Sleep quality is negatively related to video gaming volume in adults

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1 J Sleep Res. (2015) 24, Adults and video games Sleep quality is negatively related to video gaming volume in adults LIESE EXELMANS and JAN VAN DEN BULCK School for Mass Communication Research, KU Leuven, Belgium Keywords video games, PSQI, sleep latency, sleep efficiency Correspondence Liese Exelmans, School for Mass Communication Research, Parkstraat 45, 3000 Leuven, Belgium. Tel.: ; fax: ; Accepted in revised form 21 September 2014; received 3 May 2014 DOI: /jsr SUMMARY Most literature on the relationship between video gaming and sleep disturbances has looked at children and adolescents. There is little research on such a relationship in adult samples. The aim of the current study was to investigate the association of video game volume with sleep quality in adults via face-to-face interviews using standardized questionnaires. Adults (n = 844, 56.2% women), aged years old, participated in the study. Sleep quality was measured using the Pittsburgh Sleep Quality Index, and gaming volume was assessed by asking the hours of gaming on a regular weekday (Mon Thurs), Friday and weekend day (Sat Sun). Adjusting for gender, age, educational level, exercise and perceived stress, results of hierarchical regression analyses indicated that video gaming volume was a significant predictor of sleep quality (b = 0.145), fatigue (b = 0.109), insomnia (b = 0.120), bedtime (b = 0.100) and rise time (b = 0.168). Each additional hour of video gaming per day delayed bedtime by 6.9 min (95% confidence interval min) and rise time by 13.8 min (95% confidence interval min). Attributable risk for having poor sleep quality (Pittsburgh Sleep Quality Index > 5) due to gaming >1 h day was 30%. When examining the components of the Pittsburgh Sleep Quality Index using multinomial regression analysis (odds ratios with 95% confidence intervals), gaming volume significantly predicted sleep latency, sleep efficiency and use of sleep medication. In general, findings support the conclusion that gaming volume is negatively related to the overall sleep quality of adults, which might be due to underlying mechanisms of screen exposure and arousal. INTRODUCTION Media use is being increasingly labelled as a significant risk factor contributing to sleep difficulties (Cain and Gradisar, 2010). A relationship between sleep problems and media use has been established for television (Paavonen et al., 2006; Van den Bulck, 2004b), mobile phones (Nathan and Zeitzer, 2013; Van den Bulck, 2007) and internet use (Custers and Van den Bulck, 2012; Shochat et al., 2010). A growing body of literature has investigated the effects of video gaming on sleep quality: frequent video game playing before bedtime or at night has been linked to later bedtimes, a shorter sleep duration, increased sleep-onset latency and more daytime tiredness (Adam et al., 2007; Eggermont and Van den Bulck, 2006; King et al., 2013; Van den Bulck, 2004b). However, most studies to date have focused on children and adolescents rather than adults. Although video gaming is a popular pastime among adolescents (Rideout et al., 2010), previous research has identified a substantial proportion of video gamers among adults too (Lenhart et al., 2008). Indeed, gamers are found to be between 30 and 35 years old on average (Lenhart et al., 2008; Williams et al., 2008). Over half of American adults aged 18 years and older played video games, and about one in five (21%) of them played almost every day. Almost half (45.1%) of the respondents (n = 562) in a study by Weaver et al. (2009) identified themselves as gamers. In Belgium, where the current study was conducted, one in four adults were playing video games on a weekly basis (IPSOS MediaCT, 2012). While it has been found that younger adults are significantly more likely to play video games compared with older adults, studies have found that older gamers tend play more frequently. Lenhart et al. 189

