The Use of Behavior Change Techniques and Theory in Technologies for Cardiovascular Disease Prevention and Treatment in Adults: A Comprehensive Review

Overview

Journal Prog Cardiovasc Dis

Specialty Cardiology & Vascular Diseases

Date 2016 Feb 24

PMID 26902519

Citations 48

Authors

Sandra J Winter

Jylana L Sheats

Abby C King

Affiliations

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Abstract

This review examined the use of health behavior change techniques and theory in technology-enabled interventions targeting risk factors and indicators for cardiovascular disease (CVD) prevention and treatment. Articles targeting physical activity, weight loss, smoking cessation and management of hypertension, lipids and blood glucose were sourced from PubMed (November 2010-2015) and coded for use of 1) technology, 2) health behavior change techniques (using the CALO-RE taxonomy), and 3) health behavior theories. Of the 984 articles reviewed, 304 were relevant (240=intervention, 64=review). Twenty-two different technologies were used (M=1.45, SD=+/-0.719). The most frequently used behavior change techniques were self-monitoring and feedback on performance (M=5.4, SD=+/-2.9). Half (52%) of the intervention studies named a theory/model - most frequently Social Cognitive Theory, the Trans-theoretical Model, and the Theory of Planned Behavior/Reasoned Action. To optimize technology-enabled interventions targeting CVD risk factors, integrated behavior change theories that incorporate a variety of evidence-based health behavior change techniques are needed.

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References

Prestwich A, Sniehotta F, Whittington C, Dombrowski S, Rogers L, Michie S . Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Health Psychol. 2013; 33(5):465-74. DOI: 10.1037/a0032853. View

Post S, Taylor S, Sanders A, Goldfarb J, Hunt Y, Augustson E . If you build (and moderate) it, they will come: the Smokefree Women Facebook page. J Natl Cancer Inst Monogr. 2014; 2013(47):206-8. PMC: 3881994. DOI: 10.1093/jncimonographs/lgt019. View

Acker J, MacKillop J . Behavioral economic analysis of cue-elicited craving for tobacco: a virtual reality study. Nicotine Tob Res. 2013; 15(8):1409-16. PMC: 3715390. DOI: 10.1093/ntr/nts341. View

Gainforth H, West R, Michie S . Assessing Connections Between Behavior Change Theories Using Network Analysis. Ann Behav Med. 2015; 49(5):754-61. DOI: 10.1007/s12160-015-9710-7. View

Anderson-Hanley C, Snyder A, Nimon J, Arciero P . Social facilitation in virtual reality-enhanced exercise: competitiveness moderates exercise effort of older adults. Clin Interv Aging. 2011; 6:275-80. PMC: 3212419. DOI: 10.2147/CIA.S25337. View

Pericot-Valverde I, Secades-Villa R, Gutierrez-Maldonado J, Garcia-Rodriguez O . Effects of systematic cue exposure through virtual reality on cigarette craving. Nicotine Tob Res. 2014; 16(11):1470-7. PMC: 4342674. DOI: 10.1093/ntr/ntu104. View

Patrick K, Marshall S, Davila E, Kolodziejczyk J, Fowler J, Calfas K . Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). Contemp Clin Trials. 2013; 37(1):10-8. PMC: 3910290. DOI: 10.1016/j.cct.2013.11.001. View

Rivera D, Pew M, Collins L . Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction. Drug Alcohol Depend. 2006; 88 Suppl 2:S31-40. PMC: 2062527. DOI: 10.1016/j.drugalcdep.2006.10.020. View

Turolla A, Dam M, Ventura L, Tonin P, Agostini M, Zucconi C . Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial. J Neuroeng Rehabil. 2013; 10:85. PMC: 3734026. DOI: 10.1186/1743-0003-10-85. View

10.

Zullig L, Melnyk S, Goldstein K, Shaw R, Bosworth H . The role of home blood pressure telemonitoring in managing hypertensive populations. Curr Hypertens Rep. 2013; 15(4):346-55. PMC: 3743229. DOI: 10.1007/s11906-013-0351-6. View

11.

Collins L, Murphy S, Strecher V . The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007; 32(5 Suppl):S112-8. PMC: 2062525. DOI: 10.1016/j.amepre.2007.01.022. View

12.

Riley W, Rivera D, Atienza A, Nilsen W, Allison S, Mermelstein R . Health behavior models in the age of mobile interventions: are our theories up to the task?. Transl Behav Med. 2011; 1(1):53-71. PMC: 3142960. DOI: 10.1007/s13142-011-0021-7. View

13.

Arem H, Irwin M . A review of web-based weight loss interventions in adults. Obes Rev. 2010; 12(5):e236-43. PMC: 3086798. DOI: 10.1111/j.1467-789X.2010.00787.x. View

14.

Laver K, Ratcliffe J, George S, Burgess L, Crotty M . Is the Nintendo Wii Fit really acceptable to older people? A discrete choice experiment. BMC Geriatr. 2011; 11:64. PMC: 3206825. DOI: 10.1186/1471-2318-11-64. View

15.

Michie S, Prestwich A . Are interventions theory-based? Development of a theory coding scheme. Health Psychol. 2010; 29(1):1-8. DOI: 10.1037/a0016939. View

16.

Coons M, DeMott A, Buscemi J, Duncan J, Pellegrini C, Steglitz J . Technology Interventions to Curb Obesity: A Systematic Review of the Current Literature. Curr Cardiovasc Risk Rep. 2012; 6(2):120-134. PMC: 3471367. DOI: 10.1007/s12170-012-0222-8. View

17.

McEwen D, Taillon-Hobson A, Bilodeau M, Sveistrup H, Finestone H . Virtual reality exercise improves mobility after stroke: an inpatient randomized controlled trial. Stroke. 2014; 45(6):1853-5. DOI: 10.1161/STROKEAHA.114.005362. View

18.

Turner-McGrievy G, Tate D . Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention. Transl Behav Med. 2013; 3(3):287-94. PMC: 3771014. DOI: 10.1007/s13142-012-0183-y. View

19.

Park C, Choi J, Park S, Lee J, Jung H, Seol J . Comparison of the effectiveness of virtual cue exposure therapy and cognitive behavioral therapy for nicotine dependence. Cyberpsychol Behav Soc Netw. 2014; 17(4):262-7. PMC: 3976586. DOI: 10.1089/cyber.2013.0253. View

20.

Bickmore T, Schulman D, Sidner C . Automated interventions for multiple health behaviors using conversational agents. Patient Educ Couns. 2013; 92(2):142-8. PMC: 3727973. DOI: 10.1016/j.pec.2013.05.011. View