Use of handheld smart devices such as smartphones and tablet computers is prevalent globally. The smartphone ownership rate has been increasing rapidly in recent years [1
]. In The Netherlands, the rate is around 70% in the general population and over 90% in adolescents [2
]. In Switzerland, the rate in adolescents increased from around 50% to nearly 80% from 2010 to 2012 [3
]. In Germany, the rate among adolescents increased from around 25% to over 70% from 2011 to 2013 [4
]. In the United States, the rate in the general population increased from 35% to 56% from 2011 to 2013 [5
]. More than 60% of families with young children own a smartphone, and around 40% of them own tablet computers [6
]. In Asia, the smartphone ownership rate among adolescents is around 85% in South Korea, around 65% in Japan and the Philippines, over 55% in Malaysia and Hong Kong, and over 40% in China [7
]. It is comparable to the smartphone ownership rate of nearly 50% among adolescents in the United States [8
]. Nearly three-quarters of the U.S. teens have or have access to a smartphone [9
A vast majority of adolescents in Hong Kong are smart device users. Over half of primary school students and over 90% of secondary school students possess smartphones [10
]. Smart devices including smartphones and tablet computers are defined as handheld mobile electronic devices with cell-phone capability, having a browser that allows Internet access, a licensed operating system that provides a platform for third-party applications such as multimedia software and games, a touch screen input and output, and wireless connections that allow data transfer [11
Media consumption via smart devices among Hong Kong adolescents may be excessive. The American Academy of Pediatrics [14
] recommended restricting all children’s media time to a maximum of two hours per day, and this guideline has been adopted internationally to restrict all screen-based leisure activities using computers, electronic gaming devices and mobile phones among children [14
]. In Hong Kong, over 80% of school students were regular users of smartphones, and nearly 30% of them used their smartphones for at least four hours every day [17
]. Frequent and prolonged use of smart devices may increase risks of negative physical and psychosocial outcomes. These outcomes cause concern to parents, teachers and the government [10
Electronic screen-based activities have been found to be related to shorter sleep duration, lower sleep quality and daytime sleepiness among adolescents. Van den Bulck [18
] conducted a survey on 2546 Belgian adolescents with mean age 13.16 (SD = 0.43) and found that time spent on computer games was significantly associated with less time in bed at night. Dworak et al. [19
] studied 11 German adolescents with mean age 13.45 (SD = 1.04) and found that computer gaming for 1 h before bedtime was associated with longer sleep onset latency, more stage two sleep and less slow wave sleep. Weaver et al. [20
] studied 13 Australian adolescents with mean age 16.6 (SD = 1.1) and found that pre-sleep video gaming for 50 min was associated with longer sleep onset latency, reduced subjective sleepiness and change in alertness. Munezawa et al. [21
] conducted a survey on 94,777 Japanese adolescents in grades 7 to 12 and found that the percentage of respondents who often or always felt excessive daytime sleepiness was 46%; the adjusted odds of sleep for less than 6 h per night, poor sleep quality and excessive daytime sleepiness related to daily mobile phone messaging after lights out were 1.15, 1.27 and 1.50 respectively. Arora et al. [22
] conducted a survey on 632 UK adolescents aged 11–18 and found that mobile phone use at bedtime had a significant and negative direct effect on weekday sleep duration in path analysis. In Hong Kong, around 50% of school students always or occasionally had sleep depletion related to online activities [10
Use of a handheld electronic device was found to be related to physical discomfort. About 50% of Hong Kong primary school students showed symptoms of unclear vision and felt eye strain related to the use of portable electronic devices [23
]. Lui et al. [24
] conducted a survey on 464 Hong Kong primary schoolchildren aged 8–13 and found that nearly 30% of the respondents reported having bodily discomfort related to electronic gaming in one month. The exposure to handheld electronic games was significantly correlated with the incidence of bodily discomfort among the schoolchildren. Two hours’ daily use of handheld devices was significantly associated with increased risk of musculoskeletal discomfort among them. Shan et al. [25
] conducted a survey on 3016 Chinese adolescents aged 15–19 and found that the odds of neck or shoulder pain related to mobile phone use for over 2 h per day on average was 1.49, and the odds of low back pain related to mobile phone and tablet computer use were over 1.83.
