Skip to main content
SpringerLink
Log in

Health behavior models in the age of mobile interventions: are our theories up to the task?

  • Published:
Translational Behavioral Medicine

Abstract

Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. 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.

    PubMed  Google Scholar 

  2. Tate, D. F., Finkelstein, E. A., Khavjou, O., & Gustafson, A. (2009). Cost effectiveness of Internet interventions: Review and recommendations. Annals of Behavioral Medicine, 38, 40–45.

    PubMed  Google Scholar 

  3. Norman, G. J., Zabinski, M. F., Adams, M. A., Rosenberg, D. E., Yaroch, A. L., & Atienza, A. A. (2007). A review of eHealth interventions for physical activity and dietary behavior change. American Journal of Preventive Medicine, 33, 336–345.

    PubMed  Google Scholar 

  4. CTIA. (2009). http://www.ctia.org/advocacy/research/index.cfm/AID/10323

  5. International Communications Union. (2010). http://www.itu.int/ITU-d/ict/publications/idi/2010/Material/MIS_2010_Summary_E.pdf

  6. Pew Internet &American Life Project. (2010). http://www.pewInternet.org/~/media//Files/Reports/2010/PIP_Mobile_Access_2010.pdf

  7. Leena, K., Tomi, L., & Arja, R. (2005). Intensity of mobile phone use and health compromising behaviours: How is information and communication technology connected to health-related lifestyle in adolescence? Journal of Adolescence, 28, 35–47.

    PubMed  Google Scholar 

  8. Kosaraju, A., Barrigan, C. R., Poropatich, R. K., & Casscells, S. W. (2010). Use of mobile phones as a tool for United States health diplomacy abroad. Telemed J E Health., 16, 218–222.

    PubMed  Google Scholar 

  9. Kaplan, W. A. (2006). Can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries? Global Healthcare, 2, 9–22.

    Google Scholar 

  10. Pharow, P., Blobel, B., Ruotsalainen, P., Petersen, F., & Hovsto, A. (2009). Portable devices, sensors and networks: Wireless personalized eHealth services. Studies in Health Technology and Informatics, 150, 1012–1016.

    PubMed  Google Scholar 

  11. Patrick, K., Griswold, W. G., Raab, F., & Intille, S. S. (2008). Health and the mobile phone. American Journal of Preventive Medicine, 35, 177–181.

    PubMed  Google Scholar 

  12. Becker, M. H. (Ed.) (1974). The health belief model and personal health behavior. Health Education Monographs, 2, 324–473.

    Google Scholar 

  13. Godin, G., & Kok, G. (1996). The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion, 11, 87–98.

    PubMed  CAS  Google Scholar 

  14. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.

    PubMed  CAS  Google Scholar 

  15. Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12, 38–48.

    PubMed  CAS  Google Scholar 

  16. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Amer Psychol., 55, 68–78.

    CAS  Google Scholar 

  17. Ogden, J. (2003). Some problems with social cognition models: A pragmatic and conceptual analysis. Health Psychology, 22, 424–428.

    PubMed  Google Scholar 

  18. Burnett, K. F., Taylor, C. B., & Agras, W. S. (1985). Ambulatory computer-assisted therapy for obesity: A new frontier for behavior therapy. J Cons Clin Psychol., 53, 698–703.

    CAS  Google Scholar 

  19. Prue, D. M., Riley, A., Orlandi, M. A., & Jerome, A. (1990). Development of a computer-assisted smoking cessation program: A preliminary report. J Adv Med., 3, 131–139.

    Google Scholar 

  20. Brownell, K. D. (1995). Adherence to dietary regimens. 2: Components of effective interventions. Behavioral Medicine, 20, 155–164.

    PubMed  CAS  Google Scholar 

  21. Fjeldsoe, B. S., Marshall, A. L., & Miller, Y. D. (2009). Behavior change interventions delivered by mobile telephone short-message service. American Journal of Preventive Medicine, 36, 165–173.

    PubMed  Google Scholar 

  22. Krishna, S., & Boren, S. A. (2009). Healthcare via cell phone: A systematic review. Telemed e-Health., 15, 231–240.

    Google Scholar 

  23. Obermayer, J. L., Riley, W. T., Asif, O., & Jean-Mary, J. (2004). College smoking-cessation using cell phone text messaging. J Amer Coll Health, 53, 71–78.

