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Record W3132047372 · doi:10.1080/07420528.2021.1872592

Timely use of in-car dim blue light and blue blockers in the morning does not improve circadian adaptation of fast rotating shift workers

2021· article· en· W3132047372 on OpenAlexafffund
Jeanne Sophie Martin, Luc Laberge, Alexandre Sasseville, Marilie Bérubé, Samuel Alain, Joëlle Lavoie, Jérôme Houle, Marc Hébert

Bibliographic record

VenueChronobiology International · 2021
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsCégep de JonquièreUniversité du Québec à ChicoutimiUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsCircadian rhythmMorningAlertnessShift workMelatoninBlue lightMedicineNocturnalAudiologyPsychologyEndocrinologyInternal medicinePhysicsOpticsPsychiatry

Abstract

fetched live from OpenAlex

Circadian adaptation to night work usually does not occur in naturalistic conditions, largely due to exposure to low levels of light during the night and light in the morning on the way home. This leads to circadian misalignment, which has documented deleterious effects on sleep and functioning during waking hours. Chronic circadian misalignment is also being increasingly associated with long-term health comorbidities. As the circadian system is mostly sensitive to short wavelengths (i.e., blue light) and less sensitive to long wavelengths (i.e., red light), shaping light exposure in a "wavelength-wise" manner has been proposed to promote partial adaptation to night shifts, and, therefore, alleviate circadian rhythms disruption. This report presents results from two cross-over designed studies that aimed to investigate the effects of three different light conditions on circadian phase, sleepiness, and alertness of police patrol officers on a rotating shift schedule. The first study took place during summer (n = 15) and the second study (n = 25) during winter/early spring. In both studies, all participants went through three conditions composed of four consecutive night shifts: 1) in-car dim blue light exposure during the night shift and wearing of blue-blocking glasses (BBG) in the morning after 05:00 h; 2) in-car red light exposure during the night shift and wearing of BBG in the morning after 05:00 h; 3) a control condition with no intervention. To assess circadian phase position, salivary melatonin was collected hourly the night before and the night after each condition. Sleep was monitored by wrist actigraphy. Also, a 10-min Psychomotor Vigilance-Task was administered at the beginning and end of each night shift and the Karolinska Sleepiness Scale was completed every 2 h during each night shift. In the summer study, no difference was found in alertness and sleepiness between conditions. Participants though exhibited greater (≈3 h) phase delay after four consecutive night shifts in the control condition (in which morning light exposure was expected to prevent phase delay) than after the blue and red conditions (≈2 h) (in which wearing BBG were expected to promote phase delay). In the second study performed during the winter/early spring, a comparable ≈2 h phase delay was found in each of the three conditions, with no difference in alertness and sleepiness between conditions. In conclusion, participants in both studies exhibited modest phase delay across the four night shifts, even during the control conditions. Still, re-entrainment was not fast enough to produce partial circadian adaptation after four night shifts. A greater number of consecutive night shifts may be necessary to produce enough circadian alignment to elicit benefits on sleepiness and alertness in workers driving a motorized vehicle during night shifts. In-car dim blue light exposure combined with the wearing of BBG in the morning did not show the expected benefits on circadian adaptation, sleepiness, and alertness in our studies. Higher levels of light may be warranted when implementing light intervention in a motorized vehicle setting.

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)0.001
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 score0.051
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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