Seasonality of human sleep: Polysomnographic data of a neuropsychiatric sleep clinic

Age-related disturbances in rest-activity rhythms and integrity of the hippocampal network: An exploratory study

To better understand the relationship between the rest-activity rhythms and cognitive impairments during aging, we assessed the longitudinal changes in the rest-activity rhythms in an elderly population and their possible detrimental effect on the hippocampal network. This was done longitudinally in a rural cohort with two actigraphic assessments and brain imaging examinations, seven years apart. A segmentation of the hippocampus and its related structures was used to assess volumes and functional connectivity in this network based on anatomical and resting state functional data. Regression models were carried out to investigate the potential association of the evolution of sleep and rest-activity rhythms parameters with the structural and functional integrity of the hippocampal network. Our sample was composed of 33 subjects aged 75.2 ± 2.4 years old at the first time point with 40% of women. After seven years, the sleep of our participants did not change but their rest-activity rhythms did (p < 0.05), with a decrease in relative amplitude (∂RA = -0.021) and stability (∂IS = -0.044) as well as an increase in fragmentation (∂IV = +0.072). The deterioration of rest-activity rhythms was correlated with a lower anterior hippocampal volume (p corrected <0.05) while no correlation with functional connectivity was observed. These findings suggest that a degradation of rest-activity rhythms in people over 70 years old could constitute a factor of hippocampal vulnerability. Preventive interventions should consider rest-activity rhythms in the oldest-old population.

Impact of seasons and temperatures on the sleep-wake cycle in a French elderly rural population

Sleep is known to be affected by season changes in a temperate climate. Temperature changes are known to affect sleep directly, but in this context, season-related changes in the circadian rhythm may play an important role as well. The objectives of this study were to verify the effect of season and temperature on sleep parameters of elderly French subjects and to focus on the sleep-wake cycle. Sleep parameters and sleep-wake cycle parameters were analyzed through actigraphy while seasonality and temperature were acquired from recording dates and weather records from online scientific archives. ANOVAs were carried out to investigate the effect of seasons on actigraphic parameters and to calculate regression models for temperature. A sample of 157 subjects (49.7 % women) participated in the study with a mean age of 76.9 ± 4.5 years. Inter-daily stability of the sleep-wake cycle was higher in autumn and winter with a higher mean activity during those months. Time in bed was significantly longer in autumn and winter while there was a similar trend for total sleep time. Those variables changing with the seasons were negatively linked to ambient temperature. Not only sleep but also the sleep-wake cycle is impacted by seasonal changes in elderly French subjects. Seasons should be taken into consideration when planning sleep-wake cycle recordings in temperate climates, in particular for longitudinal protocols. Clinical interventions should take season-related sleep-wake cycle problems into account, particularly in the elderly, who suffer more often from sleep disorders than other age groups. Since elderly also suffer more from climate change effects, this study further adds to the demand for clinical monitoring and housing adaptations for the elderly in the future.

The Ecology of Human Sleep (EcoSleep) Cohort Study: Protocol for a longitudinal repeated measurement burst design study to assess the relationship between sleep determinants and outcomes under real-world conditions across time of year

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

Relating Photoperiod and Outdoor Temperature With Sleep Architecture in Patients With Neuropsychiatric Sleep Disorders

While artificial light in urban environments was previously thought to override seasonality in humans, recent studies have challenged this assumption. We aimed to explore the relationship between seasonally varying environmental factors and changes in sleep architecture in patients with neuropsychiatric sleep disorders by comparing two consecutive years. In 770 patients, three-night polysomnography was performed at the Clinic for Sleep & Chronomedicine (St. Hedwig Hospital, Berlin, Germany) in 2018/2019. Sleep times were adjusted to patients' preferred schedules, patients slept in, and were unaware of day-night indicators. Digital devices and clocks were not allowed. Days were spent outside the lab with work or naps not allowed. After exclusions (mostly due to psychotropic medication), analysis was conducted on the second PSG-night in 377 patients (49.1 ± 16.8 year; 54% female). Sleep parameters were plotted as 90-day moving-averages (MvA) across date-of-record. Periodicity and seasonal windows in the MvA were identified by utilizing autocorrelations. Linear mixed-effect models were applied to seasonal windows. Sleep parameters were correlated with same-day photoperiod, temperature, and sunshine duration. The MvA of total sleep time (TST) and REM sleep began a 5-month-long decline shortly after the last occurrence of freezing 24-h mean temperatures (correlation of TST between 2018 and 2019 at 2-month lag: rs361 = 0.87, p < 0.001; maximum peak-to-nadir amplitude: ΔTST ~ 62 min, ΔREM ~ 24 min). The MvA nadirs of slow wave sleep (SWS) occurred approximately at the autumnal equinox (correlation between 2018 and 2019: rs361 = 0.83, p < 0.001). Post hoc testing following significant linear mixed-effect model indicate that TST and REM sleep were longer around November till February than May till August (ΔTST = 36 min; ΔREM = 14 min), while SWS was 23 min longer around February till May than August till November. Proportional seasonal variation of SWS and of REM sleep as percentages of TST differed profoundly (SWS = 31.6%; REM = 8.4%). In patients with neuropsychiatric sleep disorders living in an urban environment, data collected in 2018 show similar patterns and magnitudes in seasonal variation of sleep architecture as the 2019 data. Interestingly, whereas SWS patterns were consistent between years with possible links to photoperiod, annual variations of TST and REM sleep seem to be related to times of outside freezing temperature. For generalization, the data need to be confirmed in a healthy population. No clinical trial was registered.

