The Psychology of Mass-Interpersonal Behavioural Change Websites: a meta-analysis

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This paper presents a meta-analysis that investigates psychological design factors that can explain the efficacy of online behavioural change interventions. It makes a clear distinction between mass-media, interpersonal and mixed, mass-interpersonal communications. To this end, a model, called ‘the Communication-Based Influence Components Model’, is used to synthesize behavioural change and persuasion taxonomies.

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The Psychology of Mass-Interpersonal Behavioural Change Websites: a meta-analysis

  1. 1. The Psychology of Mass-Interpersonal Behavioural Change Websites : a meta-analysis Brian Cugelman, Prof. Mike Thelwall, Prof. Phil Dawes University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School http://cybermetrics.wlv.ac.uk Medicine 2.0 Conference 17-18 September 2009 Toronto, Canada
  2. 2. Overview <ul><li>Background and objectives </li></ul><ul><li>Research challenges & solutions </li></ul><ul><li>Meta-analysis </li></ul><ul><li>Findings </li></ul><ul><li>Conclusions </li></ul>
  3. 3. 1. Background and Objectives
  4. 4. Examples of Online Interventions <ul><li>Don’t start smoking </li></ul><ul><li>If you started, stop </li></ul><ul><li>Exercise more </li></ul><ul><li>Drink less alcohol </li></ul><ul><li>Eat more good food </li></ul><ul><li>Eat less bad food </li></ul>
  5. 5. Synthesis Research <ul><li>1. Meta-analysis: positive results </li></ul><ul><ul><li>Portnoy et al., 2008 </li></ul></ul><ul><ul><li>Wantland et al., 2004 </li></ul></ul><ul><li>2. Systematic reviews: mixed and slightly positive </li></ul><ul><ul><li>Norman et al. (2007) </li></ul></ul><ul><ul><li>Vandelanotte et al. (2007) </li></ul></ul><ul><li>3. Real-world evaluation: unclear outcomes </li></ul><ul><ul><li>Evers et al. (2003) </li></ul></ul><ul><ul><li>Doshi et al. (2003) </li></ul></ul><ul><ul><li>Lin and Hullman (2005) </li></ul></ul>
  6. 6. Research Objectives <ul><li>Assess the efficacy of online interventions appropriate for public campaigns </li></ul><ul><li>Identify psychological design factors </li></ul><ul><li>Investigate the role of adherence (dose) </li></ul>
  7. 7. 2. Research Challenges & Solutions
  8. 8. A. Prior Studies not Generalizable to Public Campaign <ul><li>Problem: Blend voluntary with mandatory behaviours (chronic disease management) </li></ul><ul><li>Solution: More voluntary and common interventions </li></ul>
  9. 9. B: Ambiguous Online Communication Models <ul><li>Problem </li></ul><ul><ul><li>Mass-Media (one-way) </li></ul></ul><ul><ul><li>Interpersonal (two-way) </li></ul></ul><ul><li>Solution : Mass-Interpersonal </li></ul>
  10. 10. C: No Clear Design Guidelines on Online Behavioural Influence <ul><li>Problem: Too complex. Too simple. Not quite right. </li></ul><ul><li>Solution: Communication Based Influence Components Model to integrate behavioural medicine and persuasion </li></ul>
  11. 11. Communication-Based Influence Components Model Framework to describe intervention psychology Cugelman, B. Thelwall, M. Dawes, P (2009)
  12. 12. 3. Meta-Analysis
  13. 13. Conducting the Meta-Analysis <ul><li>Searched five databases + grey literature </li></ul><ul><li>Obtained 1,271 results </li></ul><ul><li>Retrieved 95 full text studies </li></ul><ul><li>Selected 31 </li></ul><ul><ul><ul><li>Primary analysis: 30 interventions from 29 studies (N=17,524) </li></ul></ul></ul>
  14. 14. 4. Findings
  15. 15. Effect Sizes Overall: d=.194, p=.000, k=30 d
  16. 16. Effect Size by Intervention Duration d
  17. 17. Dose: Three Variables COR r=.37, p<.000, k=5 Intervention Adherence Outcome Effect Size Study Adherence MR r= .481, p=.006, k=28 MR r=. 455, p=.109, k=13 COR r=.240, p<.000, k=9 COR: Correlation effect size MR: Meta-regression estimate
  18. 18. Relative Influence Components and Outcomes
  19. 19. Media Channel k % Across 30 Interventions Website & Email 20 66.7% Website 10 33.3%
  20. 20. Feedback Message k % Across 30 Interventions Tailoring 25 83.3% Personalization 12 40.0% Adaptation / Content matching 2 6.7%
  21. 21. Source Modifier k % Across 30 Interventions Attractiveness 5 16.7% Similarity 3 10.0% Credibility 1 3.3%
  22. 22. Source Encoding k % Across 30 Interventions Multiple Interactions 23 77% Single Interaction 3 10% Sequential Requests (Foot-in-the-door) 1 3%
  23. 23. Intervention Message Top 5 of 40 Behavioural Change Techniques k % Across 30 Interventions Provide information on consequences of behaviour in general 23 77% Goal setting (behaviour) 21 70% Provide feedback on performance 20 67% Prompt self-monitoring of behaviour 19 63% Provide instruction on how to perform the behaviour 18 60%
  24. 24. Audience Interpreter Top 5 of 12 Behavioural Determinants k % Across 30 Interventions Knowledge 30 100% Motivation and goals (Intention) 26 87% Social influences (Norms) 22 73% Beliefs about consequences 21 70% Skills 19 63%
  25. 25. 5. Conclusions
  26. 26. Conclusions <ul><li>Efficacy: Reasonable impact, and comparable to print, though more affordable with broad/rapid reach </li></ul><ul><li>Psychology: Most sites goal orientated, possible influence component correlation </li></ul><ul><ul><li>Communication Based Influence Components Model stood up across interventions </li></ul></ul><ul><li>Dose: study adherence, intervention adherence and ES likely related. They may be explained by motivation </li></ul>
  27. 27. Thank you University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School http://cybermetrics.wlv.ac.uk [email_address]

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