Dataset Information

A Mobile Sleep-Management Learning System for Improving Students' Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial.

ABSTRACT: BACKGROUND:Insomnia can significantly affect students' learning performance. Researchers have indicated the importance and challenge of coping with insomnia using nondrug treatments, such as cognitive behavioral therapy (CBT) for insomnia. However, it is easy for the traditional CBT for insomnia to be interrupted owing to the overly lengthy period of sleep therapy. Self-regulated learning (SRL) strategies are known to be an effective approach for helping students improve their time management, as well as their ability to set learning goals and adopt learning strategies. OBJECTIVE:The objective of this study was to propose a mobile sleep-management learning system integrated with SRL strategies and CBT. METHODS:A total of 18 undergraduate students from a university in northern Taiwan participated in the 2-week experiment of using this sleep-management system. RESULTS:The experimental results showed that the proposed approach was useful and easy for students to use. In addition, the number of students with insomnia significantly decreased; that is, the proposed approach could help students improve their sleep quality and cultivate better sleeping habits, which is important for them to enhance their learning efficiency. CONCLUSIONS:With the assistance of this proposed approach, students can plan their daily life by setting goals, applying strategies, monitoring their life habits process, and modifying strategies to cultivate good learning and healthy lifestyle habits. TRIAL REGISTRATION:Government Research Bulletin MOST104-3011-E038-001; https://www.grb.gov.tw/search/planDetail? id=11568383&docId=467988 (Archived by WebCite at http://www.webcitation.org/73MnPHNri).

SUBMITTER: Chu HC

PROVIDER: S-EPMC6239866 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A Mobile Sleep-Management Learning System for Improving Students' Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial.

Chu Hui-Chun HC Liu Yi-Meng YM Kuo Fan-Ray FR

JMIR mHealth and uHealth 20181029 10

<h4>Background</h4>Insomnia can significantly affect students' learning performance. Researchers have indicated the importance and challenge of coping with insomnia using nondrug treatments, such as cognitive behavioral therapy (CBT) for insomnia. However, it is easy for the traditional CBT for insomnia to be interrupted owing to the overly lengthy period of sleep therapy. Self-regulated learning (SRL) strategies are known to be an effective approach for helping students improve their time manag ...[more]

PMID: 30373729

Similar Datasets

Project description:IntroductionThe aim of this study was to evaluate usage habits, attitudes and perceptions towards mobile learning (m-learning), as well as to identify variables related to those attitudes amongst undergraduate dental students.Materials and methodsThe study consists of 81 dental undergraduate students who who volunteered to participate. The data collection tool consists of an m-learning attitude scale, a questionnaire, and open-ended questions. To compare the total scores and factors of m-learning attitude scale for demographic information and mobile technology usage habits of the students; the Mann-Whitney U test was used for two independent groups such as gender, presence of electronic devices, and places of Internet usage. The Kruskal-Wallis test was also used to compare the total scores and factors of m-learning attitude scale for more than two independent groups including internet usage purposes and opinions. Spearman's correlation coefficient was performed, and linear regression analysis was used to predict the change in total score according to the purposes of Internet usage.ResultsThe majority of students thought that the use of mobile devices in dentistry courses was useful and their attitudes towards m-learning were high. The students generally use the Internet for online shopping, connecting to social networks, and communication. They tend to use mobile technologies for personal use, followed by educational purposes. There were significant differences found in the m-learning attitudes for gender, having a portable power supply and use of mobile devices in dentistry courses. Communication was found significant in predicting the change in total score for the m-learning attitude scale according to the purpose of Internet usage.ConclusionDental students have generally positive attitudes towards m-learning. Students raise awareness towards the promise of m-learning in order to apply their individual technology use and learning behaviours. Designing learning materials and applications for mobile devices may increase students' performances.

Project description:BackgroundSleep disorders are composed of a group of diseases of increasing prevalence and with social-health implications to be considered a public health problem. Sleep habits and specific sleep behaviors have an influence on the academic success of students. However, the characteristics of sleep and sleep habits of university students as predictors of poor academic performance have been scarcely analyzed. In the present study, we aimed to investigate sleep habits and their influence on academic performance in a cohort of Nursing Degree students.MethodsThis was a cross-sectional and observational study. An anonymous and self-administered questionnaire was used, including different scales such as the 'Morningness and Eveningness scale', an author-generated sleep habit questionnaire, and certain variables aimed at studying the socio-familial and academic aspects of the Nursing students. The association of sleep habits and other variables with poor academic performance was investigated by logistic regression. The internal consistency and homogeneity of the 'sleep habits questionnaire' was assessed with the Cronbach's alpha test.ResultsOverall, 401 students (mean age of 22.1 ± 4.9 years, 74.8 % females) from the Nursing Degree were included. The homogeneity of the 'sleep habits questionnaire' was appropriate (Cronbach's alpha = 0.710). Nursing students were characterized by an evening chronotype (20.2 %) and a short sleep pattern. 30.4 % of the Nursing students had bad sleep habits. Regarding the academic performance, 47.9 % of the students showed a poor one. On multivariate logistic regression analysis, a short sleep pattern (adjusted OR = 1.53, 95 % CI 1.01-2.34), bad sleep habits (aOR = 1.76, 95 % CI 1.11-2.79), and age < 25 years (aOR = 2.27, 95 % CI 1.30-3.98) were independently associated with a higher probability of poor academic performance.ConclusionsAlmost 1/3 of the Nursing students were identified as having bad sleep habits, and these students were characterized by an evening chronotype and a short sleep pattern. A short sleep pattern, bad sleep habits, and age < 25 years, were independently associated with a higher risk of poor academic performance. This requires multifactorial approaches and the involvement of all the associated actors: teachers, academic institutions, health institutions, and the people in charge in university residences, among others.

Project description:BackgroundThe new coronavirus SARS-CoV-2 led to the COVID-19 pandemic starting in January 2020. The Swiss Federal Council prescribed a lockdown of nonessential businesses. Students and employees of higher education institutions had to install home offices and participate in online lectures.ObjectiveThe aim of this survey study was to evaluate lifestyle habits, such as physical activity (PA), sitting time, nutritional habits (expressed as median modified Mediterranean Diet Score [mMDS]), alcohol consumption habits, and sleeping behavior during a 2-month period of confinement and social distancing due to the COVID-19 pandemic. Survey participants were students and employees of a Swiss university of applied sciences.MethodsAll students and employees from Bern University of Applied Sciences, Department of Health Professions (ie, nursing, nutrition and dietetics, midwifery, and physiotherapy divisions) were invited to complete an anonymous online survey during the COVID-19 confinement period. Information on the lifestyle dimensions of PA, sitting time, nutritional and alcohol consumption habits, and sleep behavior was gathered using adaptations of validated questionnaires. Frequency analyses and nonparametric statistical methods were used for data analysis. Significance was set at 5% α level of error.ResultsPrevalence of non-health-enhancing PA was 37.1%, with participants of the division of physiotherapy showing the lowest prevalence. Prevalence of long sitting time (>8 hours/day) was 36.1%. The median mMDS was 9, where the maximal score was 15, with participants of the division of nutrition and dietetics being more adherent to a Mediterranean diet as compared to the other groups. Prevalence of nonadherence to the Swiss alcohol consumption recommendations was 8.3%. Prevalence of low sleeping quality was 44.7%, while the median sleeping duration was 8 hours, which is considered healthy for adult populations.ConclusionsIn the group analysis, differences in PA, sitting time, and mMDS were observed between different divisions of health professions as well as between Bachelor of Science students, Master of Science students, and employees. Therefore, public health messages regarding healthy lifestyle habits during home confinement should be more group specific. The results of this study may provide support for the implementation of group-specific health promotion interventions at universities in pandemic conditions.Trial registrationClinicalTrials.gov NCT04502108; https://www.clinicaltrials.gov/ct2/show/NCT04502108.

Dataset Information

A Mobile Sleep-Management Learning System for Improving Students' Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial.

Publications

A Mobile Sleep-Management Learning System for Improving Students' Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure