Predicting daily recovery during long-term endurance training using machine learning analysis.

ABSTRACT:

Purpose

The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures.

Methods

Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model.

Results

Prediction error (root

SUBMITTER: Rothschild JA 

PROVIDER: S-EPMC11519101 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature