Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis

Olga Perski, Ann Blandford, Robert West, Susan Michie, Olga Perski, Ann Blandford, Robert West, Susan Michie

Abstract

"Engagement" with digital behaviour change interventions (DBCIs) is considered important for their effectiveness. Evaluating engagement is therefore a priority; however, a shared understanding of how to usefully conceptualise engagement is lacking. This review aimed to synthesise literature on engagement to identify key conceptualisations and to develop an integrative conceptual framework involving potential direct and indirect influences on engagement and relationships between engagement and intervention effectiveness. Four electronic databases (Ovid MEDLINE, PsycINFO, ISI Web of Knowledge, ScienceDirect) were searched in November 2015. We identified 117 articles that met the inclusion criteria: studies employing experimental or non-experimental designs with adult participants explicitly or implicitly referring to engagement with DBCIs, digital games or technology. Data were synthesised using principles from critical interpretive synthesis. Engagement with DBCIs is conceptualised in terms of both experiential and behavioural aspects. A conceptual framework is proposed in which engagement with a DBCI is influenced by the DBCI itself (content and delivery), the context (the setting in which the DBCI is used and the population using it) and the behaviour that the DBCI is targeting. The context and "mechanisms of action" may moderate the influence of the DBCI on engagement. Engagement, in turn, moderates the influence of the DBCI on those mechanisms of action. In the research literature, engagement with DBCIs has been conceptualised in terms of both experience and behaviour and sits within a complex system involving the DBCI, the context of use, the mechanisms of action of the DBCI and the target behaviour.

Keywords: Behaviour change interventions; Conceptual framework; Digital; Engagement; Systematic review; eHealth; mHealth.

Conflict of interest statement

Ethical responsibilities of authors

All authors have approved the final manuscript and agree with its submission to Translational Behavioural Medicine. All authors have contributed equally to the scientific work and are responsible and accountable for the results. We confirm that this manuscript has not been previously published (partly or in full) and that the manuscript is not being simultaneously submitted elsewhere. We confirm that the data have not been previously reported elsewhere and that no data have been fabricated or manipulated to support our conclusions. No data, text or theories by others are presented as if they were the authors’ own. The authors have full control of all data, which are accessible upon request.

Conflict of interest

OP, SM and AB declare that they have no conflict of interest. RW undertakes research and consultancy and receives fees for speaking from companies that develop and manufacture smoking cessation medications.

Figures

Fig 1
Fig 1
PRISMA flow diagram of the study selection process [42]
Fig 2
Fig 2
Conceptual framework of direct and indirect influences on engagement with DBCIs. Transparent boxes indicate concepts. Concepts can be defined as abstract ideas that are derived from either direct or indirect evidence [149]. Blue boxes indicate attributes of concepts. Attributes can be defined as properties that characterise a concept [150]. Solid black arrows indicate relationships between concepts and attributes. Arrows with transparent heads indicate an influence of a concept.

