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Record W1989211432 · doi:10.1177/0748730413499857

Daily Physical Activity Patterns of Children with Delayed Eating Behaviors

2013· article· en· W1989211432 on OpenAlexafffund
Annette Gallant, Marie-Eve Mathieu, Jennifer D. Lundgren, Kelly C. Allison, Angelo Tremblay, Jennifer O’Loughlin, Vicky Drapeau

Bibliographic record

VenueJournal of Biological Rhythms · 2013
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité de MontréalUniversité LavalMontreal Heart Institute
FundersCanadian Institutes of Health Research
KeywordsSnackingEveningMorningPhysical activityMedicineFood intakeDemographyInternal medicinePhysical therapyObesity

Abstract

fetched live from OpenAlex

Night eating syndrome (NES) is a delayed pattern of energy intake. It is unknown if symptoms associated with this syndrome are accompanied by a delayed pattern of physical activity. This study examines the relationship between physical activity patterns and delayed eating behaviors in children. Children from the QUALITY cohort (n = 269, 45% female, aged 8-11 y) completed the Night Eating Questionnaire (NEQ), which measures NES symptoms on a continuous scale and identifies single NES symptoms. Daily accelerometer data were transformed into mean counts per wear-time minute for each hour of the day. Children with high NEQ scores had higher levels of daily (p < 0.001) and evening physical activity (p = 0.05) and reached 75% of their total daily physical activity 20 minutes later than children with low NEQ scores (p < 0.05). Excessive evening snacking and a strong urge to eat in the evening or at night were the symptoms most related to these physical activity patterns. Children with delayed eating behaviors had higher levels of physical activity in the late morning and evening and a delayed physical activity pattern compared to children with no or fewer symptoms. Future research is needed to determine if physical activity plays a role in the onset or maintenance of night eating.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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