Excessive electronic screen-based activities were found to be associated with poorer parent child relationships. Willoughby [26
] conducted a survey on 1591 Canadian adolescents studying in grades 9–12 and found that time spent on computer game use was weakly associated with parental relationships. Punamäki at al. [27
] conducted a survey on 478 grade 4 and 7 Finnish adolescents, and found that intensive digital game playing and Internet surfing were associated with poor relations with parents. Kwon et al. [28
] conducted a survey on 1136 South Korean adolescents with mean age 14.01 (SD = 0.51) and found that Internet game addiction was significantly and positively correlated with escapism and perceived hostility in parent-child relationships. Coyne et al. [29
] conducted a survey on 491 U.S. adolescents aged 12–17 and found that overall time spent on social networking was negatively correlated with the connection with parents and positively correlated with relationship aggression and delinquency. In Hong Kong, over 60% of parents had always or occasionally quarreled with their children related to use of the Internet or electronic screen products [10
Online social activities were found to be related to increased risk of cyberbullying victimization. Bossler et al. [30
] found that the frequency of posting sensitive information online, association with peers who harass online and maintaining social network sites significantly predicted online harassment victimization among adolescents. Dredge et al. [31
] stated that self-presentation on social networking sites can increase the likelihood of eliciting negative attention from potential perpetrators. Görzig and Frumkin [32
] conducted a survey on 25,142 adolescents aged 9–16 in 25 European countries and found that the odds of cyberbullying victimization on mobile phones among those who went online by using a portable handheld device and were bullied on a social networking site and on instant messaging were 1.67, 1.48 and 1.91 respectively. However, the correlation between the percentage of victimization on handheld devices among all cyberbullying victims and the percentage of handheld device use for online activities within each country was not significant. Ortega et al. [33
] conducted a survey on 2227 English, 1964 Italian and 1627 Spanish adolescents with mean age 14.20 (SD = 1.77) and found that 4.1%, 9.5% and 4.2% of the respondents, respectively, reported being victimized occasionally or more frequently in cases of mobile phone cyberbullying in 2 months. Wong et al. [34
] conducted a survey of 1917 Hong Kong adolescents aged 12–15 and found that 23% of the respondents reported being a victim of cyberbullying in one month. However, the percentage of victimization among Hong Kong adolescents on smart devices is unknown.
Smart devices can be used for purposes of either academic study or leisure among adolescents. Smart device use can facilitate inquiry-based learning, such as participating in online discussions, which benefits adolescents in terms of their competence in information technology and literacy, inquiry skills, self-efficacy and critical thinking [35
]. On the other hand, they may use smart devices for leisure activities such as Internet surfing, social networking, messaging, gaming and watching videos [7
]. Excessive smart device use should be controlled, particularly if its use for leisure is more prevalent. However, there is limited evidence on the prevalence of smart device use for the purposes of academic study or leisure, or on the differences in patterns of smart device activities within a specific timeframe between adolescents who did and did not perceive the outcomes.
The aim of this study was to investigate the prevalence and patterns of smart device activities and purposes and perceived outcomes related to smart device use, and the differences in patterns of smart device activities between adolescents who did and did not perceive the outcomes. The objectives were as follows:
To investigate the prevalence, frequency and time spent on smart device activities, overall use and whether the purpose was academic study or leisure.
To examine the prevalence and frequency of perceived outcomes related to smart device use.
To study the differences in frequency and time spent on smart device activities between adolescents who did and those who did not perceive these outcomes.
The frequency of negative outcomes related to smart device use among adolescents might be reduced if significant relationships between smart device activities and perceived outcomes can be identified, therefore health interventions could be focused on problematic behaviors in order to reduce the risk of negative outcomes.
