Paper Info
Title
Refining An Algorithm-Powered Just-In-Time Adaptive Weight Control Intervention: A Randomized Controlled Trial Evaluating Model Performance And Behavioral Outcomes
Abstract
Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions.
Year
DOI
Venue
2020
10.1177/1460458220902330
HEALTH INFORMATICS JOURNAL
Keywords
DocType
Volume
diet, machine learning, mHealth, mobile health, weight loss
Journal
26
Issue
ISSN
Citations 
4
1460-4582
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Stephanie P. Goldstein120.72
J Graham Thomas200.34
Gary D Foster300.34
Gabrielle M. Turner-McGrievy4323.50
Meghan L. Butryn520.72
James D Herbert600.34
Gerald J Martin700.34
Evan M Forman811.11