Evidence map›Paper›PMID 34386767›Full record

ArticleFrontiers in sports and active living2021

The Global Session Metric Score (GSMs): A Modified Session-Specific Exertional Index.

Hanna M Gardner, Andrew W Scheck, John R Cone, Nathaniel T Berry, Laurie Wideman

Open access · goldAbstract read
In one paragraph

Article in Frontiers in sports and active living, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 88% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 1 institution in 1 country.

Hanna M GardnerExercise Endocrinology Laboratory, Department of Kinesiology, School of Health and Human Sciences, University of North Carolina at Greensboro, Greensboro, NC, United States.
Andrew W ScheckRaleigh, NC, United States.
John R ConeAthletes Research Institute, Chapel Hill, NC, United States.
Nathaniel T BerryExercise Endocrinology Laboratory, Department of Kinesiology, School of Health and Human Sciences, University of North Carolina at Greensboro, Greensboro, NC, United States.
Laurie WidemanExercise Endocrinology Laboratory, Department of Kinesiology, School of Health and Human Sciences, University of North Carolina at Greensboro, Greensboro, NC, United States.
University of North Carolina at Greensboro · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring session training load to optimize the training stress that drives athlete adaptation and subsequent performance, is fundamental to periodization and programming. Analyzing the internal load experienced by the individual in response to the external load prescribed by coaching staff is crucial to avoid overtraining and optimize training adaptation. Subjective measures provide more information regarding individual training load, as heart rate measures alone do not account for collisions, eccentric muscle actions, muscle soreness, weather conditions, or accumulated training loads, which are paramount to the athlete experience. However, the current subjective metric for interpreting session training load (sRPE) is poorly shaped to the athlete's global response to the whole session, often showing poorer correlations to heart rate (HR) measures during intermittent or high-intensity activity. This study introduces a new metric, the Global Session Metric Score (GSMs), which creates a symmetrical relation between the verbal descriptor and numeric values, as well as more applicable session-specific verbal descriptors for the highest level of exertion. Twenty-four D1 male college soccer field players (age: 20.5 +/- 1.42) wore HR monitors and reported GSMs for all practices and games within an entire season. Linear regression with 10-fold cross validation was used to test the relation between GSMs with B-TRIMP and E-TRIMP, respectively. These models demonstrate good performance with consistency and reliability in the estimation of GSMs to predict both B-TRIMP (

Indexed as

athlete monitoringfitness periodizationperceived exertionphysiological loadsession-RPEsoccersubjective monitoring

Identifiers

PMID34386767
PMCPMC8353094
OpenAlexW3186256418

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.