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Record W4406244017 · doi:10.1515/teb-2024-0025

Impact of early and late morning supervised blood flow restriction training on body composition and skeletal muscle performance in older inactive adults

2025· article· en· W4406244017 on OpenAlexafffund
Logan E. Peskett, Amy M. Thomson, Julia K. Arnason, Yadab Paudel, Martin Sénéchal

Bibliographic record

VenueTranslational Exercise Biomedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of New Brunswick
FundersPublic Health Agency of Canada
KeywordsMorningBlood flow restrictionSkeletal muscleComposition (language)Training (meteorology)MedicinePhysical medicine and rehabilitationPhysical therapyGerontologyInternal medicineResistance trainingGeographyArt

Abstract

fetched live from OpenAlex

Abstract Objectives To investigate the impact of a supervised blood-flow restriction (BFR) training program performed at different times of the morning on body composition and muscle performance in older, inactive adults. Methods A single-arm intervention of supervised BFR resistance training was performed three times per week for six weeks. Participants (n=24; aged 65+ years) were categorized into early morning (n=13; 05:00–08:59) or late morning (n=11; 09:00–12:00) groups. Primary outcomes were changes in body composition, total work, average peak power, average peak torque, muscle strength, and physical function. Results Mixed model analysis of variance revealed a significant within-subject effect of time for all strength measures (p ranging from 0.017 to <0.001) and some physical function measures including the 30 s chair stand test, 30 s bicep curl test, and grip strength (p ranging from 0.015 to <0.001). No between-group or time by group interaction effect was observed for all outcomes. Conclusions This study showed that only six weeks of BFR training, performed at different time of the morning, did not enhance muscle mass and performance, but provided similar changes in muscle strength and some physical function tests in older adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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