Novel Energy Balance Tracking to Support Personalised AI Health Coaching: A Real-World Evaluation of the ENHANCE Framework

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 Year2024/25 festive period. Participants submitted daily blinded body weight measurements via Wi-Fi scales and EB-related questions via a mobile app, taking <2 minutes. 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.