Evidence map›Paper›PMID 41369808›Full record

ArticleSports medicine - open2025

Validating Subjective Ratings with Wearable Data for a Nuanced Understanding of Load-Recovery Status in Elite Endurance Athletes.

Lina Spetz, Johan Rogestedt, Rickard Nilsson, C Mikael Mattsson, Filip J Larsen

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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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lina SpetzDepartment of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden. lina.spetz@gih.se.ORCID http://orcid.org/0000-0001-8314-7814
Johan RogestedtSilicon Valley Exercise Analytics (svexa), Stockholm, Sweden.
Rickard NilssonSilicon Valley Exercise Analytics (svexa), Stockholm, Sweden.
C Mikael MattssonSilicon Valley Exercise Analytics (svexa), Menlo Park, CA, USA.
Filip J LarsenDepartment of Physiology, Nutrition and Biomechanics, The Swedish School of Sport and Health Sciences, GIH, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID41369808
PMCPMC12696214

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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.