Contact-free radar recordings of body movement can reflect ultradian dynamics of sleep.

This work aimed to evaluate if a contact-free radar sensor can be used to observe ultradian patterns in sleep physiology, by way of a data processing tool known as Locomotor Inactivity During Sleep (LIDS). LIDS was designed as a simple transformation of actigraphy recordings of wrist movement, meant to emphasise and enhance the contrast between movement and non-movement and to reveal patterns of low residual activity during sleep that correlate with ultradian REM/NREM cycles. We adapted the LIDS transformation for a radar that detects body movements without direct contact with the subject and applied it to a dataset of simultaneous recordings with polysomnography, actigraphy, and radar from healthy young adults (n = 12, four nights of polysomnography per participant). Radar and actigraphy-derived LIDS signals were highly correlated with each other (r > 0.84), and the LIDS signals were highly correlated with reduced-resolution polysomnographic hypnograms (rradars >0.80, ractigraph >0.76). Single-harmonic cosine models were fitted to LIDS signals and hypnograms; significant differences were not found between their amplitude, period, and phase parameters. Mixed model analysis revealed similar slopes of decline per cycle for radar-LIDS, actigraphy-LIDS, and hypnograms. Our results indicate that the LIDS technique can be adapted to work with contact-free radar measurements of body movement; it may also be generalisable to data from other body movement sensors. This novel metric could aid in improving sleep monitoring in clinical and real-life settings, by providing a simple and transparent way to study ultradian dynamics of sleep using nothing more than easily obtainable movement data.

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor.

This work aimed to evaluate whether a radar sensor can distinguish sleep from wakefulness in real time. The sensor detects body movements without direct physical contact with the subject and can be embedded in the roof of a hospital room for completely unobtrusive monitoring. We conducted simultaneous recordings with polysomnography, actigraphy, and radar on two groups: healthy young adults (n = 12, four nights per participant) and patients referred to a sleep examination (n = 28, one night per participant). We developed models for sleep/wake classification based on principles commonly used by actigraphy, including real-time models, and tested them on both datasets. We estimated a set of commonly reported sleep parameters from these data, including total-sleep-time, sleep-onset-latency, sleep-efficiency, and wake-after-sleep-onset, and evaluated the inter-method reliability of these estimates. Classification results were on-par with, or exceeding, those often seen for actigraphy. For real-time models in healthy young adults, accuracies were above 92%, sensitivities above 95%, specificities above 83%, and all Cohen's kappa values were above 0.81 compared to polysomnography. For patients referred to a sleep examination, accuracies were above 81%, sensitivities about 89%, specificities above 53%, and Cohen's kappa values above 0.44. Sleep variable estimates showed no significant intermethod bias, but the limits of agreement were quite wide for the group of patients referred to a sleep examination. Our results indicate that the radar has the potential to offer the benefits of contact-free real-time monitoring of sleep, both for in-patients and for ambulatory home monitoring.

Cardiovascular Health Effects of Shift Work with Long Working Hours and Night Shifts: Study Protocol for a Three-Year Prospective Follow-Up Study on Industrial Workers.

There is a plausible association between shift work and cardiovascular disease (CVD), which may be due to disruption of the circadian rhythm causing hormonal changes and metabolic disturbances, resulting in high blood pressure, atherosclerosis, diabetes, and being overweight. However, few studies have investigated the association between several consecutive long work shifts, including night shifts, and risk factors for developing CVD. Moreover, knowledge is lacking on factors that may modify or enhance this suggested relationship. The study period is planned from the third quarter of 2018 to the fourth quarter of 2021, and will involve 125 industrial employees at two Norwegian enterprises producing insulation. The work schedule is either rotating shiftwork (morning, evening, night) or regular day work. At baseline, we will measure blood parameters, including markers of inflammation, lipids, and glycosylated hemoglobin. We will also collect measures of blood pressure, resting heart rate, arterial stiffness, carotid intima-media thickness, and aerobic fitness. At the end of baseline data collection, a subgroup will undergo a supervised high-intensity interval training intervention for eight weeks, initiated by the Occupational Health Service. At one-year follow-up, we repeat baseline measures with added measures of heart rate variability and additional five weeks monitoring of sleep and physical activity, and assessment of respirable dust. At the two year follow-up, we will measure CVD risk factors before and after a planned three-month shutdown in one of the studied plants. We will also assess respirable dust, monitor sleep, and compile a one-year retrospective detailed overview of working hours. A final data collection, similar to the one at baseline, will be carried out after three years. We will use a comprehensive set of methods to identify the effects of shift work with long working hours and night shifts on cardiovascular health. This will provide new knowledge on the association between early manifestations of CVD and occupational exposure to shift work. Further, we can study whether work organization such as extensive overtime, sleep loss, and dust exposure have detrimental effects, and if a three-month cease in shift work or increased physical activity will modify early manifestations of CVD.