Sleep regularity is a stronger predictor of mortality risk than sleep duration: A prospective cohort study

The Wayback Machine - http://web.archive.org/web/20260623172747/https://academic.oup.com/sleep/article/47/1/zsad253/7280269 Article Navigation Navbar Search Filter Mobile Enter search term Search Abstract 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. Graphical Abstract Statement of SignificanceSleep of adequate duration is important for optimal health and longevity, but emerging evidence demonstrates that regular sleep timing may be even more important. Using objective measures of sleep in a cohort of > 60 000 individuals, we found that people with less regular sleep patterns have a higher risk of premature mortality, and that sleep regularity is a stronger predictor of mortality risk than sleep duration. These findings were robust with detailed control for confounding factors, providing evidence that sleep regularity is a key index of human health and potentially a more important marker of health than sleep duration. Introduction A large body of research has demonstrated that both subjective and objective estimates of average sleep duration are associated with mortality risk [1–9]. Several meta-analyses of subjective sleep duration have associated both short and/or long sleep duration, outside an approximate range of 7–9 hours, with higher risks for all-cause mortality [1–5]. Recently, large studies of objective sleep duration have confirmed these associations of sleep duration with all-cause mortality [6–9]. These findings are supported by results showing associations between sleep duration and many other dimensions of health [10].Maintaining optimal sleep duration is the central focus of current sleep health guidelines [11]. Recent evidence, however, indicates that sleep regularity, defined as the day-to-day consistency of sleep–wake timing, is a stronger predictor of some health outcomes than average sleep duration [12, 13]. Studies with longitudinal measures of sleep regularity have found associations between irregular sleep and adverse cardiometabolic outcomes [12, 14–17], epigenetic aging [18], depressed mood [19], and lower quality of life [13]. People with irregular sleep patterns are exposed to irregular patterns of environmental stimuli, including light, and may have irregularly timed behaviors, such as physical activity and meals. This unstable timing of both stimuli and behaviors leads to disruption of circadian rhythms, with downstream negative health effects [20]. While mortality risk has been associated with self-reported sleep regularity [21], this relationship has not been investigated prospectively in a large cohort with objective sleep data. Furthermore, the relative importance of sleep duration compared with sleep regularity for mortality risk is not known.

We aimed to assess the relationship of objectively measured sleep regularity with risk for all-cause mortality, and mortality from cardiometabolic causes and cancer, in a large cohort (N = 60 977) who wore accelerometer devices for 1 week. We also assessed whether sleep regularity was a stronger predictor of mortality risk than sleep duration. We used data from our previously reported assessment of sleep regularity in UK Biobank participants [22], where we extracted Sleep Regularity Index (SRI) scores. This metric assesses day-to-day similarity in sleep patterns, accounting for irregularity due to fragmented sleep, napping, and variable sleep onset and offset timing. Methods Overview A baseline cohort of approximately 502 000 participants aged between 40 and 69 years were recruited to the UK Biobank between 2006 and 2010 [23]. Participants completed an initial assessment at the time of recruitment, designed to capture information across a broad range of health and lifestyle factors, through questionnaires and physical measurements. Assessment centers were located to capture a range of socioeconomic, ethnic, and urban–rural distributions within the UK population. From this baseline cohort, 103 669 participants wore Axivity AX3 devices (Axivity, Newcastle upon Tyne, UK) on their dominant wrist for 7 days under free-living conditions between 2013 and 2016. Devices were tri-axial, and logged accelerometer data at 100 Hz. Invitations to participate and devices were distributed via post. Written informed consent was obtained and all data collection was conducted in accordance with the Declaration of Helsinki. See Supplementary Methods S1.1 for links to protocol and consent documents. Sleep regularity and sleep duration Sleep regularity was assessed using the SRI [24], a metric that compares the similarity of sleep patterns from one day to the next. The SRI calculates the average concordance in sleep–wake state of all epoch pairs separated by 24 hours (see Supplementary Methods S1.7 for SRI calculation formula). An SRI of 100 represents perfectly regular sleep–wake patterns, and zero represents random patterns.SRI scores were derived in this cohort in our previous work [22], and these scores were used in all analyses presented here.

