Book contents
- The Handbook of Behavior Change
- The Handbook of Behavior Change
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Sidebars
- Contributors
- 1 Changing Behavior: A Theory- and Evidence-Based Approach
- Part I Theory and Behavior Change
- Part II Methods and Processes of Behavior Change: Intervention Development, Application, and Translation
- 19 Design, Implementation, and Evaluation of Behavior Change Interventions: A Ten-Task Guide
- 20 Moving from Theoretical Principles to Intervention Strategies: Applying the Experimental Medicine Approach
- 21 Developing Behavior Change Interventions
- 22 Evaluation of Behavior Change Interventions
- 23 Implementation Science and Translation in Behavior Change
- 24 Engagement of Stakeholders in the Design, Evaluation, and Implementation of Complex Interventions
- 25 Maximizing User Engagement with Behavior Change Interventions
- 26 Cost-Effectiveness Evaluations of Behavior Change Interventions
- 27 Addressing Underserved Populations and Disparities in Behavior Change
- 28 Behavior Change in Community Contexts
- 29 Changing Behavior in the Digital Age
- 30 Critical and Qualitative Approaches to Behavior Change
- Part III Behavior Change Interventions: Practical Guides to Behavior Change
- Index
- References
29 - Changing Behavior in the Digital Age
from Part II - Methods and Processes of Behavior Change: Intervention Development, Application, and Translation
Published online by Cambridge University Press: 04 July 2020
Book contents
- The Handbook of Behavior Change
- The Handbook of Behavior Change
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Sidebars
- Contributors
- 1 Changing Behavior: A Theory- and Evidence-Based Approach
- Part I Theory and Behavior Change
- Part II Methods and Processes of Behavior Change: Intervention Development, Application, and Translation
- 19 Design, Implementation, and Evaluation of Behavior Change Interventions: A Ten-Task Guide
- 20 Moving from Theoretical Principles to Intervention Strategies: Applying the Experimental Medicine Approach
- 21 Developing Behavior Change Interventions
- 22 Evaluation of Behavior Change Interventions
- 23 Implementation Science and Translation in Behavior Change
- 24 Engagement of Stakeholders in the Design, Evaluation, and Implementation of Complex Interventions
- 25 Maximizing User Engagement with Behavior Change Interventions
- 26 Cost-Effectiveness Evaluations of Behavior Change Interventions
- 27 Addressing Underserved Populations and Disparities in Behavior Change
- 28 Behavior Change in Community Contexts
- 29 Changing Behavior in the Digital Age
- 30 Critical and Qualitative Approaches to Behavior Change
- Part III Behavior Change Interventions: Practical Guides to Behavior Change
- Index
- References
Summary
Many people are unable or unwilling to participate in face-to-face interventions for functional behavior change, and existing services often have poor fidelity to evidence-based approaches. Pervasive ownership of digital devices may offer ways to supplement and increase the reach and impact of face-to-face care. Digital approaches to support behavioral change range from informational resources, through self-guided programs or apps, to digitally-delivered or guided human interventions. Online information is well accepted, and digital interventions are increasing in acceptability and use. Digital interventions also have strong research support. For example, coached web programs for some mental health conditions have equivalent effects to face-to-face treatment. However, many digital tools have no quality or efficacy data, and more agile ways to obtain data are needed. Threats to acceptance and use of digital interventions include concerns about data security, and difficulties deciding which resources and interventions to choose. What is promising is that sound assessment tools and initiatives to provide advice are emerging. Digital tools and resources have the potential to increase the reach, impact, and cost-effectiveness of existing behavior change initiatives, although they have yet to fully impact the way services are funded and delivered. That picture is likely to change rapidly in the coming decade.