2 190 L. Exelmans and J. Van den Bulck (2008) found that one-third of the gamers aged 65 years and older played almost every day, compared with one in five for the younger group. In sum, the main goal of the current study is to explore the relationship between video gaming and sleep quality in an adult sample. MATERIALS AND METHODS Design A sample of 844 adults in Flanders, Belgium was queried about media habits and sleep in face-to-face interviews. After having received standardized interviewer training, the effects of which on improving interview quality have been reported (Loosveldt and Billiet, 1988), 44 undergraduate students of Communication Sciences conducted face-to-face interviews by means of a standardized questionnaire. A two-step sampling method was used. First, 44 cities or villages in Flanders (Belgium) were randomly selected, one for each interviewer. Second, 20 addresses were selected from the telephone directory of each city or village. The undergraduate students used a random number generator for the selection of addresses (i.e. the number of the page, the number of the column and the number of the line). Interviewers were instructed to go to the addresses in the order in which they had been selected and to proceed to the first building to the left of the building on the address list. To avoid limiting the sample to those at home and most likely to open the door, they had to interview the adult ( 18 years old) member of the household who was the first in line to celebrate his or her birthday (Oldendick and Link, 1994). When nobody answered or when the person selected was not at home, they had to try to initiate contact three times before moving on to a replacement address to reduce undersampling of those with an active lifestyle. The process stopped when the interviewer had conducted 20 interviews. The survey was presented as a study on adults leisure time activities and their general well-being. In addition to questions about their sleep behaviour and media use, the questionnaire comprised other well-being measures (stress, depression, life satisfaction) and measures of leisure time activities (exercise, going out, other types of media use). Respondents were guaranteed that all answers would be treated anonymously. The study received ethical clearance from the Faculty of Social Sciences of KU Leuven, and informed consent was obtained from all respondents. Instruments Pittsburgh Sleep Quality Index (PSQI) This measure was used with permission of the developers (Buysse et al., 1989). The index consists of 19 items assessing respondents sleep quality over the past month. The 19 items can be grouped into seven components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction), each weighed equally on a 0 3 scale. Respondents received an overall score ranging from 0 to 21, with lower scores indicating a better sleep quality. Respondents scoring below five are rated as good sleepers. Fatigue Assessment Scale (FAS) This self-report fatigue scale comprises 10 items indicating symptoms of fatigue, where respondents assess how they usually feel using a five-point scale. The scale has demonstrated good reliability and validity (Michielsen and De Vries, 2004). Items were summed yielding a total fatigue score, ranging from 0 to 50. A higher score indicates more severe symptoms of fatigue. Bergen Insomnia Scale (BIS) The BIS (Pallesen et al., 2008) comprises six items referring to symptoms of insomnia. Participants indicated how many days per week during the last month they experienced a specific symptom related to insomnia. A total insomnia score was computed, ranging from 0 to 42. Bedtime and rise time Respondents were asked to indicate at what time they usually went to bed. For the purpose of the analyses, minutes were divided by 60 and multiplied by 100. Hours were counted from 0 to 24. Hours after midnight were counted as 25 (for 01:00 hours), 26 (for 02:00 hours), etc. to create a metric variable. Video gaming volume Respondents were asked: On average, how many hours do you play video games: (1) on a regular weekday (Mon Thurs); (2) on a regular Friday; and (3) on a regular weekend day (Sat Sun)? Include playing video games on a computer or console, and games you played on the internet or social media. We distinguished Fridays from weekdays because Friday is the beginning of the weekend and many respondents may therefore play more hours of video games compared with other weekdays. Total video game playing volume per week was estimated by first multiplying volume on an average weekday by four and multiplying volume on an average weekend day by two. Next, summing these average measures of volume on weekdays and weekend days and average viewing on Fridays led to an estimated weekly average of video game playing per week. Dividing this variable by seven produced an estimate of average daily video game playing (hours). This continuous variable was also recoded into three categories: (0) 0 h day (66.4%); (1) 1 h day (22.4%); and (2) >1 h day (11.2%).