    Google Scholar 

  24. Rodgers, A., Corbett, T., Bramley, D., et al. (2005). Do u smoke after txt? Results of a randomized trial of smoking cessation using mobile phone text messaging. Tobacco Control, 14, 255–261.

    PubMed  CAS  Google Scholar 

  25. Riley, W. T., Obermayer, J., & Jean-Mary, J. (2008). Internet and mobile phone text messaging intervention for college smokers. J Amer Coll Health., 57, 245–248.

    Google Scholar 

  26. Whittaker, R., Maddison, R., McRobbie, H., et al. (2008). A multimedia mobile phone-based youth smoking cessation intervention: Findings from content development and piloting studies. Journal of Medical Internet Research, 10, e49.

    PubMed  Google Scholar 

  27. Brendryen, H., & Kraft, P. (2008). Happy Ending: A randomized controlled trial of a digital multi-media smoking cessation intervention. Addiction, 103, 478–484.

    PubMed  Google Scholar 

  28. Brendryen, H., Drozd, F., & Kraft, P. (2008). A digital smoking cessation program delivered through Internet and cell phone without nicotine replacement (happy ending): Randomized controlled trial. Journal of Medical Internet Research, 10, e51.

    PubMed  Google Scholar 

  29. Free, C., Whittaker, R., Knight, R., Abramsky, T., Rodgers, A., & Roberts, G. (2009). Txt2stop: A pilot randomized controlled trial of mobile phone-based smoking cessation support. Tobacco Control, 18, 88–91.

    PubMed  CAS  Google Scholar 

  30. Intille SS, Kukla C, Farzanfar R, Bakr W. Just-in-time technology to encourage incremental, dietary behavior change. In AMIA 2003 symposium proceedings, p 874

  31. Brendryen, H., Kraft, P., & Schaalma, H. (2010). Looking inside the black box: Using intervention mapping to describe the development of an automated smoking cessation intervention happy ending. The Journal of Smoking Cessation, 5, 29–56.

    Google Scholar 

  32. Whittaker, R., Borland, R., Bullen, C., Lin, R. B., McRobbie, H., & Rodgers, A. (2009). Mobile phone-based interventions for smoking cessation. Cochrane Database of Systematic Reviews, 4, CD006611.

    Google Scholar 

  33. Joo, N., & Kim, B. (2007). Mobile phone short message service messaging for behaviour modification in a community-based weight loss programme in Korea. Journal of Telemedicine and Telecare, 13, 416–420.

    PubMed  Google Scholar 

  34. Burke, L. E., Conroy, M. B., Sereika, S. M., et al. (2011). The effect of electronic self-monitoring on weight loss and dietary intake: A randomized behavioral weight loss trial. Obesity, 19, 338–344.

    Google Scholar 

  35. Liu, W. T., Wang, C. H., Lin, H. C., et al. (2008). Efficacy of a cell phone-based exercise programme for COPD. The European Respiratory Journal, 32, 651–659.

    PubMed  Google Scholar 

  36. Nguyen, H. Q., Gill, D. P., Wolpin, S., Steele, B. G., & Benditt, J. O. (2009). Pilot study of a cell phone-based exercise persistence intervention post-rehabilitation for COPD. Int J COPD., 4, 301–313.

    Google Scholar 

  37. Bauer, S., de Niet, J., Timman, R., & Kordy, H. (2010). Enhancement of care through self-monitoring and tailored feedback via text messaging and their use in the treatment of childhood overweight. Patient Education and Counseling, 79, 315–319.

    PubMed  Google Scholar 

  38. Hurling, R., Catt, M., De Boni, M., et al. (2007). Using Internet and mobile phone technology to deliver an automated physical activity program: Randomized controlled trial. Journal of Medical Internet Research, 9, e7.

    PubMed  Google Scholar 

  39. Beasley, J. M., Riley, W. T., Davis, A., & Singh, J. (2008). Evaluation of a PDA-based dietary assessment and intervention program: A randomized controlled trial. Journal of the American College of Nutrition, 27, 280–286.

