The Effect of a Sleep Intervention on Sleep Quality in Nursing Students: Study Protocol for a Randomized Controlled Trial

Ruiz-Zaldibar, Cayetana; Gal-Iglesias, B.; Azpeleta-Noriega, Clara; Ruíz-López, Montserrat; Pérez-Manchón, David

doi:10.3390/ijerph192113886

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…Sleep is integral to cognitive functioning, physiological tasks, and emotional control, which is necessary to maintain energy and restore body function [ 13 ]. For various reasons, healthcare students often experience poor sleep quality.…”

Section: Introductionmentioning

confidence: 99%

Identifying predictors of patient safety competency based on sleep quality in student faculty of nursing and midwifery during the internship period: a multidisciplinary study

Jafari,

Mostafazadeh,

Mojebi

et al. 2024

BMC Nurs

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Background Ensuring patient safety is of paramount importance in healthcare services. Sleep disorders not only have detrimental effects on the health of healthcare students but also significantly impair their performance, leading to an increased risk of medication errors. These errors can pose a grave threat to the safety and well-being of patients. It is crucial to address and mitigate sleep disorders among internship healthcare students to safeguard the quality of care and minimize potential patient harm. Objectives This study aimed to investigate the predictors of Patient Safety Competency (PSC) based on the sleep quality of internship healthcare students. Methods A study was conducted on 331 students from the Ardabil School of Nursing and Midwifery at Ardabil University of Medical Sciences in northwest Iran from August to December 2022. The participants were selected by stratified random sampling. Data were collected using a demographic information form, the Pittsburgh Sleep Quality Index (PSQI), and the Health Professional Education in Patient Safety Survey (H-PEPSS). The collected data were analyzed using SPSS software version 22.0. Person correlation coefficients were used to examine the relationship between PSC level, its dimensions, and sleep quality, while multiple linear regression was conducted to identify the predictors of PSC. Results The competency of nurses in patient safety was average in both classroom and clinical settings. However, their ability to work as a team with other healthcare professionals scored the lowest. In addition, the quality of sleep was found to be a predictor of patient safety competency among healthcare students during their internships. Conclusions It is important to note that healthcare students tend to have moderate patient safety competence (PSC), which is positively correlated with their sleep quality. Therefore, it is vital to identify the key factors that directly affect PSC. This would enable nursing and midwifery faculty administrators to take preventive measures to enhance patient safety competence in both classroom and clinical settings. Additionally, organizing educational workshops that engage students and improve their sleep quality could improve patient care. Practical courses are recommended for health professionals and students in clinical settings to enhance patient safety competencies. Additionally, student internships should receive hands-on training to improve teamwork and rest conditions.

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Section: Introductionmentioning

confidence: 99%

Identifying predictors of patient safety competency based on sleep quality in student faculty of nursing and midwifery during the internship period: a multidisciplinary study

Jafari,

Mostafazadeh,

Mojebi

et al. 2024

BMC Nurs

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show abstract

“…These findings emphasize the importance of evaluating sleep hygiene practices in the general population to clarify their relationship to the individual’s mental well-being and functional performance [ 6 ]. This may allow a better understanding of this problem and might help design effective awareness programs about good sleep hygiene practices for reducing the serious outcomes of this problem [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Sleep Hygiene Practices and Its Impact on Mental Health and Functional Performance Among Adults in Tabuk City: A Cross-Sectional Study