3 Sleep quality and video games in adults 191 Control variables We entered gender (0 = male, 1 = female), age, educational level, exercise and perceived stress as control variables. Among adults, the likelihood of being a video gamer has been found to be associated with age (older < younger), gender (women < men) and educational level (low or highly educated < middle educated; IPSOS MediaCT, 2012; Lenhart et al., 2008). The sample was divided into three age groups: years old (31.6%); years old (36.2%); and years old (32.2%). Educational level was measured by asking respondents about the highest educational degree they obtained: no degree; finished primary school (K6 equivalent); finished secondary school (K12 equivalent); college degree; university degree. Respondents who were students (n = 77) were included as if they had finished that level of education. Physical activity has been found to be beneficial for overall sleep quality (Youngstedt, 2005), and daily exercise is listed as one of the 10 health tips for better sleep by the National Sleep Foundation. On the other hand, higher levels of perceived stress have been associated with shorter sleep duration, more fragmented sleep, more nightmares and delayed sleep onset (Akerstedt, 2006; Lund et al., 2010). Hours of exercise were measured by asking respondents how many hours a week they exercised to the extent where they became out of breath. The perceived level of stress was assessed by a summed score on the Perceived Stress Scale. Analyses Data were analysed using statistical package SPSS for Windows (Version 22.0; Chicago, IL, USA). Correlations between sleep variables, gaming volume and control variables were computed using bivariate Pearson (for continuous data) and Spearman (for ranked data) correlation analyses. Hierarchical multiple regression analysis was used to examine the relationship between gaming volume and sleep variables. For each sleep variable, gender, age, educational level, hours of exercise per week and perceived stress were entered into the first step of a regression analysis. In the second step, video gaming volume was entered. Additionally, binomial logistic regression analysis was used to investigate the relationship between gaming volume and the binary PSQI score (PSQI score 5 = 0; PSQI score >5 = 1), and multinomial logistic regression analyses for the relationship between gaming volume and the PSQI components (scores ranging from 0 to 3). RESULTS The response rate was 43.1%. A total of 1957 addresses were visited, of which 687 (35.1%) refused participation and 426 (21.7%) did not answer the door for three separate visits. Data were carefully examined for coding errors and other abnormalities, such as outliers, true impossible values and missing values. A total of 844 questionnaires were retained for analysis, provided completed questionnaires. The sample consisted of 56.2% women and 43.8% men. Respondents were between 18 and 94 years old, with an average of 46.0 years old (SD = 17.76). About one in five (20.7%) of the respondents had at least obtained a university degree, 31.8% had received a college degree, 29.9% had a degree of secondary school and 5.7% had received a degree of primary school. Almost one in 10 respondents (9.2%) were still studying and 2.6% had no degree. Differences between the population and the sample were not significant for gender [v² (1, n = 844) = 1, P = 0.317] or age [v² (2, n = 844) = 2.67, P = 0.263]. Our sample was overrepresented by highly educated respondents [v² (2, n = 844) = 217.7, P < 0.01]. Studies have found that higher educated people are more likely to participate in surveys than less educated people (Curtin et al., 2000; Goyder et al., 2002). Reliability indicators (Cronbach s alpha) of the measures were as follows: PSQI (a = 0.61); FAS (a = 0.83); BIS (a = 0.76); Perceived Stress Scale (a = 0.83). Descriptive statistics and correlations among study variables are presented in Table 1. About one in three respondents identified themselves as a gamer (34.4%). Across the whole sample, respondents played video games for min a day [median = 0.00; interquartile range (IQR) = 21.4]. Among the gamers, the mean volume of game play per day was 67.0 min (median = 42.86; IQR = 57.14). Mean bedtime and rise time was 23:25 hours (SD = 1 : 05) and 07:30 hours (SD = 1 : 25), respectively. The mean score for the PSQI was 4.56 (SD = 2.66), which borders on the cut-off score for poor sleep quality (PSQI score >5). Gender (Spearman q = 0.184, P < 0.01) and age (Pearson r = 0.287, P < 0.01) were negatively correlated with gaming volume. Men and women differed significantly in their average gaming volume (min day): whereas men played 34.9 min day on average (SD = 70.68), women played min day on average (SD = 34.22; t = 5.307, P < 0.001). There was a significant difference between age groups for gaming volume (min day) as determined by oneway ANOVA (F 2,823 = , P < 0.001). The youngest age group (18 35 years old; M = 45.93, SD = 74.63) played video games significantly more often than the middle (36 55 years old; M = 16.45, SD = 44.80, P < 0.001) or oldest age group (56 94 years old; M = 8.11, SD = 29.24, P < 0.001). There was no significant difference between the middle age and oldest age group (P = 0.178). Correlations between sleep variables and gaming volume were computed using bivariate Pearson correlation analyses. Gaming volume was positively related with all sleeping variables, with coefficients ranging between and (P < 0.01), which, while significant, are considered to be small (Field, 2009). Age and educational level were negatively correlated with the fatigue score and rise time. Hours of exercise per week were negatively correlated with the PSQI score, the FAS and the BIS, and level of perceived stress showed a positive correlation with these variables.

