Computing circadian misalignment: Why not combine sleep timing and duration to assess accumulated sleep deficiency?

Circadian epidemiology: Structuring circadian causes of disease and practical implications

That disruptions of the body's internal clockwork can lead to negative health consequences, including cancer, is a plausible hypothesis. Yet, despite strong mechanistic and animal support, the International Agency for Research on Cancer (IARC) experts considered epidemiological evidence as limited regarding the carcinogenicity of "shift-work involving circadian disruption" (2007) and "night shift work" (2019). We use directed acyclic graphs (DAGs) to outline a concept of circadian causes that discloses challenges when choosing appropriate exposure variables. On this basis, we propose to move beyond shift-work alone as a direct cause of disease. Instead, quantifying chronodisruption as individual doses can lead to interpretable circadian epidemiology. The hypothesis is that doses of chronodisruption cause disrupted circadian organisation by leading to desynchronization of circadian rhythms. Chronodisruption can be conceptualized as the split physiological nexus of internal and external times. Biological (or internal) night - an individual's intrinsically favoured sleep time window - could be the backbone of circadian epidemiology. In practice, individual doses that cause disrupted circadian organisation are derived from the intersection of time intervals of being awake and an individual's biological night. After numerous studies counted work shifts, chronobiology may now advance circadian epidemiology with more specific dose estimation - albeit with greater challenges in measurement (time-dependent individual data) and analysis (time-dependent confounding).

Chronodisruption: Origin, Roots, and Developments of an 18-Year-Old Concept. Comment on Desmet et al. Time-Restricted Feeding in Mice Prevents the Disruption of the Peripheral Circadian Clocks and Its Metabolic Impact during Chronic Jetlag. Nutrients 2021, 13, 3846

We read with interest the article by Desmet and colleagues entitled "Time-Restricted Feeding in Mice Prevents the Disruption of the Peripheral Circadian Clocks and Its Metabolic Impact during Chronic Jetlag" [...].

Accumulation of sleep loss among shift-working truck drivers

Sleep loss is known to contribute to road traffic accidents. Professional drivers are vulnerable to curtailment of sleep due to long driving bouts and shift work. To fill in the gap in the literature related to the buildup of sleep loss in irregular shift systems, we recorded the sleep and working hours of 47 shift-working long-haul truck drivers during a two-week period. Sleep (time in bed) was verified by actigraphy and sleep logs. Sleepiness was measured using the Karolinska Sleepiness Scale (KSS). Individual sleep need was based on self-assessments. We examined the accumulated sleep versus self-reported sleep need across the study period, using midnights as points of observation, and the accumulated sleep loss within 72 h prior to shift end (sleep versus need, SVN₇₂). Across the study period, the drivers' sleep was close to their self-reported sleep need, but 45% of the drivers showed accumulated sleep loss of >6 h at least once. SVN₇₂ averaged -1.5 h and was 2.87 h shorter in connection with morning shifts compared to day or evening shifts. Night shifts showed no such difference. During days off, sleep exceeded sleep need by 1.13 h and was not dependent on the type of preceding work shift. SVN₇₂ showed small-to-medium negative associations with on-duty KSS even after accounting for sleep within the 24 h prior to the shift end. Our results show that long-haul truck drivers are exposed to severe levels of accumulated sleep loss while working irregular shifts, but they can catch up on their lost sleep, especially during days off.

Sleep and alertness in shift work disorder: findings of a field study

Purpose

Although shift work disorder (SWD) affects a major part of the shift working population, little is known about its manifestation in real life. This observational field study aimed to provide a detailed picture of sleep and alertness among shift workers with a questionnaire-based SWD, by comparing them to shift workers without SWD during work shifts and free time.

Methods

SWD was determined by a questionnaire. Questionnaires and 3-week field monitoring, including sleep diaries, actigraphy, the Karolinska Sleepiness Scale (KSS), EEG-based sleep recordings, and Psychomotor Vigilance Tasks (PVT), were used to study 22 SWD cases and 9 non-SWD workers.

Results

The SWD group had a shorter subjective total sleep time and greater sleep debt before morning shifts than the non-SWD group. Unlike the non-SWD group, the SWD group showed little compensatory sleep on days off. The SWD group had lower objective sleep efficiency and longer sleep latency on most days, and reported poorer relaxation at bedtime and sleep quality across all days than the non-SWD group. The SWD group’s average KSS-sleepiness was higher than the non-SWD group’s sleepiness at the beginning and end of morning shifts and at the end of night shifts. The SWD group also had more lapses in PVT at the beginning of night shifts than the non-SWD group.

Conclusions

The results indicate that SWD is related to disturbed sleep and alertness in association with both morning and night shifts, and to less compensatory sleep on days off. SWD seems to particularly associate with the quality of sleep.

Electronic supplementary material

The online version of this article (10.1007/s00420-018-1386-4) contains supplementary material, which is available to authorized users.

Computing sleep deficiency

Sleep deficiency is a major public health concern. Since epidemiological studies play an important role in public health evaluations, this theoretical paper pursues answers to the question: 'How can we compute sleep deficiency as informative measures of exposures or doses in observational research?' Starting from the social jetlag concept and based on the chronodisruption rationale, we illustrate and discuss five approaches (one established and four untested, each with unique strengths and limitations) to quantify sleep deficiency by focusing on the timing and duration of sleep. Hitherto, social jetlag and chronodisruption rationale were neither explicitly proposed nor developed as assessments of sleep deficiency but, as we suggest, could potentially be utilized to this end. This first foray into computing sleep deficiency in epidemiological studies makes clear that laboratory, field and epidemiological collaboration is pre-requisite to elucidating potential (co-)causal roles of sleep deficiency in disease endpoints.