Abnormally short and long sleep are associated with premature mortality, and achieving optimal sleep duration has been the focus of sleep health guidelines. Emerging research demonstrates that sleep regularity, the day-to-day consistency of sleep–wake timing, can be a stronger predictor for some health outcomes than sleep duration. The role of sleep regularity in mortality, however, has not been investigated in a large cohort with objective data. We therefore aimed to compare how sleep regularity and duration predicted risk for all-cause and cause-specific mortality. We calculated Sleep Regularity Index (SRI) scores from > 10 million hours of accelerometer data in 60 977 UK Biobank participants (62.8 ± 7.8 years, 55.0% female, median[IQR] SRI: 81.0[73.8–86.3]). Mortality was reported up to 7.8 years after accelerometer recording in 1859 participants (4.84 deaths per 1000 person-years, mean (±SD) follow-up of 6.30 ± 0.83 years). Higher sleep regularity was associated with a 20%–48% lower risk of all-cause mortality (p < .001 to p = 0.004), a 16%–39% lower risk of cancer mortality (p < 0.001 to p = 0.017), and a 22%–57% lower risk of cardiometabolic mortality (p < 0.001 to p = 0.048), across the top four SRI quintiles compared to the least regular quintile. Results were adjusted for age, sex, ethnicity, and sociodemographic, lifestyle, and health factors. Sleep regularity was a stronger predictor of all-cause mortality than sleep duration, by comparing equivalent mortality models, and by comparing nested SRI-mortality models with and without sleep duration (p = 0.14–0.20). These findings indicate that sleep regularity is an important predictor of mortality risk and is a stronger predictor than sleep duration. Sleep regularity may be a simple, effective target for improving general health and survival.

Light enhances or disrupts circadian rhythms, depending on the timing of exposure. Circadian disruption contributes to poor health outcomes that increase mortality risk. Whether personal light exposure predicts mortality risk has not been established. We therefore investigated whether personal day and night light, and light patterns that disrupt circadian rhythms, predicted mortality risk. UK Biobank participants (N = 88,905, 62.4 ± 7.8 y, 57% female) wore light sensors for 1 wk. Day and night light exposures were defined by factor analysis of 24-h light profiles. A computational model of the human circadian pacemaker was applied to model circadian amplitude and phase from light data. Cause-specific mortality was recorded in 3,750 participants across a mean (±SD) follow-up period of 8.0 ± 1.0 y. Individuals with brighter day light had incrementally lower all-cause mortality risk (adjusted-HR ranges: 0.84 to 0.90 [50 to 70th light exposure percentiles], 0.74 to 0.84 [70 to 90th], and 0.66 to 0.83 [90 to 100th]), and those with brighter night light had incrementally higher all-cause mortality risk (aHR ranges: 1.15 to 1.18 [70 to 90th], and 1.21 to 1.34 [90 to 100th]), compared to individuals in darker environments (0 to 50th percentiles). Individuals with lower circadian amplitude (aHR range: 0.90 to 0.96 per SD), earlier circadian phase (aHR range: 1.16 to 1.30), or later circadian phase (aHR range: 1.13 to 1.20) had higher all-cause mortality risks. Day light, night light, and circadian amplitude predicted cardiometabolic mortality, with larger hazard ratios than for mortality by other causes. Findings were robust to adjustment for age, sex, ethnicity, photoperiod, and sociodemographic and lifestyle factors. Minimizing night light, maximizing day light, and keeping regular light–dark patterns that enhance circadian rhythms may promote cardiometabolic health and longevity.

Light at night disrupts circadian rhythms, and circadian disruption is a risk factor for type 2 diabetes. Whether personal light exposure predicts diabetes risk has not been demonstrated in a large prospective cohort. We therefore assessed whether personal light exposure patterns predicted risk of incident type 2 diabetes in UK Biobank participants, using ∼13 million hours of light sensor data. Participants (N = 84,790, age (M ± SD) = 62.3 ± 7.9 years, 58% female) wore light sensors for one week, recording day and night light exposure. Circadian amplitude and phase were modeled from weekly light data. Incident type 2 diabetes was recorded (1997 cases; 7.9 ± 1.2 years follow-up; excluding diabetes cases prior to light-tracking). Risk of incident type 2 diabetes was assessed as a function of day and night light, circadian phase, and circadian amplitude, adjusting for age, sex, ethnicity, socioeconomic and lifestyle factors, and polygenic risk. Compared to people with dark nights (0–50th percentiles), diabetes risk was incrementally higher across brighter night light exposure percentiles (50–70th: multivariable-adjusted HR = 1.29 [1.14–1.46]; 70–90th: 1.39 [1.24–1.57]; and 90–100th: 1.53 [1.32–1.77]). Diabetes risk was higher in people with lower modeled circadian amplitude (aHR = 1.07 [1.03–1.10] per SD), and with early or late circadian phase (aHR range: 1.06–1.26). Night light and polygenic risk independently predicted higher diabetes risk. The difference in diabetes risk between people with bright and dark nights was similar to the difference between people with low and moderate genetic risk. Type 2 diabetes risk was higher in people exposed to brighter night light, and in people exposed to light patterns that may disrupt circadian rhythms. Avoidance of light at night could be a simple and cost-effective recommendation that mitigates risk of diabetes, even in those with high genetic risk. Australian Government Research Training Program.