References

    1. Rock Health. (2015). Digital health consumer adoption: 2015. Retrieved November 4, 2015, from .
    1. Fox S, Duggan M. Mobile health 2012. Pew Internet & American Life Project. 2012 Retrieved from .
    1. Kontos E, Blake KD, Chou W-YS, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. Journal of Medical Internet Research. 2014;16(7):e172. doi: 10.2196/jmir.3117.
    1. West R, Michie S. A Guide to Development and Evaluation of Digital Interventions in Healthcare. London: Silverback Publishing; 2016.
    1. Civljak M, Stead LF, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2013;7:CD007078.
    1. Whittaker, R., Borland, R., Bullen, C., Rb, L., Mcrobbie, H., & Rodgers, A. (2009). Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev, 4.
    1. Nair NK, Newton NC, Shakeshaft A, Wallace P, Teesson M. A systematic review of digital and computer-based alcohol intervention programs in primary care. Current Drug Abuse Reviews. 2015;8(2):111–118. doi: 10.2174/1874473708666150916113538.
    1. Liu F, Kong X, Cao J, Chen S, Li C, Huang J, et al. Mobile phone intervention and weight loss among overweight and obese adults: a meta-analysis of randomized controlled trials. Am J Epidemiol. 2015;181(5):337–348. doi: 10.1093/aje/kwu260.
    1. Muntaner, A., Vidal-Conti, J., & Palou, P. (2015). Increasing physical activity through mobile device interventions: a systematic review. Health Informatics Journal, 1–19. doi:10.1177/1460458214567004.
    1. Jones KR, Lekhak N, Kaewluang N. Using mobile phones and short message service to deliver self-management interventions for chronic conditions: a meta-review. Worldviews on Evidence-Based Nursing/Sigma Theta Tau International, Honor Society of Nursing. 2014;11(2):81–88. doi: 10.1111/wvn.12030.
    1. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. Journal of Medical Internet Research. 2011;13(3) doi: 10.2196/jmir.1772.
    1. Cobb NK, Graham AL, Bock BC, Papandonatos G, Abrams DB. Initial evaluation of a real-world Internet smoking cessation system. Nicotine & Tobacco Research. 2005;7(2):207–216. doi: 10.1080/14622200500055319.
    1. Tate DF, Wing RR, Winett R a. Using Internet technology to deliver a behavioral weight loss program. J Am Med Assoc. 2001;285(9):1172–1177. doi: 10.1001/jama.285.9.1172.
    1. Alexander GL, McClure JB, Calvi JH, Divine GW, Stopponi MA, Rolnick SJ, et al. A randomized clinical trial evaluating online interventions to improve fruit and vegetable consumption. Am J Public Health. 2010;100(2):319–326. doi: 10.2105/AJPH.2008.154468.
    1. The Cochrane Collaboration. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. [Updated March 2011]. (J. Higgins & S. Green, Eds.) 2011 Retrieved from .
    1. Krishnan A What are academic disciplines? NCRM Working Paper Series: ESRC National Centre for Research Methods (2009).
    1. Krishnan A Five strategies for practising interdisciplinarity. NCRM Working Paper Series: ESRC National Centre for Research Methods. (2009). Retrieved from .
    1. Csikszentmihalyi M. Flow: the Psychology of Optimal Performance. New York: Cambridge University Press; 1990.
    1. Danaher BG, Boles SM, Akers L, Gordon JS, Severson HH. Defining participant exposure measures in web-based health behavior change programs. Journal of Medical Internet Research. 2006;8(3):e15. doi: 10.2196/jmir.8.3.e15.
    1. Couper MP, Alexander GL, Zhang N, Little RJA, Maddy N, Nowak MA, et al. Engagement and retention: measuring breadth and depth of participant use of an online intervention. Journal of Medical Internet Research. 2010;12(4) doi: 10.2196/jmir.1430.
    1. Eysenbach G. The law of attrition. Journal of Medical Internet Research. 2005;7(1):e11. doi: 10.2196/jmir.7.1.e11.
    1. Consumer Health Information Corporation. Motivating patients to use smartphone health apps. (2015). Retrieved August 10, 2015, from .
    1. Bennett GG, Glasgow RE. The delivery of public health interventions via the Internet: actualizing their potential. Annu Rev Public Health. 2009;30:273–292. doi: 10.1146/annurev.publhealth.031308.100235.
    1. Brouwer W, Oenema A, Raat H, Crutzen R, De Nooijer J, De Vries NK, Brug J. Characteristics of visitors and revisitors to an Internet-delivered computer-tailored lifestyle intervention implemented for use by the general public. Health Educ Res. 2010;25(4):585–595. doi: 10.1093/her/cyp063.
    1. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JEWC. Persuasive system design does matter: a systematic review of adherence to web-based interventions. Journal of Medical Internet Research. 2012;14(6) doi: 10.2196/jmir.2104.
