Abstract
Background
Sleep regularity, defined as the consistency of sleep-wake timing across days, is increasingly recognized as an important dimension of sleep health. While inadequate sleep duration is a known risk factor for type 2 diabetes (T2D), the independent contribution of irregular sleep remains poorly understood. Evidence is particularly limited in Asian populations using objective sleep measures.
Objectives
To evaluate the association between accelerometer-derived sleep regularity and incident T2D, and to examine whether meeting recommended sleep duration mitigates the risk associated with irregular sleep patterns.
Methods
We analyzed data from 8,974 insured Chinese adults aged 40–79 years using wearable-linked medical insurance records between August 2020 and June 2023. Raw accelerometry data were collected over 7 consecutive days and processed to compute the Sleep Regularity Index (SRI). Individuals were classified into regular (SRI > 87.3), moderately irregular (71.6–87.3), and irregular (< 71.6) categories. Sleep duration was categorized as adequate or inadequate based on age-specific recommendations. Incident T2D was identified from hospital electronic health records and prescription registries during a median 3.0-year follow-up. Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) for T2D across SRI categories and in joint exposure models.
Results
Compared to individuals with irregular sleep, those with moderately irregular (HR 1.22; 95% CI: 1.08–1.39) and regular sleep (HR 1.22; 95% CI: 1.06–1.40) patterns had significantly higher risk of developing T2D. A non-linear association was observed, with the highest risk at mid-range SRI levels. Meeting sleep duration recommendations did not mitigate the elevated risk associated with irregular sleep. Notably, individuals with regular sleep but insufficient duration had a lower, though non-significant, risk of T2D (HR 0.77; 95% CI: 0.56–1.07).
Conclusions
In this database-based Chinese cohort, sleep regularity showed a non-linear association with incident T2D risk, independent of sleep duration. Public health efforts to promote consistent sleep timing may offer additional benefits for diabetes prevention beyond sleep duration alone.
Keywords: Sleep regularity, Sleep duration, Type 2 diabetes, Sleep health, Circadian rhythm
Introduction
With the growing emphasis on modifiable behavioral factors in chronic disease prevention, sleep health has garnered increasing attention in recent years [1, 2]. The global epidemics of obesity and type 2 diabetes (T2D) parallel the widespread prevalence of sleep deprivation in modern societies. Numerous epidemiological studies have consistently demonstrated a strong association between insufficient sleep and impaired metabolic function [3]. Although prior research has extensively examined sleep duration and quality, sleep regularity—defined as the consistency in bedtimes and wake times across days—remains a relatively underexplored dimension [4].
Sleep regularity plays a critical role in maintaining stable circadian rhythms [5]. The circadian system is susceptible to various disturbances, including environmental disruptions, irregular lifestyles, abnormalities in clock gene expression, and feedback from disease states. Circadian misalignment has been implicated in the pathogenesis of multiple neurological, metabolic, and immune-related disorders [6]. Accumulating evidence suggests that inconsistent sleep–wake timing may elevate the risk of cardiometabolic diseases, including T2D [7]. Experimental studies have further demonstrated that circadian disruption and irregular sleep timing can lead to insulin resistance [8], dysregulation of appetite [9], and systemic inflammation [10], all of which exacerbate metabolic dysfunction.
Despite these findings, most large-scale epidemiological studies have focused primarily on sleep duration, paying limited attention to the role of sleep timing consistency. Moreover, existing data on sleep regularity and T2D risk are largely derived from Western populations and rely heavily on self-reported sleep measures, limiting generalizability. Notably, recent evidence indicates that even individuals achieving the recommended 7–9 h of sleep per night may still face increased T2D risk if their sleep timing is inconsistent [11]. This observation challenges current public health guidelines, which often emphasize total sleep duration while overlooking the importance of temporal consistency.
To address these research gaps, we analyzed data from a large cohort of Chinese adults enrolled in the national medical insurance system, with objective sleep records obtained from wearable devices. Using passively collected data from routine health monitoring, we evaluated the association between the Sleep Regularity Index (SRI) and incident T2D. Additionally, we investigated whether meeting age-specific recommendations for sleep duration modified the association between sleep irregularity and T2D risk. By simultaneously assessing both the temporal and quantitative dimensions of sleep, this study provides novel evidence supporting the inclusion of sleep timing consistency in strategies for metabolic disease prevention.
