BackgroundIt is possible to define sleep disorders as any disturbance in sleep timing, quality, or quantity that results in daytime distress and impairment in functioning that, in turn, affects the baseline functional status of an individual. Our study aimed to describe how sleep disorders might affect pediatric inpatients at King Abdulaziz University Hospital (KAUH) as well as estimate their prevalence (2021-2022). We assessed the sleep habits using questionnaires and analyzed and combined these data to create rankings to compare the different issues affecting sleep habits in pediatric patients.MethodologyTwo scoring systems were used in this study, namely (a) the Children’s Sleep Habits Questionnaire (CSHQ) and (b) the Pediatric Sleep Questionnaire. Analyses of the data were conducted using SPSS version 23  and GraphPad Prism version 8 .ResultsThe prevalence of sleep disorders and their correlations were evaluated among 98 pediatric inpatients at KAUH, Saudi Arabia, between 2021 and 2022. The average duration of hospital stay was 11.97 ± 11.0 days (N = 78), and the average number of previous admissions was 2.85 ± 3.7 (N = 93).Conclusionssleep behavior domain of the CSHQ, most children woke up sweating, screaming, and inconsolable during the night. Furthermore, bedtime resistance and sleep anxiety were the most prevalent sleep disturbances observed in the study population.According to the  Sleep disorders are defined as any disturbance in the sleep timing, quality, or quantity that affects the baseline functional status of an individual ,2. AccorThe essentiality of sleep in maintaining body health is well known and studied, especially in the pediatric population, as their physical, psychological, and mental functions are still developing ,8 ChroniDecreased sleep during the hospitalization period is associated with abnormal vital signs such as blood pressure and blood glucose, in addition to increased recovery time -14. PediAccording to a study conducted in Chicago, several factors in hospital environments disturb sleep in pediatric patients. These include medical interventions such as vital sign monitors, continuous staff assessment, medication administration, and noise during the night . SimilarA study in Egypt concluded that sleep disturbance, excessive daytime sleepiness, and restless leg syndrome are prevalent in pediatric patients with chronic kidney disease. However, this study did not investigate these findings in pediatric inpatients . NeverthAlthough many studies have investigated sleep quality in hospitals and published recommendations to control the environmental factors that decrease sleep quality among pediatric inpatients, there is an overlap between sleep disorders and sleep problems, and the published recommendations are for improving sleep quality overall, but not specifically for inpatients with sleep disorders ,9,16,22.As there are no previous studies in this field that have focused on the effects and prevalence of sleep disorders, specifically in pediatric inpatients, this study aimed to provide new data to pediatric, psychiatric, and sleep medicine to help develop recommendations that can improve patient care and well-being.Our study aimed to describe how sleep disorders might affect pediatric inpatients at King Abdulaziz University Hospital (KAUH) as well as estimate their prevalence (2021-2022).In this cross-sectional study, identical inclusion/exclusion criteria were used in two scoring systems, namely, (A) the Children’s Sleep Habits Questionnaire (CSHQ). The CSHQ has been utilized in many studies to investigate sleep habits in young children as it is a retrospective questionnaire composed of 45 elements distributed among eight domains . For theThe PSQ and CSHQ questionnaires were translated into the Arabic language and then given to the caregivers of 98 pediatric male and female patients aged 4-14 years to be completed. The translation of the questionnaire to the Arabic language was validated by previously published studies .We excluded patients who were admitted to the hospital due to sleep disorders and non-Arabic-speaking patients and caregivers.Our study was conducted in the Department of Medicine, Psychiatry Division in KAUH, Saudi Arabia, between 2021 and 2022. Ethical approval was obtained from the Research Ethics Committee, Unit of Biomedical Ethics, King Abdulaziz University, Jeddah, and was approved by the Institutional Review Board of King Abdulaziz University (reference number: 419-21).Statistical analysisData obtained in this study were analyzed using SPSS version 23  and GraphPad Prism version 8 .Simple descriptive statistics were used to define the sociodemographic characteristics through counts and percentages for the categorical variables, while continuous variables were presented as means and standard deviations.Reliability analysis was then used with a model of alpha (Cronbach) to study the properties of the measurement scales, the items that comprise the scales, and the average interitem correlation.The chi-squared test was used to establish the relationships between categorical variables. Variables represented as means were correlated using Pearson’s correlation coefficient. Independent t-tests and one-way analysis of variance tests were employed to compare the means of two or more groups. The variables correlated were CSQH scores, PSQ scores, and bedtime and wake-up time routines of the children. These tests were performed under the assumption of a normal distribution. A p-value of <0.05 was the criterion used to discard the null hypothesis.child awakens during the night and is sweating, screaming, and inconsolable item under the sleep behavior domain, while the lowest score of 1.23 ± 1.6 (N = 95) was observed in the child falls asleep in parent’s or sibling’s bed item in the bedtime domain. In addition, reliability statistics were calculated for each domain of the CSHQ.The prevalence of sleep disorders and their correlations were evaluated among 98 pediatric inpatients at KAUH, Saudi Arabia, between 2021 and 2022. Of these, 51 (53.7%) were males, and 44 (46.3%) were females. Their age ranges were toddlers (2-5 years), school-aged (6-12 years), and adolescents (13-18 years), with percentages of 30.5, 58.9, and 10.5%, respectively. Of these, 58.2% were Saudi, and 41.8% were non-Saudi. The average duration of hospital stay was 11.97 ± 11.0 days (N = 78), and the average number of previous admissions was 2.85 ± 3.7 times (N = 93).The average scores obtained by the patients for each item within each domain of the CSHQ are shown in Figure Child often interrupts or intrudes on others item while the lowest score of 0.03 ± 0.2 (N = 95) was observed for the Seeing the child stop breathing during the night item.The frequency of answers to each item of the PSQ was evaluated Table . The reshaving no sleep problems or having sleep problems (breathing disorders) and age, gender, and nationality. Furthermore, the association between the mean PSQ results and CSHQ domains was measured , and morning wake-up (p = 0.043) according to the independent t-test at p < 0.05 level. One implication is the possibility of benefiting from integrating both scales to develop an all-inclusive scale.Interestingly, the PSQ results were found to be significantly associated with the CSHQ domains, including This study has a few limitations that need to be acknowledged. First, the sample size is small. We excluded non-Arabic-speaking parents, although 85% of the admissions were Arabic speaking. Second, this study was limited to inpatients at KAUH rather than multicenter patients, which limits its generalizability. Another limitation of this study is the use of a cross-sectional design. Objective measurements of sleep disturbances, such as polysomnography, may be more accurate than subjective measurements. Using questionnaires such as the PSQ and CSHQ is another limitation, as they do not replace the clinical diagnosis of an experienced physician.The ages of the participants varied between two and 18 years, with younger children who sleep with their parents having their parents observing them during sleep. For older children who usually sleep alone, parents may be less aware of their sleeping habits.In future investigations, we recommend comparing the sleeping habits of inpatient and outpatient children to determine whether they are affected by their surrounding environment, as well as taking into account the admitting diagnosis of the inpatients.sleep behavior domain of the CSHQ, the majority of children woke up sweating, screaming, and inconsolable during the night. Furthermore, bedtime resistance and sleep anxiety were the most prevalent sleep disturbances among the chosen population.This study estimated the prevalence of sleep disorders among pediatric inpatients using two scoring systems, namely, the CSHQ and the PSQ at KAUH, Jeddah, Saudi Arabia. In conclusion, according to the having no sleep problems or having a sleep problem (breathing disorder). Other PSQ results, such as sleep behavior, waking during the night, and morning wake-up, were significantly associated with CSHQ domains.Finally, the results revealed that age, sex, and nationality did not significantly influence the PSQ scores for


Elicitation of patients’ preferences is an integral part of shared decision-making, the recommended approach for prostate cancer decision-making. Existing decision aids for this population often do not specifically focus on patients’ preferences. Healium is a brief interactive web-based decision aid that aims to elicit patients’ treatment preferences and is designed for a low health literate population.This study used a randomized controlled trial to evaluate whether Healium, designed to target preference elicitation, is as efficacious as Healing Choices, a comprehensive education and decision tool, in improving outcomes for decision-making and emotional quality of life.Patients diagnosed with localized prostate cancer who had not yet made a treatment decision were randomly assigned to the brief Healium intervention or Healing Choices, a decision aid previously developed by our group that serves as a virtual information center on prostate cancer diagnosis and treatment. Assessments were completed at baseline, 6 weeks, and 3 months post baseline, and included decisional outcomes , and emotional quality of life (anxiety/tension and depression), along with demographics, comorbidities, and health literacy.P≤.001; Healing Choices, P≤.001), and a significant increase in satisfaction with one’s decision from 6 weeks to 3 months . Within both arms, anxiety/tension  and depression  decreased from baseline to 6 weeks, but only in the case of depression was the decrease statistically significant.A total of 327 individuals consented to participate in the study . The majority of the sample was non-Hispanic , White , married , and was on average 62.4 (SD 6.9) years old. Within both arms, there was a significant