Advertisement

Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions

      This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The first part of this paper critically reflects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote “effective engagement,” rather than simply more engagement, with “effective engagement” defined empirically as sufficient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efficient combinations of measures to develop and test multidimensional models of engagement. The final section of the paper reflects on how interventions can be designed to fit the user and their specific needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively refine the intervention to meet user requirements.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Yardley L.
        • Patrick K.
        • Choudhury T.
        • Michie S.
        Current issues and future directions for digital intervention research.
        Am J Prev Med. 2016;
        • Eysenbach G.
        The law of attrition.
        J Med Internet Res. 2005; 7: e11https://doi.org/10.2196/jmir.7.1.e11
        • Kohl L.F.M.
        • Crutzen R.
        • de Vries N.
        Online prevention aimed at lifestyle behaviours: a systematic review of reviews.
        J Med Internet Res. 2013; 7: e146https://doi.org/10.2196/jmir.2665
        • Bouvier P.
        • Lavoue E.
        • Sehaba K.
        Defining engagement and characterizing engaged-behaviors in digital gaming.
        Simul Gaming. 2014; 45: 491-507https://doi.org/10.1177/1046878114553571
        • O’Brien H.L.
        • Toms E.G.
        What is user engagement? A conceptual framework for defining user engagement with technology.
        J Assoc Inf Sci Technol. 2008; 59: 938-955https://doi.org/10.1002/asi.20801
        • Morrison L.G.
        • Yardley L.
        • Powell J.
        • Michie S.
        What design features are used in effective e-health interventions? A review using techniques from critical interpretive synthesis.
        Telemed J E Health. 2012; 18: 1-9https://doi.org/10.1089/tmj.2011.0062
        • Mohr D.C.
        • Schueller S.M.
        • Montague E.
        • Burns M.N.
        • Rashidi P.
        The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth Interventions.
        J Med Internet Res. 2014; 16: e146https://doi.org/10.2196/jmir.3077
        • Fogg B.
        A behavior model for persuasive design.
        Proceedings of the 4th International Conference on Persuasive Technology. 2009; : 40https://doi.org/10.1145/1541948.1541999
        • Oinas-Kukkonen H.
        • Harjumaa M.
        Persuasive system design: key issues, process model, and system features.
        Commun Assoc Inf Syst. 2009; 24: 485-500
        • Ritterband L.M.
        • Thorndike F.P.
        • Cox D.J.
        • Kovatchev B.P.
        • Gonder-Frederick L.A.
        A behavior change model for Internet interventions.
        Ann Behav Med. 2009; 38: 18-27https://doi.org/10.1007/s12160-009-9133-4
        • Crutzen R.
        The behavioral intervention technology model and intervention mapping: the best of both worlds.
        J Med Internet Res. 2014; 16: e188https://doi.org/10.2196/jmir.3620
        • Cugelman B.
        • Thelwall M.
        • Dawes P.
        Online interventions for social marketing health behavior change campaigns: a meta-analysis of psychological architectures and adherence factors.
        J Med Internet Res. 2011; 13: e17https://doi.org/10.2196/jmir.1367
        • Kelders S.M.
        • Kok R.N.
        • Ossebaard H.C.
        • Van Gemert-Pijnen J.E.
        Persuasive system design does matter: a systematic review of adherence to web-based interventions.
        J Med Internet Res. 2012; 14: e152https://doi.org/10.2196/jmir.2104
        • Schubart J.R.
        • Stuckey H.L.
        • Ganeshamoorthy A.
        • Sciamanna C.N.
        Chronic health conditions and Internet behavioral interventions: a review of factors to enhance user engagement.
        Comput Inform Nurs. 2011; 29: 81-92https://doi.org/10.1097/NCN.0b013e3182155274
        • Hekler E.B.
        • Michie S.
        • Rivera
        • et al.
        Developing and refining models and theories suitable for digital health interventions.
        Am J Prev Med. 2016;
        • Webb T.L.
        • Joseph J.
        • Yardley L.
        • Michie S.
        Using the Internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy.
        J Med Internet Res. 2010; 12: e4https://doi.org/10.2196/jmir.1376
        • Roshanov P.S.
        • Fernandes N.
        • Wilczynski J.M.
        • et al.
        Features of effective computerized clinical decision support systems: meta-regression of 162 randomized trials.
        BMJ. 2013; : 346https://doi.org/10.1136/bmj.f657
        • Sherrington A.