Keywords
- Type
- Chapter
- Information
- The Handbook of Behavior Change , pp. 416 - 429Publisher: Cambridge University PressPrint publication year: 2020
References
Allam, A., Kostova, Z., Nakamoto, K., & Schultz, P. J. (2015). The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: Randomized controlled trial. Journal of Medical Internet Research, 17, e14. https://doi.org/10.2196/jmir.3510Google Scholar
Appelbaum, L. G., & Erickson, G. (2018). Sports vision training: A review of the state-of-the-art in digital training techniques. International Review of Sport and Exercise Psychology, 11, 160–189. https://doi.org/10.1080/1750984x.2016.1266376Google Scholar
Batterham, P. J., Neil, A. L., Bennett, K., Griffiths, K. M., & Christensen, H. (2008). Predictors of adherence among community users of a cognitive behavior therapy website. Patient Preference and Adherence, 2, 97–105. www.dovepress.com/predictors-of-adherence-among-community-users-of-a-cognitive-behavior-peer-reviewed-article-PPAGoogle ScholarPubMed
Baumel, A., & Yom-Tov, E. (2018). Predicting user adherence to behavioral eHealth interventions in the real world: Examining which aspects of intervention design matter most. Translational Behavioral Medicine, 8, 793–798. https://doi.org/10.1093/tbm/ibx037Google Scholar
Boyle, S. C., Earle, A. M., LaBrie, J. W., & Smith, D. J. (2017). PNF 2.0? Initial evidence that gamification can increase the efficacy of brief, web-based personalized normative feedback alcohol interventions. Addictive Behaviors, 67, 8–17. https://doi.org/10.1016/j.addbeh.2016.11.024Google Scholar
Brown, A., Rice, S. M., Rickwood, D. J., & Parker, A. G. (2016). Systematic review of barriers and facilitators to accessing and engaging with mental health care among at-risk young people. Asia-Pacific Psychiatry: Official Journal of the Pacific Rim College of Psychiatrists, 8, 3–22. https://doi.org/10.1111/appy.12199Google Scholar
Carlbring, P., Andersson, G., Cuijpers, P., Riper, H., & Hedman-Lagerlöf, E. (2018). Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: An updated systematic review and meta-analysis. Cognitive Behaviour Therapy, 47, 1–18. https://doi.org/10.1080/16506073.2017.1401115Google Scholar
Cho, V., Cheng, T. C. E., & Lai, W. M. (2009). The role of perceived user-interface design in continued usage intention of self-paced learning tools. Computers and Education, 53, 216–227. https://doi.org/10.1016/j.comedu.2009.01.014Google Scholar
Clemenson, J., Rothman, M. J., Smoith, A. C., Caffery, L. J., & Danbjorg, D. B. (2017). Participatory design methods in telemedicine research. Journal of Telemedicine and Telecare, 23, 780–785. https://doi.org/10.1177/1357633X16686747Google Scholar
Conserve, D. F., Jennings, L., Aguiar, C., Shin, G., Handler, L., & Maman, S. (2017). Systematic review of mobile health behavioral interventions to improve uptake of HIV testing for vulnerable and key populations. Journal of Telehealth and Telecare, 23, 347–359. https://doi.org/10.1177/1357633X16639186Google Scholar
Council of Australian Governments. (2017). The Fifth National Mental Health and Suicide Plan. Canberra: Council of Australian Governments. www.coaghealthcouncil.gov.au/Portals/0/Fifth%20National%20Mental%20Health%20and%20Suicide%20Prevention%20Plan.pdfGoogle Scholar
Darvell, M., Kavanagh, D. J., & Connolly, J. (2015). A qualitative exploration of Internet-based treatment for comorbid depression and alcohol misuse. Internet Interventions, 2, 174–182. https://doi.org/10.1016/j.invent.2015.03.003CrossRefGoogle Scholar
Dear, B. F., Fogliati, V. J., Fogliati, R. et al. (2018). Treating anxiety and depression in young adults: A randomised controlled trial comparing clinician-guided versus self-guided Internet-delivered cognitive behavioural therapy. Australian and New Zealand Journal of Psychiatry, 52, 668–679. https://doi.org/10.1177/0004867417738055CrossRefGoogle ScholarPubMed
