Circadian Disruption from Urban Night-Time Noise and Endocrine Health Risks in Shift Workers

Abstract

Objectives:

This review examines how nighttime noise and irregular schedules influence circadian and endocrine regulation. It introduces a conceptual multilevel model to explore how these exposures may accumulate over time and contribute to long-term health risks.

Methods:

A scoping narrative review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses principles in PubMed, Scopus, Web of Science and Embase (1992–2024), including human studies on nighttime noise and shift work. Eligible studies involved adults and reported circadian, hormonal, metabolic or chronic disease outcomes. Twenty-five studies were synthesised using direction-based vote counting due to heterogeneity because meta-analysis was not feasible. The tally reflected the direction of effects rather than statistical significance. Risk-of-bias assessments informed interpretation, but not exclusion. Findings were integrated into a multilevel model integrating exposure, physiological mediation and cumulative effects.

Results:

Nighttime noise and rotating night duties shifted melatonin timing and reduced sleep continuity. Studies reported increased fasting glucose, decreased insulin sensitivity and unfavourable lipid profiles in groups exposed to these work periods. Several investigations described an increase in cardiometabolic load amongst long-term night-duty workers. In the simulation scenarios, the model followed these findings and produced circadian and metabolic changes that increased across repeated exposure cycles.

Conclusions:

This review proposes and illustrates a dynamic framework for understanding how nighttime noise and shift work may contribute to circadian and metabolic disruption over time. Its exploratory nature reflects heterogeneous evidence and a scoping design. The findings should be interpreted without causal inference.

KEY MESSAGES

• (1)Nighttime noise and rotating shifts disturb circadian rhythms and hormonal balance.
• (2)These exposures are linked to worsened sleep, reduced glucose control and increased blood pressure compared with unexposed groups.
• (3)This study develops a dynamic multilevel conceptual model that connects noise and shift work with hormonal, metabolic and cardiovascular outcomes over time.

INTRODUCTION

In urban and industrial settings, nighttime noise is associated with sleep disturbance, hypertension, cardiovascular disease and stress responses.[1] Noise-induced hearing loss remains a major concern, with rising trends reported in several populations.[2] These effects are acknowledged in the World Health Organization (WHO) Environmental Noise Guidelines for Europe, which uses L_night and L_den as reference indicators.[3] However, few studies have examined how nighttime exposure affects circadian and metabolic regulation, especially under shift work conditions.

The circadian rhythm is a 24-hour internal cycle that regulates sleep, alertness, temperature, digestion and hormone secretion. Whilst primarily synchronised by light exposure, it can be influenced by nighttime noise and irregular schedules. When internal rhythms lose alignment with external conditions, circadian disruption occurs and affects the timing of physiological functions. This disruption leads to fragmented sleep, altered hormone secretion, metabolic changes and reduced cognitive performance.[4,5,6] In occupational environments, this disruption affects functions that rely on rapid judgment and motor control, such as reaction time and decision-making. Prolonged circadian misalignment has been associated with type 2 diabetes, cardiovascular disease, gastrointestinal disorders and depression.[7] Shift workers have increased exposure to these effects because they sleep at atypical hours, and they are often exposed to noise during the day. Shift schedules include rotating night shifts, fixed night shifts and quick returns, each affecting circadian adaptation in different ways. Experimental studies have shown acute suppression of melatonin and increased evening cortisol during night work, and long-term night-shift exposure has been linked to impaired glucose-insulin regulation.[8,9] These endocrine effects have been observed in acute laboratory settings and long-term field studies.

Noise can intensify these effects. Nighttime traffic above 55 dB(A) has been associated with higher metabolic and cardiovascular strain compared with lower-exposure groups, and exposures between 40 and 50 dB(A) have been linked to increased blood pressure and adverse metabolic responses in laboratory and field studies.[10,11] Circadian disruption is accompanied by sleep disturbance, cardiovascular strain and stress responses involving the hypothalamic–pituitary–adrenal axis. These patterns appear across environmental and experimental studies.[12,13,14,15]

Reduced melatonin, increased cortisol and impaired glucose regulation have been reported in night-shift populations.[16,17] When noise coincides with irregular schedules, the burden increases, and traffic levels above 55 dB(A) during rest have been associated with increased metabolic risk.[18] Transportation noise can trigger oxidative stress and endothelial dysfunction, reflected by increased levels of inflammatory markers, such as C-reactive protein and interleukin-6, which are linked to cardiovascular disease progression.[19]

Most studies have investigated noise and shift work separately, and evidence on their combined effects is limited. This review examines how nighttime noise and shift schedules affect circadian and metabolic regulation, following a Population, Exposure, Comparator, Outcomes (PECO) structure. The main question is whether exposure to occupational or environmental nighttime noise affects circadian, endocrine or metabolic regulation in adults, including shift-working populations, compared with low or no exposure.

