University students are at risk of developing insufficient sleep and poor lifestyle behaviors. Sleep-related problems are frequently seen in nursing students. Regular exercise has a positive effect on sleep quality. In recent years, developing technological opportunities have led educators to seek different solutions in education, guidance, and counseling services aimed at protecting and improving the health of individuals. As the popularity of VR technology increases, it has a high potential to increase physical activity and improve sleep quality in university students. In the current literature, studies examining the level of physical activity and sleep quality of nursing students using VR virtual glasses are limited. This study aims to examine the effect of virtual reality exercise applications on nursing students' sleep quality and physical activity. This study was planned as a pre-test and post-test control group study. The study population comprises 60 students from the Department of Nursing and the Faculty of Health Sciences. Since the study population is accessible, no sample calculation will be made, and all students who volunteer to participate in the study and meet the inclusion criteria will be included. Randomization will divide students who meet the inclusion criteria into experimental and control groups. The aim of the study and the application will be explained to the students in the experimental group at the first meeting. They will be asked to sign written informed consent, and they will fill out data collection forms. Three sessions are planned for each student in the experimental group. Each session is planned to be held with an average interval of 96-120 hours (every 4-5 days). Data collection forms will be repeated for the students in the experimental group at the end of the third session. The aim of the study will be explained to the students in the control group at the first meeting. They will be asked to sign written informed consent, and they will fill out data collection forms. At the end of 14 days, actigraphs will be received from the control group, and post-tests will be applied. Descriptive statistics of the study data will be reported by giving numbers, percentages, mean, and standard deviation. Student t-tests, ANOVA or Mann-Whitney U, and Kruskal Wallis tests will be used to compare groups regarding the normal distribution of the data. Pearson chi-square test and Fisher exact chi-square test will be used to compare nominal or ordinal data groups.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Enrollment
60
Every three to four days (roughly 72 to 96 18 19 hours), a session was held. Students were required to use the Oculus Quest 2 and touch controllers to play immersive exergames for thirty minutes during each session. In each session, they were asked to play the games Dance Central (https://www.dancecentral.com/ ) and CREED: Rise to Glory (which the researchers loaded into the Oculus Quest 2) via the Meta Quest Gaming platform (https://www.meta.com/tr-tr/experiences/creed-rise-to27 glory-championship-edition/2366245336750543/ ).
Bartın University
Bartın, Turkey (Türkiye)
sleep quality
Pittsburgh Sleep Quality Index (PSQI): The Pittsburgh Sleep Quality Index (PSQI), developed by Buysse et al. and validated for reliability in Turkish, will be used to assess the usual sleep quality of the experimental and control groups to evaluate their homogeneity before the intervention (Ağargün et al., 1996). The PSQI evaluates seven components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. The sum of these seven components forms the total PSQI score. The total PSQI score ranges from 0 to 21, with lower scores indicating better sleep quality.
Time frame: Baseline and 8th day
Sleep efficiency
Wrist actigraphy: Wrist actigraphy was used to assess the objective sleep, which is frequently used to assess each person's level of physical activity and sleep quality in adults. Each participant in the study wore an actigraph (GT3X-Plus three-axis model, ActiGraph LLC, Pensacola, FL, USA) on their non-dominant wrist. The device was adjusted to 1-minute epochs and 30 Hz in the current study. Activity counts were analyzed using ActiLife software (version 6.13.6) based on the sleep diary entries, and the Cole-Kripke algorithm was used to score sleep on actigraphy data in order to predict bedtime and waking time. The following metric was extracted from the actigraphy-based data: sleep efficiency (SE).
Time frame: Baseline and 8th day
Wake after sleep onset (WASO)
Wrist actigraphy: Wrist actigraphy was used to assess the objective sleep, which is frequently used to assess each person's level of physical activity and sleep quality in adults. Each participant in the study wore an actigraph (GT3X-Plus three-axis model, ActiGraph LLC, Pensacola, FL, USA) on their non-dominant wrist. The device was adjusted to 1-minute epochs and 30 Hz in the current study. Activity counts were analyzed using ActiLife software (version 6.13.6) based on the sleep diary entries, and the Cole-Kripke algorithm was used to score sleep on actigraphy data in order to predict bedtime and waking time. The following metric was extracted from the actigraphy-based data: wake after sleep onset (WASO).
Time frame: Baseline and 8th day
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