Dataset Information

Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic.

ABSTRACT: Backgroud:COVID-19 pandemic has significantly affected the sleep health of local medical and nursing staff. Aim:We used wearable pulse oximeters to monitor and screen the medical and nursing staff working in hospitals designated for COVID-19 in the Wuhan area. This study aimed to establish a reliable basis to provide sleep intervention for the medical and nursing staff. Methods:Thirty medical and nursing staff members with symptoms of insomnia were instructed to wear medical ring-shaped pulse oximeters to monitor their sleep overnight. We also used the Insomnia Severity Index (ISI) and the Chinese version of the Self-Reporting Questionnaire (SRQ-20) to evaluate the severity of insomnia and mental health status, respectively, for each participant. Results:Among the 30 participants, only 26 completed the screening. Ten cases (38.5%) demonstrated moderate to severe sleep apnoea-hypopnea syndrome (SAHS) when using an oxygen desaturation index ?15 times/hour as the cut-off value. Participants with comorbid moderate to severe SAHS had significantly higher ISI and SRQ scores (p values 0.034 and 0.016, respectively) than those in the insomnia group. Correlation analysis revealed that ISI was positively correlated with total sleep time (TST) (r=0.435, p=0.026), and negatively correlated with deep sleep (r=-0.495, p=0.010); furthermore, patient SRQ scores were positively correlated with TST, sleep efficiency (SE) and REM (rapid eyes movement) sleep % (r=0.454 and 0.389, 0.512; p=0.020, 0.050 and 0.008, respectively). Stepwise logistic regression indicated that SRQ-20 and sex were risk factors for insomnia with comorbid SAHS, and their OR values were 1.516 and 11.56 (95% CI 1.053 to 2.180 and 1.037 to 128.9), respectively. Conclusion:Medical and nursing staff with insomnia showed clear signs of comorbid sleep apnoea attributable to stress. The wearable pulse oximeters accurately monitored the participants' breathing when asleep.

SUBMITTER: Zhuo K

PROVIDER: S-EPMC7299005 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic.

Zhuo Kaiming K Gao Cunyou C Wang Xiaohui X Zhang Chen C Wang Zhen Z

General psychiatry 20200616 3

<h4>Backgroud</h4>COVID-19 pandemic has significantly affected the sleep health of local medical and nursing staff.<h4>Aim</h4>We used wearable pulse oximeters to monitor and screen the medical and nursing staff working in hospitals designated for COVID-19 in the Wuhan area. This study aimed to establish a reliable basis to provide sleep intervention for the medical and nursing staff.<h4>Methods</h4>Thirty medical and nursing staff members with symptoms of insomnia were instructed to wear medica ...[more]

PMID: 32596641

Similar Datasets

Project description:BackgroundConsumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography.ObjectiveThis study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment.MethodsThe study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography.ResultsThe study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal.ConclusionsOur study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Project description:BackgroundMost of the limited number of studies that have been carried out on COVID-19 in nursing homes have not included primarily nursing staff. Nevertheless, knowledge about staff experiences will help to provide recommendations for the future.AimThe aim of this study was to describe stress experienced and interventions performed by nursing staff and to identify factors that are associated to the perceived stress among Austrian nursing home staff during the first and the second waves of COVID-19.MethodsA secondary data analysis of two cross-sectional surveys performed in 2020 and 2021 among nursing home staff was performed. We did descriptive analysis as well as univariate and multivariate logistic regression analyses.ResultsA total of 449 nurses participated in the first survey and 300 in the second survey. 12.7% experienced high stress levels in the first wave, while 26.0% experienced high stress levels in the second wave (p < 0.001). The analysis showed that nursing staff in the second wave had a 2.195-fold higher relative chance of experiencing a high stress level compared to nursing staff in the first wave (p < 0.001). Caring for COVID-19 residents (odds ratio [OR] 1.827; p = 0.007) and being female (OR 1.992; p = 0.018) also significantly increased the relative chance of experiencing a high stress level. Some protective interventions, such as the use of FFP masks, increased between the two waves, while others decreased, such as the practice of airing the residents' rooms.ConclusionAustrian nursing staff in nursing homes experienced more stress during the second wave, illustrating the heavy burden of the long pandemic on staff. Nursing management should plan appropriate supportive interventions such as psychological help, stress relief measures and financial incentives for nursing staff, especially for the identified high-risk groups.Supplementary informationThe online version of this article (10.1007/s16024-022-00395-x) contains supplementary material, which is available to authorized users.

Project description:BackgroundCOVID-19 pandemic has necessitated mandatory e-learning in medical and nursing education. How far are developing countries like India (with wide socioeconomic and cultural diversity) geared up for this challenge remains unexplored. At this critical juncture, we aim to evaluate if online teaching methods are as feasible, acceptable, and effective as in-class teaching for medical/nursing students.ObjectivesThe questionnaire captured: (1) practicability/feasibility of online classes, (2) health issues from online classes, (3) current methods for e-teaching, and (4) student attitudes and preferences.DesignCross-sectional survey.SettingsPopulation-based study in India.ParticipantsNursing and medical undergraduate students (I-IV year).MethodologyThe online questionnaire was distributed to 200 medical and nursing colleges across India. Categorical variables were analyzed using chi-square tests. Binary logistic regression was done to analyze factors predicting health issues in students. p < 0.05 was considered significant.ResultsOverall, 1541 medical and 684 nursing students completed the survey from 156 cities. The availability of laptop (p < 0.0001), Wi-Fi (p < 0.0001), dedicated room (p < 0.0001), and computer proficiency was more in students of affluent families and those from cities (p < 0.0001). Class duration >4 h/day (p < 0.0001), each class >40 min (p < 0.009) and pre-existing health issues (p < 0.0001) predicted the occurrence headache, eyestrain, anxiety, neck/back pain, and sleep disturbance. Power-point presentation was the most widely (80%) used method of teaching. Only 30% got adequate time to interact with teachers. Only 20.4% felt e-learning can replace conventional teaching. Students preferred: 3-6 classes/day, each class <40 min, 10-20 min break between classes and interactive sessions.ConclusionThere is a need to improve information and communication infrastructure to enhance feasibility of e-learning for nursing/medical students in India. There should be guidelines (number of classes/day, length of each class, break between classes, curriculum, etc) to improve the retention capacity in students and reduce health issues. Continuous feedback from teachers and students will be required to make e-learning effective.

Dataset Information

Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic.

Publications

Stress and sleep: a survey based on wearable sleep trackers among medical and nursing staff in Wuhan during the COVID-19 pandemic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets