JMIR Publications

ABSTRACT

Background:

Just-In-Time-Adaptive-Interventions (JITAIs) use real-time monitoring to deliver personalized support at optimal moments, demonstrating potential for improving lifestyle behaviors in weight management.

Objective:

The paper aims to present an overview of how Just-In-Time Adaptive Interventions (JITAIs) have been used or developed for weight management in adults with excess body weight.

Methods:

A scoping review was conducted using Arksey and O’Malley’s five-step framework. Eight electronic databases (PubMed, Cochrane-Central, Embase, CINAHL, PsycINFO, IEEE Xplore, Scopus and Web of Science) were searched from journal inception till 13 November 2024, along with grey literature and hand-searched references.

Results:

Thirty-five studies on JITAIs for weight management were included, focusing on dietary behavior (25/35, 71.4%), physical activity (20/35, 57.1%), and self-weighing (17/35, 48.6%). Types of support were prompts (n=33), feedback (n=24), recommendations of coping strategies (n=7) and educational information (n=5). 31.4% of studies used machine learning for decision-making, while the rest used rule-based algorithms. Retention rates varied from 74% to 100%, and compliance from 15.1% to 94.6%. Greater user engagement was associated with improved weight loss outcomes. Across interventions, significant improvements were observed in weight, waist circumference, body mass index and blood pressure, alongside increased physical activity, healthier dietary behaviors and reductions in sedentary time.

Conclusions:

While JITAIs show potential for improving lifestyle habits by providing the right intervention, at right time and right setting, most studies lacked theoretical grounding and were not conceptualized as JITAIs. Hence, terminology and reporting were inconsistent, which hindered evaluation and comparison across studies. Nevertheless, most studies have incorporated varied distal and proximal outcomes, behavioral theories support types, intervention delivery methods and data acquisition methods, and demonstrated positive outcomes in weight, physical activity and dietary behaviors. This review demonstrates JITAIs’ potential in weight management but highlights the field’s early stage of development. Future research should focus on improving reporting standards, integrating behavioral theories, optimizing user engagement, and exploring machine learning to enhance scalability and effectiveness.