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Background <p>Energy balance (EB) is the key determinant of fat gain, yet accurate EB tracking is difficult outside laboratory settings. Traditional methods are burdensome (e.g. food-logs) or lack daily resolution (e.g. body weight monitoring), limiting suitability for integration with free-living AI-powered health coaching.</p> Objective <p>To introduce ENHANCE—a novel framework prioritising interpretability and temporal accuracy—and demonstrate its use as a low-burden, accurate method for tracking EB using smart devices and minimal self-report, suitable for AI coaching.</p> Methods <p>This 4-week observational study spanned the Christmas to New Year 2024/25 festive period. Participants submitted daily blinded body weight measurements via Wi-Fi scales and EB-related questions via a mobile app, taking &lt;2 min. Data were used to generate five weight trends: raw (from scales), smoothed (±3-day average), piecewise (3-segments), predicted (from EB), and corrected. The correction aligned predicted and smoothed trends, using proximity and noise-weighted adjustments, producing enhanced data for AI coaching. An end-of-study questionnaire assessed acceptability and behavioural reactivity.</p> Results <p>Of 23 participants, 18 were analysed. Five were excluded due to illness (<i>n</i> = 4) or bereavement (<i>n</i> = 1). Participants completed 94% (5.1%) of body weight measurements and 100% of EB-related submissions. Questionnaire results showed low burden (1.8/5) and behavioural reactivity (1.5/5). Group-level predicted trends explained 90.4% of smoothed trend variance (R² = 0.904; mean absolute error [MAE]: 93 g). Corrected trends aligned more closely with piecewise segments than raw trends (MAE: 46 g vs 77 g). Individual-level mean EB corrections were +41 kcal/day—just 2% of reported intake. The corrected trend enhanced interpretability and plausibility while preserving real-world validity. Calculated mean net fat weight change during the monitoring phase was +0.8 kg (0.4 kg); mean net EB was +223 kcal/day (130 kcal/day).</p> Conclusions <p>This scalable method delivers the accuracy and practicality needed for real-world EB tracking—laying the foundation for continuous personalised AI coaching.</p>

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Novel energy balance tracking to support personalised AI health coaching: a real-world evaluation of the ENHANCE framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Arthur B. Daw,
  • Maxime Petit,
  • Scott A. Willis,
  • Lewis J. James,
  • James A. King

摘要

Background

Energy balance (EB) is the key determinant of fat gain, yet accurate EB tracking is difficult outside laboratory settings. Traditional methods are burdensome (e.g. food-logs) or lack daily resolution (e.g. body weight monitoring), limiting suitability for integration with free-living AI-powered health coaching.

Objective

To introduce ENHANCE—a novel framework prioritising interpretability and temporal accuracy—and demonstrate its use as a low-burden, accurate method for tracking EB using smart devices and minimal self-report, suitable for AI coaching.

Methods

This 4-week observational study spanned the Christmas to New Year 2024/25 festive period. Participants submitted daily blinded body weight measurements via Wi-Fi scales and EB-related questions via a mobile app, taking <2 min. Data were used to generate five weight trends: raw (from scales), smoothed (±3-day average), piecewise (3-segments), predicted (from EB), and corrected. The correction aligned predicted and smoothed trends, using proximity and noise-weighted adjustments, producing enhanced data for AI coaching. An end-of-study questionnaire assessed acceptability and behavioural reactivity.

Results

Of 23 participants, 18 were analysed. Five were excluded due to illness (n = 4) or bereavement (n = 1). Participants completed 94% (5.1%) of body weight measurements and 100% of EB-related submissions. Questionnaire results showed low burden (1.8/5) and behavioural reactivity (1.5/5). Group-level predicted trends explained 90.4% of smoothed trend variance (R² = 0.904; mean absolute error [MAE]: 93 g). Corrected trends aligned more closely with piecewise segments than raw trends (MAE: 46 g vs 77 g). Individual-level mean EB corrections were +41 kcal/day—just 2% of reported intake. The corrected trend enhanced interpretability and plausibility while preserving real-world validity. Calculated mean net fat weight change during the monitoring phase was +0.8 kg (0.4 kg); mean net EB was +223 kcal/day (130 kcal/day).

Conclusions

This scalable method delivers the accuracy and practicality needed for real-world EB tracking—laying the foundation for continuous personalised AI coaching.