Materials and methods
Data source and study population
1This retrospective cohort study was based on a nationwide medical insurance database in China that integrates individual-level health records, hospital claims, and wearable-linked digital health data. The database includes routinely collected information on sociodemographic characteristics, diagnoses, prescriptions, physical activity, and sleep metrics captured via commercial wrist-worn devices. Individuals aged 40–79 years with valid accelerometer-linked data between August 2020 and June 2023 were eligible for inclusion. As shown in Fig. 1, a total of 15,000 adults with linked wearable and health records were initially identified. After excluding participants with no or invalid wearable data (n = 3,000), 12,000 individuals with at least five valid days of wearable data remained. Subsequently, those with insufficient weekend wear time (n = 1,800) were excluded, leaving 10,200 individuals with a valid Sleep Regularity Index (SRI). After further excluding participants with missing baseline covariates or T2D outcome data (n = 900) and those with prevalent T2D or a diagnosis within the first year of follow-up (n = 326), the final analytical sample comprised 8,974 participants.
Fig. 1.
Flowchart showing inclusion and exclusion of study participants from initial identification to the final analytical sample
Exposure assessment
Sleep-related exposure variables were derived from accelerometry data passively collected using Axivity AX3 wrist-worn devices, deployed as part of routine digital health monitoring within the insurance-based wellness program. The devices continuously recorded raw triaxial acceleration signals at a sampling rate of 100 Hz. Data preprocessing was conducted using the open-source R package GGIR (v2.7-1), which implemented automatic calibration, detection of non-wear periods (defined as ≥ 60 min of minimal variability), and epoch-level classification of sleep and wake states.
Participants were instructed to wear the accelerometer continuously for seven consecutive days, covering both weekdays and weekends. A valid recording day was defined as having at least 16 h of wear time. To ensure sufficient temporal coverage and reliability in characterizing habitual sleep behavior, participants with at least five valid days of data—including at least one weekday and one weekend day—were included in the final analysis. This compliance threshold aligns with established protocols from large-scale studies such as the UK Biobank, which adopted ≥ 5 valid days as the standard criterion for accelerometer-based sleep research [12].
The Sleep Regularity Index (SRI) was calculated based on minute-level transitions between sleep and wake states across consecutive days, representing the probability of being in the same state at the same time on adjacent days, with scores ranging from 0 (completely irregular) to 100 (perfectly regular). SRI was computed using all available valid days across the 7-day recording period to reflect real-world sleep–wake consistency. Given that most participants contributed only one to two weekend days, restricting the calculation to workdays would have substantially reduced temporal coverage and increased interindividual variability. This full-week approach has been widely adopted in previous SRI research and provides a more stable representation of habitual sleep patterns.According to the distribution in the analytic sample, SRI was categorized into three groups: irregular (< 71.6), moderately irregular (71.6–87.3), and regular (>87.3). These cutoffs correspond to the tertiles of the SRI distribution in our analytic sample and were selected to ensure approximately balanced group sizes while maintaining statistical power for comparison. This data-driven classification strategy is commonly used in studies of behavioral exposures where noestablished clinical thresholds exist, including research on SRI in both validation cohorts and large-scale population studies [13, 14].
Daily sleep duration was computed as the average sleep time across all valid days and classified as adequate or inadequate based on age-specific recommendations: 7–9 h/day for adults aged 18–64 years and 7–8 h/day for those aged ≥ 65 years.
Outcome ascertainment
The primary outcome was incident T2D during follow-up. Diagnoses were ascertained from hospital electronic medical records and insurance billing data, and defined by ICD-10 code E11, physician-confirmed diagnosis, or prescription of antidiabetic medications (e.g., metformin, insulin). Individuals with any T2D record prior to baseline or within the first year after baseline were excluded to minimize the possibility of reverse causation.
Covariates
Baseline covariates were extracted from linked medical records and insurance files, including age, sex, moderate-to-vigorous physical activity (MVPA, minutes/day), screen time, smoking status (never, former, current), alcohol consumption frequency (times/week), coffee intake (cups/day), fruit and vegetable intake (servings/day), presence of sleep problems, medication use, mental health issues, family history of cardiovascular disease (CVD), and employment status (retired, employed with or without shift work). All covariates were selected based on prior evidence linking these factors to both sleep and type 2 diabetes (T2D).
