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Record W4414971784 · doi:10.1017/s0029665125101602

Characterising studies to inform dietary recommendations for shift workers – a systematic review

2025· article· en· W4414971784 on OpenAlexaboutno aff
A. Booker, Steven Powell, Jonathan Fallowfield, Rachel Gibson

Bibliographic record

VenueProceedings of The Nutrition Society · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studySystematic reviewPsychological interventionGrey literatureMeta-analysisRandomized controlled trialMEDLINEInclusion (mineral)Intervention (counseling)

Abstract

fetched live from OpenAlex

This abstract was awarded the student prize for best oral presentation. Shift work is integral to the operational function of emergency service and first responder professions. However, it is associated with health consequences due to chronic disruptions in circadian rhythms, (1) sleep patterns (2) and less healthy diets. (3-4) Modifiable health behaviour interventions, such as improving diet quality, may be effective to prevent, mitigate or delay the onset of non-communicable diseases linked to shift work. (5) However, there is a lack of consensus on evidence-based guidelines. (3) This review aims to evaluate the evidence to support nutritional interventions in this population. A systematic review (PROSPERO CRD42023421400) was conducted to evaluate the literature that may inform dietary guidance for shift workers. Four databases were searched (Embase, Cochrane Library, Web of Science, PubMed). An additional three databases (Police MyAthens, ProQuest, Clinical Trials registries) were searched for relevant grey literature alongside a physical search at the National Police Library. Studies were screened by two researchers and included if of a randomised controlled trial (RCT), crossover-RCT, observational study design, conducted in free-living shiftwork populations or laboratory-controlled shiftwork setting. Papers were accepted where the intervention included a dietary component, and health-related outcomes were reported. Quality assessment was assessed using the Cochrane Risk of Bias Tools. Observational studies were evaluated using the Ottawa Scale. From the 6909 articles retrieved, 43 met the inclusion criteria. Most articles reported using a crossover design (49%), followed by observational studies (33%). Where specified, most studies involved free-living participants (67%) and were carried out with nursing and healthcare professionals (40%). Of included studies, 30% were conducted under simulated conditions. Only 9% of studies were conducted in first responder groups. Years in shift work were often not reported (40%) and, likely due to the prevalence of simulated protocols, a moderate number of studies involved participants with no shift work experience (28%). The quality assessment highlighted that 66% of the included papers were judged to be of concern (15%) or a high risk of bias (51%), signifying challenges to conducting research in shift working populations. The most frequently reported intervention components were Time-Restricted Eating (21%), macronutrient adjustments (21%), and food diaries (19%). The most frequently reported outcomes were cardiometabolic health (39.5%), cognitive performance (27.9%), and sleep quality (11.6%). Heterogeneity in interventions, outcome measures and reporting across studies made it difficult to synthesise findings to inform shift worker dietary guidelines. There was an under representation of studies conducted in first responder occupations and limited studies conducted in the UK. The high risk of bias re-iterates a requirement for more rigorous research to understand the complex relationship between shift work and first responders’ health. Addressing these limitations will help contextualise findings and inform development of evidence-based dietary strategies to mitigate the adverse health effects of shift work in this critical 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

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.770
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.063
GPT teacher head0.387
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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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