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Record W4415612713 · doi:10.1123/jpah.2025-0532

A 15-Year Decline in Physical Fitness Among Children and Adolescents From the Macao Special Administrative Region (2005–2020)

2025· article· en· W4415612713 on OpenAlexaff
Siu Ming Choi, Henry Y. Dong, Siman Lei, Grant R. Tomkinson, Justin J. Lang, Cristina Cadenas‐Sánchez, Eric Tsz‐Chun Poon

Bibliographic record

VenueJournal of Physical Activity and Health · 2025
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsPublic Health Agency of CanadaUniversity of Ottawa
Fundersnot available
KeywordsPhysical fitnessPsychological interventionPhysical activityPhysical healthYoung adultHealth promotion

Abstract

fetched live from OpenAlex

BACKGROUND: Monitoring temporal trends in physical fitness provides valuable insight into population health that can help identify current and future physical health. This study examined temporal trends in physical fitness among children and adolescents aged 6-18 from the Macao Special Administrative Region between 2005 and 2020. METHODS: Representative repeated cross-sectional physical fitness data were collected in 2005, 2010, 2015, and 2020 (n = 17,987). Body size and physical fitness were objectively measured. Trends in means were calculated using general linear models. Models were adjusted for sex, age, height, and weight. Trends in distributional characteristics were assessed as the ratio of the coefficients of variation and described visually. RESULTS: Despite an increase in body size, we found significant small to large body size adjusted decline in cardiorespiratory fitness (effect size [ES] = -0.27, -0.93) and small declines in reaction time (ES = -0.21, -0.38). Declines in other fitness components varied by age, with negligible to small declines in children's (aged 6-12 y) balance (ES = -0.15) and speed (ES = -0.25) and in adolescents' (aged 13-18 y) muscular power (ES = -0.17, -0.36). A small improvement was observed in adolescents' flexibility (ES = 0.24). Mixed trends were found in distributional variability for flexibility, along with differing trends in distributional asymmetry among fitness components. CONCLUSIONS: These findings underscore the need for targeted interventions to enhance fitness levels in the young Macao population. Addressing these trends is crucial for improving the current and future health outcomes of young people in the region.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.358
Teacher spread0.323 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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