Nature and Science of Sleep最新文献

Study objectives: This study aimed to evaluate the test-retest reliability of the Epworth Sleepiness Scale (ESS) in patients with untreated obstructive sleep apnea (OSA) and investigate the effects of different follow-up techniques and various factors on ESS score discrepancies.

Methods: This study prospectively enrolled participants diagnosed with OSA at West China Hospital of Sichuan University from October 2022 to May 2023. Each participant completed a polysomnography (PSG) and the Chinese version of the ESS. Initial ESS evaluations were performed before the PSG and were reassessed either face-to-face or on telephone within a week. Analysis involved Bland-Altman plots, the intraclass correlation coefficient (ICC), and calculation of mean differences.

Results: We included 382 patients with untreated OSA, averaging 43.52 years old, with a mean body mass index (BMI) of 26.54 kg/m² and an average apnea-hypopnea index (AHI) of 47.93 events/hour. The ICC was recorded at 0.820. The signed difference in ESS scores from baseline to follow-up was 1.68 ± 2.93 overall. In OSA patients with a BMI > 28, the difference was 2.39 ± 3.46, while in those with an AHI ≥ 30, it was 1.77 ± 3.27.

Conclusion: This study underscores the significance of repeated ESS testing to improve the reliability of sleepiness evaluations in patients with OSA. Further studies should aim to confirm these findings in a broader demographic and develop refined methods for more precise sleepiness assessments among different OSA groups.

Purpose: This study assesses sleep quality amongst hemodialysis (HD) patients and identifies contributing factors, which include demographic and clinical factors and significant symptoms associated with HD (ie, fatigue and pruritus).

Patients and methods: In this cross-sectional design, 116 participants were recruited from HD units of two hospitals in Saudi Arabia. Three measures were used to identify predictors of sleep quality among HD patients, including the Pittsburgh Sleep Quality Index (PSQI), the Fatigue Severity Scale (FSS), and the 5-D itch scale. Demographic and clinical profiles were also obtained. Univariate and multiple regression analyses were used to determine significant factors associated with sleep quality during HD.

Results: The mean global PSQI score was 6.77±3.23, with 56.9% of participants scoring 5 or more, indicating poor sleep. Sleep latency achieved the highest mean PSQI score (1.70 ±0.94), and 'use of sleep medication components' the lowest (0.36 ±0.83). Increased age, lower educational status, higher fatigue, and pruritus were associated with poorer sleep. Controlling for status and age suggested that fatigue and pruritus independently influenced HD patients' sleep quality (F (4, 111) = 10.89, P = <0.001). There were positive relationships between increased levels of fatigue and pruritus and poor quality of sleep.

Conclusion: This is the first study to examine fatigue, pruritus, and sleep quality in HD patients. Findings will assist dialysis clinicians to develop appropriate management strategies to mitigate factors causing poor sleep for such patients. Intervention programs targeting self-management of fatigue and pruritus symptoms could potentially improve patients' sleep quality.

Background: Poor sleep is associated with longer recovery following adolescent concussion, making the longitudinal assessment of sleep important for monitoring recovery and identifying sleep disruptions. An important consideration for successful monitoring of sleep following concussion is the feasibility and adherence of a given sleep monitoring tool when used in an at-home environment. Understanding the usability of different sleep monitoring tools is essential for determining their applicability for longitudinal assessment in an ecologically valid environment.

Purpose: The purpose of this study was to: (1) report on the adherence and feasibility of at-home sleep monitoring in adolescents following concussion, and (2) compare outcomes of subjective and wearable measures of sleep between adolescents with and without a concussion.

Patients and methods: Participants included adolescents within 21 days of a concussion and uninjured controls that participated in four separate, prospective and longitudinal investigations of sleep following concussion. Sleep data was measured with: (1) Dreem Headband; (2) Philips Actiwatch; (3) Fitbit; and (4) subjective sleep diary. Sleep data was collected nightly, and adherence was defined as percentage of nights the participant used the sleep-monitoring tool over the study duration. Independent t-tests and effect sizes were calculated for the following sleep data outcomes as measured by each of the monitoring tools: duration, efficiency, latency, wake after sleep onset.

