METHODS: This systematic review was performed to determine the effect of ACT on insomnia and sleep quality. To collect articles, the PubMed, Web of Science (WOS), Cochrane library, Embase, Scopus, Science Direct, ProQuest, Mag Iran, Irandoc, and Google Scholar databases were searched, without a lower time-limit, and until April 2020.
RESULTS: Related articles were derived from 9 research repositories, with no lower time-limit and until April 2020. After assessing 1409 collected studies, 278 repetitive studies were excluded. Moreover, following the primary and secondary evaluations of the remaining articles, 1112 other studies were removed, and finally a total of 19 intervention studies were included in the systematic review process. Within the remaining articles, a sample of 1577 people had been assessed for insomnia and sleep quality.
CONCLUSION: The results of this study indicate that ACT has a significant effect on primary and comorbid insomnia and sleep quality, and therefore, it can be used as an appropriate treatment method to control and improve insomnia.
METHODS: A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.
RESULTS: Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).
DISCUSSION: Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.
METHODS: A web-based cross-sectional study was conducted among 1280 healthcare providers aged 18 years and older from 30 primary care clinics in Selangor, Malaysia. In this study, the Copenhagen Burnout Inventory was used to assess burnout. The results were analyzed using multiple logistic regression.
RESULTS: The prevalence of personal burnout was 41.7%, followed by work-related burnout (32.2%) and client-related burnout (14.5%). The determinants for personal burnout in this study were younger age, being a doctor, higher COVID-19 exposure risk, do not know where to seek help, inability to handle stress, poorer sleep quality score, higher total COVID-19 fear score, higher total stress score, and lower total BRS score. The determinants of work-related burnout were younger age, being a doctor, longer years of working, higher COVID-19 exposure risk, do not know where to seek help, lower altruistic score, poorer sleep quality score, higher total stress score, and lower total brief resilience score (BRS) score. The determinants of client-related burnout were doctor, single/divorced, more than one attachment site, and higher satisfaction toward the infection control, inability to handle stress, higher total depression score, and lower total BRS score.
CONCLUSION: Every fourth out of ten suffered from personal burnout, one-third from work-related burnout, and one-seventh from client-related burnout among healthcare providers during the COVID-19 pandemic. Healthcare systems must take care of healthcare workers' physical and emotional depletion, reducing the risk of burnout.
PURPOSE: Applying self-regulation theory, we conducted a randomized, controlled trial testing the efficacy of mental imagery techniques promoting arousal reduction and implementation intentions to improve sleep behavior.
METHOD: We randomly assigned 104 business employees to four imagery-based interventions: arousal reduction, implementation intentions, combined arousal reduction and implementation intentions, or control imagery. Participants practiced their techniques daily for 21 days. They completed online measures of sleep quality, behaviors, and self-efficacy at baseline and Day 21; and daily measures of sleep behaviors.
RESULTS: Participants using implementation intention imagery exhibited greater improvements in self-efficacy, sleep behaviors, sleep quality, and time to sleep relative to participants using arousal reduction and control imagery.
CONCLUSIONS: Implementation intention imagery can improve sleep behavior for daytime employees. Use of arousal reduction imagery was unsupported. Self-regulation imagery techniques show promise for improving sleep behaviors.
METHODS: Adults from the general-population (n = 392) completed online measures of Type D personality (DS14) and insomnia severity.
RESULTS: Individuals with the Type D personality trait reported significantly greater symptoms of insomnia relative to Non-Type Ds. Moreover, insomnia-symptoms were independently related to negative affectivity (NA) and social inhibition (SI) and the Type D interaction (i.e. synergistic product of SI and NA). Linear regression analysis determined that NA but not SI significantly predicted insomnia symptoms after controlling for age and sex. However, after accounting for the Type D interaction, negative affectivity remained the only significant predictor of insomnia-symptoms.
CONCLUSIONS: The Type D personality type appears to be related to insomnia-symptoms, both as a categorical and dimensional construct. These outcomes support prior research evidencing that whilst Type D personality is related to poor sleep in adolescents, NA appears to be the main contributor.
Objective: To assess whether sleep timing and napping behavior are associated with increased obesity, independent of nocturnal sleep length.
Design, Setting, and Participants: This large, multinational, population-based cross-sectional study used data of participants from 60 study centers in 26 countries with varying income levels as part of the Prospective Urban Rural Epidemiology study. Participants were aged 35 to 70 years and were mainly recruited during 2005 and 2009. Data analysis occurred from October 2020 through March 2021.
Exposures: Sleep timing (ie, bedtime and wake-up time), nocturnal sleep duration, daytime napping.
Main Outcomes and Measures: The primary outcomes were prevalence of obesity, specified as general obesity, defined as body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 30 or greater, and abdominal obesity, defined as waist circumference greater than 102 cm for men or greater than 88 cm for women. Multilevel logistic regression models with random effects for study centers were performed to calculate adjusted odds ratios (AORs) and 95% CIs.