decrease in decisional conflict from baseline to 6 weeks postbaseline (Healium, , our brief decision aid focusing on treatment preference elicitation, is as successful in reducing decisional conflict as our previously tested comprehensive decision aid, Healing Choices, and has the added benefit of brevity, making it the ideal tool for integration into the physician consultation and electronic medical record.HealiumClinicalTrials.gov NCT05800483; https://clinicaltrials.gov/study/NCT05800483 Shared decision-making (SDM), defined as a process in which both the patient and the health care professional work together to decide the best plan of care for the patient, is the recommended approach for prostate cancer treatment decision-making . All majThe use of personal, technology-based decision aids empowers patients to identify and verbalize their own preferences and bring their concerns to the clinical consultation, which encourages an SDM process. Multiple studies demonstrate that the use of personal decision aids facilitates SDM -9. To im. As the scientific base on decision-making has progressed, patients’ personal preferences have been identified as central to treatment choice selection [Our goal was to address the limitations of our prior aids  while retaining their efficacy, and simultaneously focusing on patients’ preferences. Therefore, we reconceptualized our approach to software-guided facilitation of decision-making consistent with the SDM approach and developed Healiumelection ,14. Patielection . This coelection . DespiteHealium primarily focuses on the elicitation of treatment preferences , particuTherefore, the purpose of this study is: (research question 1) to evaluate whether Healium, designed to focus on preference elicitation, is as efficacious in improving decision-making outcomes—(1) decisional conflict, (2) satisfaction with decision, and (3) preparation for decision-making, as Healing Choices in a randomized controlled trial; (research question 2) to compare the emotional quality of life—(1) anxiety/tension and (2) depression, between patients randomized to Healium and Healing Choices; and (research question 3) to assess the relationship between treatment decision and patients’ emotional quality of life—(1) anxiety/tension and (2) depression.Patients were eligible to participate in this study if they met the following inclusion criteria: (1) they had a diagnosis of localized prostate cancer and were eligible for all treatment options , (2) they had not yet made a treatment decision or begun treatment, and (3) they have basic proficiency (grade school level) in reading English.Study coordinators screened the electronic medical record to identify potentially eligible patients scheduled for diagnostic and treatment consultation visits. If eligible based on chart review, the study coordinator telephoned the patient, briefly introduced the study, obtained preliminary consent, and asked the patient to arrive 45 minutes prior to their upcoming appointment. On the day of the appointment, the study coordinator obtained written informed consent, implemented block randomization by the study site based on a predetermined randomization scheme, administered the baseline assessment, and was available to answer any questions or concerns. Participants completed additional assessments at 6-week and 3-month postbaseline, and were provided with US $30 in gift cards (US $10 per each of the 3 time points).To adhere to the structure required of a randomized controlled trial and uphold scientific rigor, we administered Healing Choices as a time and attention comparison arm in the same way as Healium . However, integrating a program with several hours of information into the clinic setting, as is the case with Healing Choices, is not feasible and Healing Choices was not designed to be used as a discussion tool for SDM.Participants assigned to the intervention arm completed the Healium program on a provided laptop computer in an internet-enabled clinic room. A member of the study team remained in the room to assist with technical questions but did not answer any disease or treatment-related questions. On average, it took participants 10-13 minutes to complete Healium.Healium is a web-based platform that employs a user-centric design and aims to appeal to a low-health literate population. It features a simple language and layout, a large font size, contrasting text and background colors, a bright color palette, and the use of short labels and headings to describe content.Healium uses plain language and easy-to-use touchscreen commands for navigation. To minimize cognitive load during decision-making, complex treatment decision-making is broken down into a series of simple gate questions that are answered in a yes or no format. The program begins by eliciting users’ preferences on whether they want to treat their prostate cancer immediately or whether they want to wait. In other words, the program offers the choice between active surveillance or active treatment . If a patient chooses active surveillance, the next page contains 4 to 5 preferences  characteristic of the selected choice. Touch or mouse controls are used to move a slider across the screen, to indicate whether the patient would be “bothered” by the selected feature . The higher the level of “bother” the larger the corresponding-colored circle grows. The circle size serves as a visual representation of the patients’ preferences in that specific area. Once all preferences are rated and submitted, the program generates a summary report, with the different colored and sized circles included. If the patient endorses that certain symptoms associated with a particular treatment are highly bothersome , the program suggests that this particular treatment choice is not compatible with the stated preferences. The user is then prompted to revisit the prior preference rating page or to “start over” again, such as exploring a different treatment modality . Such an iterative process of preference elicitation and evaluation mimics a natural decision process: As patients are presented with different considerations that they are asked to rate, they might be exposed to new, important information, that might influence their treatment choice. Users are encouraged to continue to explore the tool and discuss the treatment preference summary generated by the program with their physicians.Although not the focus of this manuscript, the development of Healium was guided by the ORBIT  model , which ePatients randomly assigned to the comparison arm received information through the Healing Choices program, accessed in the same setting and under the same conditions as patients in the intervention arm.The Healing Choices program represents a virtual health center that patients visit to obtain disease and treatment-related information. The software was designed to be open to exploration with an intuitive layout, without restrictions in terms of order of access. Information is stored in virtual rooms, such as a library, a conference room showing videos by survivors who discuss their approach to treatment, and physician offices containing videos of physicians representing different treatment specialties. All information was extensively vetted by health education experts of the National Cancer Institute’s Cancer Information Services (CIS). See Versions of Healing Choices for prostate cancer and early-stage breast cancer were evaluated in nationwide randomized controlled trials. Analyses of Healing Choices for men with prostate cancer indicated a significant intervention effect on levels of perceived decisional support, which was greatest for non-White minority participants and patients with lower educational attainment . As-treaAlthough Healing Choices is Internet based, due to a server malfunction, 27 of the 156 (17.31%) participants randomized to the comparison arm received a paper version of Healing Choices. There were no significant differences in any demographic variables between those receiving the web version of Healing Choices and those receiving the paper version of Healing Choices (data not shown).Participants completed assessments at baseline (consent), and at 6 weeks and 3 months post baseline. Areas assessed included: demographics, comorbidities, health literacy, treatment decision, decisional conflict, satisfaction with decision, preparation for decision-making, and emotional quality of life (anxiety/tension and depression).Demographic variables include age (continuous), ethnicity (Hispanic and non-Hispanic), race , annual income, highest level of education, employment status , marital status , and site of enrollment .Comorbidities were assessed with the Charlson Comorbidity Index , a widelHealth Literacy was assessed with the Newest Vital Sign (NVS) ,24, a 6-Treatment decision  was assessed at 6 weeks as either surgery, radiation, active surveillance, or others.Decisional conflict was measured with the Decisional Conflict Scale (DCS) , a well-Satisfaction With Decision Scale  is a 9-iThe Preparation for Decision-Making Scale (PDMS)  is a 10-Emotional quality of life (anxiety/tension and depression) was assessed using the relevant 5-item subscales of the short version of the Profile of Mood States (POMS) ,29.t tests were used to compare 2 different groups on continuous measures. Chi-square tests were used to compare groups on categorical variables. Paired sample 2-tailed t tests were used to compare the change in continuous measures within one group over the course of time. One-way ANOVA was used to compare more than 2 groups on continuous measures. Two-way ANOVAs were used to evaluate the main and interaction effects of 2 categorical independent variables on a continuous dependent variable.Analyses were conducted using SPSS . Total scores (both means and sums) were computed from individual items on continuous scales using the two-thirds rule . Means and SDs were calculated for continuous measures and frequencies and percentages for categorical variables. Independent sample 2-tailed This study was approved by the institutional review boards of Northwell Health (15-192) and Fox Chase Cancer Center (15-8013). The study was conducted from 2020 to 2021.In total, 327 individuals consented to participate in the study. On average, participants were 64.2 (SD 6.9) years old. The majority of the sample was non-Hispanic , White , and had an annual household income of over US $75,000 . Just over half of the sample  obtained a bachelor’s degree or higher. About half  of the sample was employed and the other half  was retired. About 3 quarters  were married or living with a partner.Of the 327 participants, 171 were randomized to the Healium intervention arm and 156 were randomized to the comparison arm . There were no significant differences in any demographic variables , nor in comorbidities, health literacy, baseline anxiety/tension, or baseline depression between the 2 arms. See