    1. Schubart JR, Stuckey HL, Ganeshamoorthy A, Sciamanna CN. Chronic health conditions and internet behavioral interventions: a review of factors to enhance user engagement. Computers, Informatics, Nursing. 2011;29(2):81–92. doi: 10.1097/NCN.0b013e3182065eed.
    1. Huberman MA, Miles MB. Handbook of Qualitative Research. Thousand Oaks: SAGE Publications; 1994. Data management and analysis methods; pp. 428–443.
    1. O’Brien HL, Toms EG. What is user engagement? A conceptual framework for defining user engagement with technology. J Am Soc Inf Sci Technol. 2008;59(6):938–955. doi: 10.1002/asi.20801.
    1. Ritterband LM, Thorndike FP, Cox DJ, Kovatchev BP, Gonder-Frederick L a. A behavior change model for internet interventions. Ann Behav Med. 2009;38:18–27. doi: 10.1007/s12160-009-9133-4.
    1. Short CE, Rebar AL, Plotnikoff RC, Vandelanotte C. Designing engaging online behaviour change interventions: a proposed model of user engagement. The European Health Psychologist. 2015;17(1):32–38.
    1. Centre for Reviews and Dissemination, U. of Y. Systematic reviews: CRD’s guidance for undertaking reviews in healthcare. (K. Khan, G. Ter Riet, J. Glanville, A. Sowden, & J. Kleijnen, Eds.) (2008). Retrieved from .
    1. Dixon-Woods M, Cavers D, Agarwal S, Annandale E, Arthur A, Harvey J, et al. Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol. 2006;6:35. doi: 10.1186/1471-2288-6-35.
    1. Dixon-Woods M, Bonas S, Booth A. How can systematic reviews incorporate qualitative research? A critical perspective. Qual Res. 2006;6(1):27–44. doi: 10.1177/1468794106058867.
    1. Entwistle V, Firnigl D, Ryan M, Francis J, Kinghorn P. Which experiences of health care delivery matter to service users and why? A critical interpretive synthesis and conceptual map. Journal of Health Services Research & Policy. 2012;17(2):70–78. doi: 10.1258/jhsrp.2011.011029.
    1. Kazimierczak KA, Skea ZC, Dixon-Woods M, Entwistle VA, Feldman-Stewart D, N’Dow JMO, MacLennan SJ. Provision of cancer information as a “support for navigating the knowledge landscape”: findings from a critical interpretive literature synthesis. Eur J Oncol Nurs. 2013;17(3):360–369. doi: 10.1016/j.ejon.2012.10.002.
    1. Morrison L, Yardley L, Powell J, Michie S. What design features are used in effective e-health interventions? A review using techniques from critical interpretive synthesis. Telemedicine and e-Health. 2012;18(2):137–144. doi: 10.1089/tmj.2011.0062.
    1. Anderson PJ. Assessment and development of executive function (EF) during childhood. Child Neuropsychology. 2002;8(2):71–82. doi: 10.1076/chin.8.2.71.8724.
    1. Thomson Reuters. EndNote X7. Philadelphia, USA 2013.
    1. Byrt T, Bishop J, Carlin JB. Bias, prevalence and kappa. J Clin Epidemiol. 1993;46(5):423–429. doi: 10.1016/0895-4356(93)90018-V.
    1. Dixon-Woods M, Sutton A, Shaw R, Miller T, Smith J, Young B, et al. Appraising qualitative research for inclusion in systematic reviews: a quantitative and qualitative comparison of three methods. Journal of Health Services Research & Policy. 2007;12(1):42–47. doi: 10.1258/135581907779497486.
    1. Barbour RS. Checklists for improving rigour in qualitative research: a case of the tail wagging the dog? Br Med J. 2001;322:1115–1117. doi: 10.1136/bmj.322.7294.1115.
    1. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097.
    1. Brown E, Cairns P. A grounded investigation of game immersion. In CHI ‘04 Extended Abstracts on Human Factors in Computing Systems. (2004) 1297–1300. ACM. doi:10.1145/985921.986048.
    1. Bianchi-Berthouze N, Kim WW, Patel D. Does body movement engage you more in digital game play? and why? In Proceedings of the International Conference on Affective Computing and Intelligent Interaction. 2007: 102–113.
    1. Chou JC, Hung C, Hung Y. Design factors of mobile games for increasing gamers’ flow experiences. In Proceedings of the 2014 I.E. ICMIT. 2014:137–139.
    1. Sharek D, Wiebe E. Measuring video game engagement through the cognitive and affective dimensions. Simulation & Gaming. 2014;45:569–592. doi: 10.1177/1046878114554176.
    1. Zhou T. Understanding the effect of flow on user adoption of mobile games. Personal & Ubiquitous Computing. 2013;17:741–748. doi: 10.1007/s00779-012-0613-3.
    1. Oh J, Sundar SS. How does interactivity persuade? An experimental test of interactivity on cognitive absorption, elaboration, and attitudes. J Commun. 2015;65:213–236. doi: 10.1111/jcom.12147.
    1. Bouvier P, Lavoue E, Sehaba K. Defining engagement and characterizing engaged-behaviors in digital gaming. Simulation & Gaming. 2014;45(4–5):491–507. doi: 10.1177/1046878114553571.
    1. Schønau-Fog H, Bjørner T. “Sure, I would like to continue”: a method for mapping the experience of engagement in video games. Bull Sci Technol Soc. 2012;32(5):405–412. doi: 10.1177/0270467612469068.
    1. Jennett C, Cox AL, Cairns P, Dhoparee S, Epps A, Tijs T, Walton A. Measuring and defining the experience of immersion in games. International Journal of Human-Computer Studies. 2008;66(9):641–661. doi: 10.1016/j.ijhcs.2008.04.004.
    1. McClure JB, Shortreed SM, Bogart A, Derry H, Riggs K, St John J, et al. The effect of program design on engagement with an internet-based smoking intervention: randomized factorial trial. Journal of Medical Internet Research. 2013;15(3):e69. doi: 10.2196/jmir.2508.
    1. Voils CI, King HA, Maciejewski ML, Allen KD, Yancy WS, Jr, Shaffer JA. Approaches for informing optimal dose of behavioral interventions. Ann Behav Med. 2014;48:392–401. doi: 10.1007/s12160-014-9618-7.
    1. Wang J, Sereika SM, Chasens ER, Ewing LJ, Matthews JT, Burke LE. Effect of adherence to self-monitoring of diet and physical activity on weight loss in a technology-supported behavioral intervention. Patient Preference and Adherence. 2012;6:221–226. doi: 10.2147/PPA.S28889.
    1. Calleja G. Digital game involvement. Games & Culture. 2007;2(3):236–260. doi: 10.1177/1555412007306206.
    1. Lin JC-C. Online stickiness: its antecedents and effect on purchasing intention. Behav Inform Technol. 2007;26(6):507–516. doi: 10.1080/01449290600740843.
    1. Han JY, Kim J-H, Yoon HJ, Shim M, McTavish FM, Gustafson DH. Social and psychological determinants of levels of engagement with an online breast cancer support group: posters, lurkers, and non-users. J Health Commun. 2012;17(3):356–371. doi: 10.1080/10810730.2011.585696.
    1. Burns CG, Fairclough SH. Use of auditory event-related potentials to measure immersion during a computer game. Int J Hum Comput Stud. 2015;73:107–114. doi: 10.1016/j.ijhcs.2014.09.002.
    1. Chiang Y-T, Lin SSJ, Cheng C-Y, Liu EZ-F. Exploring online game players’ flow experiences and positive affect. The Turkish Online Journal of Educational Technology. 2011;10(1):106–114.
    1. Chung J, Gardner HJ. Temporal presence variation in immersive computer games. International Journal of Human-Computer Interaction. 2012;28(8):511–529. doi: 10.1080/10447318.2011.627298.
    1. Fang X, Zhang J, Chan SS. Development of an instrument for studying flow in computer game play. International Journal of Human-Computer Interaction. 2013;29(7):456–470. doi: 10.1080/10447318.2012.715991.
    1. Harmat L, Manzano ÖD, Theorell T, Högman L, Fischer H, Ullén F. Physiological correlates of the flow experience during computer game playing. Int J Psychophysiol. 2015;97:1–7. doi: 10.1016/j.ijpsycho.2015.05.001.
    1. Hilvert-Bruce Z, Rossouw PJ, Wong N, Sunderland M, Andrews G. Adherence as a determinant of effectiveness of internet cognitive behavioural therapy for anxiety and depressive disorders. Behav Res Ther. 2012;50(7–8):463–468. doi: 10.1016/j.brat.2012.04.001.
    1. Lefebvre RC, Tada Y, Hilfiker SW, Baur C. The assessment of user engagement with eHealth content: the eHealth engagement scale. J Comput-Mediat Commun. 2010;15:666–681. doi: 10.1111/j.1083-6101.2009.01514.x.
    1. Martey RM, Kenski K, Folkestad J, Feldman L, Gordis E, Shaw A, et al. Measuring game engagement: multiple methods and construct complexity. Simulation & Gaming. 2014;45:528–547. doi: 10.1177/1046878114553575.
    1. Morrison L, Moss-Morris R, Michie S, Yardley L. Optimizing engagement with Internet-based health behaviour change interventions: comparison of self-assessment with and without tailored feedback using a mixed methods approach. Br J Health Psychol. 2014;19:839–855. doi: 10.1111/bjhp.12083.
    1. O’Brien HL, Toms EG. The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science & Technology. 2010;61(1):50–69. doi: 10.1002/asi.21229.
    1. Bossen D, Buskermolen M, Veenhof C, de Bakker D, Dekker J. Adherence to a web-based physical activity intervention for patients with knee and/or hip osteoarthritis: a mixed method study. Journal of Medical Internet Research. 2013;15(10):e223. doi: 10.2196/jmir.2742.
    1. Geraghty AWA, Torres LD, Leykin YAN, Mun RF. Understanding attrition from international internet health interventions: a step towards global eHealth. Health Promot Int. 2012;28(3):442–452. doi: 10.1093/heapro/das029.
    1. Arden-Close EJ, Smith E, Bradbury K, Morrison L, Dennison L, Michaelides D, Yardley L. A visualization tool to analyse usage of web-based interventions: the example of positive online weight reduction (POWeR) Journal of Medical Internet Research. 2015;2(1):e8.
    1. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. Journal of Medical Internet Research. 2013;15(4):e32. doi: 10.2196/jmir.2283.
