the psychology of mass-interpersonal behavioural change websites: a meta-analysis
DESCRIPTION
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.TRANSCRIPT
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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 Conference17-18 September 2009
Toronto, Canada
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Overview1. Background and objectives2. Research challenges & solutions3. Meta-analysis4. Findings5. Conclusions
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1. Background and Objectives
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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
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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)
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Research Objectives
1. Assess the efficacy of online interventions appropriate for public campaigns
2. Identify psychological design factors
3. Investigate the role of adherence (dose)
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2. Research Challenges &
Solutions
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A. Prior Studies not Generalizable to Public Campaign
• Problem: Blend voluntary with mandatory behaviours (chronic disease management)
• Solution: More voluntary and common interventions
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B: Ambiguous Online Communication Models
• Problem– Mass-Media (one-way)– Interpersonal (two-way)
• Solution: Mass-Interpersonal
(On
e-W
ay
) O
ne
-to
One
Impersonal
Many
Mass Media
(Tw
o-W
ay
) O
ne
-wit
h Interpersonal Mass Interpersonal
one-with-one
one-to-one
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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
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Communication-Based Influence Components Model
SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
Framework to describe intervention psychology
Cugelman, B. Thelwall, M. Dawes, P (2009)
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3. Meta-Analysis
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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)
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4. Findings
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Effect Sizes
-0.4-0.3
-0.2-0.1
0.00.1
0.20.3
0.4
Survey Only (Waitlistor Placebo)
Website Print (Major)
Overall: d=.194, p=.000, k=30
d
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Effect Size by Intervention Duration
-0.4-0.3
-0.2-0.10.0
0.10.2
0.30.40.5
0.60.7
One-time From 2 days to 1month
Beyond 1 to 4months
Beyond 4 to 7months
Beyond 7 to 13months
d
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Dose: Three Variables
COR r=.37, p<.000, k=5
Intervention
Adherence
OutcomeEffect Size
StudyAdherence MR r=.481, p=.006, k=28
MR r=.455, p=.109, k=13COR r=.240, p<.000, k=9
COR: Correlation effect sizeMR: Meta-regression estimate
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Relative Influence Components and Outcomes
876543210
Relative Behavioural Determinants (sum)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
Eff
ect
Siz
e (
d)
Print (Major)
Website
Survey Only (Waitlist or Placebo)
ControlMediaSimple
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Media Channel SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
k% Across 30
Interventions
Website & Email 20 66.7%
Website 10 33.3%
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Feedback Message SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
k% Across 30 Interventions
Tailoring 25 83.3%
Personalization 12 40.0%
Adaptation / Content matching 2 6.7%
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Source Modifier SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
k% Across 30 Interventions
Attractiveness 5 16.7%
Similarity 3 10.0%
Credibility 1 3.3%
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Source Encoding SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
k% Across 30 Interventions
Multiple Interactions 23 77%
Single Interaction 3 10%
Sequential Requests (Foot-in-the-door) 1 3%
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Intervention Message SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
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%
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Audience Interpreter SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
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%
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5. Conclusions
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Conclusions
1. Efficacy: Reasonable impact, and comparable to print, though more affordable with broad/rapid reach
2. Psychology: Most sites goal orientated, possible influence component correlation
– Communication Based Influence Components Model stood up across interventions
3. Dose: study adherence, intervention adherence and ES likely related. They may be explained by motivation
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Thank you
University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School
http://cybermetrics.wlv.ac.uk