ArticleSports medicine - open2025
Validating Subjective Ratings with Wearable Data for a Nuanced Understanding of Load-Recovery Status in Elite Endurance Athletes.
Article in Sports medicine - open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Orthometria: Metric Fixation in Digital Health and a Framework for Responsible Use in High-Performance Sport.Sports medicine - open · 2026Article
- Mobile Health (mHealth) Apps in Sport Training: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Longitudinal Development of Neuromuscular Performance and Multidirectional Speed in Youth Badminton Players: Evidence for Parallel Adaptation Trajectories.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Sub-Elite Youth Football Players: Predicting Recovery Through Machine Learning Integration of Physical, Technical, Tactical and Maturational Data.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe emergence of wearable technology offers enhanced real-time health management, including sleep, recovery, and exercise optimization. Despite their potential to monitor load-recovery parameters in elite athletes, the selection, combination, and interpretation or reliance of metrics in relation to perceived impact remain unclear.
objectiveThis study assessed data from three wearables measuring sleep, continuous glucose, and exercise, together with the Profile of Mood State (POMS) dimensions alongside subjective ratings via the Readiness Advisor application (RA app) (Silicon Valley Exercise Analytics, svexa, Menlo Park, California, USA) to evaluate their association and value in load-recovery monitoring.
methodsTwenty national team endurance athletes, competing at the highest international level, were monitored during one year of training, recovery, and competitions. Data collections were made with Global Positioning System (GPS) watches and heart rate monitors, Ōura rings (Ōura Health OY, Oulu, Finland), continuous glucose monitors, POMS questionnaires and subjective ratings in the RA app.
resultsSignificant correlations were found between each RA question and its counterpart in a linear mixed model (r values = 0.39-0.81). However, time series analysis through autoregressive integrated moving average (ARIMA analysis) revealed individual variability.
conclusionsThese findings indicate an influence of external aspects and advocate for a multifaceted approach to the assessment of load-recovery balance, well-being and performance. Moreover, personalized analyses proved more accurate than group averages, emphasizing the need for individualized monitoring. Integrating subjective and objective data appears essential for nuanced understanding of the athlete status, advancing high-performance monitoring and athletic health management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.