2.1. Study Design
The study design was a cross-sectional survey. It was a non-experimental design to collect data for studying the prevalences of variables and group differences in variables without manipulating the respondents. Demographics, behavioral and outcome variables were measured in the form of a questionnaire printed in traditional Chinese.
2.2. Sampling and Recruitment Procedure
Convenience sampling was adopted to recruit schools and their students. The inclusion criteria for schools were (1) being registered with the Hong Kong Education Bureau [39
]; (2) providing education at either primary or secondary level; and (3) being a non-special school. Registered schools in Hong Kong are supposed to follow the curriculum and assessment guidelines proposed by the Hong Kong Education Bureau [40
]. The recruited schools were assumed to be representative of Hong Kong primary and secondary schools in general. The inclusion criteria for adolescents were (1) being aged 10 to 19; (2) having experience in using smart devices; and (3) ability to complete the questionnaire without assistance.
Schools were recruited via direct contact. Contact information was obtained from the school websites. School principals were invited to consent to participate in the study on behalf of the school. The consent form and information sheet were sent to principals via email. Signed consent forms were returned to the researcher before data collection. During class time at school, students were informed that those aged 10 or above were being invited to participate in the study. The consent form and information sheet were delivered to parents by their invited children. Having been informed of the study aim, giving consent to participate in this study implied that a participant had experience of smart device use in his/her lifetime. On the next day, students who returned consent forms signed by their parents were recruited.
2.3. Data Collection
The data collection period was from July to October 2015. Questionnaires were prepared and delivered to schools by the researcher. In each school, the principal appointed a teacher or a vice principal to coordinate the study logistics. Class teachers were briefed on the aim, procedure and ethical issues of the study. They delivered the questionnaires to students who had returned consent forms and agreed to participate in the study. Students were asked to read the instructions on the questionnaire, put ticks in the box indicating their answers to the items, and fill in the blanks, date of completion, class name and class number on the questionnaire. Completed questionnaires were submitted to class teachers before or at the end of the school day. Class teachers placed the questionnaires in envelopes, class by class, and returned them to the researcher after school. In the study, 1690 students were invited in three primary schools and two secondary schools. 1494 of the students and their parents consented to participate in the survey. 1418 students completed and returned their questionnaires. The response rate was 83.9%.
2.4. Ethical Considerations and Confidentiality
The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Human Subjects Ethics Sub-committee of the Hong Kong Polytechnic University. The Reference Number was HSEARS20150629002. Permissions to conduct the study were obtained from the management committees of the recruited schools. School principals, students and parents were given consent forms and information sheets outlining the study aim, and the significance and procedure of data collection.
The study aim, the voluntary nature of participation in the study, and participants’ right to withdraw from the study without penalty were stated in the information sheet and verbally explained to respondents before data collection. Confidentiality was ensured throughout the study. No school or student names were required on the questionnaire. Demographic information was solicited on the inside page. School staff were instructed not to read the answers on the questionnaires. Only the researchers have reviewed and analyzed the raw data. Data were not used for any other purpose than the study. Hard copies of the data were stored in a secure place, and soft copies of the data were stored in encrypted memory, both of which were only accessible to the researcher.
2.5. Operational Definition of Terms
Adolescents are young people aged between 10 and 19 [41
Behavioral variables were the patterns of smart device activities, overall use, and the purpose of use for either academic study or leisure. Patterns were the frequencies and time spent on the activities, overall use and its purposes. Smart device activities were messaging [42
], browsing information [45
], gaming [1
], watching TV/movies [1
] and posting information [56
] conducted on a smartphone or tablet computer.
Outcome variables were frequencies of the outcomes related to smart device use. The outcomes were sleep deprivation [1
], eye discomfort [23
], musculoskeletal discomfort [17
], family conflict [10
] and cyberbullying victimization [10
] identified in the literature.