Air pollution, weather and positive airway pressure treatment adherence in adults with sleep apnea: a retrospective community-based repeated-measures longitudinal study

We assessed the relation between air pollution, weather, and adherence to positive airway pressure (PAP) therapy in a retrospective community-based repeated-measures study of adults with obstructive sleep apnea who purchased PAP devices from a registered provider between 2013 and 2017 (Ottawa, Ontario, Canada) and had at least one day of data. Daily PAP-derived data, air pollution, and weather databases were linked using postal code. The exposures were mean nocturnal (8:00 p.m. to 8:00 a.m.) (i) residential concentrations of nitrogen dioxide (NO2), fine particulate matter <=2.5 μm (PM2.5), ozone (O3), and Air Quality Health Index (AQHI), and (ii) temperature, relative humidity, and barometric pressure. Covariates in the main model were demographics, season, exposure year, and PAP therapy mode. We analysed 8148 adults (median age of 54 years and 61% men) and 2,071,588 days of data. Based on daily data, the median (interquartile range) daily PAP usage was 416 (323-487) min. Using mixed-effect regression analyses to incorporate daily data and clustering by individuals, we found a statistically significant decrease in adherence for increased levels of NO2, PM2.5, and AQHI. The largest effect was for NO2: a decrease in daily PAP use while comparing the highest versus lowest quartiles (Qs) was 3.4 (95% confidence interval [CI] 2.8-3.9) min. Decreased PAP adherence was also associated with increased temperature (Q4 versus Q1: 2.6 [95% CI: 1.5-3.7] min) and decreased barometric pressure (Q1 versus Q4: 2.0 [95% CI 1.5-2.5] min). We observed modest but statistically significant acute effects of air pollution and weather on daily PAP adherence.

Sleep in a 1-year diary from the mid-18th century

A 1-year systematic diary was kept by an anonymous diarist in Hamburg in the year 1755-1756. Sleep, activities, food intake, urine volume, and meteorological data were documented daily. The systematic recording of sleep and naps, with an accuracy of a quarter of an hour allowed analysis of the placement, duration, and consistency of sleep. The mean (range) night sleep duration was 6 (3-9) h. The distribution of sleep interruptions showed two peaks, short (≤30 min) and long (≥45 min) interruptions. The latter indicted nights with segmented sleep. Time in bed was subject to strong seasonal variations. Bedtime and rise time were earlier in summer than in winter and sleep latency was shorter in summer than in winter. However, sleep duration displayed a large spread in all seasons and did not differ substantially between summer and winter. Seasonal variations of sleep timing were related to the varying daylight hours during the year. Finally, the results of this individual case may prompt a more detailed investigation of the variability of sleep behaviour in the pre-industrial period.

Seasonal changes in positive airway pressure adherence

Through their effects on sleep duration, bedroom environments, and pollen allergies, seasonal variations may impact positive airway pressure (PAP) adherence. We analyzed daily PAP telemonitoring data from 25,846 adults (median age 64 years, 67.8% male) treated with PAP for at least 4 months [mean (standard deviation, SD) duration of PAP: 5.5 years (SD 4.1)] to examine seasonal changes in PAP adherence, leaks, and residual apnea-hypopnea index. We demonstrate a significant decrease in PAP adherence in June compared to January (mean (SD): 0.37 (1.54) h/night) that achieved the minimal clinically important difference (MCID) of 30 min in 13.9% of adults. Furthermore, we provide novel data supporting the association of rising temperatures with seasonal changes in PAP use. Indeed, the most pronounced decline in PAP adherence was observed during the hottest days, while PAP adherence was only slightly reduced during the coolest days of June. Clinicians should be aware of seasonal changes in PAP adherence that are likely to be exacerbated by climate change.

Seasonal Variation in the Responsiveness of the Melanopsin System to Evening Light: Why We Should Report Season When Collecting Data in Human Sleep and Circadian Studies

It is well known that variations in light exposure during the day affect light sensitivity in the evening. More daylight reduces sensitivity, and less daylight increases it. On average days, we spend less time outdoors in winter and receive far less light than in summer. Therefore, it could be relevant when collecting research data on the non-image forming (NIF) effects of light on circadian rhythms and sleep. In fact, studies conducted only in winter may result in more pronounced NIF effects than in summer. Here, we systematically collected information on the extent to which studies on the NIF effects of evening light include information on season and/or light history. We found that more studies were conducted in winter than in summer and that reporting when a study was conducted or measuring individual light history is not currently a standard in sleep and circadian research. In addition, we sought to evaluate seasonal variations in a previously published dataset of 72 participants investigating circadian and sleep effects of evening light exposure in a laboratory protocol where daytime light history was not controlled. In this study, we selectively modulated melanopic irradiance at four different light levels (<90 lx). Here, we aimed to retrospectively evaluate seasonal variations in the responsiveness of the melanopsin system by combining all data sets in an exploratory manner. Our analyses suggest that light sensitivity is indeed reduced in summer compared to winter. Thus, to increase the reproducibility of NIF effects on sleep and circadian measures, we recommend an assessment of the light history and encourage standardization of reporting guidelines on the seasonal distribution of measurements.