        • Newham J.J.
        • Bell R.
        • et al.
        Systematic review and meta-analysis of Internet-delivered interventions providing personalized feedback for weight loss in overweight and obese adults.
        Obes Rev. 2016; 17: 541-551https://doi.org/10.1111/obr/12396
        • Maher C.A.
        • Lewis L.K.
        • Ferrar K.
        • et al.
        Are health behavior change interventions that use online social networks effective? A systematic review.
        J Med Internet Res. 2014; 16: e40https://doi.org/10.2196/jmir.2952
        • Munson S.A.
        • Krupka E.
        • Richardson C.
        • Resnick P.
        Effects of public commitments and accountability in a technology-supported physical activity intervention.
        Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015; : 1135-1144https://doi.org/10.1145/2702123.2702524
        • Danaher B.G.
        • Seeley J.R.
        Methodological issues in research on web-based behavioral interventions.
        Ann Behav Med. 2009; 38: 28-39https://doi.org/10.1007/s12160-009-9129-0
        • Strecher V.J.
        • McClure J.
        • Alexander G.
        • et al.
        The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial.
        J Med Internet Res. 2008; 10: e36https://doi.org/10.2196/jmir.1002
        • Van Gemert-Pijnen J.E.
        • Kelders S.M.
        • Bohlmeijer E.T.
        Understanding the usage of content in a mental health intervention for depression: an analysis of log data.
        J Med Internet Res. 2014; 16: e27https://doi.org/10.2196/jmir.2991
        • Schneider H.
        • Moser K.
        • Butz A.
        • Alt F.
        Understanding the mechanics of persuasive system design: a mixed-method theory-driven analysis of freeletics.
        Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016; : 309-320https://doi.org/10.1145/2858036.2858290
      1. Laurie J, Blandford A. Making time for mindfulness. Int J Med Inform. In press. Online March 2, 2016. http://dx.doi.org/10.1016/j.ijmedinf.2016.02.010.

        • Graham A.L.
        • Papandonatos G.D.
        • Erar B.
        • Standton C.A.
        Use of an online smoking cessation community promotes abstinence: results of propensity score weighting.
        Health Psychol. 2015; 34: 1286-1295https://doi.org/10.1037/hea0000278
        • Sun N.
        • Rau P.P.-L.
        • Ma L.
        Understanding lurkers in online communities: a literature review.
        Comput Human Behav. 2014; 38: 110-117https://doi.org/10.1016/j.chb.2014.05.022
        • Blandford A.
        Semi-structured qualitative studies.
        in: Soegaard M. Dam R.F. 3rd ed. The Encyclopedia of Human-Computer Interaction. 52. The Interaction Design Foundation, Aarhus, Denmark2014 (Accessed November 5, 2015)
        • Lefebvre C.R.
        • Tada Y.
        • Hilfiker S.W.
        • Baur C.
        The assessment of user engagement with eHealth content: the eHealth engagement scale.
        J Comput Commun. 2010; 15: 666-681https://doi.org/10.1111/j.1083-6101.2009.01514.x
        • Warmerdam L.
        • Riper H.
        • Klein M.
        • et al.
        Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project.
        Stud Health Technol Inform. 2012; 181: 339-343https://doi.org/10.3233/978-1-61499-121-2-339
        • McClure J.B.
        • Shortreed S.M.
        • Bogart A.
        • et al.
        The effect of program design on engagement with an Internet-based smoking intervention: randomized factorial trial.
        J Med Internet Res. 2013; 15: e69https://doi.org/10.2196/jmir.2508
        • Brindal E.
        • Freyne J.
        • Saunders I.
        • et al.
        Features predicting weight loss in overweight or obese participants in a web-based intervention: randomized trial.
        J Med Internet Res. 2012; 14: e173https://doi.org/10.2196/jmir.2156
        • Morrison L.
        • Hargood C.
        • Lin S.X.
        • et al.
        Understanding usage of a hybrid website and smartphone app for weight management: a mixed-methods study.
        J Med Internet Res. 2014; 16: e201https://doi.org/10.2196/jmir.3579
        • Murray E.
        • Hekler E.B.
        • Andersson G.
        • et al.
        Evaluating digital health interventions: key questions and approaches.
        Am J Prev Med. 2016;
        • Gonzalez V.M.
        • Dulin P.
