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Record W2738374831 · doi:10.1186/s12966-017-0547-2

The use of entertainment and communication technologies before sleep could affect sleep and weight status: a population-based study among children

2017· article· en· W2738374831 on OpenAlexafffundabout
Nomathemba Dube, Kaviul Khan, Sarah Loehr, Yen Li Chu, Paul J. Veugelers

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoUniversity of Alberta
FundersWomen and Children's Health Research InstituteAlberta Health Services
KeywordsBedtimeSleep (system call)BedroomChildhood obesitySleep hygieneMedicinePopulationPsychologyObesityOverweightGerontologyPsychiatryInsomniaSleep qualityEnvironmental healthInternal medicine

Abstract

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BACKGROUND: Short sleep duration and poor sleep quality have been demonstrated to be associated with childhood obesity. It has been suggested that electronic entertainment and communication devices (EECDs) including TVs, computers, tablets, video games and cell phones interfere with sleep in children and youth. The aim of this study was to assess the impact that the use of EECDs in the hour before bedtime has on sleep and weight status to inform sleep promotion interventions and programs to prevent childhood obesity. METHODS: A provincially representative sample of 2334 grade 5 children and their parents in Alberta, Canada was surveyed. Parents reported their child's bedtime and wake-up time along with how often their child snored, felt sleepy during the day, woke-up at night and woke-up in the morning feeling unrefreshed. Sleep duration, sleep quality and sleep efficiency were derived from these indicators. Parents also reported on the presence of EECDs in their child's bedroom, while children reported use of EECDs during the day and frequency of using each of these devices during the hour before sleep. The height and weight of children were measured. Multivariable mixed effect linear and logistic regression models were used to determine how sleep duration, sleep quality, sleep efficiency and weight status are influenced by (i) access to EECDs in children's bedrooms, (ii) use of EECDs during the hour before sleep, and (iii) calming activities specifically reading during the hour before sleep. RESULTS: Sleep duration was shorter by -10.8 min (cell phone), -10.2 min (computer) and -7.8 min (TV) for those with bedroom access to and used these EECDs during the hour before sleep compared to no access and no use. Good sleep quality was hindered by bedroom access to and use of all EECDs investigated during the hour before sleep, particularly among users of cell phones (OR = 0.64, 95% CI: 0.58-0.71) and computers (OR = 0.72, 95% CI: 0.65-0.80). Very good sleep efficiency was decreased by access to and frequent use of a TV (54%), cell phone (52%), tablet (51%) and video games (51%). Odds of obesity were doubled by bedroom access to and use of a TV and computer during the hour before sleep. Children who rarely read a printed book in the bedroom during the hour before sleep had a shorter sleep duration and poorer sleep quality and sleep efficiency compared to their peers. Having access to an EECD in the bedroom was associated with increased obesity despite frequently reading during the hour before sleep. CONCLUSIONS: Our findings suggest that sleep duration, sleep quality, sleep efficiency and weight status are better among children who do not have EECDs in the bedroom and frequently read a book during the hour before sleep as opposed to those who use EECDs during this hour. Education of limits against EECD use by parents may improve sleep outcomes. These findings will inform health promotion messages and may give rise to national recommendations regarding EECD use. TRIAL REGISTRATION: ClinicalTrials.gov NCT01914185 . Registered 31 July 2013 Retrospectively registered.

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 score0.099
Threshold uncertainty score0.305

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)0.0000.000

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 head0.029
GPT teacher head0.351
Teacher spread0.323 · 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

Citations90
Published2017
Admission routes3
Has abstractyes

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