The review includes occupational and community noise sources because exposure often occurs simultaneously across work and residential environments. It introduces a dynamic multilevel model that links these exposures with circadian disruption, hormonal regulation, metabolism and recovery. The model is intended as a conceptual framework for risk assessment, not as a predictive tool calibrated for individual outcomes.

MATERIALS AND METHODS

Study Design

This study is a scoping narrative review adapted from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and designed to map physiological pathways rather than to conduct meta-analytic inference. A protocol was not prospectively registered because registration is not mandatory for scoping reviews.[20] Its purpose was to examine how chronic nighttime noise affects physiological systems through circadian misalignment. The review integrated findings from research areas rarely combined in occupational safety and health, enabling mapping of mechanistic pathways and supporting a framework to guide future studies and workplace risk assessments. Key PRISMA elements were applied, including explicit search strings, screening flow with exclusion reasons, study-level characteristics and qualitative risk-of-bias assessment. Reporting was aligned with Strengthening the Reporting of Observational Studies in Epidemiology and PRISMA recommendations, where applicable to observational evidence synthesis.

Search Strategy and Data Sources

Literature search was conducted in PubMed, Scopus, Web of Science and Embase, covering studies published between 1992 and 2024. The final search was updated in January 2025.

The search terms combined environmental or occupational noise with circadian, hormonal, metabolic or cardiovascular outcomes. The Boolean structure was used as follows: (‘night noise’ OR ‘environmental noise’ OR ‘occupational noise’) AND (‘circadian disruption’ OR ‘shift work’ OR ‘melatonin’ OR ‘cortisol’ OR ‘metabolic syndrome’).

Only peer-reviewed studies on humans published in English were included. Filters excluded animal studies and conference abstracts. Duplicate records were removed, followed by manual verification. The reference lists of all included papers were screened to identify additional studies (backward citation chasing).

The search retrieved 200 records. After screening and eligibility assessment were performed, 25 studies met the inclusion criteria, and they were included in the synthesis. Grey literature was excluded because the review focused on peer-reviewed empirical data with validated methodological quality.

Inclusion and Exclusion Criteria

Studies were included if they examined the physiological or health effects of nighttime noise or shift work in adult workers. Eligible designs were observational (cohort, case–control, or cross-sectional) and experimental human studies. Review and meta-analysis papers were screened by the evaluator only to identify primary data and to avoid duplicate cohorts.

The settings covered occupational and community environments because many shift workers are exposed to workplace and transportation noise at the same time. Exposure data had to be reported as night noise level (L_night), day-evening-night level (L_den), equivalent continuous level (LAeq) or maximum level (LAmax), all measured in dB(A). Noise metrics (L_night, L_den and LAeq) were interpreted in alignment with the WHO Environmental Noise Guidelines, and comparisons across metrics were treated conceptually where direct conversion was not feasible.

The nighttime exposure window was defined as from 23:00 to 07:00. Shift work included rotating, permanent night and extended-hour schedules.

Eligible outcomes included markers of circadian and endocrine function (melatonin, cortisol, glucose-insulin regulation and lipid profile) and cardiovascular measures (blood pressure, heart rate variability and inflammatory biomarkers). Studies had to report validated measurement methods or clinical assays. Longitudinal studies required at least 6 months of follow-up to assess time-related associations.

Reported co-exposures and confounders, such as air pollution, heat, job strain, chronotype and socioeconomic status, were recorded and considered qualitatively in the synthesis. Studies were excluded if they focused only on auditory outcomes, used animal or in vitro models, involved non-working populations, lacked quantitative exposure data or defined shift schedules, or were not peer-reviewed. Conference abstracts, editorials and duplicate datasets were excluded.

The criteria aimed to collect evidence from occupational and environmental health, endocrinology and sleep research addressing non-auditory pathways of circadian disruption.