Data processing and statistical analysis
Raw accelerometer data (.bin format) were processed through GGIR (v2.7-1) to derive sleep-wake classification, SRI, and average daily sleep duration. Individuals with < 5 valid days or invalid wear data were excluded. The primary exposure, SRI, was analyzed both categorically (three levels) and continuously. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident T2D, using age as the underlying time scale. The proportional hazards assumption was assessed with Schoenfeld residuals. Restricted cubic spline models with knots at the 10th, 33rd, and 67th percentiles of SRI were employed to evaluate potential non-linear associations between sleep regularity and T2D risk. A joint exposure analysis was also conducted by cross-classifying SRI categories with sleep duration adequacy, resulting in six mutually exclusive groups. Covariates were included based on prior knowledge and theoretical relevance. Complete-case analysis was used for missing covariate data (< 5%). All statistical analyses were conducted using R (version 4.2.3), and a two-sided p-value < 0.05 was considered statistically significant.
Clinical trial registration statement
This study was a retrospective cohort analysis using routinely collected medical records and wearable device data. Therefore, clinical trial registration was not applicable.
Results
Baseline characteristics
A total of 8,974 individuals with complete data were included in the final analytical sample. Baseline characteristics across sleep regularity categories are summarized in Table 1. The mean age of the cohort was approximately 59.6 years, and 44.6% were male. Sleep regularity, as measured by the Sleep Regularity Index (SRI), was categorized into irregular (n = 1,772), moderately irregular (n = 5,094), and regular (n = 2,108) groups. Across SRI groups, the distribution of demographic and lifestyle variables was generally comparable. The proportion of individuals meeting sleep duration recommendations was similar across groups, ranging from 50.9% in the irregular group to 52.9% in the moderately irregular group. The prevalence of self-reported sleep problems was 53.8%–55.6% across categories.
Table 1.
Baseline characteristics of participants according to sleep regularity categories
| Baseline Characteristic | Regular | Moderately Irregular | Irregular | Full Sample |
|---|---|---|---|---|
| Demographics | ||||
| Follow-up time | 2.95 (1.17) | 3.05 (1.15) | 2.99 (1.14) | 3.01 (1.16) |
| Age at enrollment | 59.77 (11.46) | 59.67 (11.50) | 59.27 (11.56) | 59.62 (11.50) |
| Male sex | 961 (45.6) | 2,254 (44.2) | 790 (44.6) | 4,005 (44.6) |
| Behavioral Factors | ||||
| MVPA | 29.44 (14.51) | 30.63 (14.85) | 30.75 (14.65) | 30.37 (14.74) |
| Screen time | 3.70 (1.00) | 3.67 (1.01) | 3.70 (1.00) | 3.68 (1.01) |
| Fruit & vegetable intake (servings/day) | 8.07 (2.56) | 8.06 (2.49) | 8.00 (2.47) | 8.05 (2.50) |
| Coffee Intake | ||||
| Coffee: None | 529 (25.1) | 1,288 (25.3) | 465 (26.2) | 2,282 (25.4) |
| Coffee: 1–3 cups | 1,171 (55.6) | 2,804 (55.0) | 955 (53.9) | 4,930 (54.9) |
| Coffee: >3 cups | 408 (19.4) | 1,002 (19.7) | 352 (19.9) | 1,762 (19.6) |
| Smoking Status | ||||
| Never smoker | 1,269 (60.2) | 2,998 (58.9) | 1,012 (57.1) | 5,279 (58.8) |
| Former smoker | 693 (32.9) | 1,718 (33.7) | 635 (35.8) | 3,046 (33.9) |
| Current smoker | 146 (6.9) | 378 (7.4) | 125 (7.1) | 649 (7.2) |
| Behavioral Factors (continued) | ||||
| Alcohol consumption frequency (times/week) | 14.34 (9.21) | 14.25 (9.25) | 14.57 (9.36) | 14.34 (9.27) |
| Medical History | ||||
| Mental health issues | 412 (19.5) | 1,012 (19.9) | 337 (19.0) | 1,761 (19.6) |
| Medication use | 449 (21.3) | 1,134 (22.3) | 390 (22.0) | 1,973 (22.0) |
| Family history of CVD | 1,188 (56.4) | 2,872 (56.4) | 1,021 (57.6) | 5,081 (56.6) |
| Employment | ||||
| Retired/not in workforce | 857 (40.7) | 1,996 (39.2) | 731 (41.3) | 3,584 (39.9) |
| Employed, not in shift work | 945 (44.8) | 2,311 (45.4) | 796 (44.9) | 4,052 (45.2) |
| Night shift work | 191 (9.1) | 534 (10.5) | 172 (9.7) | 897 (10.0) |
| Day shift work | 115 (5.5) | 253 (5.0) | 73 (4.1) | 441 (4.9) |
| Sleep-related | ||||
| Sleep problems | 1,173 (55.6) | 2,741 (53.8) | 954 (53.8) | 4,868 (54.2) |
| Meeting sleep duration guideline | 1,097 (52.0) | 2,696 (52.9) | 902 (50.9) | 4,695 (52.3) |
