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

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

  • 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
  • Overview
    • Background and objectives
    • Research challenges & solutions
    • Meta-analysis
    • Findings
    • Conclusions
  • 1. Background and Objectives
  • Examples of Online Interventions
    • Don’t start smoking
    • If you started, stop
    • Exercise more
    • Drink less alcohol
    • Eat more good food
    • Eat less bad food
  • Synthesis Research
    • 1. Meta-analysis: positive results
      • Portnoy et al., 2008
      • Wantland et al., 2004
    • 2. Systematic reviews: mixed and slightly positive
      • Norman et al. (2007)
      • Vandelanotte et al. (2007)
    • 3. Real-world evaluation: unclear outcomes
      • Evers et al. (2003)
      • Doshi et al. (2003)
      • Lin and Hullman (2005)
  • Research Objectives
    • Assess the efficacy of online interventions appropriate for public campaigns
    • Identify psychological design factors
    • Investigate the role of adherence (dose)
  • 2. Research Challenges & Solutions
  • A. Prior Studies not Generalizable to Public Campaign
    • Problem: Blend voluntary with mandatory behaviours (chronic disease management)
    • Solution: More voluntary and common interventions
  • B: Ambiguous Online Communication Models
    • Problem
      • Mass-Media (one-way)
      • Interpersonal (two-way)
    • Solution : Mass-Interpersonal
  • C: No Clear Design Guidelines on Online Behavioural Influence
    • Problem: Too complex. Too simple. Not quite right.
    • Solution: Communication Based Influence Components Model to integrate behavioural medicine and persuasion
  • Communication-Based Influence Components Model Framework to describe intervention psychology Cugelman, B. Thelwall, M. Dawes, P (2009)
  • 3. Meta-Analysis
  • Conducting the Meta-Analysis
    • Searched five databases + grey literature
    • Obtained 1,271 results
    • Retrieved 95 full text studies
    • Selected 31
        • Primary analysis: 30 interventions from 29 studies (N=17,524)
  • 4. Findings
  • Effect Sizes Overall: d=.194, p=.000, k=30 d
  • Effect Size by Intervention Duration d
  • 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
  • Relative Influence Components and Outcomes
  • Media Channel k % Across 30 Interventions Website & Email 20 66.7% Website 10 33.3%
  • Feedback Message k % Across 30 Interventions Tailoring 25 83.3% Personalization 12 40.0% Adaptation / Content matching 2 6.7%
  • Source Modifier k % Across 30 Interventions Attractiveness 5 16.7% Similarity 3 10.0% Credibility 1 3.3%
  • Source Encoding k % Across 30 Interventions Multiple Interactions 23 77% Single Interaction 3 10% Sequential Requests (Foot-in-the-door) 1 3%
  • 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%
  • 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%
  • 5. Conclusions
  • Conclusions
    • Efficacy: Reasonable impact, and comparable to print, though more affordable with broad/rapid reach
    • Psychology: Most sites goal orientated, possible influence component correlation
      • Communication Based Influence Components Model stood up across interventions
    • Dose: study adherence, intervention adherence and ES likely related. They may be explained by motivation
  • Thank you University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School http://cybermetrics.wlv.ac.uk [email_address]