Deloitte. (2018). Behaviour Unlimited: Mobil Consumer Survey 2018: The Australian Cut. www2.deloitte.com/au/mobile-consumer-surveyGoogle Scholar
Domhardt, M., Geßlein, H., von Rezori, R., & Baumeister, H. (2018). Internet- and mobile-based interventions for anxiety disorders: A meta-analytic review of intervention components. Depression and Anxiety, 36, 213–224. https://doi.org/10.1002/da.22860Google Scholar
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010). Perceived barriers and facilitators to mental health help-seeking in young people: A systematic review. BMC Psychiatry, 10, 113. www.biomedcentral.com/1471-244X/10/113Google Scholar
Heber, E., Ebert, D. D., Lehr, D. et al. (2017). The benefit of web- and computer-based interventions for stress: A systematic review and meta-analysis. Journal of Medical Internet 19, e32. https://doi.org/10.2196/jmir.5774Google Scholar
Hennemann, S., Beutel, M. E., & Zwerenz, R. (2017). Ready for eHealth? Health professionals’ acceptance and adoption of eHealth interventions in inpatient routine care. Journal of Health Communication, 22, 274–284. https://doi.org/10.1080/10810730.2017.1284286Google Scholar
Howarth, A., Quesada, J., Silva, J., Judycki, S., & Mills, P. (2018). The impact of digital health interventions on health-related outcomes in the workplace: A systematic review. Digital Health, 4, 1–18. https://doi.org/10.1177/2055207618770861CrossRefGoogle ScholarPubMed
IQVIA Institute for Human Data Science. (2017). The Growing Value of Digital Health: Evidence and Impact on Human Health and the Healthcare System. www.iqvia.com/-/media/iqvia/pdfs/institute-reports/the-growing-value-of-digital-health.pdf?_=1547778440731Google Scholar
Jackson, C. B., Quetsch, L. B., Brabson, L. A., & Herschell, A. D. (2018). Web-based training methods for behavioral health providers: A systematic review. Administration and Policy in Mental Health and Mental Health Services Research, 45(4), 587–610. https://doi.org/10.1007/s10488-018-0847-0Google Scholar
Kaker, T. B., Gustafson, D. H., & Shah, D. (2014). How can research keep up with eHealth? Ten strategies for increasing the timeliness and usefulness of eHealth research. Journal of Medical Internet Research, 16, e36. www.jmir.org/2014/2/e36Google Scholar
Kavanagh, D. J., Andrade, J., May, J., & Connor, J. P. (2014). Motivational interventions may have greater sustained impact if they trained imagery-based self-management. Addiction, 109, 1062–1063. https://doi.org/10.1111/add.12507Google Scholar
Kavanagh, D. J., Clark, D., Manicavasagar, V., Piatkowska, O. H., Rosen, A., & Tennant, C. (1993). Application of a cognitive-behavioural family intervention for schizophrenia in multi-disciplinary settings: What can the matter be? Australian Psychologist, 28, 181–188. https://doi.org/10.1080/00050069308258899CrossRefGoogle Scholar
Kavanagh, D. J., Sitharthan, T., Spilsbury, G., & Vignaendra, S. (1999). An evaluation of brief correspondence programs for problem drinkers. Behavior Therapy, 30, 641–656. https://doi.org/10.1016/S0005-7894(99)80030-6CrossRefGoogle Scholar
Kebede, M. M., Zeeb, H., Peters, M., Heise, T. L., & Pischke, C. R. (2018). Effectiveness of digital interventions for improving glycemic control in persons with poorly controlled type 2 diabetes: A systematic review, meta-analysis, and meta-regression analysis. Diabetes Technology and Therapeutics, 20, 767–782. https://doi.org/10.1089/dia.2018.0216Google Scholar
Kelders, S. M., Sommers-Spijkerman, M., & Goldberg, J. (2018). Investigating the direct impact of a gamified versus nongamified well-being intervention: An exploratory experiment. Journal of Medical Internet Research, 20, e247. www.jmir.org/2018/7/e247Google Scholar