    PubMed  Google Scholar 

  40. Haapala, I., Barengo, N. C., Biggs, S., Surakka, L., & Manninen, P. (2009). Weight loss by mobile phone: A 1-year effectiveness study. Public Health Nutrition, 12, 2382–2391.

    PubMed  Google Scholar 

  41. Atienza, A. A., King, A. C., Oliveira, B. M., Ahn, D. K., & Gardner, C. D. (2008). Using hand-held computer technologies to improve dietary intake. American Journal of Preventive Medicine, 34, 514–518.

    PubMed  Google Scholar 

  42. King, A. C., Ahn, D. K., Oliveira, B. M., Atienza, A. A., Castro, C. M., & Gardner, C. D. (2008). Promoting physical activity through hand-held computer technology. American Journal of Preventive Medicine, 34, 138–142.

    PubMed  Google Scholar 

  43. Patrick, K., Raab, F., Adams, M. A., et al. (2009). A text message-based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11, e1.

    PubMed  Google Scholar 

  44. Fjeldsoe, B. S., Miller, Y. D., & Marshall, A. L. (2010). MobileMums: A randomized controlled trial of an SMS-based physical activity intervention. Annals of Behavioral Medicine, 39, 101–111.

    PubMed  Google Scholar 

  45. Hiltz, S. R. (1988). Productivity enhancement from computer-mediated communication: A systems contingency approach. Communications of the ACM, 31, 1438–1454.

    Google Scholar 

  46. Krishna, S., & Boren, S. A. (2008). Diabetes self-management care via cell phone: A systematic review. J Diabetes Sci Technol, 2, 509–517.

    PubMed  Google Scholar 

  47. Ngo, J., Engelen, A., Molag, M., Roesle, J., Garcia-Segovia, P., & Serra-Majem, L. (2009). A review of the use of information and communication technologies for dietary assessment. British J Nutr., 101, S102–S112.

    Google Scholar 

  48. Boushey, C. J., Kerr, D. A., Wright, J., Lutes, K. D., Ebert, D. S., & Delp, E. J. (2009). Use of technology in children’s dietary assessment. Euro J Clin Nutr., 63, S50–S57.

    Google Scholar 

  49. Six, B. L., Schap, T. E., Zhu, F. M., Mariappan, A., Bosch, M., Delp, E. J., et al. (2010). Evidence-based development of a mobile telephone food record. Journal of the American Dietetic Association, 110, 74–79.

    PubMed  Google Scholar 

  50. Hynes, M., Wang, H., & Kilmartin, L. (2009). Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms. Conf Proc IEEE Eng Med Biol Soc., 2009, 5187–5190.

    PubMed  Google Scholar 

  51. Bexelius, C., Lof, M., Sandin, S., Trolle Lagerros, Y., Forsum, E., & Litton, J. E. (2010). Measure of physical activity using cell phones: Validation using criterion measures. J Med Internet Res, 29, e2.

    Google Scholar 

  52. Farmer, K. C. (1999). Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clinical Therapeutics, 21, 1074–1090.

    PubMed  CAS  Google Scholar 

  53. Naditz, A. (2008). Medication compliance—helping patients through technology: Modern “smart” pillboxes keep memory-short patients on their medical regimen. Telemed J E Health., 14, 875–880.

    PubMed  Google Scholar 

  54. Safren, S. A., Hendriksen, E. S., Desousa, N., Boswell, S. L., & Mayer, K. H. (2003). Use of an on-line pager system to increase adherence to antiretroviral medications. AIDS Care, 15, 787–793.

    PubMed  CAS  Google Scholar 

  55. Simoni, J. M., Huh, D., Frick, P. A., et al. (2009). Peer support and pager messaging to promote antiretroviral modifying therapy in Seattle: A randomized controlled trial. Journal of Acquired Immune Deficiency Syndromes, 52, 465–473.

    PubMed  Google Scholar 

  56. Koshy, E., Car, J., & Majeed, A. (2008). Effectiveness of mobile-phone short messaging service (SMS) reminders for ophthalmology outpatient appointments: Observational study. BMC Ophthalmology, 8, 9.

    PubMed  Google Scholar 

  57. Bos, A., Hoogstraten, J., & Prahl-Andersen, B. (2005). Failed appointments in an orthodontic clinic. BAm J Orthod Dentofacial Orthop, 127, 355–357.

    Google Scholar 

  58. Leong, K. C., Leong, K. W., Mastura, I., et al. (2006). The use of text messaging to improve attendance in primary care: A randomized controlled trial. Family Practice, 23, 699–705.

    PubMed  Google Scholar 

  59. Chen, Z. W., Fang, L. Z., Chen, L. Y., & Dai, H. L. (2008). Comparison of an SMS text messaging and phone reminder to improve attendance at a health promotion center: A randomized controlled trial. Journal of Zhejiang University. Science B, 9, 34–38.

    PubMed  Google Scholar 

  60. Blake, H. (2008). Mobile phone technology in chronic disease management. Nursing Standard, 23, 43–46.

    PubMed  Google Scholar 

  61. Russel-Minda, E., Jutai, J., Speechly, M., Bradley, K., Chudyk, A., & Petrella, R. (2009). Health technologies for monitoring and managing diabetes: A systematic review. J Diabetes Sci Technol., 3, 1460–1471.

    Google Scholar 

  62. Skinner, C., & Finkelstein, J. (2008). Using cell phones for chronic disease prevention and management. AMIA Annual Symposium Proceedings, 6, 1137.

    Google Scholar 

  63. Franklin, V. L., Waller, A., Pagliari, C., & Greene, S. A. (2006). A randomized controlled trial of Sweet Talk, a text messaging system to support young people with diabetes. Diabetes Med., 23, 1332–1338.

    CAS  Google Scholar 

  64. American Diabetes Association. (2009). Executive summary: Standards of medical care in diabetes—2009. Diabetes Care, 32, S6–S12.

    Google Scholar 

  65. Hedtke, P. A. (2008). Can wireless technology enable new diabetes management tools. J Diabetes Sci Technol., 2, 127–130.

    PubMed  Google Scholar 

  66. Ma, Y., Olendzki, B. C., Chiriboga, D., et al. (2006). PDA-assisted low glycemic index dietary intervention for type 2 diabetes: A pilot study. Euro J Clin Nutr., 60, 1235–1243.

    CAS  Google Scholar 

  67. Forjuoh, S. N., Reis, M. D., Couchman, G. R., & Ory, M. G. (2008). Improving diabetes self-care with a PDA in ambulatory care. Telemed Health., 14, 273–279.

    Google Scholar 

  68. Lanzola, G., Capozzi, D., D’Annunzio, G., Ferrari, P., Bellazzi, R., & Larizza, C. (2007). Going mobile with a multi-access service for the management of diabetes patients. J Diabetes Sci Technol., 1, 730–737.

    PubMed  Google Scholar 

  69. Arsand, E., Tufano, J. T., Ralston, J. D., & Hjortdahl, P. (2008). Designing mobile dietary management support technologies for people with diabetes. Journal of Telemedicine and Telecare, 14, 329–332.

    PubMed  Google Scholar 

  70. Rami, B., Popow, C., Horn, W., Waldhoer, T., & Schober, E. (2006). Telemedical support to improve glycemic control in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics, 165, 701–705.

    PubMed  Google Scholar 

  71. Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008). WellDocTM mobile diabetes management randomized controlled trial: Change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technology and Therapeutics, 10, 160–168.

    PubMed  Google Scholar 

  72. Turner, J., Larsen, M., Tarassenko, L., Neil, A., & Farmer, A. (2009). Implementation of telehealth support for patients with type 2 diabetes using insulin treatment: An exploratory study. Inform Prim Care., 17, 47–53.

    PubMed  Google Scholar 

  73. Cho, J. H., Lee, H. C., Lim, D. J., Kwon, H. S., & Yoon, K. H. (2009). Mobile communication using a mobile phone with a glucometer for glucose control in type 2 patients with diabetes: As effective as an Internet-based glucose monitoring system. Journal of Telemedicine and Telecare, 15, 77–82.

