Determining cut-off values and predictors for the Snaith-Hamilton Pleasure Scale: comparison between clinical and school settings.

BACKGROUND: Few previous studies have established Snaith-Hamilton Pleasure Scale (SHAPS) cut-off values using receiver operating characteristic curve analysis and applied these values to compare predictors of anhedonia between clinical and nonclinical groups. AIMS: To determine the optimal cut-off values for the SHAPS and use them to identify predictors of anhedonia in clinical and nonclinical groups in Taiwan. METHOD: This cross-sectional and correlational study used convenience sampling to recruit 160 patients from three hospitals and 412 students from two universities in northern Taiwan. Data analysis included receiver operating characteristic curve, univariate and multivariate analyses. RESULTS: The optimal SHAPS cut-off values were 29.5 and 23.5 for the clinical and nonclinical groups, respectively. Moreover, two-stage analysis revealed that participants in the clinical group who perceived themselves as nondepressed, and participants in the nonclinical group who did not skip classes and whose fathers exhibited higher levels of care and protection were less likely to attain the cut-off values. Conversely, participants in the nonclinical group who reported lower academic satisfaction and were unwilling to seek help from family or friends were more likely to attain the cut-off values. CONCLUSIONS: Our findings highlight the importance of optimal cut-off values in screening for depression risk within clinical and nonclinical groups. Accordingly, the development of comprehensive, individualised programmes to monitor variation trends in SHAPS scores and relevant predictors of anhedonia across different target populations is crucial.

Combining wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of two levels of OSA severity (i.e., moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using the wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index (AHI), results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between AHI and RDIPSG_TST were identified. In terms of the agreement between the two devices, the average difference between PSG-based AHI and radar-based RDIPSG_TST was 0.59 events/h, while 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy, and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.

Traces of EEG-fMRI coupling reveals neurovascular dynamics on sleep inertia.

Upon emergence from sleep, individuals experience temporary hypo-vigilance and grogginess known as sleep inertia. During the transient period of vigilance recovery from prior nocturnal sleep, the neurovascular coupling (NVC) may not be static and constant as assumed by previous neuroimaging studies. Stemming from this viewpoint of sleep inertia, this study aims to probe the NVC changes as awakening time prolongs using simultaneous EEG-fMRI. The time-lagged coupling between EEG features of vigilance and BOLD-fMRI signals, in selected regions of interest, was calculated with one pre-sleep and three consecutive post-awakening resting-state measures. We found marginal changes in EEG theta/beta ratio and spectral slope across post-awakening sessions, demonstrating alterations of vigilance during sleep inertia. Time-varying EEG-fMRI coupling as awakening prolonged was evidenced by the changing time lags of the peak correlation between EEG alpha-vigilance and fMRI-thalamus, as well as EEG spectral slope and fMRI-anterior cingulate cortex. This study provides the first evidence of potential dynamicity of NVC occurred in sleep inertia and opens new avenues for non-invasive neuroimaging investigations into the neurophysiological mechanisms underlying brain state transitions.

Association between air pollutant exposure, body water distribution and sleep disorder indices in individuals with low-arousal-threshold obstructive sleep apnoea.

BACKGROUND: Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations. METHODS: In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices. RESULTS: First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea-Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI. CONCLUSION: Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Delirium assessment tools among hospitalized older adults: A systematic review and metaanalysis of diagnostic accuracy.

Delirium is a common neuropsychiatric syndrome that is often overlooked in clinical settings. The most accurate instrument for screening delirium has not been established. This study aimed to compare the diagnostic accuracy of the 4 'A's Test (4AT), Nursing Delirium Screening Scale (Nu-DESC), and Confusion Assessment Method (CAM) in detecting delirium among older adults in clinical settings. These assessment tools feature concise item sets and straightforward administration procedures. Five electronic databases were systematically searched from their inception to September 7, 2022. Studies evaluating the sensitivity and specificity of the 4AT, Nu-DESC, and CAM against the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases as the reference standard were included. Bivariate random effects model was used to summarize the sensitivity and specificity results. A total of 38 studies involving 7378 patients were included. The 4AT, Nu-DESC, and CAM had comparable sensitivity in detecting delirium (0.76, 0.78, and 0.80, respectively). However, the specificity of the CAM was higher than that of the 4AT (0.98 vs 0.89, P = .01) and Nu-DESC 0.99 vs 0.90, P = .003). Diagnostic accuracy was moderated by the percentage of women, acute care setting, sample size, and assessors. The three tools exhibit comparable sensitivity, and the CAM has the highest specificity. Based on the feasibility of the tools, nurses and clinical staffs could employ the Nu-DESC and the 4AT on screening out positive delirium cases and integrate these tools into daily practice. Further investigations are warranted to verify our findings.

Associations between air pollution, intracellular-to-extracellular water distribution, and obstructive sleep apnea manifestations.

Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations. Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution. Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined. Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA. Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Association of Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Temporal consistency of neurovascular components on awakening: preliminary evidence from electroencephalography, cerebrovascular reactivity, and functional magnetic resonance imaging.

Sleep inertia (SI) is a time period during the transition from sleep to wakefulness wherein individuals perceive low vigilance with cognitive impairments; SI is generally identified by longer reaction times (RTs) in attention tasks immediately after awakening followed by a gradual RT reduction along with waking time. The sluggish recovery of vigilance in SI involves a dynamic process of brain functions, as evidenced in recent functional magnetic resonance imaging (fMRI) studies in within-network and between-network connectivity. However, these fMRI findings were generally based on the presumption of unchanged neurovascular coupling (NVC) before and after sleep, which remains an uncertain factor to be investigated. Therefore, we recruited 12 young participants to perform a psychomotor vigilance task (PVT) and a breath-hold task of cerebrovascular reactivity (CVR) before sleep and thrice after awakening (A1, A2, and A3, with 20 min intervals in between) using simultaneous electroencephalography (EEG)-fMRI recordings. If the NVC were to hold in SI, we hypothesized that time-varying consistencies could be found between the fMRI response and EEG beta power, but not in neuron-irrelevant CVR. Results showed that the reduced accuracy and increased RT in the PVT upon awakening was consistent with the temporal patterns of the PVT-induced fMRI responses (thalamus, insula, and primary motor cortex) and the EEG beta power (Pz and CP1). The neuron-irrelevant CVR did not show the same time-varying pattern among the brain regions associated with PVT. Our findings imply that the temporal dynamics of fMRI indices upon awakening are dominated by neural activities. This is the first study to explore the temporal consistencies of neurovascular components on awakening, and the discovery provides a neurophysiological basis for further neuroimaging studies regarding SI.

Prediction of the Duration to Next Admission for an Acute Affective Episode in Patients with Bipolar I Disorder.

Objective: Predicting disease relapse and early intervention could reduce symptom severity. We attempted to identify potential indicators that predict the duration to next admission for an acute affective episode in patients with bipolar I disorder. Methods: We mathematically defined the duration to next psychiatric admission and performed single-variate regressions using historical data of 101 patients with bipolar I disorder to screen for potential variables for further multivariate regressions. Results: Age of onset, total psychiatric admissions, length of lithium use, and carbamazepine use during the psychiatric hospitalization contributed to the next psychiatric admission duration positively. The all-in-one found that hyperlipidemia during the psychiatric hospitalization demonstrated a negative contribution to the duration to next psychiatric admission; the last duration to psychiatric admission, lithium and carbamazepine uses during the psychiatric hospitalization, and heart rate on the discharge day positively contributed to the duration to next admission. Conclusion: We identified essential variables that may predict the duration of bipolar I patients' next psychiatric admission. The correlation of a faster heartbeat and a normal lipid profile in delaying the next onset highlights the importance of managing these parameters when treating bipolar I disorder.

Can adding personalized rule-based feedback improve the therapeutic effect of self-help digital cognitive behavioral therapy for insomnia in young adults?

INTRODUCTION: Cognitive-behavioral therapy for insomnia (CBT-I) is recommended as the first-line treatment for insomnia, but low accessibility and relatively high cost limits the dissemination of the treatment. Several forms of digital CBT-I have been developed to increase the accessibility and shown to be effective; however, the treatment effect may be restricted by the lack of interaction within the treatment. The current study examines whether the therapeutic effects of self-help digital CBT-I could be enhanced by adding simple rule-based personalized feedback. METHOD: Ninety-two young adults with self-reported insomnia were randomly assigned to three groups: a self-help group (SH, n = 31), who received an eight-session email-delivered CBT-I program; a feedback group (FB, n = 31), who went through the same CBT-I program with personalized feedback; and a waitlist group (WL, n = 30). The Insomnia Severity Index (ISI) was used as the primary outcome measure, and the 16-item version of the Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16), Sleep Hygiene Practice Scale (SHPS), and sleep diary were used as the secondary outcome measures. Treatment satisfaction and adherence were also compared between the treatment groups. RESULTS: Both the SH and FB groups showed significantly more improvements in insomnia severity, sleep-related beliefs, and sleep hygiene behaviors than the WL group. Sleep onset latency and sleep efficiency in the sleep diary were also significantly improved after treatment. None of these effects significantly differed between the two treatment groups. Nonetheless, participants in the FB group reported higher treatment satisfaction than those in the SH group. CONCLUSION: This study supports the effectiveness of email-delivered self-help CBT-I for young adults with insomnia. Furthermore, while adding simple personalized feedback may not have an additional effect on sleep per se, it can enhance treatment satisfaction. This simple intervention shows promise in addressing sleep disturbance in young adults.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events.

Objectives: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Psychometric properties of Indonesian version of sleep condition indicator for screening poststroke insomnia.

BACKGROUND: No study has examined the psychometric properties of the sleep condition indicator (SCI) for screening poststroke insomnia in the Indonesian population. We aimed to develop the Indonesian version of the sleep condition indicator (ISCI) and to examine its psychometric properties for screening adult patients in late sub-acute and chronic periods after stroke. METHODS: This was a cross-sectional study with two stages. In the first stage, the English version of the SCI was translated into the ISCI using standard procedures. The psychometric properties of the ISCI were tested in the second stage. Internal consistency and test-retest reliability of ISCI were used to evaluate reliability. A confirmatory factor analysis (CFA) was performed to test construct validity. To test concurrent and convergent validity, the Indonesian version of the insomnia severity index (ISI-INA), generalized anxiety disorder questionnaire (IGAD-7), and patient health questionnaire (IPHQ-9) were used. A receiver operating characteristic (ROC) analysis was conducted to calculate the optimal cutoff score of the ISCI on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for insomnia. RESULTS: A total of 160 adults with a diagnosis of stroke for more than 3 months were included (median age of 58.5 years, 31% met the DSM-5 criteria for insomnia). The ISCI had a satisfactory Cronbach's alpha of 0.89 and test-retest reliability of 0.78. The CFA revealed that the ISCI exhibited a satisfactory model fit and was associated with the ISI-INA, IGAD-7, and IPHQ-9 (r = -0.81, -0.32, and -0.52, respectively; all P < .001). The ROC test revealed that the optimal cutoff point of ≤23 yielded the highest sensitivity (94%) and specificity (97%). CONCLUSION: The study results revealed that the 8-item ISCI is a reliable and valid screening tool for detecting insomnia symptoms according to the DSM-5 criteria in the chronic period after stroke.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study.

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aß42), and the product of the two (Aß42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAß42: 0.58 pg/mL; ΔAß42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aß42 decreased considerably in the CPAP group (ΔT-Tau: -2.22 pg/mL; ΔAß42 × T-Tau: -44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea-hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

Adverse effects of inadequate sleep duration patterns during pregnancy on toddlers suspected developmental delay: A longitudinal study.

BACKGROUND: Although studies have reported the effects of inadequate sleep on maternal health, few have examined the relationships of maternal sleep patterns with fetal health and early childhood development. This study investigated maternal sleep duration patterns from early pregnancy to 3-years postpartum and their effects on birth outcomes and child development. METHODS: This study recruited pregnant women and their partners during prenatal visits at five selected hospitals in the Taipei area; follow-up lasted from July 2011 to April 2021. A total of 1178 parents completed self-reported assessments from early pregnancy until childbirth and 544 completed eight assessments up to 3-years postpartum. Generalized estimated equation models were used for analyses. RESULTS: Group-based trajectory modeling was used to identify four trajectories of sleep duration patterns. Although maternal sleep duration was not associated with birth outcomes, maternal "short decreasing" and "stably short" sleep patterns were associated with a higher risk of suspected overall developmental delay and language developmental delay, respectively. Furthermore, an "extremely long decreasing" pattern was associated with a higher risk of suspected overall developmental delay, [adjusted odds ratio (aOR) = 2.97, 95% confidence interval (CI):1.39-6.36)], gross motor delay, (aOR = 3.14, 95% CI: 1.42-6.99) and language developmental delay (aOR = 4.59, 95% CI:1.62-13.00). The results were significant for the children of multiparous women. CONCLUSIONS: We identified a U-shaped distribution of risk between offspring developmental delay and maternal prenatal sleep duration, with the highest risk levels on both ends of the maternal prenatal sleep duration pattern. Interventions for maternal sleep are relatively straightforward to implement and should thus be a key part of standard prenatal care.

Comparative efficacy of hypnotics in young and middle-aged adults with insomnia: a systematic review and network meta-analysis.

OBJECTIVE/BACKGROUND: Insomnia is highly prevalent in modern society. However, the hierarchical selection of hypnotics in young and middle-aged adults with insomnia remains unclear. We aimed to compare the efficacy and daytime drowsiness associated with different hypnotics for treating insomnia in young and middle-aged adults. METHODS: We searched Embase, PubMed, Cochrane Library, and ProQuest Dissertations and Theses A&I databases from inception until December 15, 2021. We also manually searched reference lists and relevant publications. The literature search, data collection, and risk of bias evaluation were all carried out separately by pairs of reviewers. We included randomized control trials (RCTs) that compared hypnotics approved by the Food and Drug Administration. The R and Stata software were both used to perform the meta-analysis. RESULTS: In total, 117 RCTs comprising 22,508 participants with the age of 18 to 65 years were included. Assessment of the efficacy of the hypnotics and adverse events (drowsiness) revealed that zolpidem improved all objective sleep parameters (oTST, oSOL, oWASO, and oSE), zopiclone increased oTST and oSE and reduced oSOL, and daridorexant increased oTST and reduced oWASO. Regarding subjective sleep outcomes, zolpidem exhibited beneficial effects on sTST, sSOL, and sWASO. Zaleplon reduced sSOL, and zopiclone was the recommended hypnotic for improving SQ. Zolpidem was associated with drowsiness effect (odds ratio = 1.82; 95% confidence interval = 1.25 to 2.65). The results of sensitivity analysis remained unchanged after the exclusion of studies reporting long-term effects. CONCLUSION: Zolpidem is recommended for managing sleep-onset insomnia and sleep maintenance insomnia but should be used with caution because of daytime drowsiness effects. Daridorexant is recommended as a promising agent for managing sleep maintenance insomnia.

Post-Stroke Insomnia Increased the Risk of Cognitive Impairments: A Hospital-Based Retrospective Cohort Study.

OBJECTIVES/BACKGROUND: Insomnia is a common sleep complaint among patients who had a stroke and has been recognized as an independent risk factor for cognitive impairment. However, the relationship between poststroke insomnia and cognitive impairment over time is under-researched. Therefore, we examined the association between poststroke insomnia and the risk of cognitive impairment. PARTICIPANTS: Stroke participants who had a stroke and were 20 years and older. METHODS: This multicenter hospital-based retrospective cohort study with a 13-year follow-up period (2004-2017). The diagnosis of stroke, insomnia, and cognitive impairment was based on the International Classification of Diseases. The study participants who experienced a stroke were divided into two cohorts: those who also had insomnia and those who did not have insomnia. A Cox proportional-hazards regression model was used. RESULTS: A total of 1,775 patients with a mean age of 67.6 years were included. Of these patients, 146 and 75 patients were diagnosed with insomnia and cognitive impairment during the follow-up period, respectively. The cumulative incidence of cognitive impairment in the stroke with insomnia cohort was significantly lower than that in the stroke without insomnia cohort (log-rank test, P < .001). The adjusted hazard ratio and 95% confidence interval (CI) of the stroke with insomnia cohort indicated a higher risk of cognitive impairment compared with the stroke without insomnia cohort (adjusted hazard ratio: 2.38; 95% CI: 1.41-4.03). CONCLUSIONS: Patients who had a stroke and were diagnosed with insomnia exhibited a substantial increased risk of cognitive impairment over time.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers.

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.