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Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions.

Heather Cole-Lewis¹, Nnamdi Ezeanochie¹, Jennifer Turgiss¹

¹Johnson and Johnson Health and Wellness Solutions, Inc, New Brunswick, NJ, United States.

JMIR Formative Research

|October 12, 2019

Summary

This summary is machine-generated.

This study proposes precise definitions for user engagement in digital behavior change interventions (DBCI), distinguishing between health behavior engagement ("Big E") and intervention engagement ("Little e"). Clear measurement of these engagement types is crucial for effective health behavior change.

Keywords:

digital behavior change intervention engagement health behavior health determinants measurements user engagement

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Area of Science:

Digital Health
Behavioral Science
Health Informatics

Background:

Digital behavior change interventions (DBCI) lack precise engagement definitions, hindering measurement and evaluation.
Inconsistent definitions of "engagement" impede the assessment of DBCI effectiveness for health behavior modulation.

Purpose of the Study:

To propose discrete definitions for user engagement types in DBCI.
To emphasize the importance of precise engagement measurement for intervention efficacy.
To present a framework for measuring engagement to inform DBCI design and evaluation.

Main Methods:

Conceptualized two primary engagement categories: health behavior engagement ("Big E") and DBCI engagement ("Little e").
Bifurcated DBCI engagement into user interaction with intervention features and behavior change components.
Developed a framework and model for measuring DBCI engagement and its impact on health behavior.

Main Results:

DBCI engagement ("Little e") encompasses user interaction with intervention features and behavior change techniques.
Health behavior engagement ("Big E") is contingent upon "Little e" and the quality of behavior change components.
The proposed framework models how "Little e" drives "Big E" for improved health outcomes.

Conclusions:

Precise measurement of distinct engagement types is vital for effective DBCI.
The proposed framework offers a structured approach to measuring engagement in DBCI.
This framework can identify gaps in intervention efficacy and guide the development of more effective DBCI.