    PubMed  Google Scholar 

  74. Logan, A. G., McIsaac, W. J., Tisler, A., et al. (2007). Mobile phone-based remote patient monitoring system for management of hypertension in diabetic patients. Amer J Hypertension., 20, 942–948.

    Google Scholar 

  75. Leu, M. G., Norris, T. E., Hummel, J., Isaac, M., & Brogan, M. W. (2005). A randomized controlled trial of an automated wireless messaging system for diabetes. Diabetes Technology and Therapeutics, 7, 710–718.

    PubMed  Google Scholar 

  76. Hanauer, D. A., Wentzell, K., Laffel, N., & Laffel, L. M. (2009). Computerized automated reminder diabetes system (CARDS): Email and SMS cell phone text messaging reminders to support diabetes management. Diabetes Technology and Therapeutics, 11, 99–106.

    PubMed  Google Scholar 

  77. Strandbygaard, U., Thomsen, S. F., & Backer, V. (2010). A daily SMS reminder increases adherence to asthma treatment: A three-month follow-up study. Respiratory Medicine, 104, 166–171.

    PubMed  Google Scholar 

  78. Chung, P., Yu, T., & Scheinfeld, N. (2007). Using cellphones for teledermatology, a preliminary study. Dermatology Online Journal, 13, 2.

    PubMed  Google Scholar 

  79. Tang, F. H., Law, M. Y., Lee, A. C., & Chan, L. W. (2004). A mobile phone integrated health care delivery system for medical images. Journal of Digital Imaging, 17, 217–225.

    PubMed  Google Scholar 

  80. Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.

    PubMed  Google Scholar 

  81. National Institutes of Health. (2010). http://projectreporter.nih.gov (reporter_searchresults.cfm?&new=1&icde=5933670&loc=2&CFID=17945345&CFTOKEN=13189329). Accessed 29 Oct 2010.

  82. Skevofilakas, M., Mougiakakou, S. G., Zarkogianni, K., et al. (2007). A communication and information technology infrastructure for real-time monitoring and management of type 1 diabetes patients. Conf Proc IEEE Eng Med Biol Soc., 2007, 3685–3686.

    PubMed  Google Scholar 

  83. Holtz, B., & Whitten, P. (2009). Managing asthma with mobile phones: A feasibility study. Telemed J E Health., 15, 907–909.

    PubMed  Google Scholar 

  84. Ryan, D., Pinnock, H., Lee, A. J., et al. (2009). The CYMPLA trial. Mobile phone-based structured intervention to achieve asthma control in patients with uncontrolled persistent asthma: A pragmatic randomized controlled trial. Primary Care Respiratory Journal, 18, 343–345.

    PubMed  Google Scholar 

  85. Rosenstock, I. M. (1974). Historical origins of the Health Belief Model. Health Education Monographs, 2, 328–335.

    Google Scholar 

  86. Dunton and Atienza. (2009). The need for time-intensive information in healthful eating and physical activity research: A timely topic. JADA, 109(1), 30–35.

    Google Scholar 

  87. Boorsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110, 202–219.

    Google Scholar 

  88. Neville, L. M., O'Hara, B., & Milat, A. J. (2009). Computer-tailored dietary behaviour change interventions: A systematic review. Health Education Research, 24, 699–720.

    PubMed  Google Scholar 

  89. Lustria, M. L., Cortese, J., Noar, S. M., & Glueckauf, R. L. (2009). Computer-tailored health interventions delivered over the Web: Review and analysis of key components. Patient Education and Counseling, 74, 156–173.

    PubMed  Google Scholar 

  90. Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 15, 1–39.

    PubMed  Google Scholar 

  91. Llewelyn, S., & Hardy, G. (2001). Process research in understanding and applying psychological therapies. The British Journal of Clinical Psychology, 40, 1–21.

    PubMed  CAS  Google Scholar 

  92. Åström, K. J. & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. http://www.cds.caltech.edu/~murray/amwiki.

  93. Rivera, D. E., Pew, M. D., & Collins, L. M. (2007). Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence, 88(suppl. 2), S31–S40.