t318=1.79, P=.07), level of education , marital status , annual income , or employment status . Ethnic  and racial distribution  differed significantly by study site. Compared to the sample from Fox Chase Cancer Center, Northwell Health’s sample had more Hispanic (30% vs 70%) and Asian (23.08% vs 76.92%) patients. Consequently, the proportion of White patients was higher at Fox Chase Cancer Center, compared to Northwell Health (67.78% vs 32.22%).Of the 327 participants, 128 were recruited from Northwell Health and 199 were recruited from Fox Chase Cancer Center. There were no significant differences between the 2 sites in age  was assessed and compared across the study arm and study site. Treatment decision was significantly different between study arms, such that those patients randomized to Healium, compared to the comparison arm, were more likely to choose active surveillance and radiation, and less likely to choose surgery see . There wOur first research question aimed to evaluate whether Healium is as efficacious in improving decision-making outcomes, (1) decisional conflict, (2) satisfaction with decision, and (3) preparation for decision-making, as Healing Choices. Within both arms, there was a significant decrease in decisional conflict from baseline to 6 weeks post baseline. Although decisional conflict continued to decrease within both arms from 6 weeks to 3 months post baseline, the change was not significant see .t121=9.53, P≤.001; Clarity: t120=7.4, P≤.001; Uncertainty: t120=9.53, P≤.001; and Support: t122=7.74, P≤.001. However, the changes for all subscales from 6 weeks to 3 months postbaseline were not significant. Informed: t100=–0.19, P=.85; Clarity: t103=.97, P=.34; Uncertainty: t104=1.92, P=.06; and Support: t104=1.00, P=.32.With regard to the 4 decisional conflict subscales , there were significant decreases in decisional conflict on all subscales from baseline to 6 weeks post baseline within the Healium intervention arm. Informed: t98=8.28, P≤.001; Clarity: t99=7.71, P≤.001; Uncertainty: t98=9.93, P≤.001; and Support: t99=6.69, P≤.001. Changes for subscales from 6 weeks to 3 months postbaseline were not significant except for the uncertainty subscale, which decreased significantly from 6 weeks to 3 months. Informed: t90=–0.94, P=.35; Clarity: t90=–0.54, P=.59; Uncertainty: t90=2.64, P=.01; and Support: t88=1.40, P=.16.Within the comparison arm, there were significant decreases in decisional conflict on all subscales from baseline to 6 weeks post baseline. Informed: Within both the intervention and comparison arms, there was a significant increase in satisfaction with one’s decision from 6 weeks to 3 months .P=.90).Both Healium and Healing Choices prepared patients adequately for treatment decision-making and their consultations with the physicians. At 6 weeks post baseline, there was no significant difference between Healium and Healing Choices on the preparation for Decision Making Scale  anxiety/tension and (2) depression, as Healing Choices. Within both the intervention and comparison arms, anxiety/tension was low at baseline and decreased minimally (but not significantly) from baseline to 6 weeks. A similar pattern was found within both arms from 6 weeks to 3 months see .Within both the intervention and comparison arms, there was a significant decrease in depression from baseline to 6 weeks; however, changes in depression from 6 weeks to 3 months were not significant in both arms see .F2,185=2.62, P=.08, nor at 3 months, F2,158=1.46, P=.24. We found a significant difference in depression at 6 weeks based on treatment decision. Surgery: mean 1.53, SD 0.64; Radiation: mean 1.32, SD 0.41; Active Surveillance: mean 1.44, SD 0.47, F2,185=3.18; P=.04. This difference was no longer significant at 3 months, F2,158=1.18, P=.31.Our third research question aimed to explore the relationship between treatment decisions and emotional quality of life, (1) anxiety or tension and (2) depression. There were no significant differences in anxiety or tension across treatment decisions at 6 weeks, , consisting of physician and survivor videos, graphics, and comprehensive descriptions of treatment options, makes it impossible to use in the waiting room prior to or during patient-physician consultations. A brief tool that could be completed in a few minutes was needed to enhance the SDM experience. Second, in recent years, the decision and judgment literature has increasingly emphasized the role of patients’ preferences in treatment decision-making, especially in preference-sensitive treatment situations, as is the case with prostate cancer. Thus, an increased emphasis on eliciting patient’s preferences was needed. The Healium software program fills these two gaps: (1) it primarily focuses on the elicitation of patient preferences with regard to the 3 primary treatment options  and (2) it can be completed by patients in 10-15 minutes, thus making it possible to integrate the program either prior to, or during, the consultation process. Last, our approach to breaking down the decision steps into a series of brief yes or no questions should make the program particularly amenable for patients with low health literacy.We developed the Healium software in response to 2 primary gaps in the literature. First, it has become clear that integrating comprehensive decision and education tools with several hours of content  into the electronic medical record and the clinical consultation process is not feasible. Indeed, the extensive content of Healing ChoicesThis study was designed to demonstrate that the Healium software was as efficacious in reducing decisional conflict as our previously developed and tested program, Healing Choices for prostate cancer . The HeaIn this study, we demonstrated that both the Healing Choices and Healium programs are successful in reducing decisional conflict. A nearly 30-point drop in decisional conflict, as achieved by both programs, is clinically relevant. Indeed, it indicates a reduction from clinically significant conflict to almost no conflict at all. The lack of decisional conflict was accompanied by an increase in decisional satisfaction from 6 weeks to 3 months postbaseline assessment and there was no difference in emotional quality of life between participants receiving the 2 programs. Remarkably, patients reported low levels of anxiety or tension at baseline, which then further declined, albeit nonsignificantly. These low levels of anxiety or tension diverge from levels published in the literature, which usually show moderate to high levels of distress after a receipt of a prostate cancer diagnosis. Depression at baseline was somewhat higher, but still at a subclinical level, and declined significantly at 6 weeks. The reason for these low levels of affect prior to the physician consultation is unclear; yet, it is possible that patients’ anxiety was lower because they were focusing on the fact that they were going to resolve their cancer threat.Healium was successfully implemented within 2 separate clinic sites . Indeed, within each site, patients were recruited from Urology and Radiation Oncology, demonstrating the utility of using Healium before a consultation with either a surgical oncologist or radiation oncologist.It is noteworthy that when comparing the 2 programs, patients randomized to Healium were more likely to select active surveillance than active treatment (either surgery or radiation), while those randomized to Healing Choices were more likely to select active treatment (either surgery or radiation) than active surveillance. Evidence in the literature indicates that patients who use shared decision-making, the core of Healium, tend toward choosing less aggressive treatment options, such as active surveillance. It has been suggested that the deeper processing of the pros and cons of treatment options leads to less aggressive treatment approaches.This study was an initial step in a larger program of research. Next steps include evaluating the most efficient option for integration of Healium into clinical care , audio-recording and analyzing encounters between the patients using Healium and their physicians to identify evidence of SDM as well as that patients are voicing their preferences, and examining whether patients are more likely to receive preference congruent treatment after using Healium than without Healium.Looking ahead, it is important to highlight the specific contexts and dissemination potentials of the 2 programs given their unique characteristics. First, Healing Choices’ prime objective is patient education. This is achieved by providing comprehensive disease and treatment-related information in a centralized location, in the form of written content as well as physician and survivor videos. Healing Choices is best used after a positive prostate biopsy result and a diagnosis of prostate cancer. In contrast, Healium’s advantage is its focus on patient preference elicitation and as a communication tool for the SDM process. It is most useful for the patient to complete Healium prior to the consultation with the physician. The brief completion time of Healium makes a future integration in the clinical practice feasible and graphical nature of the preference elicitation results serves as a convenient tool for starting the SDM process.The study has a few limitations. First, although the minority representation was adequate, the patient population was well-educated and had a moderately high income. This makes our sample representative of patients who are more likely to seek second opinions or are visiting a comprehensive cancer center, but is less representative of the population at large. Related to this, failing to oversample African American patients was a missed opportunity, especially important given that African American men are disproportionately affected by prostate cancer with higher incidence and mortality rates. Second, our decision aid was designed for a low-health literate patient population; however, due to the ceiling effect in the health literacy variable, we could not examine whether there was a difference in our outcomes between those with low versus adequate health literacy. As such, this question will be the focus of future work, as testing among patients with very low levels of health literacy awaits.In sum, the results show that a brief treatment decision aid focusing on preference elicitation and designed for a low-health literate patient population is successful in reducing decisional conflict. Further, this brief-focused program is as efficacious as our previously tested comprehensive decision aid, Healing Choices for Prostate Cancer. Decisional satisfaction was equally high and emotional quality of life was not increased. The major advantage of Healium is its’ brevity, its utility as a discussion tool for SDM, and its promise of integration within the electronic medical record to further facilitate the treatment counseling process.


This systematic review and meta-analysis aims to explore changes in sleep quality and sleep disturbances in the general population from before to during the COVID-19 lockdown.The protocol was registered in PROSPERO (CRD42021256378) and the PRISMA guidelines were followed. The major databases and gray literature were systematically searched from inception to 28/05/2021 to identify observational studies evaluating sleep changes in the general population during the lockdown with respect to the pre-lockdown period. A random effects meta-analysis was undertaken for studies reporting (a) the means of the Pittsburgh Sleep Quality Index (PSQI) global scores or the means of the sleep onset latency (SOL) times (minutes - min) before and during the lockdown, (b) the percentages of poor sleep quality before and during the lockdown, or (c) the percentages of changes in sleep quality. Subgroup analysis by risk of bias and measurement tool utilized was carried out. A narrative synthesis on sleep efficiency, sleep disturbances, insomnia and sleep medication consumption was also performed.Sixty-three studies were included. A decline in sleep quality, reflected in a pooled increase in the PSQI global scores (standardized mean difference (SMD) = 0.26; 95% CI 0.17–0.34) and in SOL  were found. The percentage of individuals with poor sleep quality increased during the lockdown . Moreover, 57.3% (95% CI 50.01–61.55) of the individuals reported a change in sleep quality; in 37.3% (95% CI 34.27–40.39) of these, it was a worsening. The studies included in the systematic review reported a decrease in sleep efficiency and an increase in sleep disturbances, insomnia, and in sleep medication consumption.Timely interventions are warranted in view of the decline in sleep quality and the increase in sleep disturbances uncovered and their potentially negative impact on health. Further research and in particular longitudinal studies using validated instruments examining the long-term impact of the lockdown on sleep variables is needed.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021256378, identifier CRD42021256378. Alarmed by its severity, transmissibility, rising levels of contagion, and the strain on healthcare systems, on March 11, 2020, the World Health Organization (WHO) declared the Coronavirus disease 2019 (COVID-19) outbreak, a global pandemic , a decreThe negative influence of the COVID-19 pandemic and restrictive measures on mental wellbeing translated also in a significant impact on sleep health. Several studies have in fact described an increase in sleep duration, delayed sleep timing, and a reduction in sleep variability  during the pandemic with only a gradual return of some of these parameters to pre-pandemic levels when those measures were revoked or mitigated , 8. Not This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  and regiFour electronic academic databases , a preprint server (MedRxiv), and a gray literature database (OpenGrey) were searched systematically from inception to 28/05/2021, corresponding to the end of the 1st year of the pandemic, using the search terms listed in the All the references were downloaded in Zotero, a citation manager software used for all the steps of the studies’ selection process, from downloading and removing duplicates, to the title-abstract and full-text screenings, which were performed independently by two researchers . The reference lists of the relevant systematic reviews and of the articles identified were checked for references that might lead to additional studies. To mediate any disputes, the final decision regarding a study’s eligibility was made together with the senior authors .The inclusion and exclusion criteria used, in accordance with the PICOS  description, are outlined below.Population: general adult population (≥18 years) and studies whose samples were mainly composed of adults with adolescents making up at most 30%.Intervention(s)/exposure: COVID-19 lockdown.Outcomes: changes in sleep characteristics assessed by self-report validated instruments [e.g., Pittsburgh Sleep Quality Index – PSQI , InsomniThe PSQI is a self-report questionnaire that assesses global sleep quality relative to the previous 30 days’ time. The global score ranges from 0 to 21, with a cut-off >5 indicating “poor sleep quality.” The questionnaire also evaluates seven sleep components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, sleep medication use, and daytime dysfunction .The current study has focused on the following sleep characteristics: sleep quality, sleep onset latency , sleep efficiency  , sleep dStudy design: original observational cross-sectional, prospective or retrospective cohort studies.(1) Studies in languages other than English, Italian or Spanish;(2) Studies evaluating changes in subjects with specific diseases  and in specific groups not representing the general population .The data were extracted by two authors  using a pre-designed spreadsheet: the first author’s name, year of publication, country, study design, outcome, the sample size, the measurement tool utilized, the percentage of female participants, the participants’ ages , the assessment period, and the main study results on sleep health changes. The corresponding author was contacted whenever a study appeared incomplete or in case any clarification on the presented data was needed.The risk of bias was assessed by two independent authors  using the Newcastle Ottawa Scale (NOS) for longitudinal  and crosA meta-analysis was performed for data regarding sufficiently homogenous outcomes in terms of statistical and methodological characteristics. A narrative synthesis approach was used for those studies not included in the meta-analysis.A random-effects meta-analysis was carried out for sleep quality and sleep onset latency, using the DerSimonian and Laird method, with studies weighted according to the inverse of the standard error, using MedCalc Statistical Software version 20.118 .Studies reporting the following data were included in the meta-analysis:•the means of the PSQI global scores or the means of the sleep onset latency times (minutes - min) before and during the lockdown;•the percentages of poor sleep quality  before and during the lockdown;•the percentages of changes in the individuals who improved or worsened, or maintained the same sleep quality during the lockdown with respect to pre-lockdown levels.I2 statistic: a value of 0% indicates no observed heterogeneity, and higher values show increasing heterogeneity  , relative risks or proportions. The between-study heterogeneity was analyzed using ogeneity . The pubogeneity .T-test and Chi-squared test were used to compare subgroups.Given the different ways that the data were synthesized, whenever possible, additional stratified meta-analyses by risk of bias (NOS <5 vs. NOS ≥5) and measurement tool utilized  were conducted. via citation searches and systematic reviews were selected for full-text screening. Ninety-seven articles were selected for inclusion when the full texts were assessed. Of these, 63 contained data on outcomes pertinent to this study (N = 1), Australia (N = 2), Brazil (N = 1), China (N = 5), Cyprus (N = 1), France (N = 4), Germany (N = 3), India (N = 5), Italy (N = 8), Libya (N = 1), Multi-country (N = 9), Romania (N = 1), Russia (N = 1), Saudi Arabia (N = 1), Singapore (N = 1), Spain (N = 7), UK (N = 6), United Arab Emirates (N = 1), and USA (N = 5). Due to pandemic restrictions, most of the studies collected data through online surveys. Sixty-one studies used self-reported instruments  corresponding to a worsening in sleep quality. The analysis did not show a significant publication bias (Egger’s p = 0.568).The changes in the PSQI global scores were evaluated considering 33 outcomes reported in 14 studies –85, 88. I2 = 82.2%; not significant Egger’s publication bias). With regard to the six studies , but not significantly different.In addition, with regard to 27 outcomes in 8 studies , 85, 88  studies , 83, 84 I2 = 95.4%) (see p = 0.178).The changes in the percentages of individuals with poor sleep quality from before to during the COVID-19 lockdown were evaluated considering 19 outcomes reported in 19 studies , 85, 88..4%) see . The anaI2 = 936%; not significant Egger’s publication bias). With regard to the six studies . The two effects by risk of bias study resulted significantly different.With regard to the 13 studies , 85, 88  studies , 73, 84 I2 = 95.7%; not significant Egger’s publication bias). With regard to the six studies . A significant difference was found for the measurement tool utilized.With regard to the 13 studies , 85, 88  studies , 55, 76 I2 = 98.7%; not significant Egger’s publication bias) of participants reporting a change in sleep quality. In particular, 18.6%  reported an improvement and 37.3%  a worsening in sleep quality.The percentages of changes in individuals who improved or worsened, or maintained the same sleep quality during the lockdown with respect to pre-lockdown levels, were evaluated by 24 studies , 91, 92.I2 = 98.7%; not significant Egger’s publication bias) of the participants reporting a change in sleep quality. In particular, 19.2%  reported an improvement and 39.0%  a worsening in sleep quality. These results were also confirmed by the 14 studies  of the participants reporting a change in sleep quality. In particular, 18.1%  reported an improvement and 36.1%  a worsening in sleep quality. Subgroup analysis by risk of bias showed significant differences.With regard to the 10 studies , 89, 91  studies , 87, 92 I2 = 53% not heterogeneity; significant Egger’s publication bias) of the participants reporting a change in sleep quality. In particular, 44.2%  reported an improvement and 34.9%  a worsening in sleep quality. With regard to the 22 studies  of the participants reporting a change in sleep quality. In particular, 16.6%  reported an improvement and 37.5%  a worsening in sleep quality. Subgroup analysis by measurement tool utilized showed significant differences between the percentage of change and the percentage of improvement, but not for the percentage of worsening.With regard to the 2 studies , 65 usin studies , 91, 92 Out of the three studies not included in the meta-analysis see , 2 reporOut of 16 studies examining sleep onset latency, 6 reported data regarding both before and during the COVID-19 lockdown and were included in the meta-analysis , 88, 92;I2 = 63.7%, not shown, significant publication bias)  see .I2 = 63.1%; not significant Egger’s publication bias). With regard to the outcomes of two studies . No significant differences by risk of bias were found.The results were basically the same when the NOS quality score was stratified. With regard to 21 outcomes of the four studies , 71, 88  studies , 92 withI2 = 47.4%; not significant Egger’s publication bias). With regard to the outcomes of 3 studies . No significant differences by measurement tool utilized were found.With regard to the 20 outcomes of the 3 studies , 71, 88  studies , 83, 92 Ten studies reported 14 outcomes regarding sleep onset latency changes during the COVID-19 lockdown with respect to pre-lockdown levels: 9 , 85, 90 All of the studies, with the exception of Wang et al.’s , reporteNine studies , 85, 88 Out of ten studies , 83, 88 Thirteen studies , 73, 75 Of the eleven studies examining changes in sleep medication consumption during the COVID-19 lockdown with respect to pre-lockdown levels , 83, 88,This systematic review and meta-analysis of 63 studies aimed to examine and synthesize changes in sleep quality and sleep disturbances in the general population from before to during the COVID-19 lockdown. The main results of the meta-analysis indicated that during the lockdown, there was a significant worsening of sleep quality, and a 40% increased probability of reporting poor sleep quality. Analyses, moreover, uncovered that approximately 57% of the participants experienced a change in sleep quality, which was more frequently a worsening. Subgroup analysis showed a more marked, even if not statistically significant, worsening in sleep quality  and a significant higher increase in the percentage of individuals with poor sleep quality during the lockdown in the low quality studies with respect to the high quality ones, and in the studies using validated measurement tools with respect to those using researcher-developed ones. On the contrary, the percentages of participants reporting a change in sleep quality and of those experiencing a worsening are significantly higher in the high quality studies, as well as in the studies using validated measurement tools, in particular with regard to improvement. These results are partially in line with those presented by Jahrami et al. . The metThe decline in sleep quality and the rise in sleep disturbances emerging from our study and the possible negative consequences of these changes on mental and physical health are cause for concern. The COVID-19 pandemic was in fact characterized by more than one wave and several sleep variables seem to have been undermined by the repeated implementation of strict restrictive measures, which may have determined an onset in sleep disturbances or aggravated them. Riva et al. pointed out that the prevalence of insomnia did not diminish when pandemic restrictions were reduced or lifted but it began to normalize only several months later and it never returned to pre-pandemic levels . Two stuThe changes emerging in this study can be attributed to a variety of factors. The reduction in physical activity levels related to lockdown measures could have negatively affected sleep quality . At the via an extensive search of published peer-reviewed, preprint articles and gray literature, and to ensure the quality of the assessments and the synthesis of data. We summarized data using a random-effects model with a more conservative estimate to address heterogeneity given the many different ways the data were synthetized and cultural diversity across countries. There are nevertheless some limitations that must be considered when interpreting our results. The first one is represented by the high risk of bias of several of the studies included. As uncovered by subgroup analysis, the changes in sleep quality and in particular percent changes were more pronounced in the low quality studies with respect to the high quality ones. Another limitation is linked to the measurement tools because the use of non-validated instruments may have led to underestimated values. Moreover, because of the small number of the studies and the heterogeneity of the data collected, it was possible to carry out subgroup analysis only by risk of bias and measurement tool. Other limitations are linked to the characteristics of the studies included here. The majority adopted a cross-sectional design meaning that they did not collect data before the lockdown and thus did not allow to have an exhaustive evaluation of the changes in sleep outcomes. Most of the studies used convenience samples whose participants were for the most part recruited through social networks, excluding therefore individuals who are not regular internet users, such as elderly persons; this may have affected the generalizability of our results (Our study has several strengths. First, this systematic review and meta-analysis provided a synthesis of changes in several sleep outcomes in the general population during the lockdown, which represented an unprecedented condition within the context of a pandemic and that had a dramatic impact on daily life. Moreover, a rigorous methodological approach was used to achieve an overview of all the sleep variables linked to sleep quality and sleep disturbances  results . Almost  results , on the The findings of the present work have several research and practical implications. If on the one hand the implementation of rigid restrictive measures permitted government authorities to limit the transmission of SARS-CoV-2 infection and to mitigate its impact on health systems, on the other it has had negative consequences on sleep health in the general population. Given the importance of good sleep for our physical and mental health, sleep problems need to be detected early and treated using appropriate interventions such as the cognitive behavioral treatment for insomnia that appears to have significant effects on insomnia severity, sleep efficiency, sleep quality, sleep onset latency, and the number of awakenings .As far as research is concerned, studies evaluating if and to what degree the effects on sleep health linked to the pandemic and the restrictive measures have lingered over time are warranted. These works should seek to overcome some methodological limitations by using probability-based sampling methods, validated, standardized measurement tools, and longitudinal research designs. In addition, and as underlined by other authors, instruments that are able to describe and compare lockdown measures need to be developed .The lockdown measures have had an important impact on sleep health in the general population. The current work uncovered a worsening in sleep quality and sleep efficiency, a prolonged sleep onset latency, and an increase in sleep disturbances and insomnia. Given the risk of chronicization of sleep disturbances and their negative impact on mental and physical health, early detection and timely interventions are crucial. High quality research based on the use of validated measurement tools and longitudinal research designs is needed to evaluate the long-term effect of lockdown measures and to identify individuals at a higher risk of developing sleep disturbances and the role of moderators.FL, CT, FP, and SM conceptualized the study. FL, PS, and CT contributed to the investigation and literature search. FL contributed to the team supervision. EP and FR performed data screening and data extraction. PS contributed to the supervision, coordination of data coding, and statistical analysis. FL and PS wrote the original draft. FL, PS, CT, MN, FC, CC, SC, ER, EP, FR, FP, and SM edited and revised critically the manuscript. All authors contributed to the article and approved the submitted version.


