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Erschienen in:

01.02.2019

The history and future of digital health in the field of behavioral medicine

verfasst von: Danielle Arigo, Danielle E. Jake-Schoffman, Kathleen Wolin, Ellen Beckjord, Eric B. Hekler, Sherry L. Pagoto

Erschienen in: Journal of Behavioral Medicine | Ausgabe 1/2019

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Abstract

Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior.

Agras, W. S. (1982). Behavioral medicine in the 1980s: Nonrandom connections. Journal of Consulting and Clinical Psychology, 50, 797–803.PubMedCrossRef

Anderson, C. M., & Zhu, S. H. (2007). Tobacco quitlines: Looking back and looking ahead. Tobacco Control, 16(Suppl 1), i81–i86.PubMedPubMedCentralCrossRef

Arigo, D. (2015). Promoting physical activity among women using wearable technology and online social connectivity: A feasibility study. Health Psychology and Behavioral Medicine, 3, 391–409.CrossRef

Arigo, D., Pagoto, S., Carter-Harris, L., Lillie, S. E., & Nebeker, C. (2018). Using social media for health research: Methodological and ethical considerations for recruitment and intervention delivery. Digital Health, 4, 1–15.CrossRef

Ashrafian, H., Toma, T., Harling, L., Kerr, K., Athanasiou, T., & Darzi, A. (2014). Social networking strategies that aim to reduce obesity have achieved significant although modest results. Health Affairs, 33, 1641–1647.PubMedCrossRef

Bauer, M. S., Damschroder, L., Hagedorn, H., Smith, J., & Kilbourne, A. M. (2015). An introduction to implementation science for the non-specialist. BMC Psychology, 3, 32.PubMedPubMedCentralCrossRef

Blanchard, E. B. (1982). Behavioral medicine: Past, present, and future. Journal of Consulting and Clinical Psychology, 50, 795–796.PubMedCrossRef

Bond, D. S., Thomas, J. G., Raynor, H. A., Moon, J., Sieling, J., Trautvetter, J., et al. (2014). B-MOBILE-A smartphone-based intervention to reduce sedentary time in overweight/obese individuals: A within-subjects experimental trial. PLoS ONE, 9, e100821.PubMedPubMedCentralCrossRef

Breland, J. Y., Quintiliani, L. M., Schneider, K. L., May, C. N., & Pagoto, S. (2017). Social media as a tool to increase the impact of public health research. American Journal of Public Health, 107, 1890–1891.PubMedPubMedCentralCrossRef

Butryn, M. L., Arigo, D., Raggio, G. A., Colasanti, M., & Forman, E. M. (2016). Enhancing physical activity promotion in midlife women with technology-based self-monitoring and social connectivity: A pilot study. Journal of Health Psychology, 21, 1548–1555.PubMedCrossRef

Carr, F. (2018). Facebook is telling people their data was misused by Cambridge Analytica and they’re furious. http://time.com/5234740/facebook-data-misused-cambridge-analytica/. Access verified June 30, 2018.

Carroll, E. A., Czerwinski, M., Roseway, A., Kapoor, A., Johns, P., Rowan, K., & Schraefel, M. C. (2013). Food and mood: Just-in-time support for emotional eating. In Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference (pp. 252–257).

Carter-Harris, L., Ellis, R. B., Warrick, A., & Rawl, S. (2016). Beyond traditional newspaper advertisement: Leveraging Facebook-targeted advertisement to recruit long-term smokers for research. Journal of Medical Internet Research, 18, e117.PubMedPubMedCentralCrossRef

Chakraborty, B., Collins, L. M., Strecher, V. J., & Murphy, S. A. (2009). Developing multicomponent interventions using fractional factorial designs. Statistics in Medicine, 28, 2687–2708.PubMedPubMedCentralCrossRef

Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H., Olsen, J. M., et al. (2015). Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PLoS ONE, 10, e0139701.PubMedPubMedCentralCrossRef

