When entering the university setting, poor sleep quality is reportedly prevalent among students and has been linked to a range of adverse health outcomes, including reduced academic performance. Moreover, determinants of sleep quality are not yet fully understood. This study was designed to (1) assess the prevalence of poor sleep quality and (2) identify determinants of sleep quality in German university students. In total, 1,684 undergraduate and graduate students (50.6% female, mean age 22.87 ± 3.15 years) from multiple academic disciplines completed a cross-sectional online survey assessing socio-demographic, health, and study-related indicators and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). In our sample, 820 (48.7%) met the PSQI cut-off score (>5) for poor sleep quality. Multiple regression analysis showed that older age, being a business student, lower subjective social status, poorer self-rated health, stress, exhaustion, and poor academic performance significantly predicted poor sleep quality. Our findings document a high prevalence of poor sleep quality among university students and suggest that business students, especially, might be exposed to a greater risk for poor sleep quality. Furthermore, the results of this study are valuable for academic staff to develop tailored interventions to promote healthy sleep-in university students.

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20–50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0–79.5% and precision 77.0–79.5%; Fitbit sensitivity 61.7–78.0% and precision 72.8–73.2%; and Apple sensitivity 50.5–86.1% and precision 72.7–87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.

Sleep disturbance and deficiency are common among older adults and have been linked with dementia and all-cause mortality. Using nationally representative data, we examine the relationship between sleep disturbance and deficiency and their risk for incident dementia and all-cause mortality among older adults. The National Health and Aging Trends Study (NHATS) is a nationally-representative longitudinal study of Medicare beneficiaries in the US age 65 and older. Surveys that assessed sleep disturbance and duration were administered at baseline. We examined the relationship between sleep disturbance and deficiency and incident dementia and all-cause mortality over the following 5 years using Cox proportional hazards modeling, controlling for confounders. Among the sample (n = 2,812), very short sleep duration (≤5 hours: HR = 2.04, 95% CI: 1.26 - 3.33) and sleep latency (>30 minutes: HR = 1.45, 95% CI: 1.03 - 2.03) were associated with incident dementia in adjusted Cox models. Difficulty maintaining alertness ("Some Days": HR = 1.49, 95% CI: 1.13 - 1.94 and "Most/Every Day": HR = 1.65, 95% CI: 1.17 - 2.32), napping ("Some days": HR = 1.38, 95% CI: 1.03 - 1.85; "Most/Every Day": HR = 1.73, 95% CI: 1.29 - 2.32), sleep quality ("Poor/Very Poor": HR = 1.75, 95% CI: 1.17 - 2.61), and very short sleep duration (≤5 hours: HR = 2.38, 95% CI: 1.44 - 3.92) were associated with all-cause mortality in adjusted Cox models. Addressing sleep disturbance and deficiency may have a positive impact on risk for incident dementia and all-cause mortality among older adults.

Clinical and population health recommendations are derived from studies that include self-report. Differences in question wording and response scales may significantly affect responses. We conducted a methodological review assessing variation in event definition(s), context (i.e., work- versus free-day), and timeframe (e.g., “in the past 4 weeks”) of sleep timing/duration questions. We queried databases of sleep, medicine, epidemiology, and psychology for survey-based studies and/or publications with sleep duration/timing questions. The text of these questions was thematically analyzed. We identified 53 surveys with sample sizes ranging from 93 to 1,185,106. For sleep duration, participants reported nocturnal sleep (24/44), sleep in the past 24-hours (14/44), their major sleep episode (3/44), or answered unaided (3/44). For bedtime, participants reported time into bed (19/47), first attempt to sleep (16/40), or fall-asleep time (12/47). For wake-time, participants reported wake-up time (30/43), the time they “get up” (7/43), or their out-of-bed time (6/43). Context guidance appeared in 18/44 major sleep duration, 35/47 bedtime, and 34/43 wake-time questions. Timeframe was provided in 8/44 major sleep episode duration, 16/47 bedtime, and 10/43 wake-time questions. One question queried the method of awakening (e.g., by alarm clock), 18 questions assessed sleep latency, and 12 measured napping. There is variability in the event definition(s), context, and timeframe of questions relating to sleep. This work informs efforts at data harmonization for meta-analyses, provides options for question wording, and identifies questions for future surveys.

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