    1. Chen Z, Koh PW, Ritter PL, Lorig K, Bantum EOC, Saria S. Dissecting an online intervention for cancer survivors: four exploratory analyses of internet engagement and its effects on health status and health behaviors. Health Educ Behav. 2015;42(1):32–45. doi: 10.1177/1090198114550822.
    1. Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression. Journal of Medical Internet Research. 2009;11(2):e13. doi: 10.2196/jmir.1194.
    1. Crutzen R, Cyr D, de Vries NK. The role of user control in adherence to and knowledge gained from a website: randomized comparison between a tunneled version and a freedom-of-choice version. Journal of Medical Internet Research. 2012;14(2):e45. doi: 10.2196/jmir.1922.
    1. Cussler EC, Teixeira PJ, Going SB, Houtkooper LB, Metcalfe LL, Blew RM, et al. Maintenance of weight loss in overweight middle-aged women through the internet. Obesity. 2008;16(5):1052–1060. doi: 10.1038/oby.2008.19.
    1. Davies C, Corry K, Van Itallie A, Vandelanotte C, Caperchione C, Mummery WK. Prospective associations between intervention components and website engagement in a publicly available physical activity website: the case of 10,000 steps Australia. Journal of Medical Internet Research. 2012;14(1) doi: 10.2196/jmir.1792.
    1. Dennison L, Morrison L, Lloyd S, Phillips D, Stuart B, Williams S, et al. Does brief telephone support improve engagement with a web-based weight management intervention? Randomized controlled trial. Journal of Medical Internet Research. 2014;16(3):e95. doi: 10.2196/jmir.3199.
    1. Glasgow RE, Christiansen SM, Kurz D, King DK, Woolley T, Faber AJ, et al. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. Journal of Medical Internet Research. 2011;13(1) doi: 10.2196/jmir.1391.
    1. Manwaring JL, Bryson SW, Goldschmidt AB, Winzelberg AJ, Luce KH, Wilfley DE, Taylor CB. Do adherence variables predict outcome in an online program for the prevention of eating disorders? J Consult Clin Psychol. 2008;76(2):341–346. doi: 10.1037/0022-006X.76.2.341.
    1. Morrison C, Doherty G. Analyzing engagement in a web-based intervention platform through visualizing log-data. Journal of Medical Internet Research. 2014;16(11) doi: 10.2196/jmir.3575.
    1. Murray E, White IR, Varagunam M, Godfrey C, Khadjesari Z, McCambridge J. Attrition revisited: adherence and retention in a web-based alcohol trial. Journal of Medical Internet Research. 2013;15(8):e162. doi: 10.2196/jmir.2336.
    1. Poirier J, Cobb NK. Social influence as a driver of engagement in a web-based health intervention. Journal of Medical Internet Research. 2012;14(1):e36. doi: 10.2196/jmir.1957.
    1. Cugelman B, Thelwall M, Dawes P. Online interventions for social marketing health behavior change campaigns: a meta-analysis of psychological architectures and adherence factors. Journal of Medical Internet Research. 2011;13(1):e17. doi: 10.2196/jmir.1367.
    1. Henshaw H, McCormack A, Ferguson MA. Intrinsic and extrinsic motivation is associated with computer-based auditory training uptake, engagement, and adherence for people with hearing loss. Front Psychol. 2015;6:1–13. doi: 10.3389/fpsyg.2015.01067.
    1. Hsu C-L, Lu H-P. Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management. 2004;41:853–868. doi: 10.1016/j.im.2003.08.014.
    1. McCabe MP, Price E. Attrition from an internet-based psychological intervention for erectile dysfunction: who is likely to drop out? Journal of Sex & Marital Therapy. 2009;35(5):391–401. doi: 10.1080/00926230903065963.
    1. Postel MG, de Haan HA, ter Huurne ED, van der Palen J, Becker ES, de Jong CAJ. Attrition in web-based treatment for problem drinkers. Journal of Medical Internet Research. 2011;13(4):e117. doi: 10.2196/jmir.1811.
    1. Johansson O, Michel T, Andersson G, Paxling B. Experiences of non-adherence to internet-delivered cognitive behavior therapy: a qualitative study. Internet Interventions. 2015;2:137–142. doi: 10.1016/j.invent.2015.02.006.
    1. Sainsbury K, Mullan B, Sharpe L. Dissemination of an online theory-based intervention to improve gluten-free diet adherence in coeliac disease: the relationship between acceptability, effectiveness, and attrition. International Journal of Behavioral Medicine. 2015;22:356–364. doi: 10.1007/s12529-014-9416-4.
    1. VanDeMark NR, Burrell NR, Lamendola WF, Hoich CA, Berg NP, Medina E. An exploratory study of engagement in a technology-supported substance abuse intervention. Substance Abuse Treatment, Prevention, and Policy. 2010;5(10):1–14.
    1. Al-Asadi AM, Klein B, Meyer D. Pretreatment attrition and formal withdrawal during treatment and their predictors: an exploratory study of the anxiety online data. Journal of Medical Internet Research. 2014;16(6):e152. doi: 10.2196/jmir.2989.
    1. Habibović M, Cuijpers P, Alings M, van der Voort P, Theuns D, Bouwels L, et al. Attrition and adherence in a WEB-based distress management program for implantable cardioverter defibrillator patients (WEBCARE): randomized controlled trial. Journal of Medical Internet Research. 2014;16(2) doi: 10.2196/jmir.2809.
    1. Hebert EA, Vincent N, Lewycky S, Walsh K. Attrition and adherence in the online treatment of chronic insomnia. Behavioral Sleep Medicine. 2010;8(3):141–150. doi: 10.1080/15402002.2010.487457.
    1. Neve MJ, Collins CE, Morgan PJ. Dropout, nonusage attrition, and pretreatment predictors of nonusage attrition in a commercial web-based weight loss program. Journal of Medical Internet Research. 2010;12(4):e69. doi: 10.2196/jmir.1640.
    1. Nicholas J, Proudfoot J, Parker G, Gillis I, Burckhardt R, Manicavasagar V, Smith M. The ins and outs of an online bipolar education program: a study of program attrition. Journal of Medical Internet Research. 2010;12(5):e57. doi: 10.2196/jmir.1450.
    1. Richardson A, Graham AL, Cobb N, Xiao H, Mushro A, Abrams D, Vallone D. Engagement promotes abstinence in a web-based cessation intervention: cohort study. Journal of Medical Internet Research. 2013;15(1):e14. doi: 10.2196/jmir.2277.
    1. Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst. 2009;24(28):486–501.
    1. Hong J-C, Chiu P-Y, Shih H-F, Lin P-S. Computer self-efficacy, competitive anxiety and flow state: escaping from firing online game. The Turkish Online Journal of Educational Technology. 2012;11(3):70–76.
    1. Meischke H, Lozano P, Zhou C, Garrison MM, Christakis D. Engagement in “my child’s asthma”, an interactive web-based pediatric asthma management intervention. Int J Med Inform. 2011;80(11):765–774. doi: 10.1016/j.ijmedinf.2011.08.002.
    1. Boyle EA, Connolly TM, Hainey T, Boyle JM. Engagement in digital entertainment games: a systematic review. Comput Hum Behav. 2012;28(3):771–780. doi: 10.1016/j.chb.2011.11.020.
    1. Haines-Saah RJ, Kelly MT, Oliffe JL, Bottorff JL. Picture Me Smokefree: a qualitative study using social media and digital photography to engage young adults in tobacco reduction and cessation. Journal of Medical Internet Research. 2015;17(1):e27. doi: 10.2196/jmir.4061.
    1. Kim YH, Kim DJ, Wachter K. A study of mobile user engagement (MoEN): engagement motivations, perceived value, satisfaction, and continued engagement intention. Decis Support Syst. 2013;56:361–370. doi: 10.1016/j.dss.2013.07.002.
    1. Ludden GD, van Rompay TJ, Kelders SM, van Gemert-Pijnen JE. How to increase reach and adherence of web-based interventions: a design research viewpoint. Journal of Medical Internet Research. 2015;17(7):e172. doi: 10.2196/jmir.4201.
    1. Parks AC. A case for the advancement of the design and study of online positive psychological interventions. J Posit Psychol. 2014;9(6):502–508. doi: 10.1080/17439760.2014.936969.
    1. Horsch C, Lancee J, Beun RJ, Neerincx MA, Brinkman W-P. Adherence to technology-mediated insomnia treatment: a meta-analysis, interviews, and focus groups. Journal of Medical Internet Research. 2015;17(9):e214. doi: 10.2196/jmir.4115.
    1. Funk KL, Stevens VJ, Appel LJ, Bauck A, Brantley PJ, Champagne CM, et al. Associations of internet website use with weight change in a long-term weight loss maintenance program. Journal of Medical Internet Research. 2010;12(3):e29. doi: 10.2196/jmir.1504.
    1. Graham AL, Cha S, Cobb NK, Fang Y, Niaura RS, Mushro A. Impact of seasonality on recruitment, retention, adherence, and outcomes in a web-based smoking cessation intervention: randomized controlled trial. Journal of Medical Internet Research. 2013;15(11):e249. doi: 10.2196/jmir.2880.
    1. Peels DA, Bolman C, Golsteijn RHJ, De Vries H, Mudde AN, van Stralen MM, Lechner L. Differences in reach and attrition between web-based and print-delivered tailored interventions among adults over 50 years of age: clustered randomized trial. Journal of Medical Internet Research. 2012;14(6):e179. doi: 10.2196/jmir.2229.
    1. Steinberg DM, Levine EL, Lane I, Askew S, Foley PB, Puleo E, Bennett GG. Adherence to self-monitoring via interactive voice response technology in an eHealth intervention targeting weight gain prevention among black women: randomized controlled trial. Journal of Medical Internet Research. 2014;16(4):e114. doi: 10.2196/jmir.2996.
    1. Strecher VJ, McClure J, Alexander G, Chakraborty B, Nair V, Konkel J, et al. The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial. Journal of Medical Internet Research. 2008;10(5):e36. doi: 10.2196/jmir.1002.
    1. Wanner M, Martin-Diener E, Bauer G, Braun-Fahrländer C, Martin BW. Comparison of trial participants and open access users of a web-based physical activity intervention regarding adherence, attrition, and repeated participation. Journal of Medical Internet Research. 2010;12(1) doi: 10.2196/jmir.1361.