Results: Sleep data for a total of 183 adolescents (104 with concussion, 79 uninjured controls) was assessed. Adherence rates across all devices ranged from 53% to 98%, with the subjective sleep diary showing the highest adherence rate for both groups (concussion: 91%, control: 94%). Across the four different monitoring tools, adolescents with a concussion demonstrated longer duration, latency, wake after sleep onset, and lower (worse) efficiency, with medium to large effect sizes.

Conclusion: The results indicate that at-home sleep monitoring is a feasible approach for tracking sleep in adolescents following concussion.

Objective: There is a connection between obstructive sleep apnea (OSA) and coronary microvascular dysfunction (CMD), but the underlying mechanisms remain unclear. This study aims to evaluate the correlation between OSA-related nocturnal hypoxemia parameters and CMD.

Methods: This is an observational, single-center study that included patients who underwent polysomnography and coronary angiography during hospitalization. The presence of CMD was determined by angio-based index of microcirculatory resistance (AccuIMR). Categorical variables were compared using chi-square test or Fisher exact test. The t-test and Mann-Whitney U-test were used to compare normally and non-normally distributed continuous variables, respectively. Univariate and multivariable logistic regression analyses were performed to evaluate the relationship between nocturnal hypoxemia parameters and CMD.

Results: A total of 133 patients were included in this study, of whom 72 (54.14%) had evidence of CMD. Patients with CMD exhibited a higher prevalence of OSA and experienced more severe nocturnal hypoxia. After adjusting for potential confounding factors, minimum oxygen saturation (minSpO₂) ≤90% (OR 5.89; 95% CI 1.73-19.99; P=0.004) and the percentage of time spent with oxygen saturation below 90% (T90) ≥5% (OR 3.13; 95% CI 1.05-9.38; P=0.041) were independently associated with CMD. However, no significant association was observed between apnea-hypopnea index (AHI) and CMD.

Conclusion: Parameters of nocturnal hypoxemia are associated with CMD. Hypoxemia parameters may more sensitively reflect the correlation between OSA and CMD than AHI.

Purpose: Obstructive sleep apnoea (OSA) is a common disease that benefits from early treatment and patient support in order to prevent secondary illnesses. This study assesses the capability of the large language model (LLM) ChatGPT-4o to offer patient support regarding first line positive airway pressure (PAP) and second line hypoglossal nerve stimulation (HGNS) therapy.

Methods: Seventeen questions, each regarding PAP and HGNS therapy, were posed to ChatGPT-4o. Answers were rated by experienced experts in sleep medicine on a 6-point Likert scale in the categories of medical adequacy, conciseness, coherence, and comprehensibility. Completeness of medical information and potential hazard for patients were rated using a binary system.

Results: Overall, ChatGPT-4o achieved reasonably high ratings in all categories. In medical adequacy, it performed significantly better on PAP questions (mean 4.9) compared to those on HGNS (mean 4.6) (p < 0.05). Scores for coherence, comprehensibility and conciseness showed similar results for both HGNS and PAP answers. Raters confirmed completeness of responses in 45 of 51 ratings (88.24%) for PAP answers and 28 of 51 ratings (54.9%) for HGNS answers. Potential hazards for patients were stated in 2 of 52 ratings (0.04%) for PAP answers and none for HGNS answers.

Conclusion: ChatGPT-4o has potential as a valuable patient-oriented support tool in sleep medicine therapy that can enhance subsequent face-to-face consultations with a sleep specialist. However, some substantial flaws regarding second line HGNS therapy are most likely due to recent advances in HGNS therapy and the consequent limited information available in LLM training data.

Purpose: This study aims to develop a deep learning methodology for quantitative assessing adenoid hypertrophy in nasopharyngoscopy images and to investigate its correlation with the apnea-hypopnea index (AHI) in pediatric patients with obstructive sleep apnea (OSA).