ObjectivesThis study primarily aimed to estimate the prevalence of mental illnesses  along with the poor quality of sleep, with a secondary focus on determining whether there was an association between mental health and quality of sleep among nurses working at King Khalid University Hospital, Riyadh, Saudi Arabia, during the COVID-19 pandemic.MethodsA cross-sectional study was conducted from June to December 2021 during the COVID-19 pandemic on 309 nurses from different departments at King Khalid University Hospital. Depression, anxiety, and stress were measured by the Depression, Anxiety, and Stress Scale - 21 Items (DASS-21), and sleep quality was measured by the Pittsburg Sleep Quality Index (PSQI) via an online survey.ResultsThe prevalence of depression, anxiety, and stress was 30.4%, 43.7%, and 16.5%, respectively, while 61.5% showed poor sleep quality. The association between poor quality of sleep and the other outcome variables of mental health  was highly significant (p<0.0001).ConclusionNurses have reported an increased prevalence of depression, anxiety, stress, and poor quality of sleep during the COVID-19 pandemic, making them particularly vulnerable to mental illnesses and sleep difficulties in the event of future pandemics. There is a clear link between mental health issues and poor quality of sleep, necessitating the provision of psychological support for nurses. Enhancing sleep quality is recommended as a way to decrease the prevalence of mental health concerns. The COVID-19 pandemic has impacted life worldwide. According to the WHO, around 6.6 million deaths were globally recorded, and that number is rising . Owing tIn Riyadh, Saudi Arabia, a cross-sectional study was conducted on nurses in January 2021, and it showed a prevalence of depression symptoms in 29.2% of participants, of anxiety symptoms in 57%, and of stress symptoms in 12% . An onliIn April 2020, in Guilan province, Iran, a study conducted on 441 nurses, the majority of whom were in contact with suspected or confirmed cases of COVID-19, showed depression and anxiety in 37.4% and 38.8% of the participants, respectively . A crossA descriptive study in Turkey investigated the quality of sleep among 105 nurses working in a state hospital, using the PSQI, and revealed that 70.9% of nurses experienced poor sleep quality . A ChineThe present study primarily aimed at estimating the prevalence of mental illnesses  along with poor quality of sleep with a secondary focus on determining whether there was an association between mental illnesses and quality of sleep among nurses working in Saudi Arabia during the COVID-19 pandemic. To the best of our knowledge, studies conducted in Saudi Arabia investigating mental health and quality of sleep among nurses are scarce, especially in Riyadh. Being the capital, it has the highest population and highest number of infected individuals in Saudi Arabia. King Khalid University Hospital (KKUH), one of the leading governmental hospitals in Riyadh, was the preferred choice for performing such a study.The results of this study hopefully provided sufficient information about mental health and quality of sleep among nurses, two years following the start of the COVID-19 pandemic, giving us a greater understanding of how to deal with future ones while minimizing their negative effects. Moreover, raising awareness of mental illnesses and poor quality of sleep will be beneficial for nurses and for future researchers interested in this field.The study was approved on the third of August 2021 by the Institutional Review Board, College of Medicine, King Saud University (E-21-6169). All the procedures followed the ethical standards of which the consent was perceived. All participants' information was kept confidential.The present study is a descriptive cross-sectional study that used an online survey. This study was conducted over six months, from June to December 2021, during the COVID-19 pandemic. The subjects were nurses working at the KKUH, Riyadh, Saudi Arabia. Of them, 309 were included from different departments, using a snowball sampling technique. The inclusion criteria were nurses working at KKUH. Exclusion criteria were nurses on leave at KKUH in 2021 during data collection.The online questionnaire was constructed on Google Forms and sent by e-mail to the head nurses of different departments who were instructed to share it with nurses to fill it out.With an assumed response rate of 80%, a 95% confidence interval, and a 5% error margin, the minimum required sample size is (n = 257). The data of 309 nurses were collected between September 21, 2021, and October 13, 2021. The survey was comprised of four major parts; the first and second were the exposure variables of the participants. The first part was the sociodemographic information including gender, age, nationality, marital status, work experience, working hours per day, clinical department, and whether they were living alone or not.The second part was exposure to COVID-19. It was measured by asking nurses about the frequency of contact with COVID-19 patients   and whetDASS-21 is a collection of three self-report scales that assess depression, anxiety, and stress. Each of the three scales has seven items grouped into subscales that have comparable content. The depression scale assesses dysphoria, hopelessness, devaluation of life, self-deprecation, lack of interest/involvement, anhedonia, and inertia. The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety, and subjective experience of anxious affect. The stress scale is sensitive to levels of chronic nonspecific arousal. It assesses difficulty relaxing, nervous arousal, being easily upset/agitated, irritable/over-reactive, and impatient. The participants are asked to rate how much the statements applied to them over the past week, on a four-point scale from 0  to 3 (applied to me most of the time). Scores are calculated by summing the values and multiplying them by two for the relevant seven items and then categorized by cutoff point into normal, moderate, and severe .The Pittsburgh Sleep Quality Index (PSQI) is a 19-item self-reported questionnaire that evaluates sleep quality and disturbances over the last month. Subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, usage of sleeping medication, and daytime dysfunction are among the 19 individual items that create seven "component" scores. The total of these seven components' scores equals one global score (0-21), where more than five is considered poor quality of sleep .Data were analyzed using Statistical Product and Service Solutions (SPSS) . Descriptive statistics  were used to describe the categorical and quantitative variables. Univariate analysis was conducted using the student's t-test for independent samples. Moreover, Pearson’s chi-square test was used to assess the association between categorical study and outcome variables. Odds ratios were calculated to measure association. A p-value of ≤0.05 and 95% confidence intervals will be used to report the statistical significance and precision of the results.Out of 309 nurses, 275 (89%) were females and 34 11%) were males. The frequency of contact with COVID-19 patients was never for 27 (8.7%), rarely for 160 (51.8%), often for 81 (26.2%) and very often for 41 (13.3%). Of them, 66 (21.4%) have been infected with COVID-19. The distribution of other sociodemographic characteristics is shown in Table 1% were mNinety-four (30.4%) participants had symptoms of depression; of them, 54 (17.5%) had moderate depression, and 40 12.9%) had severe depression, while 215 (69.6%) had no symptoms of depression, and they were considered normal. Symptoms of anxiety were evident in 135 (43.7%); 64 (20.7%) of them had moderate anxiety, 71 (23%) had severe anxiety, and 174 (56.3%) were normal. Symptoms of stress were evident in 51 (16.5%) nurses; 19(6.1%) of them had moderate stress, and 32 (10.4%) had severe stress, while 258 (83.5%) had no symptoms of stress. One hundred ninety (61.5%) participants had poor quality of sleep, while 149 (38.5) were normal  was highly significant (p<0.0001); depression , anxiety , and stress  , unmarried nurses , and nurses that have less than 10 years of experience . There was no statistical significance between the depression and other study variables, as shown in Table Anxiety symptoms were more likely to manifest in nurses who were younger than 36 years of age , unmarried nurses , and nurses with less than 10 years of experience . There was no statistical significance between anxiety and the other study variables, as shown in Table Symptoms of stress were more likely to be exhibited by nurses younger than 36 years of age  and unmarried nurses . There was no statistical significance between the stress and the other study variables, as shown in Table The likelihood of developing poor sleep quality was higher in nurses who were younger than 36 years of age , unmarried nurses , and nurses that have less than 10 years of experience . No statistical significance between the poor quality of sleep and the other study variables was found, as shown in Table This study demonstrated a high prevalence of mental illnesses and poor quality of sleep symptoms among nurses working at KKUH during the COVID-19 pandemic. This suggests that poor mental health and poor quality of sleep were positively associated with COVID-19 patient care and quarantine/self-isolation experience. According to the present results, 30.4% of the nurses had symptoms of depression. Similar findings were reported in a study conducted in Riyadh, Saudi Arabia, which included 281 nurses, 29.2% of which showed moderate/severe depression . Some stRegarding anxiety, 43.7% of nurses had symptoms of anxiety, and the results of the present study are in accordance with a study conducted in China , reportiIn the present study, we found that nurses younger than 36 years old with less than 10 years of work experience, besides being unmarred are more likely to have mental illnesses and poor quality of sleep. This indicates that married older nurses with more than 10 years of work experience have a lower likelihood of developing mental illness. It may be that older nurses had more experience and were better prepared both professionally and mentally to cope with the stress during the pandemic. The results indicate that nurse managers could increase COVID-19 training and support to younger, less-experienced staff for mental health problems to be reduced . Others Anxiety and depression symptoms can be caused by a lack of support from friends and family, stress at the workplace, and concern about COVID-19 -22. PoorThe significant association between poor quality of sleep and the other outcome variables of mental health  showed the importance of evaluating the mental and sleep problems among nurses during pandemics, such as COVID-19. Sleep deprivation negatively affects the nurse's performance and can cause accidents and errors during work . EvidencRegarding the limitations of this study, the cross-sectional study type might influence the causal relationship between the sociodemographic and outcome variables . For example, the majority of participants (89%) were female, and the rest (11%) were male. Another limitation was the self-reported measurement tool, which may cause under-/overestimation by the participants, leading to information bias. Thus, the results may have been influenced by self-selection bias, as nurses who had higher levels of depression or anxiety and poor sleep quality were more likely than others to respond.Furthermore, the present study used DASS-21 and PSQI to measure the nurses' sleep, which might not be very objective. Moreover, because of the snowball sampling technique used in this study, it may be impossible to determine an exact response. Finally, the study was not conducted at the peak period of the pandemic in Saudi Arabia, and it was conducted in one hospital; thus, the generalization is limited. In addition to the factors studied, there could be other factors affecting mental health and sleep quality.In conclusion, nurses have reported an increased prevalence of depression, anxiety, stress, and poor quality of sleep during the COVID-19 pandemic, making them particularly vulnerable to mental illnesses and sleep difficulties in the event of future pandemics. There is a clear link between mental health issues and poor quality of sleep, which necessitates the provision of psychological support for nurses. Enhancing sleep quality is recommended to decrease the prevalence of mental health concerns.Future studies are recommended to assess the causal relationship between outcome variables by using different study designs. Furthermore, the long-term mental health impact of the COVID-19 pandemic among nurses needs to be addressed. To ensure the generalization factor, future studies should be performed in more than one hospital, in different regions of Saudi Arabia.