Chirico, A. M., & Stunkard, A. J. (1960). Physical activity and human obesity. New England Journal of Medicine, 263, 935–940.CrossRef

Cobiac, L. J., Vos, T., & Barendregt, J. J. (2009). Cost-effectiveness of interventions to promote physical activity: A modelling study. PLoS Medicine, 6, e1000110.PubMedPubMedCentralCrossRef

Colby, K. M., Watt, J. B., & Gilbert, J. P. (1966). A computer method of psychotherapy: Preliminary communication. The Journal of Nervous and Mental Disease, 142, 148–152.PubMedCrossRef

Cole-Lewis, H., & Kershaw, T. (2010). Text messaging as a tool for behavior change in disease prevention and management. Epidemiologic Reviews, 32, 56–69.PubMedCrossRef

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32, S112–S118.PubMedPubMedCentralCrossRef

Collins, L. M., Nahum-Shani, I., & Almirall, D. (2014). Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART). Clinical Trials, 11, 426–434.PubMedCrossRef

Conroy, D. E., Hojjatinia, S., Lagoa, C. M., Yang, C. H., Lanza, S. T., & Smyth, J. M. (2018). Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods. Psychology of Sport and Exercise. https://doi.org/10.1016/j.psychsport.2018.06.011.CrossRefPubMedPubMedCentral

Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine, 46, 649–652.PubMedCrossRef

Cummins, S. E., Bailey, L., Campbell, S., Koon-Kirby, C., & Zhu, S. H. (2007). Tobacco cessation quitlines in North America: A descriptive study. Tobacco Control, 16(Suppl 1), i9–i15.PubMedPubMedCentralCrossRef

Dehling, T., Gao, F., Schneider, S., & Sunyaev, A. (2015). Exploring the far side of mobile health: Information security and privacy of mobile health apps on iOS and Android. JMIR mHealth and uHealth, 3, e8.PubMedPubMedCentralCrossRef

Evans, W. D., Bihm, J. W., Szekely, D., Nielsen, P., Murray, E., Abroms, L., et al. (2014). Initial outcomes from a 4-week follow-up study of the Text4baby program in the military women’s population: Randomized controlled trial. Journal of Medical Internet Research, 16, e131.PubMedPubMedCentralCrossRef

Evans, W. D., Wallace, J. L., & Snider, J. (2012). Pilot evaluation of the text4baby mobile health program. BMC Public Health, 12, 1031.PubMedPubMedCentralCrossRef

Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12, 159.CrossRef

Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7, e11.PubMedPubMedCentralCrossRef

Eysenbach, G., Powell, J., Englesakis, M., Rizo, C., & Stern, A. (2004). Health related virtual communities and electronic support groups: Systematic review of the effects of online peer to peer interactions. British Medical Journal, 328, 1166.PubMedCrossRefPubMedCentral

Fahrenberg, J. (1996). Ambulatory assessment: Issues and perspectives. In J. Fahrenberg & M. Myrtek (Eds.), Ambulatory assessment: Computer-assisted psychological and psychophysiological methods in monitoring and field studies (pp. 3–20). Seattle, WA: Hogrefe and Huber.

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.PubMedCrossRef

Fox, S. (2017). The social life of health information. Washington, DC: Pew Internet & American Life Project. (2011).

Frost, J., Okun, S., Vaughan, T., Heywood, J., & Wicks, P. (2011). Patient-reported outcomes as a source of evidence in off-label prescribing: Analysis of data from PatientsLikeMe. Journal of Medical Internet Research, 13, e6.PubMedPubMedCentralCrossRef

Garnett, C., Crane, D., West, R., Brown, J., & Michie, S. (2018). The development of Drink Less: An alcohol reduction smartphone app for excessive drinkers. Translational Behavioral Medicine. https://doi.org/10.1093/tbm/iby043 CrossRefPubMedCentral

Global Wellness Institute. (2018). Global wellness: Statistics and facts. https://globalwellnessinstitute.org/press-room/statistics-and-facts/. Access verified June 11, 2018.

