Relationships Between the Usage of Televisions, Computers, and Mobile Phones and the Quality of Sleep in a Chinese Population: Community-Based Cross-Sectional Study.
ABSTRACT: BACKGROUND:No study has comprehensively investigated the association between the usage of typical screen-based electronic media devices and sleep quality in a Chinese population with individuals in a wide range of ages. OBJECTIVE:This study aimed to understand the characteristics of television (TV) viewing, computer usage, and mobile phone usage in a representative Chinese population in Macau and to examine their roles in predicting the variations in sleep quality. METHODS:This cross-sectional study was an analysis of 1500 Macau residents aged 15 to 90 years based on a community-based health needs assessment study entitled, "Healthy Living, Longer Lives." Data collection was conducted in 7 districts of Macau from 2017 to 2018 through face-to-face interviews. The durations of daily TV viewing, computer usage, and mobile phone usage were recorded in a self-administered questionnaire. The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality. RESULTS:The prevalence of TV, computer, and mobile phone usage was 78.4% (1176/1500), 51.6% (769/1490), and 85.5% (1276/1492), respectively. The average daily hours of usage were 1.75 (1.62), 1.53 (2.26), and 2.85 (2.47) hours, respectively. Females spent more time watching TV (P=.03) and using mobile phones (P=.02) and less time on the computer (P=.04) as compared to males. Older adults were more likely to watch TV while young people spent more time using the computer and mobile phones (P for all trends<.001). The mean PSQI global score was 4.79 (2.80) among the participants. Females exhibited significantly higher PSQI scores than males (5.04 vs 4.49, respectively; P<.001). No linear association was observed between the PSQI score and the amount of time spent on the 3 electronic devices (P=.58 for PSQI-TV, P=.05 for PSQI-computer, and P=.52 for PSQI-mobile phone). Curve estimation showed significant quadratic curvilinear associations in PSQI-TV (P=.003) and PSQI-computer (P<.001) among all the participants and in PSQI-mobile phone among youths (age, 15-24 years; P=.04). After adjustment of the gender, age, body mass index, demographics, and lifestyle factors, more than 3 hours of TV viewing and 4 hours of computer usage or mobile phone usage was associated with 85% (95% CI 1.04-1.87; P=.008), 72% (95% CI 1.01-2.92; P=.045), and 53% (95% CI 1.06-2.22; P=.03) greater odds of having poor sleep quality (PSQI score>5), respectively. CONCLUSIONS:The mobile phone was the most popular screen-based electronic device used in the Macau population, especially among young people. "J" shape associations were observed between sleep quality and the duration of TV viewing, computer usage, and mobile phone usage, indicating that the extreme use of screen-based electronic devices predicted poorer sleep status, whereas moderate use would be acceptable.
Project description:Background:Mobile phone addiction behaviors (MPAB) are extensively associated with several mental and sleep problems. Only a limited number of bidirectional longitudinal papers have focused on this field. This study aimed to examine the bidirectional associations of MPAB with mental distress, sleep disturbances, and sleep patterns. Methods:A total of 940 and 902 (response rate: 95.9%) students participated at baseline and one-year follow-up, respectively. Self-reported severity of mobile phone addiction was measured using Mobile Phone Involvement Questionnaire (MPIQ). Mental distress was evaluated by using Beck Depression Inventory (BDI) and Zung Self-Rating Anxiety Scale (SAS). Sleep disturbances were assessed by using Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS). Sleep patterns were evaluated by using reduced Morningness-Eveningness Questionnaire (rMEQ), weekday sleep duration, and weekend sleep duration. Results:Cross-lagged analyses revealed a higher total score of BDI, SAS, and ISI predicted a greater likelihood of subsequent MPAB, but not vice versa. We found the bidirectional longitudinal relationships between MPAB and the total score of PSQI and ESS. Besides, a higher score of MPIQ at baseline predicts a subsequent lower total score of rMEQ and shorter weekday sleep duration. Conclusions:The current study expands our understanding of causal relationships of MPAB with mental distress, sleep disturbances, and sleep patterns.
