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Record W2155708093 · doi:10.1111/jsr.12275

Associations between sleep parameters and food reward

2015· article· en· W2155708093 on OpenAlexaff
Jessica McNeil, Sébastien Cadieux, Graham Finlayson, John E. Blundell, Éric Doucet

Bibliographic record

VenueJournal of Sleep Research · 2015
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMorningCrossover studySleep (system call)Aerobic exerciseBody mass indexPsychologyMedicineAudiologySession (web analytics)Physical therapyPhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

This study examined the effects of acute, isocaloric aerobic and resistance exercise on different sleep parameters, and whether changes in these sleep parameters between sessions were related to next morning food reward. Fourteen men and women (age: 21.9 ± 2.7 years; body mass index: 22.7 ± 1.9 kg m(-) ²) participated in three randomized crossover sessions: aerobic exercise; resistance exercise; and sedentary control. Target exercise energy expenditure was matched at 4 kcal kg(-1) of body weight, and performed at 70% of VO2peak or 70% of 1 repetition-maximal. Sleep was measured (accelerometry) for 22 h following each session. The 'wanting' for visual food cues (validated computer task) was assessed the next morning. There were no differences in sleep parameters and food 'wanting' between conditions. Decreases in sleep duration and earlier wake-times were significantly associated with increased food 'wanting' between sessions (P = 0.001). However, these associations were no longer significant after controlling for elapsed time between wake-time and the food reward task. These findings suggest that shorter sleep durations and earlier wake-times are associated with increased food reward, but these associations are driven by elapsed time between awakening and completion of the food reward task.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.158
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
GPT teacher head0.423
Teacher spread0.244 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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