Sleep seasonality in alpha-synucleinopathies: A comparative study with non-neurological sleep disorder patients

INTRODUCTION

It is unknown if seasonal variation in daylight affects sleep in patients with alpha-synucleinopathies. Our objectives were to assess month of the year related changes in polysomnography (PSG) data in patients with Parkinson 's disease (PD), Lewy Body Dementia (LBD) and isolated REM sleep behavior disorder (iRBD).

METHODS

We collected PSG data from 64 PD, 30 LBD and 24 iRBD patients attending a sleep laboratory in Lisbon, Portugal, during 10 years. Each was classified according to the month of the year PSG was performed and compared with a control patient with sleep disorder, but no evidence of other neurological disorder, matched for sex, age group and PSG month. The influence of month in PSG data was assessed with mixed linear regression analysis.

RESULTS

In alpha-synucleinopathies, month showed significant interaction with N2 stage time and percentage (increase from December to April) and N3 time (peak in May). REM sleep percentage increased significantly from Spring to middle Summer. In the control group, there were significant interactions regarding Total Sleep Time and Sleep Efficiency (drop during wintertime), N2 time and REM % (increase in April and May) and Apnea-Hypopnea Index (AHI) (peak in June). There were significant associations between the term group*month and sleep efficiency and AHI, with larger monthly variation in the control group.

CONCLUSION

Seasonality had a larger impact in stage architecture in alpha-synucleinopathies, and in total sleep time, sleep efficiency and the severity of OSA in the control group. Different sleep dysfunction mechanisms could explain differences in seasonal variation.

Country differences in nocturnal sleep variability: Observations from a large-scale, long-term sleep wearable study

STUDY OBJECTIVES

Country or regional differences in sleep duration are well-known, but few large-scale studies have specifically evaluated sleep variability, either across the work week, or in terms of differences in weekday and weekend sleep.

METHODS

Sleep measures, obtained over 50 million night's sleep from ∼220,000 wearable device users in 35 countries, were analysed. Each person contributed an average of ∼242 nights of data. Multiple regression was used to assess the impact country of residence had on sleep duration, timing, efficiency, weekday sleep variability, weekend sleep extension and social jetlag.

RESULTS

Nocturnal sleep was shorter and had a later onset in Asia than other regions. Despite this, sleep efficiency was lower and weekday sleep variability was higher. Weekend sleep extension was longer in Europe and the USA than in Asia, and was only partially related to weekday sleep duration. There were also cross-country differences in social jetlag although the regional differences were less distinct than for weekend sleep extension.

CONCLUSIONS

In addition to regional differences in sleep duration, cross-country differences in sleep variability and weekend sleep extension suggest that using the latter as an indicator of sleep debt may need to be reconsidered. In countries exhibiting both short sleep and high weekday sleep variability, a culturally different means of coping with inadequate sleep is likely. Country or region differences in culture, particularly those related to work, merit closer examination as factors influencing the variability in normative sleep patterns around the world.

Investigating the relationship between sleep and migraine in a global sample: a Bayesian cross-sectional approach

BACKGROUND

There is a bidirectional link between sleep and migraine, however causality is difficult to determine. This study aimed to investigate this relationship using data collected from a smartphone application.

METHODS

Self-reported data from 11,166 global users (aged 18-81 years, mean: 41.21, standard deviation: 11.49) were collected from the Migraine Buddy application (Healint Pte. Ltd.). Measures included: start and end times of sleep and migraine attacks, and pain intensity. Bayesian regression models were used to predict occurrence of a migraine attack the next day based on users' deviations from average sleep, number of sleep interruptions, and hours slept the night before in those reporting ≥ 8 and < 25 migraine attacks on average per month. Conversely, we modelled whether attack occurrence and pain intensity predicted hours slept that night.

RESULTS

There were 724 users (129 males, 412 females, 183 unknown, mean age = 41.88 years, SD = 11.63), with a mean monthly attack frequency of 9.94. More sleep interruptions (95% Highest Density Interval (95%HDI [0.11 - 0.21]) and deviation from a user's mean sleep (95%HDI [0.04 - 0.08]) were significant predictors of a next day attack. Total hours slept was not a significant predictor (95%HDI [-0.04 - 0.04]). Pain intensity, but not attack occurrence was a positive predictor of hours slept.

CONCLUSIONS

Sleep fragmentation and deviation from typical sleep are the main drivers of the relationship between sleep and migraine. Having a migraine attack does not predict sleep duration, yet the pain associated with it does. This study highlights sleep as crucial in migraine management.