Project description:BACKGROUND:Our aim was to investigate the mediating role of worsening sleep quality in the association of the incidence of physical inactivity, high TV-viewing, and high computer/tablet use with loneliness, sadness, and anxiety. METHODS:Data of 45,161 Brazilian adults from a nationwide behavior survey, conducted between April 24th and May 24th (2020), were used. Participants reported physical inactivity (PI; <150 min/week), high TV-viewing (TV; ?4 h/day), and high computer/tablet use (PC; ?4 h/day) before and during COVID-19 quarantine (exposures). For incidence indicators, we only considered participants without the risk behavior before quarantine. Changes in sleep quality during the quarantine period (maintained/got better or worsened) were treated as a mediator. Elevated frequencies of feelings of loneliness, sadness (feel sad, crestfallen, or depressed), and anxiety (feel worried, anxious, or nervous) during the pandemic period were the study outcomes. Analyses were adjusted for sex, age group, highest academic achievement, working status during quarantine, skin color, previous diagnosis of depression, and adherence to quarantine. Mediation models were created using the Karlson Holm Breen method. RESULTS:The incidence of PI, high TV, and high PC use were associated with loneliness, sadness, and anxiety feelings. Worsening sleep quality partly mediated the association of the incidence of PI, high TV, and high PC use with loneliness (PI:30.9%; TV:19.6%; PC: 30.5%), sadness (PI:29.8%; TV:29.3%; PC: 39.1%), and anxiety (PI:21.9%; TV:30.0%; PC:38.5%). CONCLUSION:The association of the incidence of physical inactivity and sedentary behaviors with mental health indicators is partly mediated by worsening sleep quality during the COVID-19 pandemic quarantine.
Project description:BACKGROUND:This study aimed to assess the effects of restricting mobile phone use before bedtime on sleep, pre-sleep arousal, mood, and working memory. METHODS:Thirty-eight participants were randomized to either an intervention group (n = 19), where members were instructed to avoid using their mobile phone 30 minutes before bedtime, or a control group (n = 19), where the participants were given no such instructions. Sleep habit, sleep quality, pre-sleep arousal and mood were measured using the sleep diary, the Pittsburgh sleep quality index, the Pre-sleep Arousal Scale and the Positive and Negative Affect Schedule respectively. Working memory was tested by using the 0-,1-,2-back task (n-back task). RESULTS:Restricting mobile phone use before bedtime for four weeks was effective in reducing sleep latency, increasing sleep duration, improving sleep quality, reducing pre-sleep arousal, and improving positive affect and working memory. CONCLUSIONS:Restricting mobile phone use close to bedtime reduced sleep latency and pre-sleep arousal and increased sleep duration and working memory. This simple change to moderate usage was recommended to individuals with sleep disturbances.
Project description: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.
Project description:This cross-sectional study examined the associations of recreational screen time (viewing TV programs on any screen-based device and computer use, performed while sitting) with body mass index (BMI) categories and waist-to-height ratio (WHtR) tertiles in 10,228 children (mean age 11.1 years, SD 0.8). We categorized the children into Light, Medium and Heavy TV viewers and computer users, and into Low, Medium and High exercise groups. Compared with Light TV viewers, Medium (OR: 1.30, 95% CI: 1.11-1.52, when adjusted for age, sex, language, sleep duration and exercise) and Heavy (OR: 1.57, 95% CI: 1.34-1.83) TV viewers had a higher risk of being overweight. Similarly, Heavy computer users had a higher risk of being overweight (OR: 1.42, 95% CI: 1.21-1.67). We observed interactions between exercise and TV viewing (p?=?0.012) or computer use (p?=?0.010). However, Heavy TV viewers had a higher risk of being overweight in all exercise groups. The associations of TV viewing and computer use were similar with BMI and WHtR. To conclude, heavy sedentary screen time is associated with overweight and central adiposity in children. Moreover, heavy TV viewers have a higher risk for overweight and central adiposity, regardless of weekly exercise duration.
