Journal of Behavioral Medicine

, Volume 40, Issue 1, pp 85–98 | Cite as

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

  • Arlen C. MollerEmail author
  • Gina Merchant
  • David E. Conroy
  • Robert West
  • Eric Hekler
  • Kari C. Kugler
  • Susan Michie


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.


Behavior change theories Behavior change techniques Digital health Optimization Dynamic systems Social networks 



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

Compliance with ethical standards

Conflict of interest

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.

Human and animal rights and Informed consent

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.


  1. 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 CrossRefPubMedGoogle Scholar
  2. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Althoff, T., White, T. W., & Horvitz, E. (2016). Influence of Pokémon go on physical activity: Study and implications. Retrieved from
  4. 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
  5. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bandura, A. (1977a). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.CrossRefPubMedGoogle Scholar
  7. Bandura, A. (1977b). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  8. Bandura, A. (1997a). Self-efficacy: The exercise of control. New York: Freeman.Google Scholar
  9. 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 CrossRefGoogle Scholar
  10. 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.PubMedGoogle Scholar
  11. 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
  12. 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 CrossRefPubMedGoogle Scholar
  13. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. Centola, D. (2013). Social media and the science of health behavior. Circulation, 127, 2135–2144. doi: 10.1161/CIRCULATIONAHA.112.101816 CrossRefPubMedGoogle Scholar
  16. 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 CrossRefPubMedGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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 CrossRefGoogle Scholar
  19. 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), 498–504CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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 CrossRefPubMedCentralGoogle Scholar
  21. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 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 CrossRefPubMedGoogle Scholar
  23. 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 PubMedGoogle Scholar
  24. 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 CrossRefPubMedGoogle Scholar
  25. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 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 CrossRefPubMedGoogle Scholar
  28. 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 CrossRefPubMedGoogle Scholar
  29. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 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 CrossRefPubMedGoogle Scholar
  31. 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 CrossRefPubMedGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. Estrin, D. (2014). Small data, where n = me. Communications of the ACM, 57(4), 32–34.CrossRefGoogle Scholar
  34. Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. Retrieved January 20, 2016, from
  35. Greenwald, A. G. (2012). There is nothing so theoretical as a good method. Perspectives on Psychological Science, 7, 99–108. doi: 10.1177/1745691611434210 CrossRefPubMedGoogle Scholar
  36. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 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
  38. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 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 CrossRefPubMedGoogle Scholar
  40. 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 CrossRefPubMedGoogle Scholar
  41. Hirschberg, D. L., Betts, K., Emanuel, P., & Caples, M. (2014). Assessment of wearable sensor technologies for biosurveillance (Department of Defense No. ECBC-TR-1275).Google Scholar
  42. 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 CrossRefGoogle Scholar
  43. 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 CrossRefPubMedGoogle Scholar
  44. 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 CrossRefGoogle Scholar
  45. 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
  46. 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 CrossRefPubMedGoogle Scholar
  47. 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), 228–236CrossRefPubMedPubMedCentralGoogle Scholar
  48. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 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 CrossRefGoogle Scholar
  52. Lewin, K. (1951). Field theory in social science: Selected theoretical papers. In D. Cartwright (Ed.), APA PsycNET. Retrieved from
  53. Ljung, L. (1999). System identification: theory for the user (2nd ed.). Upper Saddle River, NJ: Prentice Hall PTR.Google Scholar
  54. 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 CrossRefGoogle Scholar
  55. Lorenzetti, L. (2016). This company is tackling diabetes with its “digital therapeutics.” Fortune. Retrieved from
  56. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  58. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. RoutledgeGoogle Scholar
  59. 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