2.6. Demographic Measures
Age was measured using an open-ended question and rounded up to years. Gender was measured on a nominal scale of “male” and “female”. Grade was measured on an ordinal scale of “primary 4”, “primary 5”, “primary 6”, “secondary 1”, “secondary 2”, “secondary 3”, “secondary 4”, “secondary 5” and “secondary 6”, which are equal to grades 4 to 12 in the U.S. system respectively. Family monthly income was measured on an ordinal scale of “HKD 1–4000”, “HKD 4001–8000”, “HKD 8001–12,000”, “HKD 12,001–16,000”, “HKD 16,001–20,000”, “HKD 20,001–26,000”, “HKD 26,001–33,000”, “HKD 33,001–43,000”, “HKD 43,001–65,000”, and “over HKD 65,000”, which were the 10 percentiles of Hong Kong family monthly income [66
]. One U.S. dollar is approximately equal to 7.75 Hong Kong dollars (Appendix A
2.7. Measure Frequency
The number of days on which a smart device activity was conducted, the device was used overall or for study or leisure, or an outcome was experienced in the latest week before the survey was measured on a 5-point ordinal scale with 0 = None, 1 = 1–2 days, 2 = 3–4 days, 3 = 5–6 days and 4 = Every day. In previous studies, scales such as “Yes, No” [24
] and “No or hardly ever, 1 or 2 times weekly, 3 or 4 times weekly, Most days” [67
] were used to measure frequency of outcome in the latest month before the survey. Scales such as “>1 time per day, 1–6 times per week, >4 times per month” [24
], “Never, Once a week or less, Twice a week, 3–4 times a week, 5–7 times a week” [1
], and “None, 1–3 times/month, Once/week, Several times/week, Every day” [21
] were used to measure the frequency of an activity or the device use. Recalling monthly events may involve a higher risk of bias than recalling weekly events. Options without specific and regular intervals may not allow meaningful comparisons. Therefore, recalling the frequency of events in the latest week before the survey may minimize recall bias. Relatively regular intervals in a scale may improve comparison between options as well as between respondents.
2.8. Measure Time Spent
Average time spent on a smart device activity or the device use per day on which the activity was conducted or the device was used in the week prior to the survey was measured on a 6-point ordinal scale with 1 = None, 2 = 1–60 min, 3 = 61–120 min, 4 = 121–180 min, 5 = 181–240 min and 6 = over 240 min. In previous studies, scales such as “>4 h, 2–4 h, 1–2 h, 0.5–1 h, <0.5 h” [24
] and “None, <1 h, ≥1 and <2 h, ≥2 h” [21
] were used to measure the time spent on device use. However, options in a scale should not overlap. To minimize the time gap between consecutive options, the options were set in terms of minutes. A significant number of Hong Kong children and adolescents use a smart device for more than 4 h per day [17
]. The upper bound of the scale was set at more than 240 min, which is 4 h. The durations of the outcomes were not measured because it was not practical to measure and compare them objectively among respondents.
2.9. Data Analysis
SPSS Statistics 21.0 (IBM, Armonk, NY, USA) was used to:
generate descriptive statistics of the demographics, the patterns of smart device activities, the overall use and its purposes, and the frequencies of the outcomes.
We conducted an independent sample t test to compare the mean age between genders, and a chi-square test to compare categories of grades and percentiles of income between genders.
We conducted a Mann-Whitney U test to compare the mean ranks of patterns of smart device activities between adolescents who did and did not perceive the outcomes.
We conducted complex sample binary logistic regression to examine the likelihood of the outcomes related to smart device activities, the overall use and its purposes.
Invalid responses such as missing data or more than one choice made on an item were not input into the software. System-missing values were managed by pairwise deletion in data analysis. Respondents who failed to report age or gender that met the inclusion criteria, or who responded that the pattern of smart device use overall was less than the pattern of any smart device activity or purpose of device use, were excluded from analysis. A final sample of 960 respondents proceeded to data analysis.
The Mann-Whitney U test was also adopted to study the relationships involving ordinal data. The significance level was set at 0.01 to reduce the risk of type I error, therefore test results with p < 0.01 were considered significant.
