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

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.

[1]  L. Lechner,et al.  Motivational interviewing in a web-based physical activity intervention: questions and reflections. , 2015, Health promotion international.

[2]  Deborah F Tate,et al.  Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention , 2013, Translational behavioral medicine.

[3]  J. Grimshaw,et al.  Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework , 2012, Implementation Science.

[4]  S. Michie,et al.  A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy , 2011, Psychology & health.

[5]  P. Tonin,et al.  Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial , 2013, Journal of NeuroEngineering and Rehabilitation.

[6]  H. Gainforth,et al.  Assessing Connections Between Behavior Change Theories Using Network Analysis , 2015, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[7]  Deborah L Feltz,et al.  Silence is Golden: Effect of Encouragement in Motivating the Weak Link in an Online Exercise Video Game , 2013, Journal of medical Internet research.

[8]  Paul M Cinciripini,et al.  Cue reactivity in virtual reality: the role of context. , 2011, Addictive behaviors.

[9]  Julie Ratcliffe,et al.  Is the Nintendo Wii Fit really acceptable to older people?: a discrete choice experiment , 2011, BMC geriatrics.

[10]  E. Augustson,et al.  If you build (and moderate) it, they will come: the Smokefree Women Facebook page. , 2013, Journal of the National Cancer Institute. Monographs.

[11]  Manel Nebot,et al.  The European Smoking Prevention Framework Approach (EFSA): an example of integral prevention. , 2003, Health education research.

[12]  Matthew P. Buman,et al.  A case study of BSUED : Behavioral Science-informed User Experience Design , 2011 .

[13]  B. Spring,et al.  Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. , 2015, Circulation.

[14]  Kohinoor Dasgupta,et al.  A randomized trial of an avatar-hosted multiple behavior change intervention for young adult smokers. , 2013, Journal of the National Cancer Institute. Monographs.

[15]  J. MacKillop,et al.  Behavioral economic analysis of cue-elicited craving for tobacco: a virtual reality study. , 2012, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[16]  José Gutiérrez-Maldonado,et al.  Effects of systematic cue exposure through virtual reality on cigarette craving. , 2014, Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco.

[17]  D. Lloyd‐Jones,et al.  Achieving and Maintaining Cardiovascular Health Across the Lifespan , 2014, Current Epidemiology Reports.

[18]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[19]  H. Sveistrup,et al.  Virtual Reality Exercise Improves Mobility After Stroke: An Inpatient Randomized Controlled Trial , 2014, Stroke.

[20]  A. King.  Theory’s role in shaping behavioral health research for population health , 2015, International Journal of Behavioral Nutrition and Physical Activity.

[21]  K J Calfas,et al.  Design and implementation of a randomized controlled social and mobile weight loss trial for young adults (project SMART). , 2014, Contemporary clinical trials.

[22]  M. Irwin,et al.  A review of web‐based weight loss interventions in adults , 2011, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[23]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[24]  A. Strömberg,et al.  An in-depth, longitudinal examination of the daily physical activity of a patient with heart failure using a Nintendo Wii at home: a case report. , 2013, Journal of rehabilitation medicine.

[25]  C. Anderson-Hanley,et al.  Clinical Interventions in Aging Dovepress Social Facilitation in Virtual Reality-enhanced Exercise: Competitiveness Moderates Exercise Effort of Older Adults , 2022 .

[26]  S. Michie,et al.  Does theory influence the effectiveness of health behavior interventions? Meta-analysis. , 2014, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[27]  W. Velicer,et al.  The Transtheoretical Model of Health Behavior Change , 1997, American journal of health promotion : AJHP.

[28]  W. Nilsen,et al.  Health behavior models in the age of mobile interventions: are our theories up to the task? , 2011, Translational behavioral medicine.

[29]  A. Bandura.  Social Foundations of Thought and Action: A Social Cognitive Theory , 1985 .

[30]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[31]  J. Piette,et al.  Mobile Health Devices as Tools for Worldwide Cardiovascular Risk Reduction and Disease Management , 2015, Circulation.

[32]  Anne P. Massey,et al.  Innovation in Weight Loss Intervention Programs: An Examination of a 3D Virtual World Approach , 2012, 2012 45th Hawaii International Conference on System Sciences.

[33]  Deborah L Feltz,et al.  Buddy up: the Köhler effect applied to health games. , 2011, Journal of sport & exercise psychology.

[34]  Jung-Seok Choi,et al.  Comparison of the Effectiveness of Virtual Cue Exposure Therapy and Cognitive Behavioral Therapy for Nicotine Dependence , 2014, Cyberpsychology Behav. Soc. Netw..

[35]  Cynthia M. Lakon,et al.  Development of a Twitter-Based Intervention for Smoking Cessation that Encourages High-Quality Social Media Interactions via Automessages , 2015, Journal of medical Internet research.

[36]  Deborah F. Tate,et al.  Energy expenditure and enjoyment during video game play: differences by game type. , 2011, Medicine and science in sports and exercise.

[37]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[38]  I. Ajzen.  The theory of planned behavior , 1991 .

[39]  Timothy W. Bickmore,et al.  Employing Virtual Advisors in Preventive Care for Underserved Communities: Results From the COMPASS Study , 2013, Journal of health communication.

[40]  S. Michie,et al.  Are interventions theory-based? Development of a theory coding scheme. , 2010, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[41]  Robert West,et al.  The PRIME Theory of motivation as a possible foundation for addiction treatment. , 2007 .

[42]  D. Rivera,et al.  Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction. , 2007, Drug and alcohol dependence.

[43]  F. Hu,et al.  Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. , 2012, JAMA.

[44]  Anne P. Massey,et al.  Using an Alternate Reality Game to Increase Physical Activity and Decrease Obesity Risk of College Students , 2012, Journal of diabetes science and technology.

[45]  R. Shaw,et al.  The Role of Home Blood Pressure Telemonitoring in Managing Hypertensive Populations , 2013, Current Hypertension Reports.

[46]  B. Spring,et al.  Technology Interventions to Curb Obesity: A Systematic Review of the Current Literature , 2012, Current Cardiovascular Risk Reports.

[47]  C. Sidner,et al.  Automated interventions for multiple health behaviors using conversational agents. , 2013, Patient education and counseling.

[48]  Jane D. Brown,et al.  The role of companionship, esteem, and informational support in explaining physical activity among young women in an online social network intervention , 2014, Journal of Behavioral Medicine.