| Not meeting sleep duration guideline | 1,011 (48.0) | 2,398 (47.1) | 870 (49.1) | 4,279 (47.7) |
| T2D | 1,432 (67.9) | 3,724 (73.1) | 1,362 (76.9) | 6,518 (72.6) |
Values are presented as mean (standard deviation) for continuous variables and number (percentage) for categorical variables
Sleep regularity was categorized into regular (Sleep Regularity Index [SRI] >87.3), moderately irregular (SRI 71.6–87.3), and irregular (SRI < 71.6) groups
MVPA moderate-to-vigorous physical activity, CVD cardiovascular disease, T2D type 2 diabetes
Sleep regularity and incident type 2 diabetes
During a median follow-up of 3.0 years, a total of 1,794 incident cases of type 2 diabetes were documented among 8,974 participants. As shown in Fig. 2, individuals in the moderately irregular sleep group (n = 5,094) had a significantly higher risk of developing T2D compared to those with regular sleep patterns (n = 2,108), with an adjusted hazard ratio (HR) of 1.22 (95% confidence interval [CI]: 1.08–1.39). Similarly, participants with irregular sleep (n = 1,772) also exhibited an elevated T2D risk (HR =1.22; 95% CI: 1.06–1.40) relative to the regular group.
Fig. 2.
Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes across Sleep Regularity Index (SRI) categories. The regular sleep group (n = 2,108) was used as the reference. N (Events) represents the number of participants and incident T2D cases in each group. Sleep regularity was categorized as irregular (SRI < 71.6), moderately irregular (71.6–87.3), and regular (> 87.3). SRI > 87.3 typically corresponds to individuals with bed and wake times varying by less than 30–60 min across most days of the week
These findings indicate that even moderate deviations from a consistent sleep–wake schedule are associated with a meaningful increase in diabetes risk, independent of total sleep duration.
Non-linear association of SRI with T2D risk
The association between SRI as a continuous variable and incident T2D was further examined using restricted cubic splines. As shown in Fig. 3, a non-linear relationship was observed, with a slight increase in T2D risk at mid-range SRI values (~ 70–80), followed by a decline in risk at higher SRI levels. The reference SRI was set at the lowest observed level, and the spline indicated that T2D risk did not decline monotonically with increasing regularity.
Fig. 3.
Non-linear relationship between Sleep Regularity Index (SRI) values and the risk of incident type 2 diabetes, estimated by restricted cubic spline regression. Reference value set at the 10th percentile. Shaded area indicates 95% confidence intervals
Joint association of sleep regularity and sleep duration
Joint exposure analysis of SRI category and adherence to sleep duration guidelines yielded six groups (Fig. 4). Using individuals with regular sleep and adequate sleep duration as the reference group (HR = 1.00), those with moderately irregular and irregular sleep patterns exhibited elevated but non-significant risks of T2D regardless of whether they met sleep duration guidelines. Notably, individuals in the “Regular + Inadequate sleep duration” group had a lower, though statistically non-significant, risk of T2D (HR 0.77, 95% CI: 0.56–1.07).
Fig. 4.
Joint associations between sleep regularity categories and adherence to sleep duration guidelines with the risk of incident type 2 diabetes. The reference group was “Regular + Meeting Guidelines”. Note: “Regular” corresponds to SRI > 87.3, which reflects consistent sleep–wake schedules within a 30–60 min window across at least 5–6 days per week
Discussion
This study prospectively evaluated the association between sleep regularity and the incidence of type 2 diabetes (T2D) using health insurance records from a large sample of Chinese adults. The findings revealed a non-linear relationship between the Sleep Regularity Index (SRI) and T2D risk: both moderately irregular and irregular sleep patterns were associated with a significantly higher risk of developing T2D, compared to those with regular sleep timing. Additionally, meeting age-specific recommendations for total sleep duration did not significantly mitigate the elevated T2D risk associated with inconsistent sleep timing. Notably, among individuals with insufficient sleep duration, those maintaining regular sleep timing showed a trend toward lower T2D risk, although this association did not reach statistical significance.