Goldman, J. (1961). A look at human measurements in industry. In L. E. Slater (Ed.), Interdisciplinary clinic on the instrumentation requirements for psychophysiological research. New York: Fier.

Goldstein, C. M., Minges, K. E., Schoffman, D. E., & Cases, M. G. (2017). Preparing tomorrow’s behavioral medicine scientists and practitioners: A survey of future directions for education and training. Journal of Behavioral Medicine, 40, 214–226.PubMedCrossRef

Gorman, J. R., Roberts, S. C., Dominick, S. A., Malcarne, V. L., Dietz, A. C., & Su, H. I. (2014). A diversified recruitment approach incorporating social media leads to research participation among young adult-aged female cancer survivors. Journal of Adolescent and Young Adult Oncology, 3, 59–65.PubMedPubMedCentralCrossRef

Grabosch, S., Gavard, J. A., & Mostello, D. (2014). 151: Text4baby improves glycemic control in pregnant women with diabetes. American Journal of Obstetrics and Gynecology, 210, S88.CrossRef

Han, C. J., Lee, Y. J., & Demiris, G. (2018). Interventions using social media for cancer prevention and management: A systematic review. Cancer Nursing. https://doi.org/10.1097/NCC.0000000000000534.CrossRefPubMedPubMedCentral

Head, K. J., Noar, S. M., Iannarino, N. T., & Harrington, N. G. (2013). Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Social Science and Medicine, 97, 41–48.PubMedCrossRef

Heintzman, N. D. (2016). A digital ecosystem of diabetes data and technology: Services, systems, and tools enabled by wearables, sensors, and apps. Journal of Diabetes Science and Technology, 10, 35–41.CrossRef

Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., et al. (2016). Agile science: Creating useful products for behavior change in the real world. Translational Behavioral Medicine, 6, 317–328.PubMedPubMedCentralCrossRef

Hekler, E. B., Rivera, D. E., Martin, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E., et al. (2018). Tutorial for using control systems engineering to optimize adaptive mobile health interventions. Journal of Medical Internet Research., 20, e214.PubMedPubMedCentralCrossRef

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

Hussain, M. S., Cripwell, L., Berkovsky, S., & Freyne, J. (2016). Promoting UV exposure awareness with persuasive, wearable technologies. In Proceedings of digital health innovation for consumers, clinicians, connectivity and community: Selected papers from the 24th australian national health informatics conference (HIC 2016) (pp. 48–54).

Insel, T. R. (2017). Digital phenotyping: Technology for a new science of behavior. JAMA, 318, 1215–1216.PubMedCrossRef

Iribarren, S. J., Ghazzawi, A., Sheinfil, A. Z., Frasca, T., Brown, W., Lopez-Rios, J., et al. (2018). Mixed-method evaluation of social media-based tools and traditional strategies to recruit high-risk and hard-to-reach populations into an HIV prevention intervention study. AIDS and Behavior, 22, 347–357.PubMedPubMedCentralCrossRef

Jake-Schoffman, D. E., Silfee, V. J., Waring, M. E., Boudreaux, E. D., Sadasivam, R. S., Mullen, S. P., et al. (2017). Methods for evaluating the content, usability, and efficacy of commercial mobile health apps. JMIR mHealth and uHealth, 5, e190.PubMedPubMedCentralCrossRef

Jake-Schoffman, D. E., Turner-McGrievy, G., Wilcox, S., Moore, J. B., Hussey, J. R., & Kaczynski, A. T. (2018). The mFIT (Motivating Families with Interactive Technology) Study: A randomized pilot to promote physical activity and healthy eating through mobile technology. Journal of Technology in Behavioral Science, 3(3), 179–189.CrossRef

Keefe, F. J., Buffington, A. L., Studts, J. L., & Rumble, M. E. (2002). Behavioral medicine: 2002 and beyond. Journal of Consulting and Clinical Psychology, 70, 852–856.PubMedCrossRef

Keller, P. A., Feltracco, A., Bailey, L. A., Li, Z., Niederdeppe, J., Baker, T. B., et al. (2010). Changes in tobacco quitlines in the United States, 2005–2006. Preventing Chronic Disease, 7, 1–6.

Klasnja, P., Smith, S. N., Seewald, N. J., Lee, A. J., Hall, K., & Murphy, S. A. (2017). Effects of contextually-tailored suggestions for physical activity: The HeartSteps Micro-randomized trial. Annals of Behavioral Medicine, 51, S902–S903.

Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: A national survey. JMIR mHealth and uHealth, 3, e101.PubMedPubMedCentralCrossRef

Kreuter, M. W., Strecher, V. J., & Glassman, B. (1999). One size does not fit all: The case for tailoring print materials. Annals of Behavioral Medicine, 21, 276–283.PubMedCrossRef

Larsen, G. (1949). Treatment of obesity. Tidsskrift for den Norske laegeforening, 69, 442–446.PubMed

Ledger, D., & McCaffrey, D. (2014). Inside wearables: How the science of human behavior change offers the secret to long-term engagement. https://blog.endeavour.partners/inside-wearable-how-the-science-of-human-behavior-change-offers-the-secret-to-long-term-engagement-a15b3c7d4cf3. Access verified June 20, 2018.

Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2016). Sample size calculations for micro-randomized trials in mHealth. Statistics in Medicine, 35, 1944–1971.PubMedCrossRef

Lichtenstein, E., Glasgow, R. E., Lando, H. A., Ossip-Klein, D. J., & Boles, S. M. (1996). Telephone counseling for smoking cessation: Rationales and meta-analytic review of evidence. Health Education Research, 11, 243–257.PubMedCrossRef

Lichtenstein, E., Zhu, S. H., & Tedeschi, G. J. (2010). Smoking cessation quitlines: An underrecognized intervention success story. American Psychologist, 65, 252–261.CrossRef

Lipschitz, J., & Torous, J. (2018). Why it’s so hard to figure out whether health apps work. https://slate.com/technology/2018/05/health-apps-like-headspace-are-hard-to-study-because-we-cant-make-good-placebo-apps.html. Access verified June 20, 2018.

Lustria, M. L. A., 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.PubMedCrossRef

Lustria, M. L. A., Noar, S. M., Cortese, J., Van Stee, S. K., Glueckauf, R. L., & Lee, J. (2013). A meta-analysis of web-delivered tailored health behavior change interventions. Journal of Health Communication, 18, 1039–1069.PubMedCrossRef

Mack, H. (2016). Nearly 60 percent of US smartphone owners use phones to manage health. http://www.mobihealthnews.com/content/nearly-60-percent-us-smartphone-owners-use-phones-manage-health. Access verified June 13, 2018.

Maher, C. A., Lewis, L. K., Ferrar, K., Marshall, S., De Bourdeaudhuij, I., & Vandelanotte, C. (2014). Are health behavior change interventions that use online social networks effective? A systematic review. Journal of Medical Internet Research, 16, e40.PubMedPubMedCentralCrossRef

Martinez, O., Wu, E., Shultz, A. Z., Capote, J., Rios, J. L., Sandfort, T., et al. (2014). Still a hard-to-reach population? Using social media to recruit Latino gay couples for an HIV intervention adaptation study. Journal of Medical Internet Research, 16, e113.PubMedPubMedCentralCrossRef

Mendiola, M. F., Kalnicki, M., & Lindenauer, S. (2015). Valuable features in mobile health apps for patients and consumers: Content analysis of apps and user ratings. JMIR mHealth and uHealth, 3, e40.PubMedPubMedCentralCrossRef

Michie, S., Abraham, C., Whittington, C., McAteer, J., & Gupta, S. (2009). Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychology, 28, 690–701.PubMedCrossRef