Project description:Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones. Subjects were given tri-weekly ecological momentary assessments (EMAs) on their own smartphones, and passive data including accelerometer, GPS, screen use, and anonymized call and text message logs was continuously collected. We compare the in-clinic assessment of sleep quality, assessed with the Pittsburgh Sleep Questionnaire Inventory (PSQI), to EMAs, as well as sleep estimates based on passively collected accelerometer data. EMAs and passive data classified 85% (11/13) of subjects as exhibiting high or low sleep quality compared to the in-clinic assessments among subjects who completed at least one in-person PSQI. Phone-based accelerometer data used to infer sleep duration was moderately correlated with subject self-assessment of sleep duration (r?=?0.69, 95% CI 0.23-0.90). Active and passive phone data predicts concurrent PSQI scores for all subjects with mean average error of 0.75 and future PSQI scores with a mean average error of 1.9, with scores ranging from 0-14. These results suggest sleep monitoring via personal smartphones is feasible for subjects with schizophrenia in a scalable and affordable manner.<h4>Patient monitoring</h4>SMARTPHONES CAN TRACK SCHIZOPHRENIA-RELATED SLEEP ABNORMALITIES: Smartphones may one-day offer accessible, clinically-useful insights into schizophrenia patients' sleep quality. Despite the clinical relevance of sleep to disease severity, monitoring technologies still evade convenience and reliability. In search of a preferential method, a group of Harvard University researchers led by Patrick Staples investigated the validity of data collected via patients' own mobile phones. The team, with a cohort of 17 schizophrenia patients, compared the quality of data produced by smartphone sensors and smartphone-delivered questionnaires to that of an in-clinic evaluation. The results significantly showed that smartphone monitoring could generate information that approached the accuracy of in-clinic assessments. The team noted some areas for improvement; however, this study provides convincing justifications for further research into this non-invasive, low-cost, scalable method to monitor the sleep quality of schizophrenic patients.
Project description:Background:Chinese college students are at high risk of sleep problems, and smartphone use is common among this population. However, the relationship between smartphone use characteristics and sleep problems in Chinese college students has been inadequately studied. In this preliminary study, we examined the association of poor sleep quality with smartphone use in a sample of Chinese college students from a health vocational college in Changsha, China. Methods:A total of 439 college students completed a self-report questionnaire containing the Pittsburgh Sleep Quality Index (PSQI) and questions regarding demographic information, psychosocial factors, physical health, smartphone use characteristics, and mobile phone addiction (MPA). Results:The results showed that the prevalence of poor sleep quality (PSQI > 7) in Chinese college students was 9.8%. In multiple logistic regression analysis, poor sleep quality was significantly associated with male gender (OR: 2.80, P: 0.022), not having good physical health (OR: 2.61, P: 0.020), headache (OR: 2.47, P: 0.014), more severe depressive symptoms (OR: 2.17, P: 0.049), > four years of smartphone use (OR: 3.38, P: 0.001), > five hours of daily smartphone use (OR: 2.19, P: 0.049), and more severe inability to control MPA craving (OR: 2.04, P: 0.040). Conclusion:Our findings suggest that excessive smartphone use and MPA are associated with poor sleep quality in a sample of Chinese college students from a health vocational college. Because of the limited sample representativeness and cross-sectional design of this study, large-scale prospective representative studies are warranted to confirm these associations.
Project description:We evaluated if exposure to RF-EMF was associated with reported quality of sleep in 2,361 children, aged 7 years.This study was embedded in the Amsterdam Born Children and their Development (ABCD) birth cohort study. When children were about five years old, school and residential exposure to RF-EMF from base stations was assessed with a geospatial model (NISMap) and from indoor sources (cordless phone/WiFi) using parental self-reports. Parents also reported their children's use of mobile or cordless phones. When children were seven years old, we evaluated sleep quality as measured with the Child Sleep Habits Questionnaire (CSHQ) filled in by parents. Of eight CSHQ subscales, we evaluated sleep onset delay, sleep duration, night wakenings, parasomnias and daytime sleepiness with logistic or negative binomial regression models, adjusting for child's age and sex and indicators of socio-economic position of the parents. We evaluated the remaining three subscales (bedtime resistance, sleep anxiety, sleep disordered breathing) as unrelated outcomes (negative control) because these were a priori hypothesised not to be associated with RF-EMF.Sleep onset delay, night wakenings, parasomnias and daytime sleepiness were not associated with residential exposure to RF-EMF from base stations. Sleep duration scores were associated with RF-EMF levels from base stations. Higher use mobile phones was associated with less favourable sleep duration, night wakenings and parasomnias, and also with bedtime resistance. Cordless phone use was not related to any of the sleeping scores.Given the different results across the evaluated RF-EMF exposure sources and the observed association between mobile phone use and the negative control sleep scale, our study does not support the hypothesis that it is the exposure to RF-EMF that is detrimental to sleep quality in 7-year old children, but potentially other factors that are related to mobile phone usage.