Alanazi¹,

Alanazi²,

Albuhairy³

et al. 2023

Cureus

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BackgroundPoor or imperfect sleep hygiene practices include all factors that promote arousal or disrupt the normal balance of the sleep-wake cycle. It is necessary to clarify the relationship between sleep hygiene behaviors and a person's mental health. This may allow a better understanding of this problem and might help design effective awareness programs about good sleep hygiene practices for reducing the serious outcomes of this problem. Therefore, the current study was conducted to evaluate sleep hygiene practices and assess the impact of sleep hygiene on sleep quality and the mental health of the adult population of Tabuk city, Saudi Arabia. MethodologyThis cross-sectional, survey-based study was conducted in Tabuk city, Saudi Arabia in 2022. All adult residents of Tabuk city, Saudi Arabia were invited to participate. Participants with incomplete data were excluded from the study. A self-administered questionnaire was developed by the researchers to assess sleep hygiene practices and their impact on the sleep quality and mental health of the study participants. ResultsThe study included 384 adults. There was a significant association between the frequency of sleep problems and poor sleep hygiene practices (p < 0.001). The percentage of subjects who had problems sleeping during the past three months was significantly higher among those having poor sleep hygiene practices (76.5%) than their counterparts (56.1%). The rates of excessive or severe daytime sleepiness were significantly higher among individuals with poor hygiene practices (22.5% versus 11.7% and 5.2% versus 1.2%, p = 0.001). Participants with depression were found to be significantly higher among the poor hygiene group (75.8%) in comparison to those having good hygiene practices (59.6%) (p = 0.001). ConclusionsThe findings of the present study indicate significant associations between poor sleep hygiene practices and sleep problems, daytime sleepiness, and depression among adult residents of Tabuk city,

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Novel insight into prediction model for sleep quality among college students: A LASSO-derived sleep evaluation (Preprint)

Chen¹,

Yao²,

Yang³

et al. 2023

Preprint

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UNSTRUCTURED Background: The objective of this study was to address the prevalent issue of sleep disturbance among college students, which can lead to a range of mental and physical disorders. The identification of potential predictors and the development of an accurate prediction model are essential steps for the early detection of and appropriate intervention in sleep disturbances. However, previous studies have encountered notable limitations. Objective: This study aimed to provide a fresh perspective by developing and validating a model for the prediction of sleep quality among college students, which will improve the accuracy of predictions and facilitate timely interventions. Mehods: We analyzed data from 20,645 college students between 5 April and 16 April 2022 in Fujian Province, China.First, the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire were conducted among the participants. Second, the collected data were used to select appropriate variables by comparing the outcomes of a multinomial logistic regression, LASSO regression, and Boruta feature selection. The data were then divided into a training–testing set (70%) and an independent validation set (30%) using stratified sampling. We developed and validated six machine learning techniques, which included an artificial neural network, a decision tree, a gradient-boosting tree, a k-nearest neighbor, a naïve Bayes, and a random forest. Finally, an online sleep evaluation website was established based on the best-fitting prediction model. Results: The mean global PSQI score was 6.02±3.112, and the sleep disturbance rate was 28.9% (defined as a global PSQI score of > 7 points). The LASSO regression model was preferred because it contained only the following eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, long hours online, sudden changes, and impatient closed-loop management. Among the generated models, the artificial neural network (ANN) model was proven to have the best performance, with a cutoff, AUROC, accuracy, sensitivity, specificity, precision, F1-score, and KAPPA of 0.710, 0.713 (95%CI 0.696-0.730), 0.669 (95%CI 0.669-0.669), 0.682 (95%CI 0.699-0.665), 0.637 (95%CI 0.665-0.610), 0.822 (95%CI 0.837-0.807), 0.745 (95%CI 0.729-0.795), and 0.284 (95%CI 0.313-0.255), respectively. In addition, it had a Brier score of 0.182. The calibration curves showed good agreement between the predictions and the observations. A decision curve analysis demonstrated that the model could achieve a net benefit. A clinical impact curve confirmed the high clinical efficiency of the prediction model. Conclusions: The prediction model, which incorporated eight predictors, was built using a LASSO regression and an ANN to estimate the probability of sleep disturbance among college students. This model may be utilized as an intuitive and practical tool for sleep quality predictions to support better management and healthcare on college campuses.

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The Effect of a Sleep Intervention on Sleep Quality in Nursing Students: Study Protocol for a Randomized Controlled Trial

Cited by 4 publications

References 44 publications

Identifying predictors of patient safety competency based on sleep quality in student faculty of nursing and midwifery during the internship period: a multidisciplinary study

Identifying predictors of patient safety competency based on sleep quality in student faculty of nursing and midwifery during the internship period: a multidisciplinary study

Sleep Hygiene Practices and Its Impact on Mental Health and Functional Performance Among Adults in Tabuk City: A Cross-Sectional Study

Novel insight into prediction model for sleep quality among college students: A LASSO-derived sleep evaluation (Preprint)

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