Shift work is associated with impaired alertness and performance due to sleep loss and circadian misalignment. This study examined sleep between shift types (day, evening, night), and alertness and performance during day and night shifts in 52 intensive care workers. Sleep and wake duration between shifts were evaluated using wrist actigraphs and diaries. Subjective sleepiness (Karolinska Sleepiness Scale, KSS) and Psychomotor Vigilance Test (PVT) performance were examined during day shift, and on the first and subsequent night shifts (3^rd, 4^th or 5^th). Circadian phase was assessed using urinary 6-sulphatoxymelatonin rhythms. Sleep was most restricted between consecutive night shifts (5.74 ± 1.30 h), consecutive day shifts (5.83 ± 0.92 h) and between evening and day shifts (5.20 ± 0.90 h). KSS and PVT mean reaction times were higher at the end of the first and subsequent night shift compared to day shift, with KSS highest at the end of the first night. On nights, working during the circadian acrophase of the urinary melatonin rhythm led to poorer outcomes on the KSS and PVT. In rotating shift workers, early day shifts can be associated with similar sleep restriction to night shifts, particularly when scheduled immediately following an evening shift. Alertness and performance remain most impaired during night shifts given the lack of circadian adaptation to night work. Although healthcare workers perceive themselves to be less alert on the first night shift compared to subsequent night shifts, objective performance is equally impaired on subsequent nights.

Background: Over the past 15 years, there has been substantial growth in web-based psychological interventions. We summarize evidence regarding the efficacy of web-based self-directed psychological interventions on depressive, anxiety and distress symptoms in people living with a chronic health condition.Method: We searched Medline, PsycINFO, CINAHL, EMBASE databases and Cochrane Database from 1990 to 1 May 2019. English language papers of randomized controlled trials (usual care or waitlist control) of web-based psychological interventions with a primary or secondary aim to reduce anxiety, depression or distress in adults with a chronic health condition were eligible. Results were assessed using narrative synthases and random-effects meta-analyses.Results: In total 70 eligible studies across 17 health conditions [most commonly: cancer (k= 20), chronic pain (k= 9), arthritis (k= 6) and multiple sclerosis (k= 5), diabetes (k= 4), fibromyalgia (k= 4)] were identified. Interventions were based on CBT principles in 46 (66%) studies and 42 (60%) included a facilitator. When combining all chronic health conditions, web-based interventions were more efficacious than control conditions in reducing symptoms of depressiong= 0.30 (95% CI 0.22–0.39), anxietyg= 0.19 (95% CI 0.12–0.27), and distressg= 0.36 (95% CI 0.23–0.49).Conclusion: Evidence regarding effectiveness for specific chronic health conditions was inconsistent. While self-guided online psychological interventions may help to reduce symptoms of anxiety, depression and distress in people with chronic health conditions in general, it is unclear if these interventions are effective for specific health conditions. More high-quality evidence is needed before definite conclusions can be made.

Study Objectives: Light is the main time cue for the human circadian system. Sleep and light are intrinsically linked; light exposure patterns can influence sleep patterns and sleep can influence light exposure patterns. However, metrics for quantifying light regularity are lacking, and the relationship between sleep and light regularity is underexplored. We developed new metrics for light regularity and demonstrated their utility in adolescents, across school term and vacation. Methods: Daily sleep/wake and light patterns were measured using wrist actigraphy in 75 adolescents (54% male, 17.17 ± 0.83 years) over 2 weeks of school term and a subsequent 2-week vacation. The Sleep Regularity Index (SRI) and social jetlag were computed for each 2-week block. Light regularity was assessed using (1) variation in mean daily light timing (MLiT); (2) variation in daily photoperiod; and (3) the Light Regularity Index (LRI). Associations between SRI and each light regularity metric were examined, and within-individual changes in metrics were examined between school and vacation. Results: Higher SRI was significantly associated with more regular LRI scores during both school and vacation. There were no significant associations of SRI with variation in MLiT or daily photoperiod. Compared to school term, all three light regularity metrics were less variable during the vacation. Conclusions: Light regularity is a multidimensional construct, which until now has not been formally defined. Irregular sleep patterns are associated with lower LRI, indicating that irregular sleepers also have irregular light inputs to the circadian system, which likely contributes to circadian disruption.

There is large interindividual variability in circadian timing, which is underestimated by mathematical models of the circadian clock. Interindividual differences in timing have traditionally been modeled by changing the intrinsic circadian period, but recent findings reveal an additional potential source of variability: large interindividual differences in light sensitivity. Using an established model of the human circadian clock with real-world light recordings, we investigated whether changes in light sensitivity parameters or intrinsic circadian period could capture variability in circadian timing between and within individuals. Healthy participants (n = 12, aged 18-26 years) underwent continuous light monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim-light melatonin onset (DLMO) was measured each week. Using the recorded light patterns, a sensitivity analysis for predicted DLMO times was performed, varying 3 model parameters within physiological ranges: (1) a parameter determining the steepness of the dose-response curve to light (p), (2) a parameter determining the shape of the phase-response curve to light (K), and (3) the intrinsic circadian period (tau). These parameters were then fitted to obtain optimal predictions of the three DLMO times for each individual. The sensitivity analysis showed that the range of variation in the average predicted DLMO times across participants was 0.65 h for p, 4.28 h for K, and 3.26 h for tau. The default model predicted the DLMO times with a mean absolute error of 1.02 h, whereas fitting all 3 parameters reduced the mean absolute error to 0.28 h. Fitting the parameters independently, we found mean absolute errors of 0.83 h for p, 0.53 h for K, and 0.42 h for tau. Fitting p and K together reduced the mean absolute error to 0.44 h. Light sensitivity parameters captured similar variability in phase compared with intrinsic circadian period, indicating they are viable targets for individualizing circadian phase predictions. Future prospective work is needed that uses measures of light sensitivity to validate this approach.

Key points: Shift work is highly prevalent and is associated with significant adverse health impacts. There is substantial inter‐individual variability in the way the circadian clock responds to changing shift cycles. The mechanisms underlying this variability are not well understood. We tested the hypothesis that light–dark exposure is a significant contributor to this variability; when combined with diurnal preference, the relative timing of light exposure accounted for 71% of individual variability in circadian phase response to night shift work. These results will drive development of personalised approaches to manage circadian disruption among shift workers and other vulnerable populations to potentially reduce the increased risk of disease in these populations. Night shift workers show highly variable rates of circadian adaptation. This study examined the relationship between light exposure patterns and the magnitude of circadian phase resetting in response to night shift work. In 21 participants (nursing and medical staff in an intensive care unit) circadian phase was measured using 6‐sulphatoxymelatonin at baseline (day/evening shifts or days off) and after 3–4 consecutive night shifts. Daily light exposure was examined relative to individual circadian phase to quantify light intensity in the phase delay and phase advance portions of the light phase response curve (PRC). There was substantial inter‐individual variability in the direction and magnitude of phase shift after three or four consecutive night shifts (mean phase delay −1:08 ± 1:31 h; range −3:43 h delay to +3:07 h phase advance). The relative difference in the distribution of light relative to the PRC combined with diurnal preference accounted for 71% of the variability in phase shift. Regression analysis incorporating these factors estimated phase shift to within ±60 min in 85% of participants. No participants met criteria for partial adaptation to night work after three or four consecutive night shifts. Our findings provide evidence that the phase resetting that does occur is based on individual light exposure patterns relative to an individual's baseline circadian phase. Thus, a ‘one size fits all’ approach to promoting adaptation to shift work using light therapy, implemented without knowledge of circadian phase, may not be efficacious for all individuals.

Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3–5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers.