MétaCan
Menu
Back to cohort
Record W3010488647 · doi:10.7717/peerj.8464

Physical activity, sedentary time and sleep and associations with mood states, shift work disorder and absenteeism among nurses: an analysis of the cross-sectional Champlain Nurses’ Study

2020· article· en· W3010488647 on OpenAlexafffundabout
Sonia Hajo, Jennifer L. Reed, Harleen Hans, Heather Tulloch, Robert D. Reid, Stéphanie A. Prince

Bibliographic record

VenuePeerJ · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsAbsenteeismMoodMedicinePhysical activityShift workSleep (system call)Physical therapySedentary behaviorGerontologyPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Background Research has suggested ideal combinations of sleep, physical activity (PA) and sedentary time (ST) (i.e., optimal sleep/high PA/low ST) are associated with better overall health. Previous research has shown nurses spend more than half their day sedentary, do not generally meet PA guidelines and have difficulty obtaining adequate sleep. There has been no known work to examine how combinations of sleep, PA and ST relate to the work performance and mental health of nurses. Our objective was to assess the associations of sleep, PA and ST with absenteeism, mood states and shift work disorder (SWD) in a sample of Canadian nurses. Methods A total of 342 nurses from the Champlain Nurses’ Study (mean age ± SD = 43 ± 12 years, 94% women) wore an ActiGraph GT3X accelerometer for ≥ 4 days for ≥ 10 h/day to derive time spent in moderate-to-vigorous intensity physical activity (MVPA) and ST and reported sleep time for ≥ 4 days using daily activity logs. Behavioural patterns were categorized into four groups for comparison based on opposing combinations of sleep, MVPA and ST (e.g., optimal sleep/high MVPA/low ST vs. non-optimal sleep/low MVPA/high ST). Self-reported absenteeism, mood states and SWD as measured by the Profile of Mood States (POMS) and Shift Work Disorder Screening questionnaires, respectively, were compared across combinations of high versus low MVPA and ST, and optimal vs. non-optimal sleep. Results Nurses spent an average of 444 ± 11 min/day sedentary, 14 ± 15 min/day in bouts ≥ 10 minutes of MVPA (23% met PA guidelines) and reported an average of 8 h and 39 min ± 1 h 6 min of sleep/24-h. Significant associations between behaviour groups and the POMS score and its vigor subscale, as well as SWD were observed, however, none were observed for absenteeism. The healthiest behaviour group had a significantly lower mood disturbance compared to 2/3 unhealthy behaviours and greater vigor compared to 2/3 and 3/3 unhealthy behaviours. SWD trended toward being higher amongst the group with 2/3 unhealthy behaviours. Meeting PA guidelines was associated with significantly lower total mood disturbance versus not meeting guidelines (median [IQR] = 0.4 [4.5] vs. 1.3 [4.4], Z = − 2.294, df = 1, p = 0.022), as well as lower anger, higher vigor and lower fatigue. Low ST was associated with lower POMS total mood disturbance scores versus higher ST (0.6 [4.4] vs. 1.4 [4.3], Z = 2.028, df = 1, p = 0.043), as well as higher vigor and lower fatigue. Conclusions In this sample of hospital nurses, the combined effects of sleep, PA and ST are associated with total mood disturbance and SWD. Achieving the recommended levels in all three behaviours may be beneficial in decreasing total mood disturbance and minimizing the effects of SWD. Future work is needed to address the low PA and high ST levels of nurses and to better understand how these behaviours can be improved to optimize the mental health of the health workforce.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.019
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.305
Teacher spread0.292 · 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

Labeled directly by 2 models reading the full record.

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

Citations30
Published2020
Admission routes3
Has abstractyes

Explore more

Same venuePeerJSame topicSleep and Work-Related FatigueFrench-language works237,207