A prediction model of sleep disturbances among female nurses by using the BP‐ANN

Evaluation of the correlation between sleep quality and work engagement among nurses in Shanghai during the post-epidemic era

AIM

To examine the status quo and influencing factors of sleep quality and work engagement of nurses participating in COVID-19 during the post-epidemic era and to study the relationship between them.

DESIGN

We conducted a cross-sectional survey and correlational and predictive logic to determine the association between sleep quality and work engagement among nurses in Shanghai during the post-epidemic era.

METHODS

This design involved 1060 frontline nurses in Shanghai. The Pittsburgh Sleep Quality Index questionnaire and the Utrecht Work Engagement Scale-9 scales were used for data collection.

RESULTS

This study found that the sleep quality of frontline nurses was impaired and the nurses with poor sleep accounted for 48.20% during the post-epidemic era. The work engagement of frontline nurses was at the medium level. Factors affecting nurses' sleep quality were the number of nurse night shifts, family support and nurse health. The factors affecting the nurse work engagement were monthly income, profession title, family support and self-health status. There was a positive correlation between nurses' sleep quality and work engagement.

Prediction Models for Sleep Quality Among College Students During the COVID-19 Outbreak: Cross-sectional Study Based on the Internet New Media

BACKGROUND

COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic's effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking.

OBJECTIVE

This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students.

METHODS

From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated.

RESULTS

In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively.

CONCLUSIONS

The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

Predictors of Shift Work Sleep Disorder Among Nurses During the COVID-19 Pandemic: A Multicenter Cross-Sectional Study

Background: Nurses have a high incidence of shift work sleep disorder, which places their health and patient safety in danger. Thus, exploring the factors associated with shift work sleep disorder in nurses is of great significance in improving their sleep health, nursing personnel staffing, and scheduling during the COVID-19 pandemic.

Objectives: The purpose of this study was to investigate the incidence of shift work sleep disorder during the COVID-19 pandemic and explore the factors associated with shift work sleep disorder in Chinese nurses.

Methods: This was a multicenter cross-sectional study using an online survey. Stratified cluster sampling was used to include 4,275 nurses from 14 hospitals in Shandong, China from December 2020 to June 2021. Stepwise multivariate logistic regression analysis and random forest were used to identify the factors associated with shift work sleep disorder.

Results: The prevalence of shift work sleep disorder in the sampled shift nurses was 48.5% during the COVID-19 pandemic. Physical fatigue, psychological stress, shift work more than 6 months per year, busyness during night shift, working more than 40 h per week, working more than four night shifts per month, sleeping more than 8 h before night shift, using sleep medication, irregular meals, and high-intensity physical activity were associated with increased odds of shift work sleep disorder. Good social support, good work-family balance, napping two or three times per week, resting more than one day after shifts, intervals of 8 days or more between shifts, and taking turns to rest during the night shift were associated with decreased odds of shift work sleep disorder.

Conclusions: Shift work sleep disorder may be associated with scheduling strategies and personal behavior during the COVID-19 pandemic. To reduce the incidence of shift work sleep disorders in nurses, nursing managers should increase night shift staffing, extend rest days after shift, increase night shift spacing, and reduce overtime, and nurses need to seek more family and social support and control their sleep schedules and diet.

Predicting postoperative pain following root canal treatment by using artificial neural network evaluation

This study aimed to evaluate the accuracy of back propagation (BP) artificial neural network model for predicting postoperative pain following root canal treatment (RCT). The BP neural network model was developed using MATLAB 7.0 neural network toolbox, and the functional projective relationship was established between the 13 parameters (including the personal, inflammatory reaction, operative procedure factors) and postoperative pain of the patient after RCT. This neural network model was trained and tested based on data from 300 patients who underwent RCT. Among these cases, 210, 45 and 45 were allocated as the training, data validation and test samples, respectively, to assess the accuracy of prediction. In this present study, the accuracy of this BP neural network model was 95.60% for the prediction of postoperative pain following RCT. To conclude, the BP network model could be used to predict postoperative pain following RCT and showed clinical feasibility and application value.

