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Related Experiment Video

Updated: Dec 29, 2025

Control of Eating Behavior Using a Novel Feedback System
04:48

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Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial

Stephanie P Goldstein, J Graham Thomas1, Gary D Foster2

  • 1The Warren Alpert Medical School of Brown University, USA.

Health Informatics Journal
|February 7, 2020
PubMed
Summary

Increasing questions in a weight loss app improved data quality and algorithm performance, even with slightly lower adherence. This demonstrated a trade-off between user burden and data completeness for personalized interventions.

Keywords:
dietmHealthmachine learningmobile healthweight loss

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Area of Science:

  • Behavioral Science
  • Digital Health
  • Machine Learning

Background:

  • Suboptimal weight loss outcomes are often linked to dietary lapses.
  • Mobile health interventions using ecological momentary assessment (EMA) can track lapses and triggers.
  • Personalized interventions powered by machine learning (ML) aim to improve weight loss adherence.

Purpose of the Study:

  • To evaluate the impact of increasing ecological momentary assessment (EMA) survey length on algorithm performance, app utilization, and behavioral outcomes in a mobile weight loss program.
  • To investigate the trade-off between user burden (more questions) and data completeness for machine learning (ML) model efficacy.

Main Methods:

  • A 10-week mobile weight loss program was used with 121 participants with overweight/obesity.
  • Participants were randomized to either a short EMA survey (8 questions) or a long EMA survey (17 questions) within the OnTrack app.
  • Data completeness, algorithm performance, app utilization, and behavioral outcomes were assessed.

Main Results:

  • Increased questions per EMA survey led to reduced adherence but improved data completeness.
  • Enhanced data completeness positively impacted machine learning algorithm performance.
  • No significant differences were observed in perceived effectiveness, app utilization, or behavioral weight loss outcomes between the short and long survey groups.

Conclusions:

  • While longer EMA surveys decrease adherence, they improve data completeness, which benefits machine learning algorithm performance in digital weight loss interventions.
  • The minimal differences in utilization and perceived effectiveness suggest that moderate increases in data collection within EMA surveys may not negatively impact overall behavioral outcomes.
  • Future research should explore optimal EMA design balancing data richness with user engagement for personalized digital health interventions.