The Psychology of Mass-Interpersonal Behavioural Change Websites
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Abstract
Background: As an interactive and widely-diffused media, the Internet promises health campaigners the potential to realize lower-costs, higher-impact campaigns. Despite many opportunities, health campaigners frequently apply mass-media communication models to public health interventions where interpersonal models may be more appropriate. Practitioners frequently treat the online component of mass health campaigns as just another one-to-many channel. Likewise, across the academic literature, social marketing authors rarely discuss using the Internet as more than another one-to-many communication channel.
Within the fields of e-health and persuasive technology, a growing body of research indicates that intervention websites can motivate persons to adopt healthy behaviours, such as quitting smoking, exercising more or eating better. Frequently, these online interventions are individually tailored programmes, resembling two-way interpersonal therapy. It is now conceivable that health campaigners can deploy mass-interpersonal campaigns, where online media are used to engage large populations in automated personal relationships, similar to relationships with doctors, councillors, trainers or friends.
Meta-analyses show that online interventions can outperform non web-based interventions; while web and computer-based interventions can improve health-related knowledge, attitudes, behavioural intentions and actions. These studies provide evidence that online interventions can be effective; however, the studies to date do not fully explain the range of psychology and design factors that may account for intervention success.
Objective: 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.
Methods: Following systematic review methods, papers were searched from 1999-2008 across five bibliographic databases. This resulted in 1,587 studies that were reduced to 29 relevant studies, which allocated 17,524 participants to 30 interventions.
The study examines relationships between behavioural outcomes, intervention design factors and behavioural influence techniques. A secondary analysis examines relationships between study and intervention adherence, and associations with behavioural outcomes.
Results: Interventions were selected and grouped on the basis of three control condition. Using a random effects model, the standardized mean difference effect sizes (d), 95% confidence intervals (CI), and number of interventions (k) are as follows: waitlist/placebo (d=.282, CI=.170 to .393, p=.000, k=18); static websites (d=.162, CI=.006 to .318, p=.041, k=8); major print interventions (d=-.110, CI=-.343 to .123, p=.353, k=4).
Across the various online interventions, the top three behavioural determinants targeted by interventions are knowledge, intention and social norms. The top three behavioural change techniques included providing information on consequences of behaviour, goal setting and providing feedback on performance. Results indicate a significant correlation between study adherence, intervention adherence and behavioural outcomes, while interventions consistently loosing impact over time.
Conclusions: Overall, online interventions, modelled on interpersonal interaction, work. Effective interventions follow common patters and the Communication-Based Influence Components Model is an effective framework for discovering their design patterns, and developing new interventions.
Within the fields of e-health and persuasive technology, a growing body of research indicates that intervention websites can motivate persons to adopt healthy behaviours, such as quitting smoking, exercising more or eating better. Frequently, these online interventions are individually tailored programmes, resembling two-way interpersonal therapy. It is now conceivable that health campaigners can deploy mass-interpersonal campaigns, where online media are used to engage large populations in automated personal relationships, similar to relationships with doctors, councillors, trainers or friends.
Meta-analyses show that online interventions can outperform non web-based interventions; while web and computer-based interventions can improve health-related knowledge, attitudes, behavioural intentions and actions. These studies provide evidence that online interventions can be effective; however, the studies to date do not fully explain the range of psychology and design factors that may account for intervention success.
Objective: 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.
Methods: Following systematic review methods, papers were searched from 1999-2008 across five bibliographic databases. This resulted in 1,587 studies that were reduced to 29 relevant studies, which allocated 17,524 participants to 30 interventions.
The study examines relationships between behavioural outcomes, intervention design factors and behavioural influence techniques. A secondary analysis examines relationships between study and intervention adherence, and associations with behavioural outcomes.
Results: Interventions were selected and grouped on the basis of three control condition. Using a random effects model, the standardized mean difference effect sizes (d), 95% confidence intervals (CI), and number of interventions (k) are as follows: waitlist/placebo (d=.282, CI=.170 to .393, p=.000, k=18); static websites (d=.162, CI=.006 to .318, p=.041, k=8); major print interventions (d=-.110, CI=-.343 to .123, p=.353, k=4).
Across the various online interventions, the top three behavioural determinants targeted by interventions are knowledge, intention and social norms. The top three behavioural change techniques included providing information on consequences of behaviour, goal setting and providing feedback on performance. Results indicate a significant correlation between study adherence, intervention adherence and behavioural outcomes, while interventions consistently loosing impact over time.
Conclusions: Overall, online interventions, modelled on interpersonal interaction, work. Effective interventions follow common patters and the Communication-Based Influence Components Model is an effective framework for discovering their design patterns, and developing new interventions.
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