Behavioral Components and Their Tailoring in Participatory Health Interventions for Precision Prevention

TL;DR

This paper examines how behavioral components are used in digital health interventions to promote personalized health outcomes, highlighting gaps in tailoring and effectiveness.

Contribution

The study provides a systematic mapping of behavioral intervention functions in participatory health interventions for precision prevention.

Findings

Persuasion, incentivisation, education, modeling, and coercion are commonly used across three interventions.

Restrictions and environmental restructuring are not used in any of the interventions.

Training is used in only one intervention and enablement in two.

Abstract

Objective
: To study which behavioral components are implemented within participatory health interventions for precision prevention, specifically how they are realized as part of the interventions and how the tailoring of the interventions is implemented.

Methods
: We selected three case studies of participatory health interventions for precision prevention for three different target groups (children, parents, older adults with chronic conditions). One author with a background in psychology mapped the interventions and the digital functionalities to the 9 intervention functions of the behavioral change wheel (education, persuasion, incentivisation, coercion, training, enablement, modeling, environmental restructuring, restrictions).

Results
: While the intervention functions persuasion, incentivisation, education, modeling and coercion are implemented in all three interventions under considerations, two techniques (restrictions, and environmental restructuring) were not implemented in any of the three solutions. Training was only applied in one application and enablement in two interventions. We identified significant evidence gaps in both the tailoring process and the effectiveness of behavior change techniques in precision prevention.

Conclusion
: We conclude that there is a need for more focused studies on the effects of behavior interventions functions in digital health interventions and for design guidelines to improve these interventions for personalized health outcomes, thereby advancing precision prevention in digital health.