  PubMed Central  Google Scholar 

  • Leahey, T. M., Kumar, R., Weinberg, B. M., & Wing, R. R. (2012). Teammates and social influence affect weight loss outcomes in a team-based weight loss competition. Obesity, 20, 1413–1418. doi:10.1038/oby.2012.18

    Article  PubMed  PubMed Central  Google Scholar 

  • Leroux, J. S., Moore, S., Dubé, L. (2013). Beyond the "I" in the obesity epidemic: A review of social relational and network interventions on obesity. Journal of Obesity, 2013, 1–10. doi:10.1155/2013/348249

    Article  Google Scholar 

  • Lewin, K. (1951). Field theory in social science: Selected theoretical papers. In D. Cartwright (Ed.), APA PsycNET. Retrieved from http://psycnet.apa.org/psycinfo/1951-06769-000

  • Ljung, L. (1999). System identification: theory for the user (2nd ed.). Upper Saddle River, NJ: Prentice Hall PTR.

    Google Scholar 

  • Lorencatto, F., West, R., & Michie, S. (2012). Specifying evidence-based behavior change techniques to aid smoking cessation in pregnancy. Nicotine & Tobacco Research, 14, 1019–1026. doi:10.1093/ntr/ntr324

    Article  Google Scholar 

  • Lorenzetti, L. (2016). This company is tackling diabetes with its “digital therapeutics.” Fortune. Retrieved from http://fortune.com/2016/04/22/omada-digital-health-diabetes/

  • Lyzwinski, L. N. (2014). A systematic review and meta-analysis of mobile devices and weight loss with an intervention content analysis. Journal of Personalized Medicine, 4, 311–385. doi:10.3390/jpm4030311

    Article  PubMed  PubMed Central  Google Scholar 

  • Maher, C., Ferguson, M., Vandelanotte, C., Plotnikoff, R., De Bourdeaudhuij, I., Thomas, S., Nelson-Field, K., Olds, T. (2015). A web-based, social networking physical activity intervention for insufficiently active adults delivered via Facebook app: Randomized controlled trial. Journal of Medical Internet Research, 17, e174. doi:10.2196/jmir.4086

    Article  PubMed  PubMed Central  Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Routledge

  • Martin, C. A., Rivera, D. E., & Hekler, E. B. (2016). A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control (pp. 3576–3581). New York: IEEE. doi:10.1109/ACC.2016.7525468

    Google Scholar 

  • Meeker, M. (2016). Internet trends 2016Code conference (p. Kleiner Perkins Caufield & Byers website.). Retrieved from www.kpcb.com/internet-trends

  • Merchant, G., Weibel, N., Patrick, K., Fowler, J. H., Norman, G. J., Gupta, A., Servetas, C., Calfas, K., Raste, K., Pina, L., Donohue, M., Griswold, W. G., Marshall, S. (2014). Click “Like” to change your behavior: A mixed methods study of college students’ exposure to and engagement with Facebook content designed for weight loss. Journal of Medical Internet Research, 16, e158. doi:10.2196/jmir.3267

    Article  PubMed  PubMed Central  Google Scholar 

  • Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., & French, D. P. (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychology & Health, 26, 1479–1498. doi:10.1080/08870446.2010.540664

    Article  Google Scholar 

  • Michie, S. F., Atkins, L., & West, R. (2014). In S. F. Michie (Ed.), The behaviour change wheel: A guide to designing interventions (1st ed.). London: Silverback Publishing. Retrieved from http://discovery.ucl.ac.uk/1450989/

  • Michie, S., Hardeman, W., Fanshawe, T., Prevost, A. T., Taylor, L., & Kinmonth, A. L. (2008a). Investigating theoretical explanations for behaviour change: The case study of ProActive. Psychology & Health, 23, 25–39. doi:10.1080/08870440701670588

    Article  Google Scholar 

  • Michie, S., Johnston, M., Francis, J., Hardeman, W., & Eccles, M. (2008b). From theory to intervention: Mapping theoretically derived behavioural determinants to behaviour change techniques. Applied Psychology, 57, 660–680. doi:10.1111/j.1464-0597.2008.00341.x

