MétaCan
Menu
Back to cohort

TRACKING OF PHYSICAL FITNESS FROM CHILDHOOD TO ADULTHOOD

2001· article· en· W2015004678 on OpenAlexaffabout
François Trudeau, R.J. Shephard, François Arsenault, Louis Laurencelle

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2001
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsTracking (education)Physical fitnessPhysical medicine and rehabilitationPsychologyComputer scienceMedicinePhysical therapy

Abstract

fetched live from OpenAlex

The purpose of the present study was to investigate tracking of physical fitness: muscular strength (handgrip), endurance (sit-ups), and aerobic fitness (PWC/kg) from childhood to middle age. A sub-group of 95 women (57 experimental and 38 control subjects) and 96 men (56 experimental and 40 control subjects) was randomly selected from original participants in the Trois-Rivières growth and development study. Data was gathered during the period 1970–1977 and during a recall performed in 1996–1997. Tracking between 10, 11, 12 and 35 years was measured by correlation analysis. Correlation coefficients for grip strength increased slightly at each interval 10–35, 11–35 and 12–35 years from 0.56 to 0.63 in women and from 0.45 to 0.61 in men (P < 0.001). Tracking for sit-ups was less consistent, increasing from 0.29 to 0.38 for women and 0.23 to 0.54 for men (P < 0.05), respectively, over the same intervals. Tracking of PWC/kg between 11 and 35 years was significant but low, both in women (r = 0.24) and in men (r = 0.34) (P < 0.05). We conclude that physical fitness components continually used over lifetime, such as grip strength, are relatively stable. However, components that are less likely to be used continually, such as cardiorespiratory fitness and abdominal endurance show less consistent tracking. The latter fitness components should be targeted for lifetime intervention programs. Supported by CFLRI #951R110

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 score0.511
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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

Citations6
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueMedicine & Science in Sports & ExerciseSame topicChildren's Physical and Motor DevelopmentFrench-language works237,207