Occupational and community noise exposures were extracted separately during data analysis, and overlapping cohorts were checked to avoid double-counting. The focus remained on adult worker populations even when exposure originated from community sources.

Study Quality and Risk of Bias Assessment

The quality of the included studies was evaluated on the basis of design, reporting and control of confounding factors. The assessment followed the general principles from the Newcastle-Ottawa Scale for observational studies and the Cochrane risk-of-bias approach for experimental research, adapted to occupational and environmental noise studies.

For observational designs, the evaluation considered how participants were selected; how exposure was measured; how confounders, such as age, sex, body mass index and work schedule, were handled; and how physiological outcomes were reported. Experimental studies were reviewed for exposure control, randomisation and completeness of outcome data. Each study was assessed by the evaluator using predefined criteria.

The risk of bias was classified as low, moderate or high. Most studies carried a moderate risk due to small sample sizes or partial control of confounders. Studies with unclear or missing data on noise metrics or exposure duration were rated as high risk. No study was excluded based on this evaluation, and the assessment was used to determine how comparable the findings were.

The assessment was aligned with the Risk Of Bias In Non-randomized Studies of Interventions tool and the Risk Of Bias 2, and heterogeneity in exposure metrics, threshold definitions, shift patterns and sex-specific findings was documented where reported.

Data Extraction and Synthesis

Titles and abstracts identified through database searches were screened against the PECO criteria. Full texts of potentially relevant studies were reviewed to confirm eligibility. Data extraction was conducted using a piloted Excel sheet. Extracted fields included study design, population characteristics, noise metric, exposure window, shift pattern, outcome measures and adjusted effect estimates when available.

Most of the included studies were observational, and the majority used heterogeneous exposure metrics and outcome indicators. Meta-analysis was, therefore, not feasible. A narrative synthesis was applied, grouping evidence by thematic domains to identify consistent findings and research gaps.

Data Analysis

A narrative synthesis was used to compare findings across studies. A quantitative meta-analysis was not feasible because exposure metrics, time windows and outcome definitions differed substantially between studies. Vote-counting was applied only to summarise the direction of findings, not to estimate effect magnitude or infer causality. Data were grouped by study type, population and exposure. Reported effect estimates, when available; hazard ratio (HR), risk ratio (RR) and odds ratio (OR) were reviewed to understand the direction of associations. Figures were created in Python using Matplotlib (version 3.8, Matplotlib Project, USA).

Model Development

The model was organised as a conceptual multilevel dynamic system, designed to illustrate how nighttime noise and shift timing may influence circadian alignment, physiological mediators and long-term health risk through recursive feedback processes. Nighttime noise exposure E(t) and shift work schedule S(t) were defined as external inputs acting on circadian alignment C(t). Early mediators M(t) represented short-term physiological responses such as melatonin suppression, cortisol increase and sleep disruption. Intermediate dysregulation D(t) reflected metabolic and inflammatory changes, including altered glucose regulation and oxidative stress. Long-term risk R(t) described the cumulative probability of chronic effects resulting from repeated exposure cycles.

Risk accumulation R(t) was expressed through a logistic function that captures threshold behaviour and nonlinear increases in disease probability during prolonged exposure. The combined exposure interaction λ · E(t) · S(t) represented the joint effect of noise and shift work, and memory coefficients φ and µ described biological accumulation from repeated circadian and metabolic strain. The model used initial conditions C(0) = 1 and M(0) = D(0) = R(0) = 0 and 1-hour time step for numerical integration.

Parameter values were derived from empirical studies describing hormonal, metabolic and inflammatory responses to nighttime noise and shift work. Literature-based constants (e.g., circadian period, hormonal amplitude and baseline metabolic rates) were fixed. Adaptive parameters (α, λ, φ and µ) were calibrated within reported biological ranges by using sensitivity analyses. Uncertainty and parameter interaction were explored through Monte Carlo simulations.

All computations were implemented in a standard numerical environment for differential equation modelling. The model reproduced exposure patterns typical of occupational settings and simulated the cumulative effects of noise intensity, shift duration and recovery intervals on circadian and metabolic regulation.