Kerridge, B. T., Mauro, P. M., Chou, S. P. et al. (2017). Predictors of treatment utilization and barriers to treatment utilization among individuals with lifetime cannabis use disorder in the United States. Drug and Alcohol Dependence, 181, 223–228. https://doi.org/10.1016/j.drugalcdep.2017.09.032CrossRefGoogle ScholarPubMed
Kiluk, B. D., Nich, C., Buck, M. B. et al. (2018). Randomized clinical trial of computerized and clinician-delivered CBT in comparison with standard outpatient treatment for substance use disorders: Primary within-treatment and follow-up outcomes. American Journal of Psychiatry, 175, 853–863. https://doi.org/10.1176/appi.ajp.2018.17090978Google Scholar
Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: A national survey. JMIR mHealth and uHealth, 3, e101. https://doi.org/10.2196/mhealth.4924Google Scholar
Lintvedt, O. K., Griffiths, K. M., Eisemann, M., & Waterloo, K. (2013). Evaluating the translation process of an Internet-based self-help intervention for prevention of depression: A cost-effectiveness analysis. Journal of Medical Internet Research 15, e18. www.jmir.org/2013/1/e18Google Scholar
Marasinghe, R. B., Edirippulige, S., Kavanagh, D., Smith, A., & Jiffry, M. T. M. (2012). Effect of mobile phone-based psychotherapy in suicide prevention: A randomised controlled trial in Sri Lanka. Journal of Telemedicine and Telecare, 18, 151–155. https://doi.org/10.1258/jtt.2012.SFT107Google Scholar
Neset, T. S., Opach, T., Lion, P., Lilja, A., & Johansson, J. (2016). Map-based web tools supporting climate change adaptation. Professional Geographer, 68, 103–114. https://doi.org/10.1080/00330124.2015.1033670Google Scholar
Nesvåg, S., & McKay, J. R. (2018). Feasibility and effects of digital interventions to support people in recovery from substance use disorders: Systematic Review. Journal of Medical Internet Research, 20, e255. www.jmir.org/2018/8/e255Google Scholar
Parham, S., Kavanagh, D. J., Shimada, M., May, J., & Andrade, J. (2018). Qualitative analysis of feedback on Functional Imagery Training: A novel motivational intervention for type 2 diabetes. Psychology and Health, 33, 416–429. https://doi.org/10.1080/08870446.2017.1360493CrossRefGoogle ScholarPubMed
Park, E., & Kwon, M. (2018). Health-related Internet use by children and adolescents: Systematic review. Journal of Medical Internet Research, 20, e120. www.jmir.org/2018/4/e120/Google Scholar
Pew Research Center. (2018a). Internet use by age: Surveys conducted 2000–2018. www.pewinter.net.org/chart/internet-use-by-age/Google Scholar
Pew Research Center. (2018b). Mobile fact sheet: Other devices. www.pewinternet.org/fact-sheet/mobile/Google Scholar
Randall, G. E., Wakefield, P. A., & Richards, D. A. (2012). Fidelity to assertive community treatment program standards: A regional survey of adherence to standards. Community Mental Health Journal, 48, 138–149. https://doi.org/10.1007/s10597-010-9353-xCrossRefGoogle ScholarPubMed
Richard, C., Glaser, E., & Lussier, M. T. (2017). Communication and patient participation influencing patient recall of treatment discussions. International Journal of Public Participation in Health Care and Health Policy, 20, 760–770. https://doi.org/10.1111/hex.12515Google Scholar
Robinson, N. L., Cottier, T. V., & Kavanagh, D. J. (2019). Psychosocial health interventions by social robots: Systematic review of randomized controlled trials. Journal of Medical Internet Research, 21, e13203. https://doi.org/10.2196/13203Google Scholar
SAMHSA (Substance Abuse and Mental Health Services Administration). (2018). Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health (HHS Publication No. SMA 18–5068, NSDUH Series H-53). Rockville, MD: SAMHSA. www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.pdfGoogle Scholar
