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Record W1584385382 · doi:10.1161/circ.131.suppl_1.p071

Abstract P071: How to Get a Better Night’s Sleep: Be Active and Reduce Sedentary Behaviour

2015· article· en· W1584385382 on OpenAlexaff
Lisa Kakinami, Erin K. O’Loughlin, Jennifer Brunet, Erika N. Dugas, Catherine M. Sabiston, Jennifer O’Loughlin

Bibliographic record

VenueCirculation · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsUniversity of TorontoUniversity of OttawaUniversité de MontréalConcordia University
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexMedicineSleep (system call)Physical therapyPopulationDuration (music)Sleep onsetSleep onset latencyCohortGerontologySleep qualityInsomniaInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Background: Approximately 40% of the population reports sleep problems such as poor quality sleep and insufficient sleep duration. Physical activity (PA) can help improve sleep, but data on whether PA intensity or duration is most strongly associated with sleep are lacking. In addition, given that sedentary behaviour (e.g., TV, computer use) is distinct from physical inactivity, the association between sedentary behaviour and sleep in young adults needs to be characterized. Objective: To describe the relationships between sleep quality and sleep duration and (1) frequency and duration of light, moderate, and vigorous PA, and (2) different types of sedentary behaviours (TV, computer, reading) in young adults. Methods: Self-report data for 658 participants were from the 22nd wave of the Nicotine Dependence in Teens (NDIT) cohort study (mean age=24.0 years, 46% male [300 of 658]). PA measures assessed frequency (number of days) and minutes of light, moderate and vigorous PA in the past week. Sedentary measures assessed number of hours spent reading, watching TV, and using the computer per day. Sleep measures included (1) the Pittsburgh Sleep Quality Index (PSQI) which assessed seven dimensions of sleep (daytime dysfunction, disturbances, duration, efficiency, latency, quality, use of sleeping medications), (2) general sleep quality, and (3) sleep duration in the past month. General sleep quality and sleep duration were two separate additional measures distinct from similar PSQI items (r=0.73 between general sleep quality and PSQI score; r=0.69 between sleep duration and PSQI score). Data were analyzed using multiple linear regression. Due to evidence of non-normality the PSQI score was log-transformed. Results: Controlling for age, sex, and maternal education, each additional day of light or vigorous PA was associated with 3 minutes less sleep per night (p<0.05). Each additional 10 minutes of moderate PA was associated with greater general sleep quality (β=0.004, p=0.04). TV was associated with a poorer PSQI score (β=0.01, p<0.05) and each additional hour of reading was associated with 2 minutes less sleep per night (p=0.04). Computer use was associated with a poorer PSQI score (β=0.02, p=0.005) and poorer sleep quality (β=-0.02, p=0.05). Results were similar when sedentary and PA measures were included in the same model. The inclusion of body mass index, self-rated mental and general health, and stress did not affect the results and were omitted from the final models. Conclusion: PA and sedentary behaviours are independently associated with sleep duration and quality. Sedentary behaviours are associated with poorer sleep duration and quality. In contrast, PA frequency may decrease sleep duration while PA duration may improve sleep quality. Clinicians who treat sleep problems in young adults may need to take PA and sedentary behavior into account in treatment plans.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0290.013

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 head0.126
GPT teacher head0.419
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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