RESULTS

Study Selection

The literature review identified 200 records on nighttime noise, circadian disruption and occupational health. After the inclusion and exclusion criteria were applied, 175 studies were excluded, leaving 25 for qualitative synthesis.[21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45] The sample sizes varied substantially across studies, ranging from small experimental samples (n < 20) to large population-based cohorts (n > 10,000), reflecting marked heterogeneity in study scale and design. The study selection process is shown in Figure 1.

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram showing the number of records identified, screened and excluded with reasons and studies included in the review.

A summary of the characteristics and outcomes of the studies included in the qualitative synthesis is presented in Table 1.

Table 1.

Summary of included studies and key findings

Ref.	Author(s), year	Design/Population	Key findings
[21]	Cheng et al., 2021	Cohort	Reports increased metabolic syndrome risk, triglycerides and waist circumference in nightshift workers
[22]	Soltanzadeh et al., 2024	Cohort	Reports increased fasting glucose, LDL, triglycerides and decreased HDL levels in rotating shift workers
[23]	Wang et al., 2011	Observational cohort	Reports increased BMI, insulin resistance and hypertension in long-term night-shift workers
[24]	Knutsson et al., 1999	Case–control study	Reports increased odds of myocardial infarction amongst rotating shift workers
[25]	De Bacquer et al., 2009	Cohort	Reports increased metabolic syndrome incidence in rotating shift workers
[26]	Pan et al., 2011	Two prospective cohorts	Reports increased type 2 diabetes risk in rotating night workers, with a duration–response pattern partly explained by weight gain
[27]	Kawada, 2021	Risk assessment letter	Reports an association between nighttime traffic noise and cardiovascular mortality
[28]	Morikawa et al., 2005	Prospective cohort	Reports increased diabetes incidence in shift workers, especially in two-shift systems
[29]	Leproult et al., 2014	Experimental	Reports increased insulin resistance and inflammation during circadian misalignment
[30]	Eze et al., 2017	Cohort	Reports associations amongst nighttime noise, melatonin-related genes and glucose control
[31]	Asare-Anane et al., 2015	Cross-sectional study	Reports increased BMI, fasting glucose, HbA1c, hs-CRP and dyslipidaemia in shift workers
[32]	Fontana et al., 2019	Experimental	Reports altered circadian gene expression in the auditory system following noise exposure
[33]	Lu et al., 2017	Cross-sectional study	Reports an increase in metabolic syndrome rates, resistin and WBC count in shift workers
[34]	Scheer et al., 2009	Experimental	Reports impaired glucose and cardiovascular regulation during circadian misalignment
[35]	Scott et al., 1997	Pilot study	Reports increased depression rates amongst shift workers
[36]	Münzel et al., 2018	Experimental and cohort studies	Reports increased oxidative stress, inflammation and endothelial dysfunction with traffic noise exposure
[37]	Vetter et al., 2016	Prospective cohort	Reports increased coronary heart disease risk in rotating night workers
[38]	Yeo et al., 2022	Cross-sectional SEM study	Reports that sleep disturbance, depressive symptoms and reduced cognitive efficiency predict work errors, with effects in shift workers
[39]	Lin et al., 2024	Cross-sectional study	Reports increased burnout scores with increasing residential noise exposure, partly mediated by sleep deprivation
[40]	Pietroiusti et al., 2010	Prospective cohort study	Reports an increase in metabolic syndrome incidence amongst night-shift workers
[41]	Wirtz and Nachreiner, 2010	Comparative study	Reports worsened health and well-being with extended work hours
[42]	Virkkunen et al., 2007	Cohort	Reports associations amongst shift work, sleep disruption and cardiometabolic indicators in working populations
[43]	Cagnacci et al., 1992	Experimental study	Reports that melatonin manipulation shifts the circadian rhythm of core temperature
[44]	Zhang et al., 2014	Genomic analysis	Reports circadian gene activity patterns across human tissues
[45]	Zeitzer et al., 2000	Experimental study	Reports dose-dependent melatonin suppression and phase delays with evening light exposure

The findings were organised into four domains: hormonal effects, sleep disorders, metabolic markers and chronic disease risks. Evidence from observational, experimental and review studies was integrated to identify patterns, inconsistencies and gaps. Figure 2 illustrates the multilevel model mapping exposures to nighttime noise E(t) and shift work S(t) to circadian alignment C(t), early mediators M(t) (melatonin and sleep parameters), intermediate dysregulation D(t) (glucose regulation, lipid profiles and blood pressure) and long-term risk R(t). Feedback loops and memory terms (φ, µ) capture cumulative effects over repeated exposure cycles.