    PubMed  Google Scholar 

  94. Navarro-Barrientos, J. E., Rivera, D. E., & Collins, L. M. (2010). A dynamical systems model for understanding behavioral interventions for weight loss. In S. K. Chai, J. J. Salerno, & P. L. Mabry (Eds.), 2010 international conference on social computing, behavioral modeling, and prediction (SBP 2010) (pp. 170–179). Heidelberg: Springer.

    Google Scholar 

  95. Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008). The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 4, e1000092.

    PubMed  Google Scholar 

  96. Saper, C. B., Chou, T. C., & Scammell, T. E. (2001). The sleep switch: Hypothalamic control of sleep and wakefulness. Trends Neuroscience., 24, 726–731.

    CAS  Google Scholar 

  97. Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience, 7, 907–915.

    PubMed  CAS  Google Scholar 

  98. Lewis, F. L., & Vamvoudakis, K. G. (2010). Reinforcement learning for partially observable dynamic processes: Adaptive dynamic programming using measured output data. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 41, 14–25.

    Google Scholar 

  99. Bequette, B. W. (2009). Glucose clamp algorithms and insulin time–action profiles. J Diabetes Sci Technol., 3, 1005–1013.

    PubMed  Google Scholar 

  100. Nandola, N., Rivera, D. E. (2010). A robust model predictive control formulation for hybrid systems with application to adaptive behavioral interventions. In American Control Conference, Baltimore, MD, pp. 6286–6292.

  101. Schwartz, J. D., & Rivera, D. E. (2010). A process control approach to tactical inventory management in production-inventory systems. Int J Production Economics, 125, 111–124.

    Google Scholar 

  102. Ogunnaike, B. A., & Ray, W. H. (1994). Process dynamics, modeling, and control. New York: Oxford University Press.

    Google Scholar 

  103. Ljung, L. (1999). System identification: Theory for the user. Prentice Hall information and system sciences series. New York: Prentice Hall.

    Google Scholar 

  104. Ramsay, J., & Silverman, B. (2005). Functional data analysis. New York: Springer.

    Google Scholar 

  105. Camacho, E. F., & Bordons, C. (2004). Model predictive control (2nd ed.). New York: Springer.

    Google Scholar 

  106. Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196.

    PubMed  Google Scholar 

  107. O’Grady, L., Witteman, H., Bender, J. L., Urowitz, S., Wiljer, D., & Jaded, A. R. (2009). Measuring the impact of a moving target: Towards a dynamic framework for evaluating collaborative adaptive interactive technologies. Journal of Medical Internet Research, 11, e20.

    PubMed  Google Scholar 

  108. Resnicow, K., Strecher, V., Couper, M., et al. (2010). Methodologic and design issues in patient-centered e-health research. American Journal of Preventive Medicine, 38, 98–102.

    PubMed  Google Scholar 

  109. Downer, S. R., Meara, J. G., & Da Costa, A. C. (2005). Use of SMS text messaging to improve outpatient adherence. MJA, 183, 366–368.

    PubMed  Google Scholar 

  110. Downer, S. R., Meara, J. G., Da Costa, A. C., & Sethuraman, K. (2006). SMS text messaging improves outpatient attendance. Australian Health Review, 30, 389–396.

    PubMed  Google Scholar 

  111. Foley, J., & O’Neill, M. (2009). Use of mobile short message service (SMS) as a reminder: The effect on patient attendance. European Archives of Paediatric Dentistry, 10, 15–18.

    PubMed  CAS  Google Scholar 

  112. da Costa, T. M., Salomao, P. L., Martha, A. S., Pisa, I. T., & Sigulem, D. (2010). The impact of short message service text messages sent as appointment reminders to patients’ cell phones at outpatient clinics in Sao Paulo, Brazil. International Journal of Medical Informatics, 79, 65–70.

    PubMed  Google Scholar 

  113. Kwon, H. S., Cho, J. H., Kim, H. S., et al. (2004). Development of web-based diabetic patient management system using short message service (SMS). Diabetes Research and Clinical Practice, 66S, S133–S137.

    Google Scholar 

  114. Ostojic, V., Cvoriscec, B., Ostojic, S. B., Reznikoff, D., Stipic-Markovic, A., & Tudjman, Z. (2005). Improving asthma control through telemedicine: A study of short-message service. Telemed J E Health., 11, 28–35.