Patients and methods: A total of 1642 nasopharyngoscopy images were collected from pediatric patients aged 3 to 12 years. After excluding images with obscured secretions, incomplete adenoid exposure, 1500 images were retained for analysis. The adenoid-to-nasopharyngeal (A/N) ratio was manually annotated by two experienced otolaryngologists using MATLAB's imfreehand tool. Inter-annotator agreement was assessed using the Mann-Whitney U-test. Deep learning segmentation models were developed with the MMSegmentation framework, incorporating transfer learning and ensemble learning techniques. Model performance was evaluated using precision, recall, mean intersection over union (MIoU), overall accuracy, Cohen's Kappa, confusion matrices, and receiver operating characteristic (ROC) curves. The correlation between the A/N ratio and AHI, derived from polysomnography, was analyzed to evaluate clinical relevance.

Results: Manual evaluation of adenoid hypertrophy by otolaryngologists (p=0.8507) and MATLAB calibration (p=0.679) demonstrated high consistency, with no significant differences. Among the deep learning models, the ensemble learning-based SUMNet outperformed others, achieving the highest precision (0.9616), MIoU (0.8046), overall accuracy (0.9182), and Kappa (0.87). SUMNet also exhibited superior consistency in classifying adenoid sizes. ROC analysis revealed that SUMNet (AUC=0.85) outperformed expert evaluations (AUC=0.74). A strong positive correlation was observed between the A/N ratio and AHI, with the correlation coefficients for SUMNet-derived ratios ranging from r=0.9052 (tonsils size+1) to r=0.4452 (tonsils size+3) and for expert-derived ratios ranging from r=0.4590 (tonsils size+1) to r=0.2681 (tonsils size+3).

Conclusion: This study introduces a precise and reliable deep learning-based method for quantifying adenoid hypertrophy and addresses the challenge posed limited sample sizes in deep learning applications. The significant correlation between adenoid hypertrophy and AHI underscores the clinical utility of this method in pediatric OSA diagnosis.

Purpose: Benzodiazepine receptor agonists (BZRAs), including benzodiazepines (BZDs) and Z drugs, are widely prescribed for anxiety and sleep. Therefore, issues of tolerance, dependence and adverse effects are of concern. Recent studies suggested a potential link between BZRAs and hearing problems. However, the actual relationship was still unclear. Accordingly, this study aims to investigate the actual association between BZRA use and risk of sudden sensorineural hearing loss (SSNHL) using population data.

Patients and methods: This study used the Taiwan Longitudinal Health Insurance Database. 137,277 BZRA users and 1,328,554 nonusers were identified for relevant analyses. We used cohort design with inverse-probability treatment weighting (IPTW) strategy to balance the baseline differences of demographics and comorbidities between two groups. The 5-year incidence of SSNHL was followed. Cox proportional-hazard regression analyses were used to estimate the hazard ratios (HRs).

Results: BZRA users showed an increased 5-year SSNHL risk (adjusted HR: 1.244) after weighting. Subgroup and sensitivity analyses produced consistent results. Notably, SSNHL risk was higher among young BZRA users (adjusted HR: 1.397). BZRA users had the highest SSNHL risk in the first year (adjusted HR: 2.037) after IPTW.

Conclusion: BZRA use elevated the risk of SSNHL, particularly in young adults and in the first year. This emphasises the importance for physicians and policymakers should be aware of the potential hearing difficulties among BZRA users and take necessary examinations.

Background: Multimorbidity, defined as the coexistence of two or more chronic diseases, is highly prevalent among the elderly population and is associated with adverse outcomes. However, little is known about its relationship with sleep issues, particularly in this demographic. Therefore, this study aimed to investigate its association with sleep quality and duration among the elderly.

Methods: This cross-sectional study was conducted in Emin County, Xinjiang, China, which included a population aged 60 years and above. We employed the Pittsburgh Sleep Quality Index (PSQI) score to assess sleep quality and duration. Multimorbidity was determined through self-reports, physical examination, blood tests, and imaging. Logistic regression analyses were used to explore the association between multimorbidity and sleep patterns, adjusting for confounders.