Figure 2.

Multilevel dynamic model of circadian disruption caused by work and environmental factors, showing sequential responses from body clock alignment (level 1) to immediate changes in melatonin and sleep (level 2); metabolic effects, such as glucose, lipids and blood pressure (level 3) and long-term health outcomes, including metabolic syndrome and coronary risk (level 4).

The outcomes were extracted in accordance with the study design and measurement timing. Acute hormonal responses, such as melatonin, were measured during the nocturnal period or the first waking phase following night work. Sleep latency and efficiency were assessed during night rest. Metabolic indicators were obtained during clinical assessment. Long-term clinical outcomes, such as incident hypertension or diabetes, were reported in cohort studies.

Hormonal Markers and Circadian Phase Shifts

Experimental works reported changes in melatonin timing during controlled circadian misalignment. The studies described delayed secretion and shifts in circadian phase during and after periods of altered sleep timing.[43,45] These changes appeared together with variation in sleep timing and efficiency, creating a mismatch between work schedules and internal circadian signals.[31,34] Across the available evidence, melatonin timing emerged as the main hormonal marker of circadian disruption, with consistent phase delays during repeated misalignment.[43,45]

Sleep Outcomes and Fragmentation

Research from study designs reported changes in sleep timing and continuity during rotating night duties. Field studies described lengthened sleep latency, irregular sleep periods and increased awakenings in workers exposed to repeated night-work cycles.[31,35] Experimental protocols reported reductions in sleep efficiency and interruptions of nocturnal continuity during circadian misalignment.[34] In real-world settings, irregular programmes of night duties relate to worsened sleep and increased daytime fatigue in groups that cycle often between day and night work.[38] Disturbed sleep and limited nocturnal recovery appear in parallel, and repeated cycles can increase physiological strain across consecutive work periods.[31,34]

Metabolic Indicators and Early Physiological Strain

Research on rotating night duties reported early metabolic changes in working populations. Studies described increased fasting glucose and reduced insulin sensitivity during periods of circadian misalignment.[26,31] An experimental work reported increased postprandial glucose and insulin during misaligned sleep-wake cycles, reflecting strain on short-term metabolic control.[34] Field studies linked rotating schedules with increased waist circumference and altered lipid profiles, findings consistent with early metabolic deviation.[21,40] Workers exposed to irregular programmes of night duties presented features associated with metabolic syndrome, including central adiposity and disturbed glucose regulation.[26,31] These markers appeared in early stages of exposure and pointed towards growing physiological load during repeated night-work cycles.[31,34]

Long-Term Health Risks and Chronic Outcomes

Prolonged exposure to rotating night duties is linked to early markers of cardiometabolic load. Field studies reported increased fasting glucose and waist circumference and altered lipid profiles in workers with repeated cycles of night duties.[21,40] Experimental works supported these observations, noting deviations in glucose handling during periods of circadian misalignment.[26,34] Workers exposed to irregular programmes over extended periods presented features associated with metabolic syndrome, including central adiposity and disturbed glucose regulation.[26,31] These findings point to an accumulation of metabolic strain during recurrent misalignment and suggest that schedule design plays a role in how early these deviations appear.[31,40] The available studies did not provide data on inflammatory markers or cancer outcomes, so long-term observations remain limited to metabolic indicators.

Data Analysis Summary

A vote-counting assessment was performed across the four outcome domains summarised in Figure 3. In the hormonal domain, both studies evaluating melatonin reported measurable alterations in nocturnal secretion and circadian timing, resulting in complete agreement across available evidence. Sleep outcomes were examined in seven studies, and all documented disruptions in at least one parameter of sleep structure, including timing, efficiency or continuity. Metabolic indicators were assessed in five studies, each reporting changes involving glucose regulation, insulin sensitivity or lipid profiles. Chronic disease outcomes were described in six studies, and all identified elevated cardiometabolic risk amongst workers exposed to rotating or night-shift schedules. Across the domains, the synthesis showed a consistent pattern, with every included study reporting an observable effect related to circadian misalignment or night-shift exposure.

Figure 3.

Vote-counting analysis illustrating the proportion of studies reporting effects across the four outcome domains: hormonal responses, sleep outcomes, metabolic indicators and chronic disease risk. All studies within each domain reported measurable effects related to circadian misalignment or night-shift exposure.

Experimental, field and observational studies reported similar results. Groups exposed to rotating night duties showed disrupted sleep, altered metabolic markers and signs of cardiometabolic load. These results appeared across different study designs and populations, as shown in Figure 4.

Figure 4.

Evidence matrix summarising the presence of effects across outcome domains and study designs. Dark cells indicate combinations where at least one study reported an effect on sleep outcomes, metabolic markers, blood pressure or metabolic syndrome/type 2 diabetes. Empty cells indicate the absence of available evidence for that specific pairing.

Conceptual Model Development

This study proposes a framework that extends circadian models beyond light exposure to include occupational factors, such as nighttime noise and shift timing. The conceptual model introduces threshold dynamics, showing that disruption increases once exposures pass a critical level. It adds interaction terms to capture the joint effect of noise and shift work and a stochastic component to reflect individual variability. These extensions provide a new way to connect occupational exposures with circadian disruption, physiological mediators and long-term disease risk [Figure 5].

Figure 5.

Conceptual model of indirect pathways showing how nighttime noise and shift timing act as exposure inputs that combine into a joint exposure term, leading to circadian disruption, physiological mediators, metabolic dysregulation and long-term chronic disease risk.

The model was developed to represent how occupational exposures act on circadian processes and propagate towards chronic outcomes. Nighttime noise E(t) and irregular work schedules S(t) are defined as external forcing factors on the circadian system C(t), and physiological mediators M(t) provide feedback across levels. The model is intended for exploratory hypothesis generation, not precise prediction.

The temporal evolution of circadian misalignment is expressed as follows[46,47]:

where C(t) is circadian misalignment, k_c​ is natural re-entrainment capacity, E(t) nocturnal noise, α denotes exposure forcing, S(t) refers to schedule irregularity, β is schedule weight, λ refers to noise–shift interaction, E_tot​(t) is the combined exposure term, E₀​ is the exposure threshold, θ refers to slope and γM(t) indicates the physiological feedback from mediators such as melatonin and cortisol.

The mediator dynamics are expressed as follows[48]:

where M(t) represents melatonin, cortisol and autonomic activity; k_m refers to homeostatic recovery; δ is the coupling between circadian disruption and mediators; η is the influence of occupational modifiers; O(t) represents contextual factors and ξ(t) denotes the stochastic variability with variance σ².

The propagation into metabolic and inflammatory dysregulation, D(t), is represented as follows[46,48]:

where D(t) includes insulin resistance, dyslipidaemia and inflammation; k_d is metabolic compensation; θ_m is mediator contribution and ψ_c​ is circadian contribution.

Chronic disease risk, R(t), is expressed as a cumulative process as follows[47,48]:

where R(t) is cumulative risk, φ refers to translation from dysregulation and µ denotes the system’s recovery capacity.

The recursive interactions across levels are expressed in discrete time as follows[46]:

C_(t+1)=C_t+[-k_cC_t+αf(e_tot,t)+γM_t],

M_(t+1)=M_t+[-k_mM_t+δC_t+ηO_t+ξ_t],

D_(t+1)=D_t+[-k_dD_t+θ_mM_t+ψ_cC_t],

R_(t+1)=R_t+[ℓD_t-μR_t],

where the subscript t denotes the time step, with E_tot,t, M_t​, and O_t​ representing the exposure, mediators and contextual factors at that step, respectively, and ξ_t is the stochastic input.

The logistic term yields minimal effects below E_0​ and saturating responses above it. The interaction λE(t)S(t) captures the combined effects of noise and shift schedules, and the stochastic component ξ(t) represents interindividual variability and produces a distribution of trajectories across workers.

This conceptual framework is intended as an exploratory mechanistic representation rather than as a finalised predictive model.