SPSS Complex Sample was used to address the clustering effect of schools. Primary schools and secondary schools were analyzed separately. There was no probability proportional to size (PPS) sampling because convenience sampling was adopted to recruit clusters and respondents. Therefore equal probability sampling was assumed.
There were only three out of 528 local primary schools [68
] and two out of 476 local secondary schools [69
] being sampled which were less than 5% of the school populations. Still, there was finite population correction (FPC) in estimation for sampling without replacement (WOR). Population sizes of strata including gender in each grade varied. Sample weights of strata were calculated according to the census data [70
There were no obvious normal distributions of responses in histograms and Q-Q plots. Regarding items measuring the purposes, activities or outcomes, the percentages of univariate outliers ranged between 2.5% and 22.1% in 12 items and between 5% and 22.2% in seven items respectively in the included primary school students and secondary school students.
The smart device activities, the overall use and its purposes were measured at ordinal level. In the test of parallel lines, there were either zero −2 log likelihood in the general model or no convergence of logit models. The proportional odds assumption was not fulfilled.
Therefore categories denoting number of days other than none of the days were collapsed into a single category in a dichotomous scale measuring an outcome. Binary logistic regression was used to examine the likelihood of the outcomes related to smart device use. Adjusted chi-squared tests of model effects were adopted to analyze clustered data [71
]. Sequential Bonferroni method was used to adjust for p
value because it is considered more powerful than traditional Bonferroni method [72
]. The significance level was 0.05.
Demographics having associations with outcome variables by Spearman’s rho ≥0.2 were identified as covariates. The criteria of multicollinearity among purposes, activities and overall use of smart device were variance inflation factor >10 and bivariate Spearman’s rho ≥0.7 [73
Purposes, activities and overall use of smart device having significant associations with outcome variables in Spearman’s correlation were included in a regression model when they met the criterion of variance inflation factor ≤10 with covariates controlled; had stronger associations with an outcome among those showing multicollinearity with Spearman’s rho ≥0.7; and contributed to a model with the least number of variables showing quasi-complete separations.
Cases were identified as outliers and excluded from analysis when the absolute values of standardized residuals were larger than 2.5 with covariates controlled in binary logistic regression [74
]. Linearities between independent variables and log odds were checked with Box-Tidewell method [75
]. Variables with significant interactions with their natural logs in the models were excluded from analysis.
Among primary school students, frequencies and time spent on activities, frequencies of leisure and overall use of smart device, time spent on leisure and overall use, and time spent on leisure use and gaming showed multicollinearity with Spearman’s rho ≥0.7. Only grade was identified as covariate of cyberbullying victimization, however, none of the purposes, activities or overall use of smart device showed significant association with the victimization. Two and three outlying cases in the regression models of musculoskeletal discomfort and family conflict were excluded from analysis respectively. Time spent on study use and frequency of gaming were excluded from the models of eye discomfort and family conflict respectively because of non-linearity.
Among secondary school students, frequencies and time spent on watching TV/movie, posting and gaming, and time spent on leisure and overall use of smart device showed multicollinearity with Spearman’s rho ≥0.7. Age and grade were identified as covariates of sleep deprivation and were controlled in the regression model. Seven and thirty-three outlying cases in the models of family conflict and cyberbullying victimization were excluded from analysis respectively. Frequency of browsing was excluded from the model of cyberbullying victimization because of quasi-complete separation.
