Key result
A SARIMA model reduces resting heart rate prediction error by ~29% versus a naive baseline.
Why the study?
Does a statistical time-series forecasting model (SARIMA) accurately predict resting heart rate from wearable device data in an endurance athlete?
Case Report (n=1)
No
Does a statistical time-series forecasting model (SARIMA) accurately predict resting heart rate from wearable device data in an endurance athlete?
Absolute Event Rate: 1.712% vs 2.404%
A continuously updated SARIMA model using previous-day resting heart rate and trimmed average active heart rate can forecast future resting heart rate in an endurance athlete better than a naive baseline.
Supports SARIMA feasibility for resting heart rate prediction in one athlete; leaves open validation and clinical utility in larger cohorts.
BACKGROUND: For endurance athletes, resting heart rate (RHR) is a well-known indicator of training load, physiological status, and readiness for upcoming training. Predicting the following day's RHR would enable athletes and coaches to optimize training plans and make timely load adjustments. OBJECTIVE: The aim of this study was to develop a statistical time-series forecasting model for RHR. METHODS: Daily heart rate (HR) data (624 valid observations) collected from the personal wearable device of a single endurance runner (n=1) were used to establish a statistical time-series RHR forecasting model. This model was built using an autoregressive integrated moving average (ARIMA) model from the sktime package. The research framework evaluates wearable RHR forecasting models across 3 distinct phases. Phase 1 compared a naive persistence baseline against 4 dynamic seasonal autoregressive integrated moving average (SARIMA) models (using 1-day and 7-day rolling forecasts, with and without exogenous features) on a 75-25 train-test split. Phase 2 conducted a leave-one-feature-out ablation analysis on the top-performing model from phase 1. Finally, phase 3 assessed real-world "cold-start" viability by training a streamlined SARIMA model on the first 21 days of data. RESULTS: In phase 1, the baseline naive forecaster yielded a mean absolute error (MAE) of 2.404. The SARIMA model using a 1-day rolling forecast with exogenous features and a chronological 75-25 split achieved an MAE of 1.712. An ablation analysis in phase 2 revealed that previous-day RHR and trimmed average active HR were the primary predictive drivers. Consequently, in phase 3, the streamlined SARIMA model, trained on just 21 days of initial data and using only the previous-day RHR and trimmed average active HR as 2 training features, demonstrated performance comparable to that of the full-history model. CONCLUSIONS: In the current case study, we preliminarily verified that a continuously updated SARIMA model trained on sufficient features can forecast future RHR in a single runner. Further study with a larger number of participants and the inclusion of more exogenous features will be needed to verify the framework's applicability.
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Chen et al. (2026) conducted a case report in Resting heart rate forecasting in endurance athletes (n=1). SARIMA model with exogenous features vs. Naive persistence baseline was evaluated on Mean absolute error (MAE) of resting heart rate prediction. A SARIMA model using a 1-day rolling forecast with exogenous features predicted resting heart rate with a mean absolute error of 1.712, compared to 2.404 for a naive persistence baseline.
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