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Theoretical Perspectives of Adherence to Web-Based Interventions: a Scoping Review

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Abstract

Purpose

The purpose of this paper is to review the literature as this relates to theoretical perspectives of adherence to web-based interventions, drawing upon empirical evidence from the fields of psychology, business, information technology and health care.

Methods

A scoping review of the literature utilising principles outlined by Arksey and O’Malley was undertaken.

Results

Several relevant theoretical perspectives have emerged, eight of which are charted and discussed in this review. These are the Internet Intervention Model, Persuasive Systems Design, the ‘PERMA’ framework, the Support Accountability Model, the Model of User Engagement, the Technology Acceptance Model, the Unified Theory of Acceptance and Use of IT and the Conceptual Model of User Engagement.

Conclusions

The findings of the review indicate that an interdisciplinary approach, incorporating a range of technological, environmental and individual factors, may be needed in order to comprehensively explain user adherence to web-based interventions.

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References

  1. Bewick BM, Trusler K, Barkham M, Hill AJ, Cahill J, Mulhern B. The effectiveness of web-based interventions designed to decrease alcohol consumption—a systematic review. Prev Med. 2008;47(1):17–26.

    Article  PubMed  Google Scholar 

  2. Neve M, Morgan PJ, Jones P, Collins C. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev. 2010;11(4):306–21.

    Article  CAS  PubMed  Google Scholar 

  3. Copeland J, Martin G. Web-based interventions for substance use disorders: a qualitative review. J Subst Abus Treat. 2004;26(2):109–16.

    Article  Google Scholar 

  4. Powell J, Hamborg T, Stallard N, Burls A, McSorley J, Bennett K, et al. Effectiveness of a web-based cognitive-behavioral tool to improve mental well-being in the general population: randomized controlled trial. J Med Internet Res. 2013;15(1):e2.

    Article  PubMed Central  Google Scholar 

  5. Murray E, White IR, Varagunam M, Godfrey C, Khadjesari Z, McCambridge J. Attrition revisited: adherence and retention in a web-based alcohol trial. J Med Internet Res. 2013;15(8):83–93.

    Article  Google Scholar 

  6. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. 2012;14(6):e152.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wangberg SC, Bergmo TS, Johnsen J. Adherence in internet-based interventions. Patient Prefer Adherence. 2008;2:57–65.

    PubMed  PubMed Central  Google Scholar 

  9. Vandelanotte C, Müller AM, Short CE, Hingle M, Nathan N, Williams SL, et al. Past, present, and future of eHealth and mHealth research to improve physical activity and dietary behaviors. J Nutr Educ Behav. 2016;48(3):219–28. e1.

    Article  PubMed  Google Scholar 

  10. Prestwich A, Sniehotta FF, Whittington C, Dombrowski SU, Rogers L, Michie S. Does theory influence the effectiveness of health behavior interventions? Meta-analysis. Am Psychol Assoc; 2014.

  11. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res. 2004;6(4):e40.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Michie S, Johnston M, Francis J, Hardeman W, Eccles M. From theory to intervention: mapping theoretically derived behavioural determinants to behaviour change techniques. Appl Psychol. 2008;57(4):660–80.

    Article  Google Scholar 

  13. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2016:1–14.

  14. 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. J Med Internet Res. 2011;13(4):e117-e.

    Article  Google Scholar 

  15. Neil A, Batterham P, Christensen H, Bennett K, Griffiths K. Predictors of adherence by adolescents to a cognitive behavior therapy website in school and community-based settings. J Med Internet Res. 2009;11(1):e6.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rothert K, Strecher VJ, Doyle LA, Caplan WM, Joyce JS, Jimison HB, et al. Web-based weight management programs in an integrated health care setting: a randomized, controlled trial. Obesity. 2006;14(2):266–72.

    Article  PubMed  Google Scholar 

  17. Kelders SM, Van Gemert-Pijnen JE, Werkman A, Nijland N, Seydel ER. Effectiveness of a Web-based intervention aimed at healthy dietary and physical activity behavior: a randomized controlled trial about users and usage. J Med Internet Res. 2011;13(2):e32.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Balmford J, Borland R, Benda P. Patterns of use of an automated interactive personalized coaching program for smoking cessation. J Med Internet Res. 2008;10(5):e54.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schubart JR, Stuckey HL, Ganeshamoorthy A, Sciamanna CN. Chronic health conditions and internet behavioral interventions: a review of factors to enhance user engagement. CIN: Comput Inform Nurs. 2011;29(2):81–92.