Mohr, D. C., Schueller, S. M., Riley, W. T., Brown, C. H., Cuijpers, P., Duan, N., et al. (2015). Trials of intervention principles: Evaluation methods for evolving behavioral intervention technologies. Journal of Medical Internet Research, 17, e166.PubMedPubMedCentralCrossRef

Moreno, M. A., Waite, A., Pumper, M., Colburn, T., Holm, M., & Mendoza, J. (2017). Recruiting adolescent research participants: In-person compared to social media approaches. Cyberpsychology, Behavior, and Social Networking, 20, 64–67.CrossRef

Muench, F., & Baumel, A. (2017). More than a text message: Dismantling digital triggers to curate behavior change in patient-centered health interventions. Journal of Medical Internet Research, 19, e147.PubMedPubMedCentralCrossRef

Müller, A. M., Maher, C. A., Vandelanotte, C., Hingle, M., Middelweerd, A., Lopez, M. L., et al. (2018). Physical activity, sedentary behavior, and diet-related eHealth and mHealth research: Bibliometric analysis. Journal of Medical Internet Research, 20, e122.PubMedPubMedCentralCrossRef

Murray, E., Hekler, E. B., Andersson, G., Collins, L. M., Doherty, A., Hollis, C., et al. (2016). Evaluating digital health interventions. American Journal of Preventive Medicine, 51, 843–851.PubMedPubMedCentralCrossRef

Murray, J. M., Brennan, S. F., French, D. P., Patterson, C. C., Kee, F., & Hunter, R. F. (2017). Effectiveness of physical activity interventions in achieving behaviour change maintenance in young and middle aged adults: A systematic review and meta-analysis. Social Science and Medicine, 192, 125–133.PubMedCrossRef

Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015). Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychology, 34, 1209–1219.PubMedCentralCrossRef

Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., et al. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52, 446–462.PubMedCrossRef

National Institute on Alcohol Abuse and Alcoholism (NIAAA). (2018). A second challenge competition for the wearable alcohol biosensor. https://www.niaaa.nih.gov/research/niaaa-research-highlights/second-challenge-competition-wearable-alcohol-biosensor. Access verified June 30, 2018.

National Institutes of Health. (2018a). All of Us Research Program. https://allofus.nih.gov/. Access verified June 30, 2018.

National Institutes of Health. (2018b). Innovation Corps (I-Corps™) at NIH Program for NIH and CDC Translational Research (Admin Supp). https://grants.nih.gov/grants/guide/pa-files/PA-18-314.html. Access verified June 20, 2018.

Nebeker, C., Linares-Orozco, R., & Crist, K. (2015). A multi-case study of research using mobile imaging, sensing and tracking technologies to objectively measure behavior: Ethical issues and insights to guide responsible research practice. Journal of Research Administration, 46, 118–137.

Nikolaou, C. K., & Lean, M. E. J. (2017). Mobile applications for obesity and weight management: Current market characteristics. International Journal of Obesity, 41, 200–202.PubMedCrossRef

Pagoto, S., Schneider, K., Jojic, M., DeBiasse, M., & Mann, D. (2013). Evidence-based strategies in weight-loss mobile apps. American Journal of Preventive Medicine, 45, 576–582.PubMedCrossRef

Pagoto, S., Tulu, B., Agu, E., Waring, M. E., Oleski, J. L., & Jake-Schoffman, D. E. (2018). Using the Habit App for weight loss problem solving: Development and feasibility study. JMIR mHealth & uHealth, 6, e145.CrossRef

Pagoto, S., & Waring, M. E. (2016). A call for a science of engagement: Comment on Rus and Cameron. Annals of Behavioral Medicine, 50(5), 690–691.PubMedCrossRef

Pagoto, S., Waring, M. E., May, C. N., Ding, E. Y., Kunz, W. H., Hayes, R., et al. (2016). Adapting behavioral interventions for social media delivery. Journal of Medical Internet Research, 18, e24.PubMedPubMedCentralCrossRef

