Evidence map›Paper›PMID 41824225›Full record

ReviewSports medicine (Auckland, N.Z.)2026

Monitoring Training Effects in Athletes: A Multidimensional Framework for Decision-Making.

André Rebelo, Chris Bishop, Robin T Thorpe, Anthony N Turner, Tim J Gabbett

Abstract readReview
In one paragraph

Review in Sports medicine (Auckland, N.Z.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

André RebeloCIDEFES, Research Center in Sport, Physical Education, and Exercise and Health, Lusófona University, Lisbon, Portugal. andre94rebelo@hotmail.com.ORCID http://orcid.org/0000-0003-2441-9167
Chris BishopFaculty of Science and Technology, London Sport Institute, Middlesex University, London, UK.ORCID http://orcid.org/0000-0002-1505-1287
Robin T ThorpeRed Bull Performance Centre, 2700 Pennsylvania Ave, Santa Monica, CA, 90404, USA.
Anthony N TurnerFaculty of Science and Technology, London Sport Institute, Middlesex University, London, UK.ORCID http://orcid.org/0000-0002-5121-432X
Tim J GabbettGabbett Performance Solutions, Brisbane, QLD, 4011, Australia.ORCID http://orcid.org/0000-0002-9950-5505

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Athlete monitoring is widely used to support training and recovery decisions in elite sport, yet practitioners often face challenges related to data quality, feasibility, and the interpretation of short-term readiness signals within longer-term training adaptation. This narrative review synthesizes conceptual and applied developments in athlete monitoring through the lens of 'training effects', encompassing positive adaptation, maintenance, or maladaptation arising from training, competition, and contextual stressors. We distinguish assessment as isolated or periodic measurement from monitoring as repeated, systematic data collection used to track change over time. Building on contemporary conceptual models, readiness is positioned as an operational proxy for training effects that can inform day-to-day decision making when interpreted longitudinally and within context. We integrate the Minimal, Adequate, and Accurate framework to support tool selection that is economical in resource use, sufficient to meet clearly defined objectives, and grounded in valid and reliable measurement. Tools and metrics are organized according to the primary construct they inform: training load, athlete state and training response. We summarize practical considerations across neuromuscular, subjective, physiological, biochemical, and sleep-related indicators, emphasizing interpretive scope, measurement variability, and implementation constraints. To operationalize individualized monitoring, we outline pragmatic approaches using athlete-specific baselines and distribution-based thresholds (e.g., standard deviation intervals, minimum detectable change), alongside decision-making considerations related to Type I and Type II errors. Overall, this framework aims to reconcile scientific rigor with real-world feasibility, supporting practitioner decision making while acknowledging that monitoring should function as a decision-support process rather than a stand-alone determinant of performance outcomes.

Indexed as

AthletesAthletic PerformanceDecision MakingPhysical Conditioning, HumanAdaptation, PhysiologicalHumans

Identifiers

PMID41824225
PMCPMC13388359

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.