    1. Jahangiry L, Shojaeizadeh D, Montazeri A, Najafi M. Adherence and attrition in a web-based lifestyle intervention for people with metabolic syndrome. Iranian Journal of Public Health. 2014;43(9):1248–1258.
    1. Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors. Journal of Medical Internet Research. 2013;15(2):e37. doi: 10.2196/jmir.2281.
    1. Mahmassani HS, Chen RB, Huang Y, Williams D, Contractor N. Time to play? Activity engagement in multiplayer online role-playing games. Transportation Research Record: Journal of the Transportation Research Board. 2010;2157:129–137. doi: 10.3141/2157-16.
    1. Ferguson MA, Henshaw H. Computer and internet interventions to optimize listening and learning for people with hearing loss: accessibility, use, and adherence. Am J Audiol. 2015;24:338–343. doi: 10.1044/2015_AJA-14-0090.
    1. Weston A, Morrison L, Yardley L, Van Kleek M, Weal M. Measurements of engagement in mobile behavioural interventions? In Digital Health. 2015:1–8.
    1. Donovan E, Mahapatra PD, Green TC, Chiauzzi E, Mchugh K, Hemm A, et al. Efficacy of an online intervention to reduce alcohol-related risks among community college students. Addiction Research & Theory. 2015;23(5):437–447. doi: 10.3109/16066359.2015.1043625.
    1. Khadjesari Z, Murray E, Kalaitzaki E, White IR, McCambridge J, Thompson SG, et al. Impact and costs of incentives to reduce attrition in online trials: two randomized controlled trials. Journal of Medical Internet Research. 2011;13(1):e26. doi: 10.2196/jmir.1523.
    1. An LC, Perry CL, Lein EB, Klatt C, Farley DM, Bliss RL, et al. Strategies for increasing adherence to an online smoking cessation intervention for college students. Nicotine & Tobacco Research. 2006;8(December):S7–S12. doi: 10.1080/14622200601039881.
    1. Brouwer W, Kroeze W, Crutzen R, de Nooijer J, de Vries NK, Brug J, Oenema A. Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review. Journal of Medical Internet Research. 2011;13(1):e2. doi: 10.2196/jmir.1639.
    1. Cairns P, Cox AL, Day M, Martin H, Perryman T. Who but not where: the effect of social play on immersion in digital games. Int J Hum Comput Stud. 2013;71:1069–1077. doi: 10.1016/j.ijhcs.2013.08.015.
    1. Morris RR, Schueller SM, Picard RW. Efficacy of a web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: randomized controlled trial. Journal of Medical Internet Research. 2015;17(3):e72. doi: 10.2196/jmir.4167.
    1. Crutzen R, Cyr D, Larios H, Ruiter RAC, De Vries NK. Social presence and use of internet-delivered interventions: a multi-method approach. PLoS One. 2013;8(2):e57067. doi: 10.1371/journal.pone.0057067.
    1. Ben-Zeev D, Kaiser SM, Krzos I. Remote “hovering” with individuals with psychotic disorders and substance use: feasibility, engagement, and therapeutic alliance with a text-messaging mobile interventionist. Journal of Dual Diagnosis. 2014;10(4):197–203. doi: 10.1080/15504263.2014.962336.
    1. Miller, A. S., Cafazzo, J. A., & Seto, E. (2014). A game plan: gamification design principles in mHealth applications for chronic disease management. Health Informatics Journal, 1–10. doi:10.1177/1460458214537511.
    1. Brigham TJ. An introduction to gamification: adding game elements for engagement. Medical Reference Services Quarterly. 2015;34(4):471–480. doi: 10.1080/02763869.2015.1082385.
    1. Richardson CR, Buis LR, Janney AW, Goodrich DE, Sen A, Hess ML, et al. An online community improves adherence in an internet-mediated walking program. Part 1: results of a randomized controlled trial. Journal of Medical Internet Research. 2010;12(4):e71. doi: 10.2196/jmir.1338.
    1. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. Preventive. 2005;40:54–59.
    1. Irvine AB, Russell H, Manocchia M, Mino DE, Cox Glassen T, Morgan R, et al. Mobile-web app to self-manage low back pain: randomized controlled trial. Journal of Medical Internet Research. 2015;17(1) doi: 10.2196/jmir.3130.
    1. Lin H, Wu X. Intervention strategies for improving patient adherence to follow-up in the era of mobile information technology: a systematic review and meta-analysis. PLoS One. 2014;9(8):e104266. doi: 10.1371/journal.pone.0104266.
    1. Kok G, Bockting C, Burger H, Smit F, Riper H. Mobile cognitive therapy: adherence and acceptability of an online intervention in remitted recurrently depressed patients. Internet Interventions. 2014;1:65–73. doi: 10.1016/j.invent.2014.05.002.
    1. van den Berg MH, Ronday HK, Peeters AJ, Voogt-van der Harst EM, Munneke M, Breedveld FC, Vliet Vlieland TPM. Engagement and satisfaction with an internet-based physical activity intervention in patients with rheumatoid arthritis. Rheumatology. 2007;46(3):545–552. doi: 10.1093/rheumatology/kel341.