3.1. Demographic Characteristics
There were 960 respondents in the final sample (Table 1
). Ages ranged from 10 to 19, the mean was 13.80 and the standard deviation (SD) was 1.93. In the sample, 52.6% were male and 47.4% were female. According to the Hong Kong Census and Statistics Department [66
], the estimated Hong Kong male to female ratio was 1.06 for both age groups of 10–14 and 15–19 in 2015. In the sample of this study, the male to female ratio was 1.11, which was close to the estimation. Respectively, 4.7%, 8%, 16%, 14.4%, 17.2%, 20.4%, 15.2% and 4.1% of respondents were studying in grades 5 to 12. Ages and grades roughly followed normal distribution in histograms. There were 650 respondents who reported family monthly incomes and 5.4%, 4.2%, 9.1%, 10.3%, 5.8%, 9.4%, 11.1%, 10.8%, 16.2% and 17.8% of them belonged to the 1st to 10th percentiles respectively, while over 50% of them were in the highest four percentiles. There were no significant differences between genders in age, grade or family monthly income.
3.2. Patterns of Smart Device Activities, Overall Use and Its Purposes
Patterns of smart device activities, overall use and its purposes were measured in terms of days in a week and minutes per day on average. Among the respondents, 0.6%, 2.3%, 4.9%, 6.3% and 85.9% of them used a smart device overall for none of the days to every day respectively, and 0.6%, 11.3%, 23.6%, 20.2%, 14.5% and 29.8% of them spent no time to over 240 min per day respectively on smart device use overall (Table 2
). Nearly 65% of the sample used a smart device for more than 2 h per day overall. Over 80% of the sample used a smart device for leisure on at least 5 days in a week, and nearly 50% spent more than 2 h per day using a smart device for leisure. Over 35% the respondents used a smart device for study on at least 5 days in a week, and nearly 15% spent more than 2 h per day using a smart device for study. Over 65% of the respondents used messaging and nearly 50% browsed information and played games on at least 5 days in a week respectively. Nearly 35% of the sample watched TV/movies and 25% posted information on at least 5 days in a week. Around 25% of the respondents used messaging, browsed information, played games and watched TV/movies for more than 2 h per day respectively. Nearly 15% of the respondents posted information for over 2 h per day.
There were significantly higher percentages of those who use smart device for more than 2 days per week overall, for study, leisure, posting and particularly messaging and browsing in secondary school students than the percentages in primary school students (Table 3
There were significantly higher percentages of those who spent more than 2 h per day on smart device use overall, for study, leisure, posting and particularly messaging and browsing in secondary school students than the percentages in primary school students (Table 4
3.3. One-Week Prevalence of Perceived Outcomes Related to Smart Device Use
The five outcomes measured in relation to smart device use were sleep deprivation, eye discomfort, musculoskeletal discomfort, family conflict and cyberbullying victimization. The number of days on which an outcome was experienced in the week prior to the survey was measured. Nearly 49%, 46%, 37%, 21% and 5% of the respondents experienced sleep deprivation, eye discomfort, musculoskeletal discomfort, family conflict and cyberbullying victimization respectively on at least one day each week (Table 5
). Nearly 27%, 14%, 14%, 7% and 3% of the respondents experienced sleep deprivation, eye discomfort, musculoskeletal discomfort, family conflict and cyberbullying victimization respectively on at least 3 days in a week. More adolescent smart device users perceived physical outcomes than psychosocial outcomes such as family conflict and cyberbullying victimization.
3.4. Differences in Patterns of Smart Device Activities Between Adolescents Who Did and Did Not Perceive The Outcomes
The Mann-Whitney U test was used to analyze the differences in patterns of smart device activities between adolescents who did and did not perceive the outcomes (Table 6
). The patterns were measured by number of days in a week and minutes per day on average. Among the significant results, the mean ranks of the frequencies or time spent on smart device activities among adolescents who perceived the outcomes were higher than for those who did not.
Adolescents who perceived sleep deprivation, family conflict or cyberbullying victimization spent significantly more time per day on messaging (z = −2.71 to −4.40), browsing information (z = −3.73 to −5.65), gaming (z = −3.37 to −3.82) and watching TV/movies (z = −3.30 to −5.81), with larger medians than those who did not perceive these outcomes. Those who perceived cyberbullying victimization also spent significantly more time per day on posting information (z = −4.04).
