Sleep and Biological Rhythms最新文献

The purpose of this study was to assess sleep quality in patients undergoing total hip arthroplasty (THA) from preoperatively to 12 weeks postoperatively and to establish a risk predictor for postoperative sleep disturbance to enable early care and intervention. A self-designed data collection form was used. Patients were assessed preoperatively and at 5 postoperative time points using visual analog scale (VAS) for pain, sleep quality and neuropsychological status with the following assessment tools: the Chinese versions of the Pittsburgh Sleep Quality Index (CPSQI), the Epworth Sleepiness Scale (CESS), the Zung Self-Rating Anxiety Scale (ZSAS) and the Epidemiological Studies Depression Scale (CESD). Univariate and multivariate logistic regression analysis was used for the identification of risk factors for postoperative sleep disturbance. The receiver operating characteristic (ROC) curve was plotted to evaluate the regression model. Of the 290 eligible patients, 193 (133 women) were included in the study. There was a 60.6% prevalence of preoperative sleep disturbance. The CPSQI score increased significantly at 2 weeks postoperatively compared to preoperative baseline, but appeared to decrease at 4 weeks postoperatively. Multivariate logistic regression analysis showed that pain (VAS score: OR = 1.202 [95% CI = 1.002-1.446, P < 0.05]), daytime sleepiness (CESS score: OR = 1.134 [95% CI = 1.015-1.267, P < 0.05]) and anxiety (ZSAS score: OR = 1.396 [95% CI = 1.184-1.645, P < 0.001]) were risk factors associated with postoperative sleep disturbance at 2 weeks. The ROC curve showed that the AUC was 0.762, the sensitivity was 83.19% and the specificity was 64.86%. Postoperative sleep disturbance is highly prevalent in the first 2 weeks after THA. The risk prediction model constructed according to the above factors has good discriminant ability for the risk prediction of sleep disturbance after THA. The use of this risk prediction model can improve the recognition of patients and medical providers and has good ability to guide clinical nursing observation and early screening of sleep disturbance after THA.

The purpose of this study was to assess the effect of a digital patient support (DPS) tool, complementary to standard care on continuous or automatic positive airway pressure (auto)CPAP adherence and daytime sleepiness after 12 weeks in patients diagnosed with severe obstructive sleep apnea (OSA). All patients with apnea-hypopnea index (AHI) ≥ 30 per hour were prospectively included and randomized to receive standard care (SC) or standard care with personalized DPS via a mobile app prototype version (SC + DPS). Patients in the SC + DPS arm received additionally automated feedback on their therapy, motivational messages and therapy recommendations. 100 patients completed the study (SC: 50, SC + DPS: 50). No differences were found in characteristics of SC vs. SC + DPS (mean ± SD) for age (53.9 ± 10.8 vs. 51.7 ± 12.3 years), initial diagnostic apnea-hypopnea index (51.1 ± 15.5 vs. 50.9 ± 17.7 events/h), BMI (33.8 ± 6.7 vs. 33.5 ± 4.5 kg/m), and Epworth Sleepiness Scale (ESS) baseline score (9.5 ± 4.2 vs. 9.1 ± 5.2). After 12 weeks, mean ESS score was significantly lower (SC: 7.6 ± 4.1 vs. SC + DPS: 5.5 ± 3.9; p = 0.006) in the SC + DPS group vs. standard care group. Therapy adherence was significantly higher (SC: 268.7 ± 122.1 vs. SC + DPS: 338.8 ± 106.8 min; p = 0.002) in the SC + DPS group compared to standard care group. No difference was found in the residual AHI between both groups. However, SC + DPS group showed a trend towards fewer phases with increased leakage compared to SC group. Intention-to-treat analysis (112 (56/56) patients) showed similar results. After 12 weeks, (auto)CPAP adherence and daytime sleepiness improved significantly in patients with severe OSA using the digital patient support tool. Clinical Trial Registration (retrospectively registered): Registry: NCT05440279; Title: Effects of Telemedical Support on Therapeutic Results of CPAP Patients; URL: https://clinicaltrials.gov/ct2/show/NCT05440279; Date of registration: June 30, 2022.

Supplementary information: The online version contains supplementary material available at 10.1007/s41105-023-00479-9.

Purpose: The aim of the present study was to examine the psychometric properties of the Turkish version of the Pre-sleep Arousal Scale (PSAS), which measures pre-sleep arousal, a significant predictor of insomnia symptoms.

Methods: 651 participants were recruited via social media and the Internet. Confirmatory factor analysis was conducted in the total sample (65.28% females; M_age1 = 28.09 ± 14.00). Convergent, divergent, incremental, and known-groups validity and internal consistency coefficients were assessed in a subsample of 556 participants (62.77% females; M_age2 = 29.25 ± 14.81). A second separate sample of 88 participants (80.68% females; M_age3 = 22.19 ± 4.98) was used to evaluate three-week test-retest reliability.

Results: The results of factor analysis confirmed the two-factor structure of the Turkish PSAS with cognitive (PSAS-C) and somatic (PSAS-S), similar to the original scale. The correlations of the PSAS with convergent and divergent measures showed that the Turkish form had good convergent and acceptable divergent validity. PSAS-C and PSAS-S were able to explain an 18% additional variance in insomnia severity beyond depression and anxiety, an 18% additional variance in depression beyond insomnia severity, and a 35% additional variance in anxiety beyond insomnia severity. Moreover, insomnia patients had significantly higher PSAS-C and PSAS-S scores than good sleepers. Finally, the PSAS, PSAS-C, and PSAS-S had satisfactory internal consistency coefficients (α = 0.92, 0.91, and 0.86, respectively) and three-week test-retest correlations (ICC = 0.82, 0.82, and 0.71, respectively).

Conclusion: The Turkish form of the PSAS was a valid and reliable measure of pre-sleep arousal and can be utilized in sleep studies.

Supplementary information: The online version contains supplementary material available at 10.1007/s41105-023-00483-z.

As lifestyles have shifted to favor nighttime activities, daytime sleepiness and sleep-related problems have become increasingly common in Japan. Excessive daytime sleepiness (EDS) is an adverse consequence of sleep loss and an important public health concern. EDS may cause academic difficulties, behavioral abnormalities, and psychological dysfunction; therefore, it is a particularly important issue among university students. We conducted a cross-sectional study to investigate the prevalence of EDS and its associated lifestyle factors among Japanese university students. A questionnaire was completed by 1470 first-year university students, aged 19.0 (± 1.0) years. Using the questionnaire, we collected information on (1) demographic variables, (2) lifestyle variables, and (3) sleep habits and daytime sleepiness. Daytime sleepiness was measured using the Japanese version of the Epworth Sleepiness Scale, a frequently used subjective scale for assessing sleepiness. The overall prevalence of EDS was 57% (53% in men and 61% in women). Multivariate logistic regression analysis revealed that the following factors were associated with EDS: female sex, exercise habits, long commuting times, later wake-up times, and shorter sleep duration. Given that more than 50% of first-year university students reported having EDS, interventions should be considered to decrease its risk, including educational programs that provide strategies to extend sleep duration and delay wake-up time. Such strategies may also be valuable for students with other potential risk factors, such as exercise habits or long commute times, that are associated with EDS.

We examined the effect of sleep and resilience on stress responses in female Japanese university students during the COVID-19 pandemic. Sleep was measured using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J), stress response was evaluated using the Stress Response Scale-18 (SRS-18), and resilience using the Resilience Scale for Students (RS-S). Multiple regression analysis showed that approximately 40% of the SRS-18 score could be explained by PSQI-J score and RS-S score. Subjective sleep quality, daytime dysfunction, and global score of PSQI-J had direct influences on SRS-18.

Although associations among insomnia, cognitive impairment, and stroke have been demonstrated, whether insomnia increases the risk of cognitive impairment after stroke remains unclear. The aim of this study was to examine whether insomnia complaints moderated the association between stroke and cognitive impairment in older adults. This study was a secondary data analysis that used data from the National Health Interview Survey 2009. A total of 447 older adults with a mean age of 74.63 years (50.1% men) were included. Self-reported insomnia and stroke occurrence were determined using a questionnaire. Cognitive impairment was assessed using the Mini-Mental State Examination. We used multivariate logistic regression to analyze the association between insomnia complaints and cognitive impairment. Participants were categorized into four groups: those with stroke and insomnia (58), those with stroke without insomnia (91), those without stroke with insomnia (116), and those without stroke or insomnia (182). The prevalence of insomnia complaints was 38.9%, and the frequency of poststroke cognitive impairment was 50.3%. After controlling for potential confounders, participants with stroke (with or without insomnia) had a significantly higher risk of cognitive impairment than those without stroke or insomnia (adjusted odds ratios: 4.16 and 2.91, 95% confidence intervals: 1.91-9.07 and 1.56-5.43, respectively). Stroke with or without insomnia complaints was associated with a higher risk of cognitive impairment relative to older adults without stroke or insomnia. The risk of cognitive impairment was the highest among participants with both stroke and insomnia.