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Record W4415151474 · doi:10.1249/mss.0000000000003795

Physical Activity and Exercise Intensity Terminology: A Joint American College of Sports Medicine (ACSM) Expert Statement and Exercise and Sport Science Australia (ESSA) Consensus Statement

2025· article· en· W4415151474 on OpenAlexaff
David J. Bishop, Belinda R. Beck, Stuart Biddle, Keri L. Denay, Alessandra Ferri, Martin J. Gibala, Samuel Headley, Andrew M. Jones, Mary E. Jung, Matthew J-C Lee, Trine Moholdt, Robert U. Newton, Sophia Nimphius, Linda S. Pescatello, Nicholas J. Saner, C. Tzarimas

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia, Okanagan CampusMcMaster University
Fundersnot available
KeywordsTerminologyCLARITYSports medicinePosition statementStatement (logic)Physical activityCardiorespiratory fitnessPerception

Abstract

fetched live from OpenAlex

ABSTRACT: The evidence supporting the many beneficial effects of physical activity, including exercise, is overwhelming. This has led to numerous publications, statements, and position stands providing evidence-based recommendations to realize the performance-enhancing and therapeutic benefits of exercise. However, one factor hampering research and limiting the adoption of these recommendations is the inconsistent use of terminology associated with different exercise intensities. The goal of this international group of researchers and practitioners, therefore, was to propose standardized physical activity and exercise intensity terminology that has utility across all ages, sexes, genders, physical abilities, conditions, applications, and activities. After much discussion, we propose a standard terminology for physical activity, exercise, and sport and human performance comprising five exercise intensities: very low, low, moderate, high, and very high. We also propose five different descriptors for the perception of effort that align with the five intensities we have suggested: very easy, easy, somewhat hard, hard, and very hard. To enable consistent use of these descriptors with both cardiorespiratory and resistance exercise, we suggest not using descriptors such as light, heavy, weak, or strong (which might be perceived as only being applicable to describing load). We appreciate that some fields have long-established terminology and may be reluctant to change. Nonetheless, at a minimum, the terminology proposed here allows for more clarity when comparing the different exercise intensity descriptors currently used by different fields. Finally, we hope this will be an important "first step" in harmonizing the descriptions of exercise intensity across the fields of physical activity for public health, exercise science, and sport science.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.065
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0070.005
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0060.004
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.368
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2025
Admission routes1
Has abstractyes

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