Simulation Scenario

The simulation examined how repeated circadian misalignment moves through the elements of the conceptual model. Each cycle corresponded to 24 hours, and the simulation included five consecutive cycles (120 hours in total). Rotating shifts were entered in the model as the external term S(t). Successive cycles change circadian timing in ways described in experimental work on melatonin phase changes.[43,45] The simulated curves move in the same direction as findings from field studies, with reduced nighttime stability and shifts in sleep timing similar to those reported in workers on night duties. The model produced gradual shifts in circadian timing and lowered nighttime stability. These changes resemble observations from shift-working groups, where sleep duration and sleep efficiency vary across night schedules.[31,34] The simulation extended these changes into the metabolic part of the model, where the output matches the deviations described in studies on glucose handling and early metabolic strain during rotating work. Repeated cycles created an accumulated load that mirrors reports of cardiometabolic strain in long-term night-work routines.[26,31] Repeated cycles led to a cumulative direction of change consistent with reports of cardiometabolic load in long-term shift work.[31,40]

The simulation can help to observe how strain increases during repeated misalignment and offers a comparison of roster options, recovery periods and individual differences. These sequences are exploratory, they are not numerical predictions and can support early assessments of scheduling alternatives.

DISCUSSION

These findings should be interpreted as associations rather than evidence of direct causality, given that most included studies were observational and heterogeneous in exposure assessment.

Integrated Effects of Circadian Disruption

This review draws on experimental findings and field observations to outline how circadian disruption can develop across work cycles. Melatonin timing changes reported in laboratory studies are consistent with sleep disturbance and early metabolic deviation described in workers exposed to rotating schedules.[31,38,43] These observations support a broader view in which circadian misalignment affects several physiological systems rather than a single pathway. The combination of disturbed sleep and metabolic imbalance indicates that irregular work patterns can generate cumulative strain that increases across repeated exposure cycles.[26,34] For working populations, these changes may appear before clinical outcomes and highlight the value of early markers in monitoring programmes.[21,31] The results from different study designs showed comparable effects across work settings and pointed to the influence of roster structure and recovery periods on physiological load.[34,41] These findings underline the importance of examining how schedule design, recovery time and individual tolerance shape the pace of circadian strain over time.[35,41]

Evidence Inconsistencies and Research Gaps

The results were not uniform across study designs. Short-term experimental works often recorded modest or temporary physiological changes, whereas studies covering longer periods of rotating night duties reported clearer and more persistent alterations.[23,32,34] This contrast suggests that acute laboratory protocols capture early deviations, whereas repeated misalignment in real work settings allows physiological strain to grow across sleep and metabolic processes.[26,31] Observational studies described variation between individuals, pointing to differences in tolerance and recovery capacity amongst workers.[21,39]

The combination of short-term deviations and findings from cross-sectional and cohort studies helps explain why controlled experiments may show limited changes, whereas population-based research reports increased cardiometabolic strain in groups exposed to prolonged misalignment.[31,40] These observations revealed several gaps, including uncertainty over the duration of misalignment needed to produce measurable physiological load, the role of recovery intervals in reducing strain and the contribution of factors, such as age, accumulated sleep debt or baseline metabolic status.[37,41]

Extending Circadian Models with Occupational Exposures

Circadian regulation was firstly described as a response to light and darkness, and newer studies indicated that changes in sleep timing and core physiological rhythms can influence circadian phase.[43,45] This framework builds on these models by examining shift work as an external driver of misalignment on the basis of evidence of altered melatonin secretion, disrupted sleep patterns and early metabolic deviations in rotating and night-shift schedules.[31,34] Recursive feedback patterns, such as fragmented sleep contributing to reduced capacity for metabolic recovery, suggest how disruption may reinforce itself over time. Memory terms represent potential accumulation of strain from repeated misalignment, increasing baseline vulnerability even when individual episodes are brief.

Compared with existing circadian models, this framework introduces three exploratory extensions: nonlinearity with threshold and saturation effects, interindividual variability and interaction between schedule characteristics. Firstly, it replaces linear assumptions with a logistic formulation that allows low levels of misalignment to have minimal effect, whereas more persistent exposures may accelerate disruption. Secondly, it incorporates a stochastic component to capture interindividual variability, producing a distribution of potential responses rather than a single trajectory. Thirdly, it includes interaction terms related to shift timing and recovery intervals, indicating that scheduling patterns may exert a combined influence on circadian stability, a factor not addressed in light-dominant circadian models.