Pagoto, S. L., Waring, M. E., Schneider, K. L., Oleski, J. L., Olendzki, E., Hayes, R. B., et al. (2015). Twitter-delivered behavioral weight-loss interventions: A pilot series. JMIR Research Protocols, 4, e123.PubMedPubMedCentralCrossRef

Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. JAMA, 313, 459–460.PubMedCrossRef

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

Paul, M. J., Sarker, A., Brownstein, J. S., Nikfarjam, A., Scotch, M., Smith, K. L., & Gonzalez, G. (2016). Social media mining for public health monitoring and surveillance. In Biocomputing 2016: Proceedings of the Pacific symposium (pp. 468–479).

Payne, H. E., Lister, C., West, J. H., & Bernhardt, J. M. (2015). Behavioral functionality of mobile apps in health interventions: A systematic review of the literature. JMIR mHealth and uHealth, 3, e20.PubMedPubMedCentralCrossRef

Peake, J., Kerr, G. K., & Sullivan, J. P. (2018). A critical review of consumer wearables, mobile applications and equipment for providing biofeedback, monitoring stress and sleep in physically active populations. Frontiers in Physiology, 9, 743. https://doi.org/10.3389/fphys.2018.00743.PubMedPubMedCentralCrossRef

Perski, O., Blandford, A., West, R., & Michie, S. (2016). Conceptualising engagement with digital behaviour change interventions: A systematic review using principles from critical interpretive synthesis. Translational Behavioral Medicine, 7, 254–267.PubMedCentralCrossRef

Pew Research Center. (2018a). Mobile fact sheet. http://www.pewinternet.org/fact-sheet/mobile/. Access verified June 14, 2018.

Pew Research Center. (2018b). Social media fact sheet. http://www.pewinternet.org/fact-sheet/social-media/. Access verified June 14, 2018.

Pustozerov, E., & Albrecht, U. V. (2016). Evaluation of mHealth applications security based on application permissions. Studies in Health Technology and Informatics, 226, 241–244.PubMed

Ramo, D. E., Thrul, J., Delucchi, K. L., Hall, S., Ling, P. M., Belohlavek, A., & Prochaska, J. J. (2018). A randomized controlled evaluation of the tobacco status project, a Facebook intervention for young adults. Addiction. https://doi.org/10.1111/add.14245.PubMedCrossRefPubMedCentral

RecycleHealth. (2018). What we do. http://www.recyclehealth.com/what-we-do.html. Access verified June 30, 2018.

Reuters. (2017). mHealth Market Worth $23 Billion in 2017 and Estimated to Grow at a CAGR of more than 35% over the next three years. https://www.reuters.com/brandfeatures/venture-capital/article?id=4640. Access verified June 10, 2018.

Riley, W. T., Glasgow, R. E., Etheredge, L., & Abernethy, A. P. (2013). Rapid, responsive, relevant (R3) research: A call for a rapid learning health research enterprise. Clinical Translational Medicine, 2, 10.PubMedCrossRefPubMedCentral

Riordan, B. C., Conner, T. S., Flett, J. A., & Scarf, D. (2015). A brief orientation week ecological momentary intervention to reduce university student alcohol consumption. Journal of Studies on Alcohol and Drugs, 76, 525–529.PubMedCrossRef

Rivera, J., McPherson, A., Hamilton, J., Birken, C., Coons, M., Iyer, S., et al. (2016). Mobile apps for weight management: A scoping review. JMIR mHealth and uHealth, 4, e87.PubMedPubMedCentralCrossRef

Robinson, M. N., Tansil, K. A., Elder, R. W., Soler, R. E., Labre, M. P., Mercer, S. L., et al. (2014). Mass media health communication campaigns combined with health-related product distribution: A community guide systematic review. American Journal of Preventive Medicine, 47, 360–371.PubMedCrossRef