Results: A total of 8205 elderly participants were included, of whom 66.8% suffered from multimorbidity. Participants with multimorbidity exhibited higher total PSQI scores [6 (3,9)], and a higher percentage of poor sleep quality (50.6%), compared to those without multimorbidity. Multimorbidity was significantly associated with the presence of poor sleep quality (OR = 1.27, 95% CI: 1.14-1.41, P < 0.001) before and after adjusting for confounders. The risk of having poor sleep quality significantly increased as the number of multimorbidities increased. The OR (95% CI) values were 1.16 (1.02,1.32) for two diseases, 1.54 (1.26,1.90) for ≥5 diseases. In the adjusted model for total participants, having four diseases (OR = 1.26, 95% CI: 1.05-1.51, p = 0.013) and five or more diseases (OR = 1.29, 95% CI: 1.03-1.61, p = 0.029) were associated with shorter sleep duration. Furthermore, those with five or more diseases associated with longer sleep duration (OR = 1.40, 95% CI: 1.00-1.95, p = 0.057).

Conclusion: There is a significant association between multimorbidity and poor sleep quality in older community dwellers, which may provide clues for disease prevention.

Objective: Sleep disorders are common in Alzheimer's disease (AD) patients and can impair the glymphatic system, leading to cognitive decline. This study aimed to investigate whether AD patients with sleep disorders exhibit worse glymphatic function and more severe cognitive impairment compared to those without sleep disorders and to explore the underlying molecular imaging mechanisms.

Methods: This study included 40 AD patients with sleep disorders (ADSD), 39 cognitively matched AD patients without sleep disorders (ADNSD), and 25 healthy middle-aged and elderly controls (NC). Participants underwent functional magnetic resonance imaging (fMRI), and cognitive and sleep assessments. The ALPS (Along the Perivascular Space) index was calculated, followed by intergroup comparisons, correlation analyses, and mediation analyses. The diagnostic utility of the ALPS index was assessed using a receiver operating characteristic (ROC) curve.

Results: The ALPS index was lower in the ADNSD and ADSD groups compared to the NC group. In the ADSD group, PSQI scores were negatively correlated with MMSE scores. The ALPS index was positively correlated with MMSE scores and negatively with PSQI scores. Mediation analyses indicated that the ALPS index partially mediated the effect of sleep disturbances on cognitive impairment (indirect effect = -0.134; mediation effect = 30.505%). The area under the ROC curve (AUROC) for distinguishing ADSD from ADNSD was 0.86, with a cutoff ALPS index value 1.309.

Conclusion: Sleep disorders worsen glymphatic function and cognitive impairment in AD patients. The ALPS index partially mediates the impact of sleep disorders on cognitive function and shows moderate accuracy in distinguishing between patients with ADSD and ADNSD.

Purpose: It is presumed by many that acute sleep loss results in degraded in-game esports (competitive, organized video game play) performance. However, this has not been experimentally investigated to date. The objective of the current experiment was to elucidate whether ~29hrs of total sleep deprivation impacts in-game performance for the popular esport Rocket League.

Patients and methods: Twenty skill-matched pairs (N = 40 total) were recruited. Within each pair, one participant was assigned to an intervention group (TSD), while the other was assigned to a control group (CON). Two test sessions occurred; one while both participants were rested (baseline), and the other while the CON participant was rested but the TSD participant was sleep deprived (experimental).

Results: Following total sleep deprivation, TSD participants reported higher Karolinska Sleepiness Scale-measured subjective sleepiness and lower subjective alertness and motivation, as well as worsened PVT response speed and ~5 times greater PVT lapse incidence, and worsened response speed on a two-choice categorization task. However, overall in-game Rocket League performance did not worsen due to total sleep deprivation. Exploratory analyses of performance indicators suggest a potential shift toward a simpler and safer strategy following sleep deprivation.

Conclusion: Following a bout of ~29hrs total sleep deprivation, overall in-game Rocket League performance remained unaffected. This presents as a promising finding given the high potential for acute pre-competition sleep disturbance in esports, though habitual sleep remains a concern for esport athletes.