Project description:The present cross-sectional study examined the relations of bedtime mobile phone use to cognitive functioning, academic performance, and sleep quality in a sample of undergraduate students. Three hundred eighty-five undergraduate students completed a self-administered questionnaire containing sociodemographic variables, bedtime mobile phone use, the Pittsburgh Sleep Quality Index, and the Cambridge Neuropsychological Test Automated Battery (attention and verbal memory). At bivariate level, increased scores in bedtime mobile phone use were significantly correlated with decreased scores in academic performance and sleep quality. Our multivariate findings show that increased scores in bedtime mobile phone use uniquely predicted decreased scores in academic performance and sleep quality, while controlling for gender, age, and ethnicity. Further untangling the relations of bedtime mobile phone use to academic performance and sleep quality may prove complex. Future studies with longitudinal data are needed to examine the bidirectional effect that bedtime mobile phone use may have on academic performance and sleep quality.
Project description:<h4>Background</h4>Substance use disorder research and practice have not yet taken advantage of emerging changes in communication patterns. While internet and social media use is widespread in the general population, little is known about how these mediums are used in substance use disorder treatment.<h4>Objective</h4>The aims of this paper were to provide data on patients' with substance use disorders mobile phone ownership rates, usage patterns on multiple digital platforms (social media, internet, computer, and mobile apps), and their interest in the use of these platforms to monitor personal recovery.<h4>Methods</h4>We conducted a cross-sectional survey of patients in 4 intensive outpatient substance use disorder treatment facilities in Philadelphia, PA, USA. Logistic regressions were used to examine associations among variables.<h4>Results</h4>Survey participants (N=259) were mostly male (72.9%, 188/259), African American (62.9%, 163/259), with annual incomes less than US $10,000 (62.5%, 161/259), and averaged 39 (SD 12.24) years of age. The vast majority of participants (93.8%, 243/259) owned a mobile phone and about 64.1% (166/259) owned a mobile phone with app capabilities, of which 85.1% (207/243) accessed the internet mainly through their mobile phone. There were no significant differences in age, gender, ethnicity, or socio-economic status by computer usage, internet usage, number of times participants changed their phone, type of mobile phone contract, or whether participants had unlimited calling plans. The sample was grouped into 3 age groups (Millennials, Generation Xers, and Baby Boomers). The rates of having a social media account differed across these 3 age groups with significant differences between Baby Boomers and both Generation Xers and Millennials (P<.001 in each case). Among participants with a social media account (73.6%, 190/259), most (76.1%, 144/190) reported using it daily and nearly all (98.2%, 186/190) used Facebook. Nearly half of participants (47.4%, 90/190) reported viewing content on social media that triggered substance cravings and an equal percentage reported being exposed to recovery information on social media. There was a significant difference in rates of reporting viewing recovery information on social media across the 3 age groups with Baby Boomers reporting higher rates than Millennials (P<.001). The majority of respondents (70.1%, 181/259) said they would prefer to use a relapse prevention app on their phone or receive SMS (short message service) relapse prevention text messages (72.3%, 186/259), and nearly half (49.1%, 127/259) expressed an interest in receiving support by allowing social media accounts to be monitored as a relapse prevention technique.<h4>Conclusions</h4>To our knowledge, this is the first and largest study examining the online behavior and preferences regarding technology-based substance use disorder treatment interventions in a population of patients enrolled in community outpatient treatment programs. Patients were generally receptive to using relapse prevention apps and text messaging interventions and a substantial proportion supported social media surveillance tools. However, the design of technology-based interventions remains as many participants have monthly telephone plans which may limit continuity.