Podarcis siculus. The present study aimed to investigate the effects of the herbicide on the female reproductive system of this lizard in order to have a full picture of Gly-induced reproductive impairment. The animals were exposed to 0.05 and 0.5 μg/kg of pure Gly by gavage for 3 weeks. The results demonstrated that Gly, at both doses tested, profoundly interfered with ovarian function. It induced germ cells’ recruitment and altered follicular anatomy by anticipating apoptotic regression of the pyriform cells. It also induced thecal fibrosis and affected oocyte cytoplasm and zona pellucida organizations. At the functional levels, Gly stimulated the synthesis of estrogen receptors, suggesting a serious endocrine-disrupting effect. Overall, the follicular alterations, combined with those found at the level of the seminiferous tubules in males, suggest serious damage to the reproductive fitness of these non-target organisms, which over time could lead to a decline in survival.Glyphosate (Gly) is a broad-spectrum herbicide widely used thanks to its high efficiency and low toxicity. However, evidence exists of its toxic effects on non-target organisms. Among these, the animals inhabiting agricultural fields are particularly threatened. Recent studies demonstrated that exposure to Gly markedly affected the morphophysiology of the liver and testis of the Italian field lizard  Modern intensive practices in agriculture provide for a wide use of herbicides and, among these, of glyphosate (Gly). In 2020, the global production of this synthetic molecule was around 700,000 tons , and theRegarding wild species that may be exposed to glyphosate in their natural environment, studies have mainly focused on aquatic organisms, as they can easily come into contact with wastewater from the irrigation of cultivated fields. Glyphosate is in fact highly soluble in water . In bothPodarcis siculus. Inhabiting areas intended for agriculture, lizards can easily be exposed to this herbicide, through contaminated water and food . P. siculus is a good bioindicator in ecotoxicology; several studies have demonstrated that this lizard can be contaminated in its natural habitat by herbicides, pesticides, and manure, which modify its health status and give rise to measurable biological responses [Recently, the effects of Gly were determined on the wall lizard esponses ,22,23.P. siculus [The severe liver fibrosis, the activation of the antioxidant defence machinery, and the xenoestrogenic effects of Gly in  siculus  prompted siculus .P. siculus. For this purpose, the structural and functional organization of the ovarian follicles following oral exposure to the herbicide was investigated. In particular, the effects on the number of germ cells and follicles were determined, while epithelial/thecal alterations were detected by cytological investigations. Immunocytochemistry detected alterations (1) in PCNA expression, indicative of alterations in proliferation rate [The aim of this work was to expand the study to the female reproductive system of ion rate ; (2) in ion rate ; and (3)ion rate ,24.P. siculus ovaries are characterized by a cluster structure, with prefollicular cells located in two germinal beds and follicles showing a complex epithelium, functional to the maturation and growth of the oocyte. The primary follicles are surrounded by a monolayer of small stem cells, which divide rapidly into two cells, one that maintains a stem function and remains close to the external connective theca and another that contacts the oocyte, forming an intercellular bridge. Then, follicle cells markedly increase in size and change shape, becoming pyriform cells [P. siculus, reproduction occurs with two or three ovulatory waves in spring/summer, followed by a summer resting period and a fall recrudescence, in which ovarian functions are partly resumed but quite soon arrested by the beginning of the winter stasis [rm cells . The pyrrm cells . At thisrm cells . In P. sr stasis .The results demonstrated that glyphosate profoundly interferes with ovarian structure and function. It anticipates germ cells’ recruitment and alters follicular anatomy by anticipating apoptotic regression of the pyriform cells. It also induces thecal fibrosis and affects the oocyte cytoplasm and the organization of the zona pellucida. At the functional level, Gly stimulates estrogen receptors’ synthesis, suggesting a severe endocrine-disrupting effect.The ovary of control animals contains an average of 458 ± 24 standard deviation (SD) prefollicular oogonia and oocytes and 13.2 ± 0.75 SD previtellogenic follicles, ranging from 80 to about 1400 microns in diameter A,B. EarlIn sections, control oocytes show a homogenous, fibro-granular cytoplasm delimited by a continuous zona pellucida and a multi-layered epithelium. This is typically polymorphic, with large pyriform cells occupying most of the thickness, small cells located immediately under the connective theca, and intermediate cells close to the zona pellucida C.In glyphosate-treated follicles, no matter the dose, the epithelium shows several late D or earlPicrosirius red (PSR) staining shows a dose-dependent increase in matrix deposition in both low- and high-dose glyphosate-treated samples A,C,E.PSR staining observed under polarized light allows recognition of thin immature collagen fibers  from the thick mature collagen fibers . The results indicate that immature and mature collagens increased dose-dependently in treated samples with respect to controls A. Newly In control follicles, PAS intensely stains the connective theca and the zona pellucida. Follicle cells’ cytoplasm is moderately stained as the oocyte cytoplasm A,B. AfteIn controls, the three lectins stain the zona pellucida; WGA A and ConAfter exposure to glyphosate, no matter the dose, UEA E,H and CIn control follicles, a low and diffuse signal for E-cadherin is found on the zona pellucida and on the epithelium, while a moderately intense signal is on the external theca A,B. In aIn controls, the immunohistochemical signal for PCNA antigen is present in the theca cells and within small stem cells, located immediately below the theca, as well as on small cells that migrate to the zona pellucida  and diffER-α  and induced the recruitment of extra primary follicles. Significantly, recruitment is not accompanied by oogonial proliferation or the appearance of new leptotene oocytes; therefore, more than true recruitment, it appears that glyphosate has anticipated differentiation, an effect parallel to the anticipated apoptosis in the follicular epithelium. A check for an anticipated exposure of GnRH receptors might prove that this is the case .It is impossible to know if the newly recruited oocytes would have been ovulated because, in Podarcis, the maximum number of eggs is strictly controlled and depends on female size more than on environmental cues ,70. It iIn conclusion, exposure to glyphosate results in structural and functional damage. These injuries, combined with those found on the morpho-physiology of the testis in male specimens , suggestPodarcis siculus females were captured in late October in the outskirts of Naples , maintained in soil-filled terrariums under natural temperature and photoperiod , and fed with insect larvae. After 7 days of acclimatization, the animals were randomly assigned to three groups (n = 8): groups 1 and 2 were exposed to the active ingredient glyphosate (Gly) at doses of 0.05  and 0.5  μg/kg body weight, respectively; group 3 were left as control animals. Gly was administered by pipetting the corresponding aqueous solution (50 µL) into the mouth, three times a week, for 3 weeks; control animals received pure water. At the end of the treatments, the animals were killed by decapitation after anesthesia with ketamine hydrochloride , at 325 μg/g body weight; ovaries were excised and processed for histological and immunocytochemical analyses.Ovaries were fixed in Bouin’s solution, dehydrated in a graded series of ethanol, clarified in xylene, and embedded in paraffin. Sections (7 μm thick) were dewaxed, rehydrated, and stained with Mallory’s trichrome  to show ® software (version 1.53t) was used and the scale bar was reported on each image for calibration. The results were analyzed for significance by the Student’s t-test. Statistically significant differences among groups were tested by analysis of variance (ANOVA) or the Kruskal–Wallis test. Data are presented as average ± SD and p-values <0.05 were considered statistically significant. Furthermore, Cohen’s d was used to calculate the effect size through a standardized mean difference. Cohen’s d = (x1 − x2)/s, where x1 and x2 are the sample means of group 1 and group 2, respectively, and s is the standard deviation of the population from which the two groups were taken. All of the stained sections were observed using a Zeiss Axioskop microscope and the images were acquired using Axiovision 4.7 Software  [Sections were also stained with Picrosirius red to show collagen; polarized light images were acquired with a polarized light microscope. To quantitatively determine the proportion of mature (red) and immature (green) collagen fibers, randomly selected images were resolved into their hue, saturation, and brightness (HSB) components using the “color threshold” function of ImageJ® software (version 1.53t). The hue component was retained, and a histogram of hue frequency was obtained from the resolved 8-bit hue images (containing 256 colors). The following hue definitions were used: red 0–51 and green 52–120 ,76. CollGermany) .2O2 in methanol to block the endogenous peroxidase [Sections mounted on polylysine glass slides were treated with 10 mM citrate buffer pH 6.0 to unmask the antigens and incubated in 2.5% Hroxidase ,79. Non-