    Article  Google Scholar 

  • Michie, S., & Prestwich, A. (2010). Are interventions theory-based? Development of a theory coding scheme. Health Psychology, 29, 1–8. doi:10.1037/a0016939

    Article  PubMed  Google Scholar 

  • Michie, S., Whittington, C., Hamoudi, Z., Zarnani, F., Tober, G., & West, R. (2012). Identification of behaviour change techniques to reduce excessive alcohol consumption: Behaviour change and excessive alcohol use. Addiction, 107, 1431–1440. doi:10.1111/j.1360-0443.2012.03845.x

    Article  PubMed  Google Scholar 

  • Michie, S., Wood, C. E., Johnston, M., Abraham, C., Francis, J. J., & Hardeman, W. (2015). Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data). Health Technology Assessment, 19, 1–188. doi:10.3310/hta19990

    Article  Google Scholar 

  • Mohr, D. C., Cuijpers, P., & Lehman, K. (2011). Supportive accountability: A model for providing human support to enhance adherence to eHealth interventions. Journal of Medical Internet Research, 13, e30. doi:10.2196/jmir.1602

    Article  PubMed  PubMed Central  Google Scholar 

  • Moller, A. C., Deci, E. L., & Ryan, R. M. (2006). Choice and ego-depletion: The moderating role of autonomy. Personality and Social Psychology Bulletin, 32, 1024–1036. doi:10.1177/0146167206288008

    Article  PubMed  Google Scholar 

  • Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015). Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychology, 34, 1209–1219. doi:10.1037/hea0000306

    Article  PubMed Central  Google Scholar 

  • Nandola, N., & Rivera, D. (2013). An improved formulation of hybrid model predictive control with application to production-inventory systems, IEEE Xplore, 21, 121-135. Retrieved January 20, 2016, from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6112190 doi: 10.1109/TCST.2011.2177525

  • Ng, J. Y. Y., Ntoumanis, N., Thogersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., Williams, G. C. (2012). Self-determination theory applied to health contexts: A meta-analysis. Perspectives on Psychological Science, 7, 325–340. doi:10.1177/1745691612447309

    Article  PubMed  Google Scholar 

  • Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, aac4716–aac4716. doi:10.1126/science.aac4716

    Article  Google Scholar 

  • Pagoto, S., Schneider, K., Jojic, M., Debiasse, M., & Mann, D. (2013). Evidence-based strategies in weight-loss mobile apps. American Journal of Preventive Medicine, 45, 576–582. doi:10.1016/j.amepre.2013.04.025

    Article  PubMed  Google Scholar 

  • Pagoto, S., & Waring, M. E. (2016). A call for a science of engagement: Comment on Rus and Cameron. Annals of Behavioral Medicine, 50, 690–691. doi:10.1007/s12160-016-9839-z

    Article  PubMed  Google Scholar 

  • Pellegrini, C. A., Hoffman, S. A., Collins, L. M., & Spring, B. (2014). Optimization of remotely delivered intensive lifestyle treatment for obesity using the multiphase optimization strategy: Opt-In study protocol. Contemporary Clinical Trials, 38, 251–259. doi:10.1016/j.cct.2014.05.007

    Article  PubMed  PubMed Central  Google Scholar 

  • Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. Penguin

  • Perrin, A., & Duggan, M. (2015). Americans’ internet access: 20002015. Pew Research Center. Retrieved from http://www.pewinternet.org/2015/06/26/americans-internet-access-2000-2015/

  • Peters, G.-J. Y., de Bruin, M., & Crutzen, R. (2015). Everything should be as simple as possible, but no simpler: towards a protocol for accumulating evidence regarding the active content of health behaviour change interventions. Health Psychology Review, 9, 1–14. doi:10.1080/17437199.2013.848409

    Article  PubMed  Google Scholar 

  • Poncela-Casasnovas, J., Spring, B., McClary, D., Moller, A. C., Mukogo, R., Pellegrini, C. A., et al. (2015). Social embeddedness in an online weight management programme is linked to greater weight loss. Journal of the Royal Society, Interface, 12, 20140686. doi:10.1098/rsif.2014.0686

    Article  PubMed  PubMed Central  Google Scholar 

  • Prestwich, A., Sniehotta, F. F., Whittington, C., Dombrowski, S. U., Rogers, L., & Michie, S. (2014). Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 33, 465–474. doi:10.1037/a0032853

    Article  Google Scholar 

  • Ratti, C., Turgeman, Y. J., & Alm, E. (2014). Smart toilets and sewer sensors are coming. Wired. Retrieved December 31, 2016, from http://www.wired.co.uk/article/yaniv-j-turgeman

  • Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.

