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Record W2073518570 · doi:10.1123/jpah.2014-0159

Low-Cost and Scalable Classroom Equipment to Promote Physical Activity and Improve Education

2014· article· en· W2073518570 on OpenAlexaboutno aff
Shelly McCrady-Spitzer, Chinmay U. Manohar, Gabriel A. Koepp, James A. Levine

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersMayo Clinic
KeywordsPhysical activityQuarter (Canadian coin)Intervention (counseling)Physical educationPhysical therapyMedicinePsychologyMathematics educationNursing

Abstract

fetched live from OpenAlex

BACKGROUND: We tested a low-cost and scalable set of classroom equipment, called Active Classroom Equipment, which was designed to promote physical activity while children learn. We hypothesized the Active Classroom Equipment would be associated with increased physical activity without impairing learning. METHODS: Fourteen first-grade students in a public elementary school (7 females, 7 males, aged 6.9 ± (SD) 0.4 years, 24 ± 5.4 kg, BMI 15.8 ± 2.6 kg/m2) used the Active Classroom Equipment for 30 minutes each day throughout the school year. Five-day physical activity was measured using validated triaxial accelerometers at baseline (before the intervention began) and on 4 sequential occasions during the 9-month intervention. RESULTS: For the baseline period, 5-day physical activity averaged 157 ± 65 AU/min. When the 14 children accessed the Active Classroom Equipment, their mean 5-day physical activity was 229 ± 103 Acceleration Units (AU)/ min (P < .0001). There were sequential increases in physical activity over the 9-month intervention (Quarter 1: 163 ± 94 AU/min, Quarter 2: 227 ± 108 AU/min, Quarter 3: 278 ± 61 AU/min, Quarter 4: 305 ± 65 AU/min). Students' Dynamic Indicators of Basic Early Literacy Skills (DIBELS) scores improved. CONCLUSION: Active Classroom Equipment may be one approach to increase physical activity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.340
Teacher spread0.317 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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