Dear Editor, Human health and behavior are regulated by a complex and extensive network of circadian clocks. These clocks are entrained by rhythmic signals in the environment, such as daily light exposure. In individuals who have irregular sleep schedules, these signals that furnish time-of-day information to the circadian system are less robust, which may cause circadian disruption and poor health outcomes [ 1]. Sleep regularity can be quantified using the Sleep Regularity Index (SRI) [ 2], a metric that compares sleep patterns between consecutive days (sleeping at similar times each day results in a high SRI). The SRI captures day-to-day variability in bedtime, waketime, sleep duration, naps, and awakenings during sleep [ 3]. Lower SRI has been associated with substantially increased risk for obesity, diabetes, cardiovascular disease, hypertension, and depressed mood [ 4–6]. Although sleep regularity is now recognized as a critical dimension of sleep health, there are barriers to measuring and reporting sleep regularity consistently. First, there are no open-source options for calculating sleep regularity, meaning it cannot be computed with the same ease as other common sleep metrics. Second, there is a lack of clear benchmarks for what represents a high or low level of sleep regularity at a population level, both for the SRI and other sleep regularity metrics [ 3, 4, 6]. We developed an open-source package for computing SRI from accelerometer data, and we applied it to the single largest accelerometer sample available to researchers, within the UK Biobank.

Importance: Light at night causes circadian disruption, which is a known risk factor for adverse cardiovascular outcomes. However, it is not well understood of cardiovascular diseases. Objective: To assess whether day and night light exposure is associated with incidence of cardiovascular diseases, and whether associations of light with cardiovascular diseases differ according to genetic susceptibility, sex, and age. Design, Setting, and Participants: This prospective cohort study analyzed cardiovascular disease records across 9.5 years (June 2013 to November 2022) from UK Biobank participants who wore light sensors in a naturalistic setting. Data were analyzed from September 2024 to July 2025. Exposure: Approximately 13 million hours of light exposure data, tracked by wrist-worn light sensors (1 week each), categorized into the 0 to 50th, 51st to 70th, 71st to 90th, and 91st to 100th percentiles. Main Outcomes and Measures: Incidence of coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, and stroke after light tracking were derived from UK National Health Service records. Risks of cardiovascular diseases were assessed using Cox proportional hazards models (3 primary models adjusted at 3 levels) and reported as hazard ratios (HRs). Results: A total of 88 905 individuals were included (mean [SD] age, 62.4 [7.8] years; 50 577 female [56.9%]). Compared with individuals with dark nights (0-50th percentiles), those with the brightest nights (91st-100th percentiles) had significantly higher risks of developing coronary artery disease (adjusted HR [aHR], 1.32; 95% CI, 1.18-1.46), myocardial infarction (aHR, 1.47; 95% CI, 1.26-1.71), heart failure (aHR, 1.56; 95% CI, 1.34-1.81), atrial fibrillation (aHR, 1.32; 95% CI, 1.18-1.46), and stroke (aHR, 1.28; 95% CI, 1.06-1.55). These associations were robust after adjusting for established cardiovascular risk factors, including physical activity, smoking, alcohol, diet, sleep duration, socioeconomic status, and polygenic risk. Larger-magnitude associations of night light with risks of heart failure ( P for interaction = .006) and coronary artery disease ( P for interaction = .02) were observed for females, and larger-magnitude associations of night light with risks of heart failure ( P for interaction = .04) and atrial fibrillation ( P for interaction = .02) were observed for younger individuals in this cohort. Conclusions and Relevance: In this cohort study, night light exposure was a significant risk factor for developing cardiovascular diseases among adults older than 40 years. These findings suggest that, in addition to current preventive measures, avoiding light at night may be a useful strategy for reducing risks of cardiovascular diseases.

Little is known about the interrelationships between sleep regularity, obstructive sleep apnea (OSA) and important health markers. This study examined whether irregular sleep is associated with OSA and hypertension, and if this modifies the known association between OSA and hypertension. Six hundred and two adults (age mean(SD) = 56.96(5.51) years, female = 60%) from the Raine Study who were not evening or night shift workers were assessed for OSA (in-laboratory polysomnography; apnea–hypopnea index ≥ 15 events/hour), hypertension (doctor diagnosed, or systolic blood pressure ≥140 mmHg and/or diastolic ≥90 mmHg) and sleep (wrist actigraphy for ≥5 days). A sleep regularity index (SRI) was determined from actigraphy. Participants were categorized by tertiles as severely irregular, mildly irregular, or regular sleepers. Logistic regression models examined the interrelationships between SRI, OSA and hypertension. Covariates included age, sex, body mass index, actigraphy sleep duration, insomnia, depression, activity, alcohol, smoking, and antihypertensive medication. Compared to regular sleepers, participants with mildly irregular (OR 1.97, 95% confidence intervals [CI] 1.20 to 3.27) and severely irregular (OR 2.06, 95% CI: 1.25 to 3.42) sleep had greater odds of OSA. Compared to those with no OSA and regular sleep, OSA and severely irregular sleep combined had the highest odds of hypertension (OR 2.34 95% CI: 1.07 to 5.12; p for interaction = 0.02) while those with OSA and regular/mildly irregular sleep were not at increased risk (p for interaction = 0.20). Sleep irregularity may be an important modifiable target for hypertension among those with OSA.