Simulation Results and Model Applicability

The simulation followed trends described in experimental and field studies. Repeated misalignment shifted circadian timing and reduced nighttime stability, in line with phase delays seen in controlled protocols and with sleep changes reported in groups working night duties.[31,34,43,45] These shifts continued through the metabolic part of the model, where the simulation produced changes similar to early alterations in glucose handling and metabolic strain described in observational work.[26,31] As cycles accumulated, the direction of change converged with findings from studies that report cardiometabolic load in long-term rotating duties.[31,40] These results suggest that the model can support exploratory work on scheduling choices. Simulations can compare forward and backward rotation, lengthened recovery intervals or shortened night-shift sequences. Additional scenarios may test the value of brief recovery breaks during extended shifts or the influence of individual tolerance on the pace of circadian disruption. The model can help estimate how different scheduling patterns may shape the build-up of physiological strain over time.

Implications for Occupational Health Practice and Policy

The review suggests that rotating and night-shift schedules may disturb circadian timing and produce early physiological strain. These changes affect sleep, metabolic regulation and cardiometabolic load, and they should be considered in workplace risk assessment.[31,34] Evidence across study designs suggests that the structure of the schedule and the time available for recovery influence the pace at which strain develops.[34,41]

The model can support exploratory work on scheduling strategies. Simulations may compare limits on consecutive night shifts, the effect of prolonged recovery intervals or the difference between forward and backward rotation. Additional scenarios may test shortened night-shift sequences or brief recovery breaks during extended shifts.[34,40] These applications can guide early discussions about work standards that incorporate circadian considerations whilst still requiring feasibility assessment before policy use.[31,40]

Public Health Implications

The findings indicate that rotating and night-shift schedules may disturb circadian timing and contribute to early metabolic strain, aspects relevant for population-level monitoring.[31,34] Studies in working populations point to increased metabolic risk, where misalignment persists across multiple shift cycles.[21,26]

Surveillance systems often record shift history without indicators of circadian disruption, which may overlook early changes related to sleep, glucose handling and cardiometabolic load.[31,40] Research on recovery intervals and roster structure indicates that schedule design affects the rate at which strain develops.[34,41]

The model can help explore how surveillance programmes may integrate circadian indicators. Combinations of shift sequence, recovery time and simple sleep metrics could support the identification of groups with repeated misalignment.[43,45] Populations with irregular schedules and limited recovery may face increased cumulative load, as seen in studies on metabolic disruption and sleep variation amongst shift workers.[26,31]

These groups may benefit from periodic checks of sleep and metabolic status, using practical markers that detect early deviations from circadian stability.[21,31] Such approaches remain exploratory, and they could require structured evaluation before being included in routine public health surveillance.[34,40]

Limitations and Future Research

The evidence base is heterogeneous, mostly cross-sectional and concentrated in high-income urban areas. These features reduce causal certainty and limit the generalisability of the model to other populations and work settings. Reliance on self-reported outcomes further weakens the validity, and thresholds for melatonin, cortisol or metabolic change remain uncertain. Amongst the included studies, exposure assessment methods varied considerably. Some studies used modelled or averaged community noise, whereas others used direct measurements in the workplace. Shift work exposure was defined either through administrative records or self-reported schedules. These inconsistencies may lead to measurement error and affect comparability across studies. For these reasons, the model should be considered exploratory rather than predictive. These limitations should be considered when interpreting the findings, especially regarding generalisability and potential exposure misclassification. Future research should focus on long-term cohorts with objective exposure and health measures, mechanistic studies on inflammation and autonomic regulation and validation of simulation parameters in diverse occupational contexts.

CONCLUSIONS

Rotating and night-shift schedules can alter circadian timing and create early physiological strain. Experimental and field studies describe shifts in melatonin phase, changes in sleep and early metabolic deviations in workers exposed to repeated misalignment. These changes can build up over time when shift cycles continue with limited recovery.

The conceptual model places these processes in a single structure. Circadian timing, sleep and metabolic responses are treated as connected elements that influence on another during repeated misalignment. The model incorporates individual variation and possible threshold effects, which may help explain why workers respond differently to similar schedules.

The simulations help to test scheduling options before they are applied in practice. Shift sequence, recovery time and the length of night-shift blocks can be compared to observe how strain develops across cycles. The results are exploratory, yet they outline directions for future research on roster design and early physiological monitoring in groups exposed to recurrent misalignment.

Availability of Data and Materials

All data generated or analyzed during this study are included in this published article.

Author Contributions

The author confirms their sole responsibility for the study’s conception, results, and manuscript preparation.

Ethics Approval and Consent to Participate

Not applicable.

Financial Support and Sponsorship

This research received no external funding.

Conflicts of Interest

There are no conflicts of interest.