Just-In-Time Adaptive Interventions for Weight Management Among Adults With Excess Body Weight: Scoping Review

Area of Science:

• Behavioral Science
• Digital Health
• Obesity Research

Background:

• Just-in-time adaptive interventions (JITAIs) leverage real-time monitoring for personalized support in lifestyle behavior change.
• These interventions demonstrate significant potential for improving weight management outcomes in adults with excess body weight.

Purpose of the Study:

• To provide a comprehensive overview of the application and development of JITAIs for adult weight management.
• To identify common characteristics, intervention components, and outcomes of JITAI-based weight management strategies.

Main Methods:

• A scoping review was conducted following the Arksey and O'Malley framework and PRISMA-ScR guidelines.
• Data were extracted from eight electronic databases and gray literature, with two independent reviewers performing the extraction.
• Nonlinear, inductive qualitative content analysis was employed to synthesize findings from 35 included studies.

Main Results:

• Included studies focused on dietary behavior (71.4%), physical activity (57.1%), and self-weighing (48.6%).
• Intervention components included prompts, feedback, coping strategies, and educational information, with 31.4% utilizing machine learning.
• Positive outcomes observed include improvements in weight, waist circumference, BMI, blood pressure, physical activity, and dietary behaviors, with high retention rates (74-100%).

Conclusions:

• JITAIs show potential for improving lifestyle habits through timely, personalized interventions, but most studies lack theoretical grounding and consistent reporting.
• Despite challenges, JITAIs demonstrate positive effects on weight, physical activity, and diet, indicating their potential in weight management.
• Future research should prioritize standardized reporting, integration of behavioral theories and machine learning, and enhanced user engagement through passive sensing and adaptive feedback.