    Article  Google Scholar 

  • Rhodes, R. E., & Nigg, C. R. (2011). Advancing physical activity theory: A review and future directions. Exercise and Sport Sciences Reviews, 39, 113–119. doi:10.1097/JES.0b013e31821b94c8

    Article  PubMed  Google Scholar 

  • Riley, W. T., Martin, C. A., Rivera, D. E., Hekler, E. B., Adams, M. A., Buman, M. P., Pavel, M., & King, A. C. (2016). Development of a dynamic computational model of social cognitive theory. Translational Behavioral Medicine, 6(4), 483–495. 10.1007/s13142-015-0356-6.

    Article  PubMed  Google Scholar 

  • Riley, W. T., & Rivera, D. E. (2014). Methodologies for optimizing behavioral interventions: introduction to special section. Translational Behavioral Medicine, 4, 234–237. doi:10.1007/s13142-014-0281-0

    Article  PubMed  PubMed Central  Google Scholar 

  • Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., & Mermelstein, R. (2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Translational Behavioral Medicine, 1, 53–71. doi:10.1007/s13142-011-0021-7

    Article  PubMed  PubMed Central  Google Scholar 

  • Sanou, B. (2015). ICT data and statistics division: Facts & figures. Geneva, Switzerland: International Telecommunication Union (ITU). Retrieved from https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf

  • Schoffman, D. E., Turner-McGrievy, G., Jones, S. J., & Wilcox, S. (2013). Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: Just fun and games? Translational Behavioral Medicine, 3, 320–325. doi:10.1007/s13142-013-0206-3

    Article  PubMed  PubMed Central  Google Scholar 

  • Sepah, S. C., Jiang, L., & Peters, A. L. (2015). Long-term outcomes of a web-based diabetes prevention program: 2-Year results of a single-arm longitudinal study. Journal of Medical Internet Research, 17, e92. doi:10.2196/jmir.4052

    Article  PubMed  PubMed Central  Google Scholar 

  • Silva, M. N., Marques, M. M., & Teixeira, P. J. (2014). Testing theory in practice: The example of self-determination theory-based interventions. European Health Psychologist, 16, 171–180.

    Google Scholar 

  • Smith, K. P., & Christakis, N. A. (2008). Social networks and health. Annual Review of Sociology, 34, 405–429. doi:10.1146/annurev.soc.34.040507.134601

    Article  Google Scholar 

  • Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27, 2322–2329. doi:10.1016/j.chb.2011.07.011

    Article  Google Scholar 

  • Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., Rivera, D. E., Spring, B., Michie, S., Asch, D. A., Sanna, A., Salcedo, V. T., Kukakfa, R., Pavel, M. (2015). Building new computational models to support health behavior change and maintenance: New opportunities in behavioral research. Translational Behavioral Medicine, 5, 335–346. doi:10.1007/s13142-015-0324-1

    Article  PubMed  PubMed Central  Google Scholar 

  • Strecher, V. J., McClure, J. B., Alexander, G. L., Chakraborty, B., Nair, V. N., Konkel, J. M., Greene, S. M., Collins, L. M., Carlier, C. C., Wieseb, C. J., Little, R. J., Pomerleau, C. S., Pomerleau, O. F. (2008). Webbased smoking-cessation programs: Results of a randomized trial. American Journal of Preventive Medicine, 34(5), 373–381.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sutton, S. (2010). Using social cognition models to develop health behaviour interventions: The theory of planned behaviour as an example. In D. P. French, K. Vedhara, A. A. Kaptein, & J. Weinman (Eds.), Health Psychology (2nd ed., Vol. 122). New York: BPS Blackwell.