Sander, L., Rausch, L., & Baumeister, H. (2016). Effectiveness of internet-based interventions for the prevention of mental disorders: A systematic review and meta-analysis. JMIR Mental Health, 3, e38. www.jmir.org/2016/3/e38/Google Scholar
Schleibe, M., Reichelt, J., Bellman, M., & Kirch, W. (2015). Acceptance factors of mobile apps for diabetes by patients aged 50 or older: A qualitative study. Medicine 2.0, 4, e1. www.medicine20.com/2015/1/e1/CrossRefGoogle Scholar
Schoeppe, S., Alley, S., Van Lippevelde, W. et al. (2016). Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: A systematic review. The International Journal of Behavioral Nutrition and Physical Activity, 13, 127. https://doi.org/10.1186/s12966-016-0454-yGoogle Scholar
Sijbrandij, M., Kunovsk, I., & Cuijpers, P. (2016). Effectiveness of internet-delivered cognitive behavioral therapy for posttraumatic stress disorder: A systematic review and meta-analysis. Depression and Anxiety, 33, 783–791. https://doi.org/10.1002/da.22533Google Scholar
Solbrig, L., Whalley, B., Kavanagh, D. J. et al. (2018). Functional imagery training versus motivational interviewing for weight loss: A randomised controlled trial of brief individual interventions for overweight and obesity. International Journal of Obesity, 43, 883–894. https://doi.org/10.1038/s41366-018-0122-1Google Scholar
Stoyanov, S., Hides, L., Kavanagh, D. J., Tjondronegoro, D., Zelenko, O., & Mani, M. (2015). Mobile application rating scale (MARS). A new tool for assessing the quality of health-related mobile applications. JMIR mHealth and uHealth, 3, e27. https://mhealth.jmir.org/2015/1/e27/Google Scholar
Stoyanov, S., Hides, L., Kavanagh, D. J., & Wilson, H. (2016). Development and validation of the user version of the mobile application rating scale (uMARS). JMIR mHealth and uHealth, 4, e72. https://doi.org/10.2196/mhealth.5849Google Scholar
Sucala, M., Cuijpers, P., Muench, F. et al. (2017). Anxiety: There is an app for that. A systematic review of anxiety apps. Depression and Anxiety, 34, 518–525. https://doi.org/10.1002/da.22654Google Scholar
Sztein, D., Koransky, C. E., Fegan, L., & Himelhoch, S. (2018). Efficacy of cognitive behavioural therapy delivered over the Internet for depressive symptoms: A systematic review and meta-analysis. Journal of Telemedicine and Telecare, 24, 527–539. https://doi.org/10.1177/1357633X17717402Google Scholar
Titov, N., Dear, B., Nielssen, O. et al. (2018). ICBT in routine care: A descriptive analysis of successful clinics in five countries. Internet Interventions, 13, 108–115. https://doi.org/10.1016/j.invent.2018.07.006Google Scholar
Titov, N., Dear, B., Staples, L. G. et al. (2017). The first 30 months of the MindSpot Clinic: Evaluation of a national e-mental health service against project objectives. Australian and New Zealand Journal of Psychiatry, 51, 1227–1239. https://doi.org/10.1177/0004867416671598CrossRefGoogle ScholarPubMed
We Are Social, & Hootsuite. (2018). Digital in 2018: Global Overview. https://wearesocial.com/blog/2018/01/global-digital-report-2018Google Scholar
Whiteford, H. A., Buckingham, W. J., Harris, M. G. et al. (2014). Estimating treatment rates for mental disorders in Australia. Australian Health Review, 38, 80–85. https://doi.org/10.1071/AH13142Google Scholar
Zachariae, R., Lyby, M. S., Ritterband, L. M., & O’Toole, M. S. (2016). Efficacy of internet-delivered cognitive-behavioral therapy for insomnia: A systematic review and meta-analysis of randomized controlled trials. Sleep Medicine Reviews, 30, 1–10. https://doi.org/10.1016/j.smrv.2015.10.004Google Scholar
Zeng, E. Y., Heffner, J. L., Copeland, W. K., Mull, K. E., & Bricker, J. B. (2016). Get with the program: Adherence to a smartphone app for smoking cessation. Addictive Behaviors, 63, 120–124. https://doi.org/10.1016/j.addbeh.2016.07.007Google Scholar