Compared with previous studies, most of which have been conducted in Western populations, our findings show certain distinctions. Prior research has generally suggested that irregular sleep is closely linked to adverse cardiometabolic outcomes [15–18]. However, such studies often relied on self-reported sleep data and assumed a linear association, without adequately addressing potential non-linear patterns. A recent UK Biobank study reported a monotonic increase in T2D risk with increasing degrees of sleep irregularity [19, 20]. In contrast, our spline regression analysis demonstrated a U-shaped relationship, indicating that individuals with either extremely regular or moderately irregular sleep timing may be at higher risk. This divergence may reflect population-specific factors, such as differences in work-rest schedules, meal timing, nighttime light exposure, and social engagement between urban and rural residents in China, all of which may influence circadian alignment and, in turn, modulate the relationship between sleep patterns and metabolic health.
From a mechanistic perspective, sleep regularity may influence glucose metabolism via its impact on circadian regulation [21]. Inconsistent sleep–wake timing can disrupt the rhythmic secretion of hormones regulated by the hypothalamic–pituitary–adrenal (HPA) axis [22–24] thereby reducing insulin sensitivity [25, 26] and impairing glucose tolerance [27–29]. On the other hand, extremely regular sleep schedules may reflect behavioral rigidity, social isolation, or underlying subclinical health conditions. Furthermore, our findings show that inconsistent sleep timing is associated with increased T2D risk even among individuals who meet recommended sleep duration, highlighting the potential independent role of sleep timing regularity in metabolic risk stratification.
To enhance the interpretability and practical relevance of our findings, we sought to contextualize the SRI thresholds in behavioral terms. Based on post hoc analysis, participants classified as having regular sleep patterns (SRI > 87.3) typically maintained consistent bedtimes and wake times within a 30–60 min window on at least 5 to 6 days per week. In contrast, individuals in the irregular category (SRI < 71.6) often showed variability exceeding 90 min from day to day. These behavioral approximations may provide clinicians and public health practitioners with practical guidance: namely, that minimizing daily variability in sleep-wake timing and maintaining regularity across most days of the week may confer protective benefits for glycemic health, regardless of sleep duration.
This study offers several strengths. First, the use of real-world clinical data from a national health insurance database enhances the generalizability and applicability of the results. Second, the large sample size and standardized data processing strengthen the robustness and reproducibility of the findings. Nonetheless, certain limitations should be acknowledged. Despite adjusting for numerous covariates, residual confounding cannot be entirely ruled out. Additionally, information on shift work, dietary timing, and individual chronotype was not available, which may have introduced unmeasured effect modification. Finally, although the use of routine healthcare data helps mitigate selection bias, individuals with comprehensive insurance-linked health records may differ from the general population in health awareness and healthcare-seeking behaviors.
Conclusion
In this large, database-based Chinese cohort, sleep regularity exhibited a significant non-linear association with the risk of incident type 2 diabetes, independent of sleep duration. Both moderately irregular and highly regular sleep timing patterns were associated with an increased risk, suggesting that optimal metabolic health may require not only sufficient sleep duration but also an appropriate degree of variability in sleep timing. Notably, adherence to recommended sleep duration did not attenuate the elevated risk associated with irregular sleep patterns. These findings highlight the importance of incorporating sleep regularity into public health recommendations for diabetes prevention. Specifically, maintaining consistent bed and wake times within a 30–60 min range on at least 5 to 6 days per week may provide additional protection against diabetes risk, beyond achieving recommended sleep duration.
Authors’ contributions
Y.L. and W.Z. contributed equally to this work and share first authorship. Y.L. and X.W. conceptualized and designed the study. W.Z. performed statistical analyses and drafted the initial manuscript. Q.Y. contributed to data interpretation and assisted in manuscript revision. X.W. supervised the study and provided critical revisions. All authors read and approved the final manuscript.
Funding
No funding was received for this study.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Shanghai Sixth People’s Hospital (Approval No: 2025-KY-135(K)). Due to the retrospective nature of the study, which used anonymized medical insurance data and routinely collected accelerometer data without involving direct contact with participants, informed consent was waived by the Ethics Committee. All procedures performed in this study complied with institutional guidelines and the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yubin Liu and Wenbo Zhao contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.