Results: Overall, 136 652 participants (81 652 [59.8%] women; mean [SD] age, 51.0 [9.8] years) were included in analysis. A total of 27 195 participants (19.9%) had general obesity, and 37 024 participants (27.1%) had abdominal obesity. The mean (SD) nocturnal sleep duration was 7.8 (1.4) hours, and the median (interquartile range) midsleep time was 2:15 am (1:30 am-3:00 am). A total of 19 660 participants (14.4%) had late bedtime behavior (ie, midnight or later). Compared with bedtime between 8 pm and 10 pm, late bedtime was associated with general obesity (AOR, 1.20; 95% CI, 1.12-1.29) and abdominal obesity (AOR, 1.20; 95% CI, 1.12-1.28), particularly among participants who went to bed between 2 am and 6 am (general obesity: AOR, 1.35; 95% CI, 1.18-1.54; abdominal obesity: AOR, 1.38; 95% CI, 1.21-1.58). Short nocturnal sleep of less than 6 hours was associated with general obesity (eg, <5 hours: AOR, 1.27; 95% CI, 1.13-1.43), but longer napping was associated with higher abdominal obesity prevalence (eg, ≥1 hours: AOR, 1.39; 95% CI, 1.31-1.47). Neither going to bed during the day (ie, before 8pm) nor wake-up time was associated with obesity.
Conclusions and Relevance: This cross-sectional study found that late nocturnal bedtime and short nocturnal sleep were associated with increased risk of obesity prevalence, while longer daytime napping did not reduce the risk but was associated with higher risk of abdominal obesity. Strategic weight control programs should also encourage earlier bedtime and avoid short nocturnal sleep to mitigate obesity epidemic.
METHODS: A comprehensive search was conducted in CENTRAL, MEDLINE, SCOPUS, Google Scholars, World Health Organization Trials Portal, ClinicalTrials.gov, Clinical Trial Registry of India, and AYUSH Research Portal for all appropriate trials. Randomized controlled trials that examined the effect of Ashwagandha extract versus placebo on sleep in human participants 18 years old and above were considered. Two authors independently read all trials and independently extracted all relevant data. The primary outcomes were sleep quantity and sleep quality. The secondary outcomes were mental alertness on rising, anxiety level, and quality of life.
RESULTS: A total of five randomized controlled trials containing 400 participants were analyzed. Ashwagandha extract exhibited a small but significant effect on overall sleep (Standardized Mean Difference -0.59; 95% Confidence Interval -0.75 to -0.42; I2 = 62%). The effects on sleep were more prominent in the subgroup of adults diagnosed with insomnia, treatment dosage ≥600 mg/day, and treatment duration ≥8 weeks. Ashwagandha extract was also found to improve mental alertness on rising and anxiety level, but no significant effect on quality of life. No serious side effects were reported.
CONCLUSION: Ashwagandha extract appears to has a beneficial effect in improving sleep in adults. However, data on the serious adverse effects of Ashwagandha extract are limited, and more safety data would be needed to assess whether it would be safe for long-term use.
METHOD: A multicenter, prospective, randomized, parallel-design, open label interventional study to estimate the effectiveness of zolpidem (10 mg) oral tablets versus acupressure on sleep quality and quality of life in patients with CKD-aP on hemodialysis. A total of 58 hemodialysis patients having sleep disturbance due to CKD-aP completed the entire 8-week follow-up. The patients were divided into a control (acupressure) group of 28 patients and an intervention (zolpidem) group of 30 patients.
RESULTS: A total of 58 patients having CKD-aP and sleep disturbance were recruited. In the control group there was a reduction in the PSQI score with a mean ± SD from 12.28 ± 3.59 to 9.25 ± 3.99, while in the intervention group the reduction in PSQI score with a mean ± SD was from 14.73 ± 4.14 to 10.03 ± 4.04 from baseline to endpoint. However, the EQ5D index score and EQ-visual analogue scale (VAS) at baseline for the control group with a mean ± SD was 0.49 ± 0.30 and 50.17 ± 8.65, respectively, while for the intervention group the values were 0.62 ± 0.26 and 47.17 ± 5.82, respectively. The mean EQ5D index score in the control group improved from 0.49 ± 0.30 to 0.53 ± 0.30, but in the intervention group there was no statistical improvement in mean EQ5D index score from 0.62 ± 0.26 to 0.62 ± 0.27 from baseline to week 8. The EQ 5D improved in both groups and the EQ-VAS score was 2.67 points higher at week 8 as compared to baseline in the control group, while in the intervention group the score was 3.33 points higher at week 8 as compared to baseline. Comparing with baseline, the PSQI scores were significantly reduced after week 4 and week 8 (P = sleep quality and quality of life among CKD-aP patients on hemodialysis has been observed in both the control and intervention groups. Zolpidem and acupressure safety profiling showed no severe adverse effect other that drowsiness, nausea and daytime sleeping already reported in literature of zolpidem.
Methods: One hundred participants (50 good sleepers; 50 poor sleepers) were asked to choose between 2 written scenarios to answer 1 of 2 questions: "Which describes a better (or worse) night of sleep?". Each scenario described a self-reported experience of sleep, stringing together 17 possible determinants of sleep quality that occur at different times of the day (day before, pre-sleep, during sleep, upon waking, day after). Each participant answered 48 questions. Logistic regression models were fit to their choice data.
Results: Eleven of the 17 sleep quality parameters had a significant impact on the participants' choices. The top 3 determinants of sleep quality were: Total sleep time, feeling refreshed (upon waking), and mood (day after). Sleep quality judgments were most influenced by factors that occur during sleep, followed by feelings and activities upon waking and the day after. There was a significant interaction between wake after sleep onset and feeling refreshed (upon waking) and between feeling refreshed (upon waking) and question type (better or worse night of sleep). Type of sleeper (good vs poor sleepers) did not significantly influence the judgments.
Conclusions: Sleep quality judgments appear to be determined by not only what happened during sleep, but also what happened after the sleep period. Interventions that improve mood and functioning during the day may inadvertently also improve people's self-reported evaluation of sleep quality.