Adolescents who perceived eye and musculoskeletal discomfort spent significantly more time per day on browsing information (z = −3.52 to −5.96) and watching TV/movies (z = −2.77 to −5.92) with larger medians than those who did not perceive these outcomes. Those who perceived musculoskeletal discomfort also spent significantly more time per day on messaging (z = −4.36).
Adolescents who perceived musculoskeletal discomfort and sleep deprivation browsed information (z = −4.00 to −4.13) and watched TV/movies (z = −3.69 to −4.17) on significantly more days in a week and with larger medians than those who did not perceive these outcomes.
Adolescents who perceived family conflict and cyberbullying victimization spent time on gaming (z = −2.81 to −3.85) on significantly more days in a week and with larger medians than those who did not perceive these outcomes. Adolescents who perceived family conflict spent time gaming (z = −3.85) and watching TV/movies (z = −3.27) on significantly more days in a week and with larger medians than those who did not perceive the outcome. Adolescents who perceived cyberbullying victimization spent time on browsing (z = −3.09) and posting information (z = −3.68) significantly more days in a week and with larger medians than those who did not perceive the outcome.
3.5. Complex Sample Binary Logistic Regression Estimating Likelihood of Outcomes of Smart Device Use
Estimates of binary logistic regression in a complex sample of primary school students were shown in Table 7
. More time spent on smart device use overall was significantly associated with higher odds of family conflict (OR
= 3.40, 95% CI (1.07, 10.82), p
= 0.02). Higher frequency of study use was significantly related to lower odds of eye discomfort (OR
= 0.65, 95% CI (0.48, 0.88), p
= 0.001) and family conflict (OR
= 0.37, 95% CI (0.12, 1.12), p
= 0.04) respectively. More time spent on gaming was significantly related to lower odds of musculoskeletal discomfort (OR
= 0.48, 95% CI (0.32, 0.72), p
Estimates of binary logistic regression in a complex sample of secondary school students were shown in Table 8
. More time spent on smart device use overall was significantly associated with higher odds of musculoskeletal discomfort (OR
= 1.15, 95% CI (0.99, 1.34), p
= 0.03) and sleep deprivation (OR
= 1.14, 95% CI (1.00, 1.29), p
= 0.02) respectively. Higher frequency of leisure use was significantly associated with higher odds of family conflict (OR
= 1.31, 95% CI (1.06, 1.61), p
= 0.004) and sleep deprivation (OR
= 1.29, 95% CI (1.01, 1.66), p
= 0.02) respectively.
More time spent on watching TV/movie was significantly associated with higher odds of musculoskeletal discomfort (OR = 1.29, 95% CI (1.17, 1.41), p < 0.001), eye discomfort (OR = 1.09, 95% CI (1.01, 1.18), p = 0.01), sleep deprivation (OR = 1.28, 95% CI (1.13, 1.46), p < 0.001) and family conflict (OR = 1.24, 95% CI (1.08, 1.43), p < 0.001) respectively.
More time spent on browsing was significantly associated with higher odds of musculoskeletal discomfort (OR = 1.36, 95% CI (1.12, 1.64), p < 0.001), eye discomfort (OR = 1.20, 95% CI (1.04, 1.38), p = 0.004) and family conflict (OR = 1.22, 95% CI (1.00, 1.50), p = 0.03) respectively.
More time spent on posting was significantly associated with higher odds of cyberbullying victimization (OR = 2.43, 95% CI (0.91, 6.48), p = 0.04). On the other hand, more time spent on messaging was significantly related to lower odds of eye discomfort (OR = 0.88, 95% CI (0.80, 0.97), p = 0.002) and family conflict (OR = 0.85, 95% CI (0.75, 0.97), p = 0.01) respectively. More time spent on gaming was significantly related to lower odds of musculoskeletal discomfort (OR = 0.83, 95% CI (0.73, 0.94), p = 0.001).
Age was controlled in the model of sleep deprivation in the complex sample of secondary school students. Older age was significantly associated with higher odds of sleep deprivation (OR = 1.23, 95% CI (1.11, 1.35), p < 0.001).