Sleep behavior traits and associations with opioid-related adverse events: a cohort study.

STUDY OBJECTIVES: Opioid-related adverse events (OAEs), including opioid use disorders, overdose, and death, are serious public health concerns. OAEs are often associated with disrupted sleep, but the long-term relationship between poor sleep and subsequent OAE risk remains unknown. This study investigates whether sleep behavior traits are associated with incident OAEs in a large population cohort. METHODS: 444 039 participants (mean age ± SD 57 ± 8 years) from the UK Biobank reported their sleep behavior traits (sleep duration, daytime sleepiness, insomnia-like complaints, napping, and chronotype) between 2006 and 2010. The frequency/severity of these traits determined a poor sleep behavior impacts score (0-9). Incident OAEs were obtained from hospitalization records during 12-year median follow-up. Cox proportional hazards models examined the association between sleep and OAEs. RESULTS: Short and long sleep duration, frequent daytime sleepiness, insomnia symptoms, and napping, but not chronotype, were associated with increased OAE risk in fully adjusted models. Compared to the minimal poor sleep behavior impacts group (scores of 0-1), the moderate (4-5) and significant (6-9) groups had hazard ratios of 1.47 (95% confidence interval [1.27, 1.71]), p < 0.001, and 2.19 ([1.82, 2.64], p < 0.001), respectively. The latter risk magnitude is greater than the risk associated with preexisting psychiatric illness or sedative-hypnotic medication use. In participants with moderate/significant poor sleep impacts (vs. minimal), subgroup analysis revealed that age <65 years was associated with a higher OAE risk than in those ≥65 years. CONCLUSIONS: Certain sleep behavior traits and overall poor sleep impacts are associated with an increased risk for opioid-related adverse events.

Daytime napping and Alzheimer's dementia: A potential bidirectional relationship.

INTRODUCTION: Daytime napping is frequently seen in older adults. The longitudinal relationship between daytime napping and cognitive aging is unknown. METHODS: Using data from 1401 participants of the Rush Memory and Aging Project, we examined the longitudinal change of daytime napping inferred objectively by actigraphy, and the association with incident Alzheimer's dementia during up to 14-year follow-up. RESULTS: Older adults tended to nap longer and more frequently with aging, while the progression of Alzheimer's dementia accelerates this change by more than doubling the annual increases in nap duration/frequency. Longer and more frequent daytime naps were associated with higher risk of Alzheimer's dementia. Interestingly, more excessive (longer or more frequent) daytime napping was correlated with worse cognition a year later, and conversely, worse cognition was correlated with more excessive naps a year later. DISCUSSION: Excessive daytime napping and Alzheimer's dementia may possess a bidirectional relationship or share common pathophysiological mechanisms.

Actigraphy-Based Sleep Detection: Validation with Polysomnography and Comparison of Performance for Nighttime and Daytime Sleep During Simulated Shift Work.

Purpose: Actigraphy-based sleep detection algorithms were mostly validated using nighttime sleep, and their performance in detecting daytime sleep is unclear. We evaluated and compared the performance of Actiware and the Cole-Kripke algorithm (C-K) - two commonly used actigraphy-based algorithms - in detecting daytime and nighttime sleep. Participants and Methods: Twenty-five healthy young adults were monitored by polysomnography and actigraphy during two in-lab protocols with scheduled nighttime and/or daytime sleep (within-subject design). Mixed-effect models were conducted to compare the sensitivity, specificity, and F1 score (a less-biased measure of accuracy) of Actiware (with low/medium/high threshold setting, separately) and C-K in detecting sleep epochs from actigraphy recordings during nighttime/daytime. t-tests and intraclass correlation coefficients were used to assess the agreement between actigraphy-based algorithms and polysomnography in scoring total sleep time (TST). Results: Sensitivity was similar between nighttime (Actiware: 0.93-0.99 across threshold settings; C-K: 0.61) and daytime sleep (Actiware: 0.93-0.99; C-K: 0.66) for both the C-K and Actiware (daytime/nighttime×algorithm interaction: p > 0.1). Specificity for daytime sleep was lower (Actiware: 0.35-0.54; C-K: 0.91) than that for nighttime sleep (Actiware: 0.37-0.62; C-K: 0.93; p = 0.001). Specificity was also higher for C-K than Actiware (p < 0.001), with no daytime/nighttime×algorithm interaction (p > 0.1). C-K had lower F1 (nighttime = 0.74; daytime = 0.77) than Actiware (nighttime = 0.95-0.98; daytime = 0.90-0.91) for both nighttime and daytime sleep (all p < 0.05). The daytime-nighttime difference in F1 was opposite for Actiware (daytime: 0.90-0.91; nighttime: 0.95-0.98) and C-K (daytime: 0.77; nighttime: 0.74; interaction p = 0.003). Bias in TST was lowest in Actiware (with medium-threshold) for nighttime sleep (underestimation of 5.99 min/8h) and in Actiware (with low-threshold) for daytime sleep (overestimation of 17.75 min/8h). Conclusion: Daytime/nighttime sleep affected specificity and F1 but not sensitivity of actigraphy-based sleep scoring. Overall, Actiware performed better than the C-K algorithm. Actiware with medium-threshold was the least biased in estimating nighttime TST, and Actiware with low-threshold was the least biased in estimating daytime TST.

Association of Poor Sleep Burden in Middle Age and Older Adults With Risk for Delirium During Hospitalization.

BACKGROUND: Delirium is a distressing neurocognitive disorder recently linked to sleep disturbances. However, the longitudinal relationship between sleep and delirium remains unclear. This study assessed the associations of poor sleep burden, and its trajectory, with delirium risk during hospitalization. METHODS: About 321 818 participants from the UK Biobank (mean age 58 ± 8 years [SD]; range 37-74 years) reported (2006-2010) sleep traits (sleep duration, excessive daytime sleepiness, insomnia-type complaints, napping, and chronotype-a closely related circadian measure for sleep timing), aggregated into a sleep burden score (0-9). New-onset delirium (n = 4 775) was obtained from hospitalization records during a 12-year median follow-up. About 42 291 (mean age 64 ± 8 years; range 44-83 years) had repeat sleep assessment on average 8 years after their first. RESULTS: In the baseline cohort, Cox proportional hazards models showed that moderate (aggregate scores = 4-5) and severe (scores = 6-9) poor sleep burden groups were 18% (hazard ratio = 1.18 [95% confidence interval: 1.08-1.28], p < .001) and 57% (1.57 [1.38-1.80], p < .001), more likely to develop delirium, respectively. The latter risk magnitude is equivalent to 2 additional cardiovascular risks. These findings appeared robust when restricted to postoperative delirium and after exclusion of underlying dementia. Higher sleep burden was also associated with delirium in the follow-up cohort. Worsening sleep burden (score increase ≥2 vs no change) further increased the risk for delirium (1.79 [1.23-2.62], p = .002) independent of their baseline sleep score and time lag. The risk was highest in those younger than 65 years at baseline (p for interaction <.001). CONCLUSION: Poor sleep burden and worsening trajectory were associated with increased risk for delirium; promotion of sleep health may be important for those at higher risk.