    PubMed  Google Scholar 

  20. Brouwer W, Kroeze W, Crutzen R, de Nooijer J, de Vries NK, Brug J, et al. Which intervention characteristics are related to more exposure to internet-delivered healthy lifestyle promotion interventions? A systematic review. J Med Internet Res. 2011;13(1):e2.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Beatty L, Binnion C. A systematic review of predictors of, and reasons for, adherence to online psychological interventions. Int J Behav Med. 2016;23(6):776–94.

    Article  PubMed  Google Scholar 

  22. Rogers Everett M. Diffusion of innovations. New York. 1995;12.

  23. Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression: systematic review. J Med Internet Res. 2009;11(2):e13.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Christensen H, Mackinnon A. The law of attrition revisited. J Med Internet Res. 2006;8(3):e20.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cavanagh K. Turn on, tune in and [don’t] drop out: engagement, adherence, attrition, and alliance with internet-based interventions. In: Bennett-Levy J, Richards DA, Farrand P, Christensen H, Griffiths KM, Kavanaugh DJ, et al., editors. Oxford guide to low intensity CBT interventions. Oxford guides in cognitive behavioural therapy. New York: Oxford University Press; 2010. p. 227–33.

    Google Scholar 

  26. Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

    Article  Google Scholar 

  28. Maxwell JA. Qualitative research design: an interactive approach. Thousand Oaks: Sage publications; 2012.

    Google Scholar 

  29. Ritterband LM, Thorndike FP, Cox DJ, Kovatchev BP, Gonder-Frederick LA. A behavior change model for internet interventions. Ann Behav Med. 2009;38(1):18–27.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst. 2009;24(1):28.

    Google Scholar 

  31. Ludden GDS, van Rompay TJL, Kelders SM, van Gemert-Pijnen JEWC. How to increase reach and adherence of web-based interventions: a design research viewpoint. J Med Internet Res. 2015;17(7):e172-e.

    Article  Google Scholar 

  32. Short CE, Rebar AL, Plotnikoff RC, Vandelanotte C. Designing engaging online behaviour change interventions: a proposed model of user engagement. Eur Health Psychol. 2015;17(1):32–8.

    Google Scholar 

  33. Petty RE, Cacioppo JT. The elaboration likelihood model of persuasion. Communication and persuasion: Springer; 1986. p. 1–24.

  34. Beatty L, Binnion C. A systematic review of predictors of, and reasons for, adherence to online psychological interventions. Int J behav Med. 2016;23(6):776–94.

    Article  PubMed  Google Scholar 

  35. 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):938–55.

    Article  Google Scholar 

  36. Crutzen R, Cyr D, de Vries NK. Bringing loyalty to E-health: theory validation using three internet-delivered interventions. J Med Internet Res. 2011;13(3):107–17.

    Article  Google Scholar 

  37. Mohr D, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011;13(1):e30.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Davis FD Jr. A technology acceptance model for empirically testing new end-user information systems: theory and results. Cambridge: Massachusetts Institute of Technology; 1986.

    Google Scholar 

  39. 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. J Med Internet Res. 2014;16(3):e95.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003:425–78.

  41. Sun H, Zhang P. Causal relationships between perceived enjoyment and perceived ease of use: an alternative approach. J Assoc Inf Syst. 2006;7(9):24.

    Google Scholar 

  42. Attuquayefio S, Addo H. Review of studies with UTAUT as conceptual framework. Eur Sci J, ESJ. 2014;10(8).

  43. Taiwo AA, DOWNE AG. The theory of user acceptance and use of technology (UTAUT): a meta-analytic review of empirical findings. J Theor Appl Inf Technol. 2013;49(1).

  44. Khechine H, Lakhal S, Ndjambou P. A meta-analysis of the UTAUT model: eleven years later. Can J Adm Sci/Rev Can Sci Adm. 2016;33(2):138–52.

    Article  Google Scholar 

  45. Farrer LM, Griffiths KM, Christensen H, Mackinnon AJ, Batterham PJ. Predictors of adherence and outcome in internet-based cognitive behavior therapy delivered in a telephone counseling setting. Cogn Ther Res. 2014;38(3):358–67.

    Article  Google Scholar 

  46. Fogg BJ. Persuasive technologies. Commun ACM. 1999;42(5):27–9.

    Article  Google Scholar 

  47. Fogg BJ. Persuasive technology: using computers to change what we think and do. Ubiquity. 2002;2002:5.

    Article  Google Scholar 

  48. Fogg BJ, editor A behavior model for persuasive design. Proceedings of the 4th international Conference on Persuasive Technology; 2009: ACM.

  49. Wildeboer G, Kelders SM, van Gemert-Pijnen JEWC. The relationship between persuasive technology principles, adherence and effect of web-based interventions for mental health: a meta-analysis. Int J Med Inform. 2016;96:71–85.

    Article  PubMed  Google Scholar 

  50. Wildeboer L. Does the number of persuasive technology principles used in web-based interventions concerning mental health affect the effectiveness: a meta-analysis: University of Twente; 2015.

  51. Pohlmeyer A. Design for happiness. Interfaces. 2012;92(8–11).

  52. Seligman ME. Flourish: a visionary new understanding of happiness and well-being. Policy. 2011;27(3):60–1.

    Google Scholar 

  53. Titov N, Dear BF, Johnston L, Lorian C, Zou J, Wootton B, et al. Improving adherence and clinical outcomes in self- guided internet treatment for anxiety and depression: randomised controlled trial. PLoS ONE. 2013;8(7).

  54. Furmark T, Carlbring P, Hedman E, Sonnenstein A, Clevberger P, Bohman B, et al. Guided and unguided self-help for social anxiety disorder: randomised controlled trial. Br J Psychiatry. 2009;195(5):440–7.

    Article  PubMed  Google Scholar 

  55. Alfonsson S, Olsson E, Linderman S, Winnerhed S, Hursti T. Is online treatment adherence affected by presentation and therapist support? A randomized controlled trial. Comput Hum Behav. 2016;60:550–8.

    Article  Google Scholar 

  56. Mohr DC, Duffecy J, Ho J, Kwasny M, Cai X, Burns MN, et al. 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):1–9.

    Article  Google Scholar 

  57. Duffecy J, Sanford S, Wagner L, Begale M, Nawacki E, Mohr DC. Project onward: an innovative e-health intervention for cancer survivors. Psycho-Oncology. 2013;22(4):947–51.

    Article  PubMed  Google Scholar 

  58. Pilutti L, Dlugonski D, Sandroff B, Klaren R, Motl R. Randomized controlled trial of a behavioral intervention targeting symptoms and physical activity in multiple sclerosis. Mult Scler J. 2014;20(5):594–601.

    Article  CAS  Google Scholar 

  59. Schueller SM, Mohr DC, editors. Initial field trial of a coach-supported web-based depression treatment. Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare; 2015: ICST [Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering].

  60. Zarski A-C, Lehr D, Berking M, Riper H, Cuijpers P, Ebert DD. Adherence to internet-based mobile-supported stress management: a pooled analysis of individual participant data from three randomized controlled trials. J Med Internet Res. 2016;18(6).

  61. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading: Addison-Wesley; 1975.

  62. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci. 2000;46(2):186–204.

    Article  Google Scholar 

  63. King WR, He J. A meta-analysis of the technology acceptance model. Inf Manag. 2006;43(6):740–55.

    Article  Google Scholar 

  64. Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev. 2007;64(6):650–72.

    Article  PubMed  Google Scholar 

  65. Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010;43(1):159–72.

    Article  PubMed  Google Scholar 

  66. Ma Q, Liu L. The technology acceptance model: a meta-analysis of empirical findings. J Organ End User Comput [JOEUC]. 2004;16(1):59–72.

    Article  Google Scholar 

  67. Yousafzai SY, Foxall GR, Pallister JG. Technology acceptance: a meta-analysis of the TAM: part 1. J Model Manag. 2007;2(3):251–80.

    Article  Google Scholar 

  68. Murugesh-Warren A, Dubb S, Sudbury D, Nnajiuba U, Abdel-Gadir S, Caris J. An extension of the UTAUT 2 with a focus of age in healthcare: what do different ages want? Int J Integrat Care [IJIC]. 2015;15:1–3.

    Google Scholar 

  69. Csikszentmihalyi M. Flow and the psychology of discovery and invention. Harper Collins: New York; 1996.

    Google Scholar 

  70. Beardsley MC. Some persistent issues in aesthetics. The aesthetic point of view: selected essays 1982:285–7.

  71. Stephenson W. The play theory of mass communication. New Brunswick: Transaction Publishers; 1964.

  72. Toms EG. Information interaction: providing a framework for information architecture. J Assoc Inf Sci Technol. 2002;53(10):855–62.