New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

Haloketones (HKs) is one class of disinfection by-products (DBPs) which is genetically toxic and mutagenic. Monitoring HKs in drinking water is important for drinking water safety, yet it is a time-consuming and laborious job. Developing predictive models of HKs to estimate their occurrence in drinking water is a good alternative, but to date no study was available for HKs modeling. This study was to explore the feasibility of linear, log linear regression models, back propagation (BP) as well as radial basis function (RBF) artificial neural networks (ANNs) for predicting HKs occurrence (including dichloropropanone, trichloropropanone and total HKs) in real water supply systems. Results showed that the overall prediction ability of RBF and BP ANNs was better than linear/log linear models. Though the BP ANN showed excellent prediction performance in internal validation (N₂₅ = 98-100%, R² = 0.99-1.00), it could not well predict HKs occurrence in external validation (N₂₅ = 62-69%, R² = 0.202-0.848). Prediction ability of RBF ANN in external validation (N₂₅ = 85%, R² = 0.692-0.909) was quite good, which was comparable to that in internal validation (N₂₅ = 74-88%, R² = 0.799-0.870). These results demonstrated RBF ANN could well recognized the complex nonlinear relationship between HKs occurrence and the related water quality, and paved a new way for HKs prediction and monitoring in practice.

Association of occupational stress, period circadian regulator 3 (PER3) gene polymorphism and their interaction with poor sleep quality

Occupational stress is associated with sleep quality among workers and the human variable number tandem repeat (VNTR) polymorphism of the period circadian regulator 3 (PER3) gene relates to sleep-wake regulation. The main aims of the present study were to examine the effects of PER3 VNTR genotypes, occupational stress, and their interactions on sleep quality. A cross-sectional study was conducted and 729 workers were recruited in Sichuan. Sleep quality were assessed using the Pittsburgh Sleep Quality Index. Occupational stress was measured using the Generic Job Stress Questionnaire. PER3 genotypes were determined with polymerase chain reaction. High and medium occupational stress were linked to a higher risk of poor sleep quality than low levels. Unconditional logistic regression indicated that PER3 genotype was significantly associated with sleep quality, and an increased risk of poor sleep of >1.5-times was observed in those with the allele 5 compared to allele 4. The 5/5 genotype was associated with both sleep latency and sleep duration. Crossover analysis showed an occupational stress × PER3 interaction. Compared to subjects with both low and medium occupational stress and 4/4 + 4/5 genotype, those with both high occupational stress and 5/5 genotype had a higher risk of poor sleep quality. Stratified logistic analyses found that compared with low and medium occupational stress, high occupational stress increased the risk of poor sleep by more than five-times in 5/5 genotype carriers. Occupational stress and PER3 genotype had both separate and combined effects on poor sleep quality of workers. The results suggest that occupational stress may increase the risk of poor sleep quality through interaction with the PER3 gene polymorphism.

Factors associated with workplace fatigue among midwives in southern China: A multi-centre cross-sectional study

AIMS

To identify the level of workplace fatigue among midwives and factors influencing their fatigue.

BACKGROUND

Midwives who play an important role in medical care are prone to experience workplace fatigue, which negatively affects their well-being and work quality.

METHODS

A multi-centre cross-sectional study was conducted among 666 Chinese midwives from 38 hospitals in March 2019. Data were collected by four questionnaires of self-designed demographic questions, the Pittsburgh Sleep Quality Index, the Social Support Self-Rating Scale and the 14-item Fatigue Scale. Descriptive statistics, univariate analysis and multiple linear regression were used to analyse the data.

RESULTS

Midwives had moderate levels of fatigue with the mean scores of physical fatigue, mental fatigue and total fatigue being 9.53, 6.25 and 15.79, respectively. Multiple linear regression results showed that sleep quality, social support, job satisfaction, occupational injuries, adverse life events, frequency of irregular meals and employment type were statistically significant factors influencing fatigue among the participants.

CONCLUSIONS

Physical and mental fatigue were generally common among midwives and were affected by personal-related and work-related factors, sleep quality and social support.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurse administrators have the opportunity to advocate for improved health policy under the two children rule to prevent workplace fatigue amongst midwives.