Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 14, 2026
Open Peer Review Period: Jan 11, 2026 - Jan 20, 2026
Date Accepted: Sep 3, 2026
(closed for review but you can still tweet)
Using Statistical Time-Series Forecasting to Predict Resting Heart Rate Based on Wearable-Device Data Sources: A Case Study for a Distance Runner
ABSTRACT
Background:
For endurance athletes, resting heart rate (RHR) is a well-known indicator for estimating training load status. A method for predicting the coming day's RHR would be valuable for training adjustments.
Objective:
The aim of this study is to develop a machine learning (ML) model for forecasting resting heart rate (RHR).
Methods:
Daily data (valid sample n = 624) collected from the personal wearable device of a single endurance athlete were used to establish the ML model. This ML model was built using an autoregressive integrating moving average (ARIMA) model from the Sktime package. Using heart rate as the sole data source, various strategies were employed to train the model, such as using 1-day or 7-day batch datasets and including or excluding additional features.
Results:
Models trained with all strategies showed mean forecasting values (mean = 49.371, 49.225, 49.782 & 50.101) close to observed values (mean = 49.724). However, the models trained with additional features generated a more reasonable trend (variance = 3.807 & 0.924 for 7 & 1-day batch) than those trained with only resting heart rate as the sole data source (variance = 0 for both 7 & 1-day batch).
Conclusions:
The results of the current study verified that a SARIMA model trained with sufficient features is capable of forecasting future RHR. Moreover, the present study found training the model on a weekly basis to be the best practice. Clinical Trial: N/A
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