    PubMed  Google Scholar 

  115. Kim, H. S., Yoo, Y. S., & Shim, H. S. (2005). Effects of an Internet-based intervention on plasma glucose levels in patients with type 2 diabetes. Journal of Nursing Care Quality, 20, 335–340.

    PubMed  Google Scholar 

  116. Kim, H. S., Kim, N. C., & Ahn, S. H. (2006). Impact of a nurse short message service intervention for patients with diabetes. Journal of Nursing Care Quality, 21, 266–271.

    PubMed  Google Scholar 

  117. Kim, H. S., & Jeong, H. S. (2007). A nurse short message service by cellular phone in type-2 diabetic patients for six months. Journal of Clinical Nursing, 16, 1082–1087.

    PubMed  Google Scholar 

  118. Benhamou, P. Y., Melki, V., Boizel, R., et al. (2007). One-year efficacy and safety of Web-based follow-up using cellular phone in type 1 diabetic patients under insulin pump therapy: The PumpNet study. Diabetes Metabol., 33, 220–226.

    Google Scholar 

  119. Yoon, K. H., & Kim, H. S. (2008). A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Research and Clinical Practice, 79, 256–261.

    PubMed  Google Scholar 

  120. Newton, K. H., Wiltshire, E. J., & Elley, C. R. (2009). Pedometers and text messaging to increase physical activity: Randomized controlled trial of adolescents with type 1 diabetes. Diabetes Care, 32, 813–815.

    PubMed  Google Scholar 

  121. Prabhakaran, L., Chee, W. Y., Chua, K. C., Abisheganaden, J., & Wong, W. M. (2010). The use of text messaging to improve asthma control: A pilot study using mobile phone short messaging service (SMS). Journal of Telemedicine and Telecare, 16, 286–290.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Heart, Lung, and Blood Institute, NIH, 6701 Rockledge Dr, Room 10224, MSC 7936, Bethesda, MD, 20892-7936, USA

    William T Riley PhD

  2. School for Engineering of Matter, Transport, and Energy, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ, 85287-6106, USA

    Daniel E Rivera PhD

  3. National Institute of Health, National Cancer Institute, 6130 Executive Boulevard, EPN 4082, Bethesda, MD, 20892-7335, USA

    Audie A Atienza PhD

  4. Office of Behavioral and Social Science Research, NIH, 31 Center Dr., Room B1C19, MSC 2027, Bethesda, MD, 20892-2027, USA

    Wendy Nilsen PhD

  5. National Institute of Mental Health, NIH, 6001 Executive Boulevard, Room 6226, MSC 9615, Bethesda, MD, 20892-9615, USA

    Susannah M Allison PhD

  6. Department of Psychology and Public Health, Health Research and Policy Center, University of Illinois at Chicago, 850 West Jackson Boulevard, Suite 400, Chicago, IL, 60607, USA

    Robin Mermelstein PhD

Authors
  1. William T Riley PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel E Rivera PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Audie A Atienza PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wendy Nilsen PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susannah M Allison PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robin Mermelstein PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William T Riley PhD.

Additional information

Implications

Practice: Mobile technologies are rapidly evolving as a method for delivering health behavior interventions that can be tailored to the individual throughout the intervention, but the content and timing of these interventions have not been consistently grounded in health behavior theories, so practitioners need to consider the theoretical and empirical basis of mobile health behavior interventions.

Policy: Investment in the development of mobile health behavior interventions needs to be balanced with investment in theoretically grounded content development and evaluation procedures that are responsive to this rapidly evolving area.

Research: In addition to the responsive evaluation of mobile health behavior interventions, researchers need to utilize these applications to test and advance more dynamic health behavior theories, taking advantage of control systems engineering and other dynamic feedback models to advance new theories that can be better applied to the intensive adaptability possible from mobile health behavior interventions.

About this article

Cite this article

Riley, W.T., Rivera, D.E., Atienza, A.A. et al. Health behavior models in the age of mobile interventions: are our theories up to the task?. Behav. Med. Pract. Policy Res. 1, 53–71 (2011). https://doi.org/10.1007/s13142-011-0021-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13142-011-0021-7

Keywords

Search

Navigation