        Comparison of a smartphone app for alcohol use disorders with an Internet-based intervention plus bibliotherapy: a pilot study.
        J Consult Clin Psychol. 2015; 83: 335-345https://doi.org/10.1037/a0038620
        • Lippke S.
        • Corbet J.M.
        • Lange D.
        • Parschau L.
        • Schwarzer R.
        Intervention engagement moderates the dose-response relationships in a dietary intervention.
        Dose Response. 2016; 14https://doi.org/10.1177/1559325816637515
        • Mohr D.C.
        • Schueller S.M.
        • Riley W.T.
        • et al.
        Trials of intervention principles: evaluation methods for evolving behavioral intervention technologies.
        J Med Internet Res. 2015; 17: e166https://doi.org/10.2196/jmir.4391
        • Patrick K.
        • Hekler E.B.
        • Estrin D.
        • et al.
        Rapid rate of technological development and its implications for research on digital behavior change interventions.
        Am J Prev Med. 2016;
        • Fairclough S.H.
        • Gilleade K.
        • Ewing K.C.
        • Roberts J.
        Capturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation.
        Int J Auton Adapt Commun Syst. 2013; 6: 63https://doi.org/10.1504/IJAACS.2013.050694
        • Crutzen R.
        • Cyr D.
        • Larios H.
        • Ruiter R.A.C.
        • de Vries N.K.
        Social presence and use of Internet-delivered interventions: a multi-method approach.
        PLoS One. 2013; 8: e57067https://doi.org/10.1371/journal.pone.0057067
        • Bradbury K.
        • Dennison L.
        • Little P.
        • Yardley L.
        Using mixed methods to develop and evaluate an online weight management intervention.
        Br J Health Psychol. 2015; 20: 45-55https://doi.org/10.1111/bjhp.12125
        • van Gemert-Pijnen J.E.
        • Nijland N.
        • van Limburg M.
        • et al.
        A holistic framework to improve the uptake and impact of eHealth technologies.
        J Med Internet Res. 2011; 13: e111https://doi.org/10.2196/jmir.1672
        • Yardley L.
        • Morrison L.
        • Bradbury K.
        • Muller I.
        • Yardley L.
        The person-based approach to intervention development: application to digital health-related behavior change interventions.
        J Med Internet Res. 2015; 17: e30https://doi.org/10.2196/jmir.4055
        • Vandelanotte C.
        • Muller A.M.
        • Short C.E.
        • et al.
        Past, present and future of eHealth and mHealth research to improve physical activity and dietary behaviors.
        J Nutr Educ Behav. 2016; 48: 219-228https://doi.org/10.1016/j.neb.2015.12.006
        • Mackert M.
        • Champlin S.
        • Holton A.
        • Muñoz I.
        • Damásio M.
        eHealth and health literacy: a research methodology review.
        J Comput-Mediat Comm. 2014; 12: 516-528https://doi.org/10.1111/jcc4.12044
        • Barry M.
        • D׳Eath M.
        • Sixsmith J.
        Interventions for improving population health literacy: insights from a rapid review of the evidence.
        J Health Commun. 2013; 18: 1507-1522https://doi.org/10.1080/10810730.2013.840699
        • Glasgow R.
        Interactive media for diabetes self-management: issues in maximizing public health impact.
        Med Decis Making. 2010; 30: 745-758https://doi.org/10.1177/0272989X10385845
        • Jacobs R.J.
        • Lou J.Q.
        • Ownby R.L.
        • Caballero J.
        A systematic review of eHealth interventions to improve health literacy.
        Health Informatics J. 2016; 22: 81-98https://doi.org/10.1177/1460458214534092
        • Rowsell A.
        • Muller I.
        • Murray E.
        • et al.
        Views of people with high and low levels of health literacy about a digital intervention to promote physical activity for diabetes: a qualitative study in five countries.
        J Med Internet Res. 2015; 17: e230https://doi.org/10.2196/jmir.4999
        • Thomas J.G.
        • Bond D.S.
        Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: implications for JITAI optimization.
        Health Psychol. 2015; 34: 1261-1267https://doi.org/10.1037/hea0000304
        • Riley W.T.
        • Serrano K.J.
        • Nilsen W.
        • Atienza A.A.
        Mobile and wireless technologies in health behavior and the potential for intensively adaptive interventions.
        Curr Opin Psychol. 2015; 5: 67-71https://doi.org/10.1016/j.copsyc.2015.03.024
        • Lustria M.L.A.
