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Record W2329254534 · doi:10.1249/mss.0000000000000258

Physical Activity of Children and Academic Achievement

2014· letter· en· W2329254534 on OpenAlexaff
Roy J. Shephard

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2014
Typeletter
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysical activityPsychologyAccelerometerMedicineDevelopmental psychologyPhysical therapyComputer science

Abstract

fetched live from OpenAlex

Dear Editor-in-Chief I read with interest the recent article of Syväoja et al. (4) showing that although the self-reported physical activity of 12-yr-old children was directly associated with teacher gradings of academic attainment, uniaxial accelerometer measurements of daily physical activity did not show such an association. One potential issue is the accuracy of self-reports. Many children substantially overestimate their absolute levels of physical activity (2). Nevertheless, most authors would accept that questionnaires are capable of ranking interindividual differences in habitual physical activity and, thus, of demonstrating correlations between habitual activity and other variables such as academic attainment. A uniaxial accelerometer provides the observer with objective data, but there are several pitfalls to accurate interpretation of activity counts. Syväoja et al. (4) based their analysis upon students who provided at least 500 min of data on each of two weekdays and one weekend day. However, a much larger volume of information is needed to provide an accurate picture of a person’s activity over an entire year (5). Furthermore, if the device is worn for only a short period, reactive effects may lead to an upward skewing of readings in a proportion of the students (1). Moreover, the accelerometer underestimates or fails to record many childhood activities. Syväoja et al. (4) note skateboarding, but one may add swimming and cycling, to the list of poorly identified activities. Finally, and perhaps most critically, activity counts are averaged over the entire day, whereas in terms of academic learning, the critical factor may be the intervention of a period of vigorous activity during the time that the child is attending school. Syväoja et al. (4) suggest that their study is the first to look at relations between objectively measured physical activity and academic performance. In terms of accelerometer measurements, they are correct. However, an alternative approach to this question is to impose a substantial and known fraction of the child’s daily physical activity through an hour of vigorous daily classroom activity, using a quasi-experimental design. The Trois Rivières study carried out such an investigation for a 6-yr period and thus demonstrated that children who received a daily hour of added specialist-taught physical activity had a better level of academic achievement than their peers who received only a nominal weekly amount of physical education from their homeroom teachers, whether their performance was determined by local classroom appraisals or by province-wide examinations (3). In conclusion, our quasi-experimental study would seem to support the questionnaire rather than the accelerometer data. Moreover, given the known arousing effect of physical activity, one important variable to consider in all future research on this topic would seem to be the time relationships between periods of physical activity and academic instruction. No funding was received for this study, and there is no conflict of interest.

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)0.003
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.003

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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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 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

Citations5
Published2014
Admission routes1
Has abstractyes

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