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Record W4392202955 · doi:10.1016/j.smhs.2024.02.006

Exercise snacks and physical fitness in sedentary populations

2024· review· en· W4392202955 on OpenAlexaff
Tutu Wang, Ismail Laher, Shunchang Li

Bibliographic record

VenueSports Medicine and Health Science · 2024
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
FundersServierNational Natural Science Foundation of China
KeywordsPhysical activityPhysical fitnessPhysical exercisePublic healthSedentary lifestyleEnvironmental healthHealth benefitsMedicineGerontologyPsychologyPhysical therapy

Abstract

fetched live from OpenAlex

Physical inactivity remains a pressing global public health concern. Prolonged periods of sedentary behavior have been linked to heightened risks of non-communicable diseases such as cardiovascular diseases and type 2 diabetes, while engaging in any form of physical activity can elicit favorable effects on health. Nevertheless, epidemiological research indicates that people often struggle to meet recommended physical activity guidelines, citing time constraints, lack of exercise equipment, and environmental limitations as common barriers. Exercise snacks represents a time-efficient approach with the potential to improve physical activity levels in sedentary populations, cultivate exercise routines, and enhance the perception of the health benefits associated with physical activity. We review the existing literature on exercise snacks, and examine the effects of exercise snacks on physical function and exercise capacity, while also delving into the potential underlying mechanisms. The objective is to establish a solid theoretical foundation for the application of exercise snacks as a viable strategy for promoting physical activity and enhancing overall health, particularly in vulnerable populations who are unable to exercise routinely.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.228
GPT teacher head0.510
Teacher spread0.283 · 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 designOther design
Domainnot available
GenreReview

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

Citations38
Published2024
Admission routes1
Has abstractyes

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