        • Noar S.M.
        • Cortese J.
        • et al.
        A meta-analysis of web-delivered tailored health behavior change interventions.
        J Health Commun. 2013; 18: 1039-1069https://doi.org/10.1080/10810730.2013.768727
        • Deci E.L.
        • Ryan R.M.
        Self-determination theory.
        in: Van Lange P.A.M. Kruglanski A.W. Higgins E.T. Handbook of Theories of Social Psychology. Elsevier, New York, NY2011: 416-433
        • Ryan R.M.
        • Deci E.L.
        Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.
        Am Psychol. 2000; 55: 68https://doi.org/10.1037/0003-066X.55.1.68
        • Kreuter M.W.
        • Strecher V.J.
        • Glassman B.
        One size does not fit all: the case for tailoring print materials.
        Ann Behav Med. 1999; 21: 276-283https://doi.org/10.1007/BF02895958
        • Kreuter M.W.
        • Farrell D.W.
        • Olevitch L.R.
        • Brennan L.K.
        Tailoring Health Messages: Customizing Communication With Computer Technology.
        Routledge, New York2013
        • Baumeister H.
        • Reichler L.
        • Munzinger M.
        • Lin J.
        The impact of guidance on Internet-based mental health interventions—a systematic review.
        Internet Interv. 2014; 1: 205-215https://doi.org/10.1016/j.invent.2014.08.003
        • Kleiboer A.
        • Donker T.
        • Seekles W.
        • et al.
        Randomized controlled trial on the role of support in Internet-based problem solving therapy for depression and anxiety.
        Behav Res Ther. 2015; 72: 63-71https://doi.org/10.1016/j.brat.2015.06.013
        • Newman M.G.
        • Szkodny L.E.
        • Llera S.J.
        • Przeworski A.
        A review of technology-assisted self-help and minimal contact therapies for anxiety and depression: is human contact necessary for therapeutic efficacy?.
        Clin Psychol Rev. 2011; 31: 89-103https://doi.org/10.1016/j.cpr.2010.09.008
        • Riper H.
        • Blankers M.
        • Hadiwijaya H.
        • et al.
        Effectiveness of guided and unguided low-intensity Internet interventions for adult alcohol misuse: a meta-analysis.
        PLoS One. 2014; 9: e99912https://doi.org/10.1371/journal.pone.0099912
        • Zachariae R.
        • Lyby M.S.
        • Ritterband L.M.
        • O’Toole M.S.
        Efficacy of Internet-delivered cognitive-behavioral therapy for insomnia—a systematic review and meta-analysis of randomized controlled trials.
        Sleep Med Rev. 2015; 30: 1-10https://doi.org/10.1016/j.smrv.2015.10.004
        • Knowles S.
        • Toms G.
        • Sanders C.
        • et al.
        Qualitative meta-synthesis of user experience of computerized therapy for depression and anxiety.
        PLoS One. 2014; 9: e84323https://doi.org/10.1371/journal.pone.0084323
        • Santer M.
        • Muller I.
        • Yardley L.
        • et al.
        Supporting self-care for families of children with eczema with a web-based intervention plus health care professional support: pilot randomized controlled trial.
        J Med Internet Res. 2014; 16: e70https://doi.org/10.2196/jmir.3035
        • Mohr D.C.
        • Cuijpers P.
        • Lehman K.
        Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions.
        J Med Internet Res. 2011; 13: e30https://doi.org/10.2196/jmir.1602
        • van der Vaart R.
        • Witting M.
        • Riper H.
        • et al.
        Blending online therapy into regular face-to-face therapy for depression: content, ratio and preconditions according to patients and therapists using a Delphi study.
        BMC Psychiatry. 2014; 14: 355https://doi.org/10.1186/s12888-014-0355-z
        • Smit E.S.
        • Hoving C.
        • Cox V.C.M.
        • de Vries H.
        Influence of recruitment strategy on the reach and effect of a web-based multiple tailored smoking cessation intervention among Dutch adult smokers.
        Health Educ Res. 2012; 27: 191-199https://doi.org/10.1093/her/cyr099
        • Spring B.
        • Duncan J.M.
        • Janke E.A.
        • et al.
        Integrating technology into standard weight loss treatment.
        JAMA Intern Med. 2013; 173: 105https://doi.org/10.1001/jamainternmed.2013.1221