Observational studyInternational journal of obesity (2005)2026
Novel energy balance tracking to support personalised AI health coaching: a real-world evaluation of the ENHANCE framework.
Observational study in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEnergy 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.
objectiveTo 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.
methodsThis 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.
resultsOf 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).
conclusionsThis scalable method delivers the accuracy and practicality needed for real-world EB tracking-laying the foundation for continuous personalised AI coaching.
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