Rus, H. M., & Cameron, L. D. (2016). Health communication in social media: Message features predicting user engagement on diabetes-related Facebook pages. Annals of Behavioral Medicine, 50, 678–689.PubMedCrossRef

Sackett, D. L., Rosenberg, W. M., Gray, J. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: What it is and what it isn’t. British Medical Journal, 312, 71–72.PubMedCrossRefPubMedCentral

Samdal, G. B., Eide, G. E., Barth, T., Williams, G., & Meland, E. (2017). Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; systematic review and meta-regression analyses. International Journal of Behavioral Nutrition and Physical Activity, 14, 42.CrossRef

Sazonov, E., Lopez-Meyer, P., & Tiffany, S. (2013). A wearable sensor system for monitoring cigarette smoking. Journal of Studies on Alcohol and Drugs, 74, 956–964.PubMedPubMedCentralCrossRef

Schoffman, D. E., Turner-McGrievy, G., Jones, S. J., & Wilcox, S. (2013). Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: Just fun and games? Translational Behavioral Medicine, 3, 320–325.PubMedPubMedCentralCrossRef

Schulmann, J. L., & Reisman, J. M. (1959). An objective measurement of hyperactivity. American Journal of Mental Deficiency, 64, 455–456.

Schwitzgebel, R. L. (1968). Survey of electromechanical devices for behavior modification. Psychological Bulletin, 70(6p1), 444–459.PubMedCrossRef

Segarra, L. M. (2018). Under Armour data breach exposes 150 million MyFitnessPal accounts. http://time.com/5222015/under-armour-myfitnesspal-data-breach/. Access verified June 30, 2018.

Shaffer, J. A., Kronish, I. M., Falzon, L., Cheung, Y. K., & Davidson, K. W. (2018). N-of-1 randomized intervention trials in health psychology: A systematic review and methodology critique. Annals of Behavioral Medicine, 52, 731–742.PubMedPubMedCentralCrossRef

Shaw, R. J., & Johnson, C. M. (2011). Health information seeking and social media use on the Internet among people with diabetes. Online Journal of Public Health Informatics, 3, 3561.CrossRef

Silfee, V. J., Haughton, C. F., Jake-Schoffman, D. E., Lopez-Cepero, A., May, C. N., Sreedhara, M., et al. (2018). Objective measurement of physical activity outcomes in lifestyle interventions among adults: A systematic review. Preventive Medicine Reports, 11, 74–80.PubMedPubMedCentralCrossRef

Smith, W., Ploderer, B., Wadley, G., Webber, S., & Borland, R. (2017). Trajectories of engagement and disengagement with a story-based smoking cessation app. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3045–3056).

Stiles-Shields, C., Kwasny, M. J., Cai, X., & Mohr, D. C. (2014). Therapeutic alliance in face-to-face and telephone-administered cognitive behavioral therapy. Journal of Consulting and Clinical Psychology, 82, 349–354.PubMedPubMedCentralCrossRef

Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16, 199–202.CrossRef

Stunkard, A. (1960). A method of studying physical activity in man. The American Journal of Clinical Nutrition, 8, 595–601.CrossRef

Stunkard, A., & Pestka, J. (1962). The physical activity of obese girls. American Journal of Diseases of Children, 103, 812–817.PubMed

Suls, J., Bunde, M., Martin, R., & Barnett, K. (2006). Hystersisters online: Social support and social comparison among hysterectomy patients on the Internet. Annals of Behavioral Medicine, 31, 271–278.PubMedCrossRef

Tate, D. F., & Zabinski, M. F. (2004). Computer and Internet applications for psychological treatment: Update for clinicians. Journal of Clinical Psychology, 60, 209–220.PubMedCrossRef

Teixeira, V., Voci, S. M., Mendes-Netto, R. S., & da Silva, D. G. (2018). The relative validity of a food record using the smartphone application MyFitnessPal. Nutrition & Dietetics, 75, 219–225.CrossRef

The Nielson Company. (2014). Tech-Styles: Are consumers really interested in wearing tech on their sleeves? http://www.nielsen.com/us/en/insights/news/2014/tech-styles-are-consumers-really-interested-in-wearing-tech-on-their-sleeves.html. Access verified June 30, 2018.

