DEFINING WHAT WORKS IN TAILORING: A META-ANALYSIS OF COMPUTERTAILORED INTERVENTIONS FOR HEALTH BEHAVIOR CHANGE

By Paul Krebs, James Prochaska, J. Rossi. Preventive Medicine, 2010

816 citations recorded by the source corpus.

Objective Computer-tailored interventions have become increasingly common for facilitating improvement in behaviors related to chronic disease and health promotion. A sufficient number of outcome studies from these interventions are now available to facilitate the quantitative analysis of effect sizes, permitting moderator analyses that were not possible with previous systematic reviews. Method The present study employs meta-analytic techniques to assess the mean effect for 88 computer-tailored interventions published between 1988 and 2009 focusing on four health behaviors: smoking cessation, physical activity, eating a healthy diet, and receiving regular mammography screening. Effect sizes were calculated using Hedges g. Study, tailoring, and demographic moderators were examined by analyzing between-group variance and meta-regression. Results Clinically and statistically significant overall effect sizes were found across each of the four behaviors. While effect sizes decreased after intervention completion, dynamically tailored interventions were found to have increased efficacy over time as compared with tailored interventions based on one assessment only. Study effects did not differ across communication channels nor decline when up to three behaviors were identified for intervention simultaneously. Conclusion This study demonstrates that computer-tailored interventions have the potential to improve health behaviors and suggests strategies that may lead to greater effectiveness of these techniques.

Subfields: Applied Psychology, Physiology

View Paper: https://www.semanticscholar.org/paper/9c3c3c4ca7a738881c22b3db5df72130f985899f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

1. Robert J. DeRubeis
2. S. Noar
3. L. Liebenberg
4. J. Amirkhan
5. M. Ungar
6. Bonnie Auyeung

In Physiology

1. S. Noar

Citations in the corpus

• The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “This form of personalization and customization has proven effective in contexts like smoking ces­ sation, breast cancer screening, and physical activity 214,215 .”
• A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions (2013, background): “These findings are largely consistent with recent research (Krebs et al., 2010; Prochaska, 2008).”