Dataset Information

Effects of Mobile Use on Subjective Sleep Quality.

ABSTRACT: Purpose:The objective of this study was to find out the association between mobile use and physiological parameters of poor sleep quality. It also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) and their effects on sleep in mobile users. Materials and Methods:This cross-sectional study was conducted on 1925 students (aged 17-23yrs) from multiple Colleges of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The study tools used were Pittsburgh sleep quality index (PSQI) and MRSRF online questionnaires. Results:The mean age (±SD) of participants was 19.91 ± 2.55 years. Average mobile screen usage time was 8.57±4.59/24 hours, whereas average mobile screen usage time in the bed after the lights have been turned off was 38.17±11.7 minutes. Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. "Screen usage time of ?8 hours" was positively correlated with sleep disturbances and decrease in the length of actual sleeping time (p =0.023 and 0.022). "Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off" showed positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). "Keeping the mobile near the pillow while sleeping" was also positively correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001). Conclusion:This study concludes that using mobile screen ?8 hours/24 hours, using the mobile for at least 30 minutes before sleeping after the lights have been turned off and keeping the mobile near the pillow are positively associated with poor sleep quality. Moreover, we observed that MRSRF were highly prevalent amongst the mobile users.

SUBMITTER: Rafique N

PROVIDER: S-EPMC7320888 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Effects of Mobile Use on Subjective Sleep Quality.

Rafique Nazish N Al-Asoom Lubna Ibrahim LI Alsunni Ahmed Abdulrahman AA Saudagar Farhat Nadeem FN Almulhim Latifah L Alkaltham Gaeda G

Nature and science of sleep 20200623

<h4>Purpose</h4>The objective of this study was to find out the association between mobile use and physiological parameters of poor sleep quality. It also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) and their effects on sleep in mobile users.<h4>Materials and methods</h4>This cross-sectional study was conducted on 1925 students (aged 17-23yrs) from multiple Colleges of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The study tools used were Pittsb ...[more]

PMID: 32607035

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Project description:BackgroundSleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications.ObjectiveOur goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model.MethodsWe used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting.ResultsMost models (on either of the outcome variables) were not informative on the validation set (ie, predicted R2≤0). However, our best models achieved R2 values of 0.658, 0.779, and 0.074 for SSQ, NA, and depression, respectively on the training set and R2 values of 0.348, 0.103, and 0.025, respectively on the test set.ConclusionsThe approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic.

Project description:Background and objectivesSleep disturbance is often associated with migraine. However, there is a paucity of research investigating objective and subjective measures of sleep in patients with migraine. This meta-analysis aims to determine whether there are differences in subjective sleep quality measured using the Pittsburgh Sleep Quality Index (PSQI) and objective sleep architecture measured using polysomnography (PSG) between adult and pediatric patients and healthy controls.MethodsThis review was preregistered on PROSPERO (CRD42020209325). A systematic search of 5 databases (Embase, MEDLINE, Global Health, APA PsycINFO, and APA PsycArticles, last searched on December 17, 2020) was conducted to find case-control studies that measured PSG or PSQI in patients with migraine. Pregnant participants and those with other headache disorders were excluded. Effect sizes (Hedges g) were entered into a random effects model meta-analysis. Study quality was evaluated with the Newcastle Ottawa Scale and publication bias with the Egger regression test.ResultsThirty-two studies were eligible, of which 21 measured PSQI or Migraine Disability Assessment Test in adults, 6 measured PSG in adults, and 5 measured PSG in children. The overall mean study quality score was 5/9; this did not moderate any of the results and there was no risk of publication bias. Overall, adults with migraine had higher PSQI scores than healthy controls (g = 0.75, p < 0.001, 95% confidence interval [CI] 0.54-0.96). This effect was larger in those with a chronic rather than episodic condition (g = 1.03, p < 0.001, 95% CI 0.37-1.01; g = 0.63, p < 0.001, 95% CI 0.38-0.88, respectively). For polysomnographic studies, adults and children with migraine displayed a lower percentage of rapid eye movement sleep (g = -0.22, p = 0.017, 95% CI -0.41 to -0.04; g = -0.71, p = 0.025, 95% CI -1.34 to -0.10, respectively) than controls. Pediatric patients displayed less total sleep time (g = -1.37, p = 0.039, 95% CI -2.66 to -0.10), more wake (g = 0.52, p < 0.001, 95% CI 0.08-0.79), and shorter sleep onset latency (g = -0.37, p < 0.001, 95% CI -0.54 to -0.21) than controls.DiscussionPeople with migraine have significantly poorer subjective sleep quality and altered sleep architecture compared to healthy individuals. Further longitudinal empirical studies are required to enhance our understanding of this relationship.

Dataset Information

Effects of Mobile Use on Subjective Sleep Quality.

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Effects of Mobile Use on Subjective Sleep Quality.

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