Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers

Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study

BACKGROUND

Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients' hesitancy to undergo demanding and costly overnight polysomnography tests.

OBJECTIVE

We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test.

METHODS

We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve.

RESULTS

In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age.

CONCLUSIONS

SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

Influencing factors and risk prediction modeling of maternal postpartum depression: a cross-sectional study in Chinese puerperal women of sitting the month

Objective

This study aims to investigate the occurrence of maternal postpartum depression (PPD) during menstruation and analyze the influencing factors and risk prediction modeling of maternal PPD in Chinese puerperal women of sitting the month.

Methods

A total of 286 mothers were selected using convenience sampling, who came for a routine postpartum follow-up visit were surveyed, including face-to-face, telephone, and online. They completed questionnaires including the basic profile questionnaire, Postpartum Partner Support Scale (PPSS), Edinburgh PPD Scale (EPDS), Parenting Self-Efficacy Scale (SICS), and Simple Coping Style Questionnaire (SCSQ), who were advised to complete the survey alone, in private, reducing the impact of husband's presence on the quality of the questionnaire. Variables showing statistical significance in the one-way analysis were further analyzed using logistic regression analysis. The predictive value of the logistic regression model was analyzed using the Receiver Operating Characteristic Curve (ROC), and the predictive reliability was expressed as the area under the ROC [Area Under the Curve (AUC)].

Results

The total score of PPD was 7.78 ± 4.57, and 22 people (7.69%) experienced depression during the postpartum period. PPD was found to be correlated with postpartum partner support, positive coping, negative coping, and parenting self-efficacy, with correlation coefficient values of -0.63, 0.62, 0.56, and - 0.70, respectively (all p < 0.05). Logistic regression analysis revealed that postpartum partner support and parenting self-efficacy were independent factors influencing PPD, with odds ratios (95% confidence intervals) of 0.76 (0.61 ~ 0.94) and 0.83 (0.75 ~ 0.93), respectively both p < 0.05.The area under the curve, sensitivity, and specificity for postpartum partner support and parenting self-efficacy were 1.00 (95% confidence intervals 0.99 ~ 1.00), 99.24, and 90.91%.

Conclusion

Postpartum partner support and parenting self-efficacy independently predict the occurrence of PPD. Healthcare professionals and maternal families should prioritize timely attention to maternal partner support and parenting issues to reduce the occurrence of PPD.

Coronasomnia in Employees without a Direct Contact with COVID-19 Infected Patients in Their Workplace

Background: The aim of this analysis was to explore coronasomnia in second line workers. Methods: Data were collected via in an online questionnaire. Patients with new onset or aggravation of insomnia were defined as cases and those without insomnia as controls. Differences among groups were studied by nonparametric tests; the correlation among variables was assessed using regression, followed by Bonferroni adjustment. Results: There were 377 responders, grouped into 129 cases and 248 controls. Younger age (Odds Ratio = 0.97, p = 0.021), women (OR = 2.46, p = 0.016), workers belonging to a vulnerable group (OR = 2.36, p = 0.007), and those with previous history of insomnia (OR = 38.76, p = 0.00) were associated with coronasomnia. Increased home duties were directly related to insomnia (OR = versus home support which were indirectly associated). The constant preoccupation for SARS-CoV-2 media reports (OR = 3.6, p = 0.00009) and media consumption were components of the coronasomnia. In the adjusted models, the personal medical history, and the anxiety created by media alerts maintained their significance. Conclusion: Preventive measures to reduce the occurrence of insomnia in times of social stress in nonessential occupations should focus on health vulnerable groups, persons with previous history of insomnia and who develop anxiety from media.

Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health

The aim of this study was to develop and validate a prediction model to evaluate the risk of poor sleep quality. We performed a cross-sectional study and enrolled 1,928 college students from five universities between September and November 2021. The quality of sleep was evaluated using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). Participants were divided into a training (n = 1,555) group and a validation (n = 373) group. The training group was used to establish the model, and the validation group was used to validate the predictive effectiveness of the model. The risk classification of all participants was performed based on the optimal threshold of the model. Of all enrolled participants, 45.07% (869/1,928) had poor sleep quality (PSQI score ≧ 6 points). Multivariate analysis showed that factors such as older age, a higher grade, previous smoking, drinking, midday rest, chronic disease, anxiety, and stress were significantly associated with a higher rate of poor sleep quality, while preference for vegetables was significantly associated with better sleep quality, and all these variables were included to develop the prediction model. The area under the curve (AUC) was 0.765 [95% confidence interval (CI): 0.742-0.789] in the training group and 0.715 (95% CI: 0.664-0.766) in the validation group. Corresponding discrimination slopes were 0.207 and 0.167, respectively, and Brier scores were 0.195 and 0.221, respectively. Calibration curves showed favorable matched consistency between the predicted and actual probability of poor sleep quality in both groups. Based on the optimal threshold, the actual probability of poor sleep quality was 29.03% (317/1,092) in the low-risk group and 66.03% (552/836) in the high-risk group (P < 0.001). A nomogram was presented to calculate the probability of poor sleep quality to promote the applicationof the model. The prediction model can be a helpful tool to stratify sleep quality, especially among university students. Some intervention measures or preventive strategies to quit smoking and drinking, eat more vegetables, avoid midday rest, treat chronic disease, and alleviate anxiety and stress may be considerably beneficial in improving sleep quality.