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Health Behavior Models in the Age of Mobile Interventions: Are Our Theories Up to the Task?

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Date 2011 Jul 29
PMID 21796270
Citations 484
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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.

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References
1.
Rami B, Popow C, Horn W, Waldhoer T, Schober E . Telemedical support to improve glycemic control in adolescents with type 1 diabetes mellitus. Eur J Pediatr. 2006; 165(10):701-5. DOI: 10.1007/s00431-006-0156-6. View

2.
Bandura A . Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001; 52:1-26. DOI: 10.1146/annurev.psych.52.1.1. View

3.
Ma Y, Olendzki B, Chiriboga D, Rosal M, Sinagra E, Crawford S . PDA-assisted low glycemic index dietary intervention for type II diabetes: a pilot study. Eur J Clin Nutr. 2006; 60(10):1235-43. DOI: 10.1038/sj.ejcn.1602443. View

4.
Yoon K, Kim H . A short message service by cellular phone in type 2 diabetic patients for 12 months. Diabetes Res Clin Pract. 2007; 79(2):256-61. DOI: 10.1016/j.diabres.2007.09.007. View

5.
Hynes M, Wang H, Kilmartin L . Off-the-shelf mobile handset environments for deploying accelerometer based gait and activity analysis algorithms. Annu Int Conf IEEE Eng Med Biol Soc. 2009; 2009:5187-90. DOI: 10.1109/IEMBS.2009.5333715. View