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

Overview1. Background and objectives2. Research challenges & solutions3. Meta-analysis4. Findings5. 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

1. Assess the efficacy of online interventions appropriate for public campaigns

2. Identify psychological design factors

3. 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

(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

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

SourceInterpreter

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AudienceInterpreter

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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

-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

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

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

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

Media Channel SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

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k% Across 30

Interventions

Website & Email 20 66.7%

Website 10 33.3%

Feedback Message SourceInterpreter

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AudienceInterpreter

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k% Across 30 Interventions

Tailoring 25 83.3%

Personalization 12 40.0%

Adaptation / Content matching 2 6.7%

Source Modifier SourceInterpreter

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k% Across 30 Interventions

Attractiveness 5 16.7%

Similarity 3 10.0%

Credibility 1 3.3%

Source Encoding SourceInterpreter

InterventionMessage

AudienceInterpreter

FeedbackMessage

Media ChannelContext

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k% Across 30 Interventions

Multiple Interactions 23 77%

Single Interaction 3 10%

Sequential Requests (Foot-in-the-door) 1 3%

Intervention Message SourceInterpreter

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AudienceInterpreter

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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 SourceInterpreter

InterventionMessage

AudienceInterpreter

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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

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

Thank you

University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School

http://cybermetrics.wlv.ac.uk

b.cugelman@wlv.ac.uk