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The pleasure of moving: A compositional data analysis of the association between replacing sedentary time with physical activity on affective valence in daily life

2024· article· en· W4401971219 on OpenAlexaffabout
Matthew Bourke, Sophie M. Phillips, Jenna D. Gilchrist, Eva Pila

Bibliographic record

VenuePsychology of sport and exercise · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsUniversity of WaterlooLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPsychologyPleasureAssociation (psychology)Valence (chemistry)Physical activitySedentary behaviorEmotional valenceDevelopmental psychologyCognitive psychologySocial psychologyCognitionChemistryPsychotherapistPsychiatryPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

= 0.013). Nevertheless, engaging in less sedentary time than usual and instead engaging in physical activity was significantly related to more positive affective valence. Considering light intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) separately, replacing time spent sedentary with time engaged in MVPA and LPA both had a significant positive association on affective valence, although the association with MVPA was stronger than the association with LPA. The results provide unique insights into how replacing sedentary time with physical activity in daily life, especially MVPA, may be associated with more feelings of pleasure. These results may be useful to help inform the development of just-in-time adaptive interventions.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.036
GPT teacher head0.321
Teacher spread0.285 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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