    Article  Google Scholar 

  73. O’Brien H. Theoretical perspectives on user engagement. In: O’Brien H, Cairns P, editors. Why engagement matters: cross-disciplinary perspectives and innovations on user engagement with digital media. New York: Springer Publishing Company, Incorporated; 2016. p. 1–26.

  74. Short CE, Vandelanotte C, Dixon MW, Rosenkranz R, Caperchione C, Hooker C, et al. Examining participant engagement in an information technology-based physical activity and nutrition intervention for men: the manup randomized controlled trial. JMIR Res protoc. 2014;3(1):e2.

    Article  PubMed  PubMed Central  Google Scholar 

  75. O’Brien HL, Toms EG. Examining the generalizability of the User Engagement Scale (UES) in exploratory search. Inf Process Manag. 2013;49(5):1092–107.

    Article  Google Scholar 

  76. O’Brien H, Cairns P. Why engagement matters: cross-disciplinary perspectives and innovations on user engagement with digital media. New York: Springer Publishing Company, Incorporated; 2016.

    Google Scholar 

  77. Wiebe EN, Lamb A, Hardy M, Sharek D. Measuring engagement in video game-based environments: investigation of the user engagement scale. Comput Hum Behav. 2014;32:123–32.

    Article  Google Scholar 

  78. van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, et al. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13(4):e111.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Stegemann SK, Weg R, Ebenfeld L, Thiart H, Berking M, Funk B. Towards measuring user engagement in internet interventions for common mental disorders. In: Proceedings of the HCI 2012, the 26th BCS Conference on Human Computer Interaction, Birmingham, 12–14 September 2012. p. 1–4. Available from http://ewic.bcs.org/. Accessed 12 Dec 2016.

  80. Guertler D, Vandelanotte C, Kirwan M, Duncan MJ. Engagement and nonusage attrition with a free physical activity promotion program: the case of 10,000 steps Australia. J Med Internet Res. 2015;17(7):e176-e.

    Article  Google Scholar 

  81. Donkin L, Glozier N. Motivators and motivations to persist with online psychological interventions: a qualitative study of treatment completers. J Med Internet Res. 2012;14(3):284–95.

    Article  Google Scholar 

  82. Donkin L, Hickie IB, Christensen H, Naismith SL, Neal B, Cockayne NL, et al. Rethinking the dose-response relationship between usage and outcome in an online intervention for depression: randomized controlled trial. J Med Internet Res. 2013;15(10):e231.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Alfonsson, S. Treatment adherence in internet-based CBT. The effects of presentation, support and motivation. PhD [dissertation]. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine. Uppsala: Acta Universitatis Upsaliensis; 2016. Available from: Uppsala universitetsbibliotek.

  84. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med. 2016;51(5):833–42.

    Article  PubMed  Google Scholar 

  85. Kelders SM, Oinas-Kukkonen H, Oörni A, van Gemert-Pijnen JE. Health behavior change support systems as a research discipline; a viewpoint. Int J Med Inform. 2016;96:3–10.

    Article  PubMed  Google Scholar 

  86. Sundström C, Gajecki M, Johansson M, Blankers M, Sinadinovic K, Stenlund-Gens E, et al. Guided and unguided internet-based treatment for problematic alcohol use—a randomized controlled pilot trial. PLoS One. 2016;11(7):e0157817.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Johansson M, Sinadinovic K, Hammarberg A, Sundström C, Hermansson U, Andreasson S, et al. Web-based self-help for problematic alcohol use: a large naturalistic study. Int J Behav Med. 2016:1–11.

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Authors and Affiliations

  1. Department of Nursing and Health Care, School of Health Sciences, Waterford Institute of Technology, Waterford, Ireland

    Cathal Ryan & Michael Bergin

  2. School of Health Sciences, Waterford Institute of Technology, Waterford, Ireland

    John SG Wells

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  1. Cathal Ryan
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  2. Michael Bergin
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  3. John SG Wells
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Correspondence to Cathal Ryan.

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Funding

This review was funded through the Waterford Institute of Technology PhD Scholarship Programme.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Ryan, C., Bergin, M. & Wells, J.S. Theoretical Perspectives of Adherence to Web-Based Interventions: a Scoping Review. Int.J. Behav. Med. 25, 17–29 (2018). https://doi.org/10.1007/s12529-017-9678-8

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