Conclusions: Up to a fourth of eyes had worse pain after discharge. Older age, initial intraocular pressure, and pain during the procedure were risk factors for higher levels of overnight pain.Background: This study aimed to assess the pain experienced during micropulse transscleral laser therapy (MPTLT) and overnight thereafter and explore the factors associated with the pain. Methods: This prospective study included 100 eyes of 81 glaucoma patients undergoing MPTLT under retrobulbar anesthesia. All patients were asked to rate both types of pain using a numerical rating scale (NRS). The risk factors were explored using multivariable mixed-effects ordinal logistic regression. Results: The mean (SD) NRS pain score during the procedure was 3.57 (3.41) (range 0–10), which included no, mild, moderate, and severe pain in 30 (30%), 33 (33%), 17 (17%), and 20 (20%) eyes, respectively. The mean (SD) NRS score of overnight pain was 2.99 (2.28) (range 0–9), which included no, mild, moderate, and severe pain in 17 (17%), 59 (59%), 17 (17%), and 7 (7%) eyes, respectively. Twenty-seven (27%) eyes reported worse pain overnight than during the procedure. Increased age, initial intraocular pressure, and pain during the procedure were significantly associated with increased overnight pain ( Micropulse transscleral laser therapy (MPTLT) is one of the latest laser treatments for glaucoma. The mechanism of MPTLT involves the use of a diode laser to deliver repetitive short pulses of energy in an on-and-off manner. The ‘on’ mode involves the emission of an 810-nm-wavelength laser to produce thermal energy that is absorbed by the ciliary body, resulting in a decrease in aqueous production. The ‘off’ mode involves a resting period that allows the surrounding tissues to cool down to minimize complications from thermal energy. The use of MPTLT is increasing due to its good efficacy and safety profile .MPTLT, as with conventional cyclophotocoagulation, is not a painless laser procedure and requires anesthesia. As MPTLT is a relatively new technology, there is no consensus on anesthesia guidelines. Thus, there have been several reports on local anesthesia practices, including the retrobulbar block , the perMPTLT, as an office-based procedure, has a number of advantages over operating room procedure in terms of convenience, cost, and efficiency ,7,8. AddDespite receiving local anesthesia, some patients who undergo MPTLT report pain that might affect their quality of life and ability to perform activities of daily living. Few studies have evaluated pain due to MPTLT, and most have focused only on pain during the procedure ,12,13,14This study was a non-comparative, prospective, and interventional trial. We enrolled all glaucoma patients scheduled for MPTLT at the King Chulalongkorn Memorial Hospital between March 2020 and March 2021. All participants were required to fulfill the following criteria: (1) 18 years old or older, (2) indicated or scheduled for MPTLT, (3) able and willing to attend follow-up appointments after the procedure, and (4) able to provide informed consent to participate in this study. Patients with painful blind eyes were excluded from the study.This study was conducted in accordance with the tenets of the Declaration of Helsinki and approved by the local ethical committee, the Institutional Review Board of the Faculty of Medicine Chulalongkorn University, Bangkok, Thailand. Written informed consent was obtained from all study participants.® 2%, Recipharm, Monts, France). The globes were assessed for akinesia 5 min after the injection. Rarely, a second injection of 1.5–2.5 mL lidocaine was administered if the globes did not exhibit akinesia. The total dose of lidocaine was less than 5 mL. Topical lidocaine hydrochloride 2%  was administered subsequently, and experienced ophthalmologists performed MPTLT using the IRIDEX Cyclo G6® Glaucoma Laser System with Micropulse P3® Glaucoma device  according to the manufacturer’s instructions. The laser settings were as follows: power, 2000 mW; duty cycle, 31.3%; and duration, 180 s for first-time MPTLT and 280 s for repeat MPTLT. The power was gradually decreased to 1800, 1500, and 1000 mW if the patient experienced unbearable pain. The laser probe was placed adjacent to the limbus and moved in a sliding motion along the sclera, sparing the 3 o’clock, 9 o’clock, and scleral thinning areas.Patients scheduled for MPTLT were premedicated with 50 mg of oral tramadol and 2 mg of oral diazepam. All patients received retrobulbar anesthesia with 2.5 mL of lidocaine hydrochloride 2%  and patched for 3 h. Paracetamol was administered as a “prn” prescription. All patients continued the same antiglaucoma medication that was taken before the procedure.After the laser treatment, the operated eyes were treated with one drop of topical cyclopentolate hydrochloride 1%  and one drop of topical prednisolone acetate 1% . Both types of pain were recorded using the numerical rating scale (NRS), which ranged from 0 to 10. Jensen’s classification of pain was used to categorize the NRS score as no pain (NRS 0), mild pain (1–4), moderate pain (5–6), and severe pain (7–10) .The data of individual participants, including age, sex, presence of diabetes mellitus, glaucoma type, inflammation, initial intraocular pressure (IOP), concurrent pilocarpine use, concurrent steroid use, repeated treatment, total energy, and total treatment area, were collected. The baseline IOP was measured using a Goldman applanation tonometer immediately before the laser treatment.p-values < 0.1 in univariable analysis. The NRS of “pain during the procedure” was included as a covariate in the “overnight pain” model. A p-value < 0.05 was considered statistically significant.Statistical analysis was performed using STATA 16.0 . The mean and standard deviation of continuous variables are presented as descriptive statistics. Frequency and percentage are used to describe categorical variables. One-way analysis of variance (ANOVA) and chi-square tests were used to test the differences in clinical characteristics across pain levels. The association between the clinical characteristics and pain was further investigated using multivariable mixed-effects ordinal logistic regression for factors with p = 0.02). Eight patients did not complete the MPTLT procedure due to unbearable pain. A total of 100 eyes from 81 patients who underwent MPTLT were included in this study. No two eyes underwent MPTLT on the same day. The demographic and clinical characteristics of the patients are shown in Both pain during the procedure and overnight pain were classified into groups, as shown in p > 0.10).The baseline factors, including age, sex, underlying diabetes mellitus, glaucoma type, inflammation, initial IOP, concurrent pilocarpine use, concurrent steroid use, repeated treatment, total energy, and total treatment area, were analyzed for their association with pain during the procedure; however, none showed statistical significance . The results of each model are shown in For the analysis of overnight pain, factors, including age, initial IOP, repeated treatment, total energy, treatment duration, and pain during the procedure, were included in the multivariable model. Given the collinearity between the total energy and treatment duration, independent models were used to analyze the two variables separately. Mixed-effects logistic regression showed that pain during the procedure, age, and initial IOP were significantly associated with the intensity of overnight pain, with higher values indicating greater severity  and 24 patients (24%) who underwent MPTLT reported moderate or greater pain during the procedure and overnight, respectively. We identified advanced age, high initial IOP, and high level of pain during the procedure as the factors associated with increased pain experienced overnight after the procedure.MPTLT-related pain has been reported in the literature. Despite MPTLT being a non-incisional procedure, our study found that more than one-third of patients still experienced moderate to severe pain during the procedure. Popa et al. compared the pain experienced during MPTLT with that experienced during continuous wave cyclophotocoagulation (CW-CPC). Although they reported that the pain experienced during MPTLT was less than that during CW-CPC, the average pain score during MPTLT was as high as 6.02, which was considered as moderate pain .According to our findings, 83% of patients experienced some degree of overnight pain, with 24% reporting moderate to severe pain. In addition, one-fourth of patients reported worse pain overnight than during the procedure. There were only two previous studies that reported the pain assessed at an early follow-up visit. The incidence ranged from 5.8% to 18.4%, and the degree of pain was mild. However, all previous studies utilized low total energy levels, whereas our study employed higher energy.Overnight pain can develop after the effect of retrobulbar anesthesia has worn off. Possible contributors to overnight pain include intraocular inflammation, ciliary muscle spasm, and ocular surface pain. Lim et al. reported that 33.3% of patients who underwent MPTLT experienced postoperative anterior chamber inflammation . JohnstoThis study found that overnight pain was associated with advanced age, initial IOP, and level of pain during the procedure. Although aging is often related to an increase in pain threshold, several psychological investigations have revealed a decrease in pain tolerance and an increase in the duration of hyperalgesia after tissue injury in elderly individuals ,21. The This was the first study to assess pain during the procedure and early postoperative pain after MPTLT as a primary objective. One limitation of this study was that all patients received retrobulbar anesthesia with a specific pre-medication pain control regimen before the laser treatment and one dose of cyclopentolate immediately after. Therefore, these results may not be generalizable to other anesthetic techniques or protocols. Second, pre-laser analgesic medication can affect the pain assessment. In this study, all patients received analgesics before the treatment, which should have theoretically reduced pain perception. Thus, the frequency and severity of pain in our study could have been underestimated. Third, the exact amount of lidocaine used for retrobulbar anesthesia was not recorded. The lidocaine dosage might influence the management of pain overnight. However, since the duration of action of lidocaine is less than two hours, it is less likely that its effect can alleviate pain throughout the night. We believe that our retrobulbar protocol, with the typical dose and short duration of action of lidocaine, had a lesser impact on the measurement of overnight pain. Fourth, overnight pain was recalled at follow-up and could have been affected by recall bias. Lastly, there was no exact dose or time when patients took the analgesics after the laser treatment.In conclusion, pain due to MPTLT was frequent and could be severe both during the procedure and overnight thereafter. Thus, management for both types of pain should be seriously considered, especially in at-risk patients, such as elderly individuals, those with high IOP, and those who experienced a high level of pain during the procedure.