    Google Scholar 

  • Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods.. doi:10.1177/0261927X09351676

    Google Scholar 

  • Taylor, S., Sanders, A., Keefe, B., Vargo, A., Hunt, Y., & Augustson, E. (2013). Smokefree.gov: 10 years of disseminating evidence-based cessation interventions. Presented at the 141st APHA Annual Meeting (November 2–November 6, 2013), APHA. Retrieved from https://apha.confex.com/apha/141am/webprogramadapt/Paper281216.html

  • The Rise of the Cheap Smartphone. (2014). The economist. Retrieved from http://www.economist.com/news/business/21600134-smartphones-reach-masses-host-vendors-are-eager-serve-them-rise-cheap

  • Timms, K. P., Martin, C. A., Rivera, D. E., Hekler, E. B., & Riley, W. (2014). Leveraging intensive longitudinal data to better understand health behaviors. In 2014 36th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 6888–6891). doi:10.1109/EMBC.2014.6945211

  • Topol, E. J. (2013). The creative destruction of medicine: How the digital revolution will create better health care (1st pbk. ed). New York: Basic Books.

    Google Scholar 

  • Turkle, S. (2015). Reclaiming conversation: The power of talk in a digital age. New York: Penguin Press.

    Google Scholar 

  • Turkle, S. (2016). The empathy gap: Digital culture needs what talk therapy offers. Psychtherapy Networker. Retrieved from https://www.psychotherapynetworker.org/magazine/article/1051/the-empathy-gap

  • Ubhi, H. K., Michie, S., Kotz, D., Wong, W. C., & West, R. (2015). A mobile app to aid smoking cessation: Preliminary evaluation of SmokeFree28. Journal of Medical Internet Research, 17, e17. doi:10.2196/jmir.3479

    Article  PubMed  PubMed Central  Google Scholar 

  • Wagner, K. (2016). How many people are actually playing Pokémon Go? Here’s our best guess so far. Retrieved July 27, 2016, from http://www.recode.net/2016/7/13/12181614/pokemon-go-number-active-users

  • Walther, J. B. (1996). Computer-mediated communication impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43.

    Article  Google Scholar 

  • Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12, e4. doi:10.2196/jmir.1376

    Article  PubMed  PubMed Central  Google Scholar 

  • Weinstein, N. D. (2007). Misleading tests of health behavior theories. Annals of Behavioral Medicine, 33, 1–10. doi:10.1207/s15324796abm3301_1

    Article  PubMed  Google Scholar 

  • West, R., Evans, A., & Michie, S. (2011). Behavior change techniques used in group-based behavioral support by the english stop-smoking services and preliminary assessment of association with short-term quit outcomes. Nicotine & Tobacco Research, 13, 1316–1320. doi:10.1093/ntr/ntr120

    Article  Google Scholar 

  • Wyrick, D. L., Rulison, K. L., Fearnow-Kenney, M., Milroy, J. J., & Collins, L. M. (2014). Moving beyond the treatment package approach to developing behavioral interventions: Addressing questions that arose during an application of the multiphase optimization strategy (MOST). Translational Behavioral Medicine, 4, 252–259. doi:10.1007/s13142-013-0247-7

    Article  PubMed  PubMed Central  Google Scholar 

  • Yang, C.-H., Maher, J. P., & Conroy, D. E. (2015). Implementation of behavior change techniques in mobile applications for physical activity. American Journal of Preventive Medicine, 48, 452–455. doi:10.1016/j.amepre.2014.10.010

    Article  PubMed  Google Scholar 

  • Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., et al. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51, 833–842. doi:10.1016/j.amepre.2016.06.015

    Article  PubMed  Google Scholar 

  • Young, S. D., Holloway, I., Jaganath, D., Rice, E., Westmoreland, D., & Coates, T. (2014). Project HOPE: Online social network changes in an HIV prevention randomized controlled trial for African American and Latino men who have sex with men. American Journal of Public Health, 104, 1707–1712. doi:10.2105/AJPH.2014.301992

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank Rachel Kornfield and Nadyah Mohiuddin for feedback on earlier versions of this manuscript.

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Correspondence to Arlen C. Moller.

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Arlen C. Moller, Gina Merchant, David E. Conroy, Robert West, Eric Hekler, Kari C. Kugler, and Susan Michie declare that they have no conflict of interest.

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All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.

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Moller, A.C., Merchant, G., Conroy, D.E. et al. Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential. J Behav Med 40, 85–98 (2017). https://doi.org/10.1007/s10865-016-9818-7

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