  60. Meeker, M. (2016). Internet trends 2016Code conference (p. Kleiner Perkins Caufield & Byers website.). Retrieved from
  61. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  62. 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 CrossRefGoogle Scholar
  63. 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
  64. 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 CrossRefGoogle Scholar
  65. 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 CrossRefGoogle Scholar
  66. Michie, S., & Prestwich, A. (2010). Are interventions theory-based? Development of a theory coding scheme. Health Psychology, 29, 1–8. doi: 10.1037/a0016939 CrossRefPubMedGoogle Scholar
  67. 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 CrossRefPubMedGoogle Scholar
  68. 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 CrossRefGoogle Scholar
  69. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  70. 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 CrossRefPubMedGoogle Scholar
  71. 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 CrossRefPubMedCentralGoogle Scholar
  72. 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 doi: 10.1109/TCST.2011.2177525
  73. 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 CrossRefPubMedGoogle Scholar
  74. Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, aac4716–aac4716. doi: 10.1126/science.aac4716 CrossRefGoogle Scholar
  75. 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 CrossRefPubMedGoogle Scholar
  76. 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 CrossRefPubMedGoogle Scholar
  77. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. PenguinGoogle Scholar
  79. Perrin, A., & Duggan, M. (2015). Americans’ internet access: 20002015. Pew Research Center. Retrieved from
  80. 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 CrossRefPubMedGoogle Scholar
  81. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  82. 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 CrossRefGoogle Scholar
  83. Ratti, C., Turgeman, Y. J., & Alm, E. (2014). Smart toilets and sewer sensors are coming. Wired. Retrieved December 31, 2016, from
  84. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.CrossRefGoogle Scholar
  85. 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 CrossRefPubMedGoogle Scholar
  86. 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.CrossRefPubMedGoogle Scholar
  87. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  88. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  89. Sanou, B. (2015). ICT data and statistics division: Facts & figures. Geneva, Switzerland: International Telecommunication Union (ITU). Retrieved from
  90. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  91. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  92. 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
  93. 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 CrossRefGoogle Scholar
  94. 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 CrossRefGoogle Scholar
  95. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  96. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  97. 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
  98. Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods.. doi: 10.1177/0261927X09351676 Google Scholar
  99. Taylor, S., Sanders, A., Keefe, B., Vargo, A., Hunt, Y., & Augustson, E. (2013). 10 years of disseminating evidence-based cessation interventions. Presented at the 141st APHA Annual Meeting (November 2–November 6, 2013), APHA. Retrieved from
  100. 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
  101. 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
  102. Turkle, S. (2015). Reclaiming conversation: The power of talk in a digital age. New York: Penguin Press.Google Scholar
  103. Turkle, S. (2016). The empathy gap: Digital culture needs what talk therapy offers. Psychtherapy Networker. Retrieved from
  104. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  105. Wagner, K. (2016). How many people are actually playing Pokémon Go? Here’s our best guess so far. Retrieved July 27, 2016, from
  106. Walther, J. B. (1996). Computer-mediated communication impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43.CrossRefGoogle Scholar
  107. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  108. Weinstein, N. D. (2007). Misleading tests of health behavior theories. Annals of Behavioral Medicine, 33, 1–10. doi: 10.1207/s15324796abm3301_1 CrossRefPubMedGoogle Scholar
  109. 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 CrossRefGoogle Scholar
  110. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  111. 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 CrossRefPubMedGoogle Scholar
  112. 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 CrossRefPubMedGoogle Scholar
  113. 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 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Arlen C. Moller
    • 1
    • 2
    Email author
  • Gina Merchant
    • 3
  • David E. Conroy
    • 4
  • Robert West
    • 5
  • Eric Hekler
    • 6
  • Kari C. Kugler
    • 4
  • Susan Michie
    • 5
  1. 1.Illinois Institute of TechnologyChicagoUSA
  2. 2.Northwestern UniversityChicagoUSA
  3. 3.University of California, San DiegoSan DiegoUSA
  4. 4.The Pennsylvania State UniversityState CollegeUSA
  5. 5.University College LondonLondonUK
  6. 6.Arizona State UniversityTempeUSA

Personalised recommendations