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Record W2158873210 · doi:10.3109/07420528.2013.789894

Effects of Filtering Visual Short Wavelengths During Nocturnal Shiftwork on Sleep and Performance

2013· article· en· W2158873210 on OpenAlexafffund
Shadab A. Rahman, Colin M. Shapiro, Flora Wang, Hailey Ainlay, Syeda Kanza Kazmi, Theodore J. Brown, Robert F. Casper

Bibliographic record

VenueChronobiology International · 2013
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsMount Sinai HospitalOntario College of Art and DesignUniversity of TorontoLunenfeld-Tanenbaum Research Institute
FundersCanadian Institutes of Health Research
KeywordsCircadian rhythmPolysomnographyMelatoninAlertnessNocturnalSleep (system call)Sleep onsetMedicineActigraphySleep StagesDaytimeAudiologyAnesthesiaInternal medicineInsomniaApneaPsychiatry

Abstract

fetched live from OpenAlex

Circadian phase resetting is sensitive to visual short wavelengths (450-480 nm). Selectively filtering this range of wavelengths may reduce circadian misalignment and sleep impairment during irregular light-dark schedules associated with shiftwork. We examined the effects of filtering short wavelengths (<480 nm) during night shifts on sleep and performance in nine nurses (five females and four males; mean age ± SD: 31.3 ± 4.6 yrs). Participants were randomized to receive filtered light (intervention) or standard indoor light (baseline) on night shifts. Nighttime sleep after two night shifts and daytime sleep in between two night shifts was assessed by polysomnography (PSG). In addition, salivary melatonin levels and alertness were assessed every 2 h on the first night shift of each study period and on the middle night of a run of three night shifts in each study period. Sleep and performance under baseline and intervention conditions were compared with daytime performance on the seventh day shift, and nighttime sleep following the seventh daytime shift (comparator). On the baseline night PSG, total sleep time (TST) (p < 0.01) and sleep efficiency (p = 0.01) were significantly decreased and intervening wake times (wake after sleep onset [WASO]) (p = 0.04) were significantly increased in relation to the comparator night sleep. In contrast, under intervention, TST was increased by a mean of 40 min compared with baseline, WASO was reduced and sleep efficiency was increased to levels similar to the comparator night. Daytime sleep was significantly impaired under both baseline and intervention conditions. Salivary melatonin levels were significantly higher on the first (p < 0.05) and middle (p < 0.01) night shifts under intervention compared with baseline. Subjective sleepiness increased throughout the night under both conditions (p < 0.01). However, reaction time and throughput on vigilance tests were similar to daytime performance under intervention but impaired under baseline on the first night shift. By the middle night shift, the difference in performance was no longer significant between day shift and either of the two night shift conditions, suggesting some adaptation to the night shift had occurred under baseline conditions. These results suggest that both daytime and nighttime sleep are adversely affected in rotating-shift workers and that filtering short wavelengths may be an approach to reduce sleep disruption and improve performance in rotating-shift workers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.406

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 head0.008
GPT teacher head0.235
Teacher spread0.228 · 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 designBench or experimental
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

Citations65
Published2013
Admission routes2
Has abstractyes

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