Thomas, J. G., & Bond, D. S. (2015). Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization. Health Psychology, 34, 1261–1267.PubMedCentralCrossRef

Topolovec-Vranic, J., & Natarajan, K. (2016). The use of social media in recruitment for medical research studies: A scoping review. Journal of Medical Internet Research, 18, e286.PubMedPubMedCentralCrossRef

Torous, J., & Nebeker, C. (2017). Navigating ethics in the digital age: Introducing Connected and Open Research Ethics (CORE), a tool for researchers and institutional review boards. Journal of Medical Internet Research, 19, e38.PubMedPubMedCentralCrossRef

Tudor-Locke, C., & Lutes, L. (2009). Why do pedometers work? Sports Medicine, 39, 981–993.PubMedCrossRef

Turner-McGrievy, G. M., Hales, S. B., Schoffman, D. E., Valafar, H., Brazendale, K., Weaver, R. G., et al. (2016). Choosing between responsive-design websites versus mobile apps for your mobile behavioral intervention: Presenting four case studies. Translational Behavioral Medicine, 7(2), 224–232.PubMedCentralCrossRef

Turner-McGrievy, G. M., & Tate, D. F. (2013). Weight loss social support in 140 characters or less: Use of an online social network in a remotely delivered weight loss intervention. Translational Behavioral Medicine, 3, 287–294.PubMedPubMedCentralCrossRef

Waring, M. E., Jake-Schoffman, D. E., Holovatska, M. M., Mejia, C., Williams, J. C., & Pagoto, S. L. (2018). Social media and obesity in adults: A review of recent research and future directions. Current Diabetes Report, 18(6), 34. https://doi.org/10.1007/s11892-018-1001-9 CrossRef

Whittaker, R., Matoff-Stepp, S., Meehan, J., Kendrick, J., Jordan, E., Stange, P., et al. (2012). Text4baby: Development and implementation of a national text messaging health information service. American Journal of Public Health, 102, 2207–2213.PubMedPubMedCentralCrossRef

Wicks, P., Mack Thorley, E., Simacek, K., Curran, C., & Emmas, C. (2018). Scaling PatientsLikeMe via a “generalized platform” for members with chronic illness: Web-based survey study of benefits arising. Journal of Medical Internet Research, 20, e175.PubMedPubMedCentralCrossRef

Wolin, K., & Pagoto, S. L. (2018). What is preventing academic and industry collaborations in the development of health promotion technologies? http://www.pchalliance.org/news/what-preventing-academic-and-industry-collaborations-development-health-promotion-technologies. Access verified June 20, 2018.

Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., et al. (2016). Understanding and promoting effective engagement with digital behavior change interventions. American Journal of Preventive Medicine, 51, 833–842.PubMedCrossRef

Zhao, J., Freeman, B., & Li, M. (2016). Can mobile phone apps influence people’s health behavior change? An evidence review. Journal of Medical Internet Research, 18, e287.PubMedPubMedCentralCrossRef

Titel: The history and future of digital health in the field of behavioral medicine
verfasst von: Danielle Arigo
Danielle E. Jake-Schoffman
Kathleen Wolin
Ellen Beckjord
Eric B. Hekler
Sherry L. Pagoto
Publikationsdatum: 01.02.2019
Verlag: Springer US
Erschienen in: Journal of Behavioral Medicine / Ausgabe 1/2019
Print ISSN: 0160-7715
Elektronische ISSN: 1573-3521
DOI: https://doi.org/10.1007/s10865-018-9966-z

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The history and future of digital health in the field of behavioral medicine

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