    1. Stark S, Snetselaar L, Piraino B, Stone A, Kim S, Hall B, Burke LE. PDA self-monitoring adherence rates in two dialysis dietary intervention pilot studies: BalanceWise-HD and BalanceWise-PD. J Ren Nutr. 2011;21(6):492–498. doi: 10.1053/j.jrn.2010.10.026.
    1. Mohr DC, Duffecy J, Ho J, Kwasny M, Cai X, Burns MN, Begale M. A randomized controlled trial evaluating a manualized TeleCoaching protocol for improving adherence to a web-based intervention for the treatment of depression. PLoS One. 2013;8(8):e70086. doi: 10.1371/journal.pone.0070086.
    1. Klein M, Mogles N, Wissen AV. Intelligent mobile support for therapy adherence and behavior change. J Biomed Inform. 2014;51:137–151. doi: 10.1016/j.jbi.2014.05.005.
    1. McCambridge J, Kalaitzaki E, White IR, Khadjesari Z, Murray E, Linke S, et al. Impact of length or relevance of questionnaires on attrition in online trials: randomized controlled trial. Journal of Medical Internet Research. 2011;13(4):e96. doi: 10.2196/jmir.1733.
    1. Helander E, Kaipainen K, Korhonen I, Wansink B. Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study. Journal of Medical Internet Research. 2014;16(4):e109. doi: 10.2196/jmir.3084.
    1. Whiteside U, Lungu A, Richards J, Simon GE, Clingan S, Siler J, et al. Designing messaging to engage patients in an online suicide prevention intervention: survey results from patients with current suicidal ideation. Journal of Medical Internet Research. 2014;16(2):e42. doi: 10.2196/jmir.3173.
    1. Jennings M. Proceedings of the 2000 ACM SIGCPR Conference on Computer Personnel Research. New York: ACM; 2000. Theory and models for creating engaging and immersive e-commerce websites; pp. 77–85.
    1. Park N, Min K, Jin SA, Kang S. Effects of pre-game stories on feelings of presence and evaluation of computer games. Int J Hum Comput Stud. 2010;68:822–833. doi: 10.1016/j.ijhcs.2010.07.002.
    1. Hwang M-Y, Hong J-C, Hao Y-W, Jong J-T. Elders’ usability, dependability, and flow experiences on embodied interactive video games. Educ Gerontol. 2011;37(8):715–731. doi: 10.1080/03601271003723636.
    1. Chapman P, Selvarajah S, Webster J. Engagement in multimedia training systems. In Proceedings of the 32nd Hawaii International Conference on System Sciences 1999; 0: 1–9. Washington, DC: IEEE. doi:10.1109/HICSS.1999.772808.
    1. Liu S, Liao H, Pratt JA. Impact of media richness and flow on e-learning technology acceptance. Comput Educ. 2009;52:599–607. doi: 10.1016/j.compedu.2008.11.002.
    1. Miller AS, Cafazzo JA, Seto E. A game plan: gamification design principles in mHealth applications for chronic disease management. Health Informatics Journal. 2014
    1. Lieberman DZ. Effects of a personified guide on adherence to an online program for alcohol abusers. Cyberpsychology & Behavior. 2006;9(5):603–607. doi: 10.1089/cpb.2006.9.603.
    1. Bellg AJ, Borrelli B, Resnick B, Hecht J, Minicucci DS, Ory M, et al. Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. Health Psychol. 2004;23(5):443–451. doi: 10.1037/0278-6133.23.5.443.
    1. Borrelli B. The assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials. J Public Health Dent. 2011;71:S52–S63. doi: 10.1111/j.1752-7325.2011.00233.x.
    1. Ubhi HK, Michie S, Kotz D, Wong WC, West R. A mobile app to aid smoking cessation: preliminary evaluation of SmokeFree28. Journal of Medical Internet Research. 2015;17(1):e17. doi: 10.2196/jmir.3479.
    1. Chinn PL, Kramer MK. Theory and nursing: a systematic approach. St. Louis: Mosby-Year Book; 1991.
    1. Fiannaca A, La Rosa M, Rizzo R, Urso A, Gaglio S. An ontology design methodology for Knowledge-Based systems with application to bioinformatics. In Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 I.E. Symposium. 2012:85–91.
    1. Weber R. Evaluating and developing theories in the information systems discipline. J Assoc Inf Syst. 2012;13(1):1–30.
    1. O’Brien HL, Toms EG. The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science & Technology. 2010;61(1):50–69. doi: 10.1002/asi.21229.
    1. Stone AA, Shiffman S. Ecological momentary assessment (EMA) in behavorial medicine. Ann Behav Med. 1994;16(3):199–202.
    1. Haukkala, A., Uutela, A., Vartiainen, E., Mcalister, A., & Knekt, P. (2000). Depression and smoking cessation: the role of motivation and self-efficacy. Addict Behav, 25. doi:10.1016/S0306-4603(98)00125-7.
    1. Linde JA, Jeffery RW, Levy RL, Sherwood NE, Utter J, Pronk NP, Boyle RG. Binge eating disorder, weight control self-efficacy, and depression in overweight men and women. Int J Obes. 2004;28(3):418–425. doi: 10.1038/sj.ijo.0802570.

Source: PubMed

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