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Record W2164470978 · doi:10.1145/2658537.2658693

Decreasing sedentary behaviours in pre-adolescents using casual exergames at school

2014· article· en· W2164470978 on OpenAlexafffund
Yue Gao, Kathrin Gerling, Regan L. Mandryk, Kevin G. Stanley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsCasualPhysical activityPsychologySedentary behaviorPerceived exertionPhysical therapyApplied psychologyMedicine

Abstract

fetched live from OpenAlex

There are risks to too much sedentary behaviour, regardless of a person's level of physical activity, particularly for children. As exercise habits instilled during childhood are strong predictors of healthy lifestyles later in life, it is important that schools break up long sedentary periods with short periods of physical activity. Casual exergames are an appealing option for schools who wish to engage adolescents, and have been shown to provide exertion levels at recommended values, even when played for only 10 minutes. In this paper we describe a preliminary survey with teachers of a local school that informed the deployment of a casual exergame with a group of pre-adolescent students from the same school. We show that students preferred the game to traditional exercise, that the game was able to generate appropriate levels of exertion in pre-adolescents, and that students have a sophisticated understanding of the role of exercise in their lives. Overall, we establish the feasibility of casual exergames for combating sedentary behavior in preteen classrooms.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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

Citations32
Published2014
Admission routes2
Has abstractyes

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