SRI scores were calculated using epoch-by-epoch sleep–wake state across each participant’s 1-week recording. Sleep–wake state was estimated using “GGIR”, a validated, widely used open-source R package for estimating sleep–wake from accelerometer data [25, 26], and “sleepreg”, an R package developed by our group to accurately calculate SRI scores from “GGIR” output [22]. The “sleepreg” package uses sustained inactivity data to account for naps, fragmented sleep, and large periods of wake during sleep in its calculation of SRI scores, which could not be achieved using “GGIR” summary output alone. This method accounted for device non-wear, and excluded instances of miscalculated sleep onset and offset. It also allowed patterns including more than one sleep episode within a 24-hour period to be accurately represented. Valid SRI scores were calculated in participants with at least 120 hours (i.e., 5 days) of 24-hour-separated epoch pairs after non-wear removal and exclusion of miscalculated days. Further detail is included in Supplementary Methods S1.6-8, and all scripts used in generating SRI scores are included in the “sleepreg” package, which is freely available on GitHub [https://github.com/dpwindred/sleepreg].Sleep duration was calculated on a daily basis for each individual, as the duration of sustained inactivity between daily sleep onset and sleep offset times estimated by GGIR. Daily sleep durations were extracted in the same study days used to calculate SRI scores, and participant-level average sleep duration was calculated across these days. Similarly, participant-level average mid-sleep timing was calculated from daily mid-sleep, defined as the clock time halfway between sleep onset and sleep offset. Intraindividual variability in sleep onset and offset timing was calculated as the standard deviation of daily sleep onset and offset times for each individual. Mortality records Mortality data were received from NHS Digital (England) and NHS Central Register (Scotland). Records include date of death and primary cause of death, diagnosed in accordance with the ICD-10 [27]. Records from June 2013 to March 2021 were included. Cardiometabolic mortality was defined according to ICD-10 diseases of the circulatory system, or endocrine and metabolic diseases (I05-I89, E00-E90).

Predominant circulatory causes of death were ischemic heart disease (I20-I25); cerebrovascular diseases (I60-I69); other heart disease (I30-I52); diseases of the arteries, arterioles, and capillaries (I70-I79); and hypertensive diseases (I10-I15). Predominant endocrine and metabolic causes of death were diabetes mellitus (E10-E14); metabolic disorders (E70-E90); and obesity (E65-E68). Cancer was defined as any cause of death by malignant or benign neoplasms (C00-C97, D10-48). Predominant causes of death by cancer were malignant neoplasms of digestive organs (C15-C26); respiratory and intrathoracic organs (C30-C39); lymphoid and hematopoietic tissue (primary; C81-C96); and breast (C50). Covariates Average physical activity was defined as average device acceleration across the recording, after exclusion of low-quality data and periods of non-wear, as described in previous work [28], and was derived from the same accelerometer records used to estimate sleep–wake state. Additional covariates were collected during an initial assessment between 2006 and 2010, including: self-reported ethnic background; employment status; yearly household income; Townsend Deprivation Index (average material deprivation of a participant’s residential location); weekly social activities; frequency of social visits; smoking status; urban or rural postcode; rotating shift work status; prescription of medication for hypertension or cholesterol; diagnosis of cancer, diabetes, or vascular conditions; body mass index (BMI); cholesterol ratio; frequency of depressed mood, unenthusiasm/disinterest, tenseness/restlessness, tiredness/lethargy; and visitation to a general practitioner or psychiatrist for mental health concerns. See Supplementary Methods S1.2-3 for detailed descriptions of covariates. Statistical analyses SRI and sleep duration were split into quintiles. Hazards of mortality were estimated for each of the top four SRI and sleep duration quintiles compared to their respective lowest quintiles, which were hypothesized to have the highest mortality risk. This approach allowed for unspecified non-linearity in sleep/mortality relationships.