4 192 L. Exelmans and J. Van den Bulck Table 1 Descriptive statistics and correlations M SD Gaming volume PSQI score ** 3. Fatigue score ** 0.343** 4. Insomnia score ** 0.675** 0.453** 5. Bedtime ** Rise time ** 0.076* 0.147** ** 7. Gender 0.184** ** 0.101** 0.163** Age ** ** 0.077* 0.189** 0.085* Educational level ** 0.072* * ** 10. Exercise ** 0.100** 0.083* ** 0.121** 0.117** 11. Perceived stress ** 0.482** 0.382** ** 0.237** ** PSQI, Pittsburgh Sleep Quality Index. Gaming volume = h day 1. Exercise = h week 1. Bivariate Pearson and Spearman rho correlation analyses were conducted. *P < 0.05, **P < Gaming volume and sleep variables After entering the various control variables to the model, gaming volume positively predicted all sleeping variables (Table 2). In other words, more hours of video game play per day was associated with higher levels of fatigue (b = 0.109, P < 0.01), more symptoms of insomnia (b = 0.120, P < 0.01), later bedtime (b = 0.100, P < 0.01) and later rise time (b = 0.168, P < 0.001; Fig. 1). The changes in R² for each model were significant when adding gaming volume as a predictor in the final step. For bedtime and rise time, the degree of change was calculated using the unstandardized b-values. Each additional hour of video gaming per day delayed bedtime by 6.9 min [95% confidence interval (CI) min] and rise time by 13.8 min (95% CI min). Gaming volume and PSQI score For the PSQI score, results indicated that the more hours respondents played video games per day, the worse was their overall sleep quality (b = 0.145, P < 0.001; Fig. 1). Logistic regression analysis indicated that the odds of having a poor sleep quality (PSQI score >5) increased by 31.0% per extra hour of video game play per day [Exp (B) = 1.309, P < 0.01, 95% CI: ]. Compared with respondents who did not play video games, respondents who played >1 h day (n = 93) were 2.75 times more likely to have poor sleep quality [Exp (B) = 2.746, P < 0.001, 95% CI: ]. Playing video games for more than 1 h day doubles the odds of having a poor sleep quality. Attributable risk is the difference in prevalence between exposed and nonexposed groups, and thus reflects the proportion of prevalence (in our case: poor sleep quality) attributable to the exposure (in our case: video gaming) in relation to all cases (Webb, Bain, & Pirozzo, 2006). In the group of respondents who play >1 h of video games per day, 29.5% of the prevalence of poor sleep quality could be attributed to their game play. There was no significant increase in odds when comparing those who did not play video games and those who played less than 1 h day, and thus no attributable risks were calculated. Gaming volume and PSQI components Every component has a range of 0 3 points in the PSQI: a score of 0 indicates no difficulty; while a score of 3 indicates severe difficulty (Buysse et al., 1989). In the current study, the last two categories of every component were merged, given the small proportion of respondents in the highest category. The results are therefore computed with components ranging from 0 to 2. Gaming volume significantly predicted sleep latency, sleep efficiency and, to a lesser extent, subjective sleep quality and the use of sleep medication.

5 Sleep quality and video games in adults 193 Table 2 Hierarchical multiple regression analyses for sleeping variables PSQI score Fatigue score Insomnia score Bedtime Rise time b ΔR² b ΔR² b ΔR² b ΔR² b ΔR² Step 1 Gender *** *** *** 0.167*** 0.067*** *** Age 0.098** 0.179*** * * 0.115* * Educational level 0.088* 0.118** Exercise Perceived stress 0.322*** 0.428*** 0.384*** Step 2 Gender *** ** ** 0.145*** 0.009** *** Age 0.147*** 0.142*** * * Educational level ** Exercise Perceived stress 0.323*** 0.429*** 0.385*** Gaming volume 0.145*** 0.109** 0.120** 0.100** 0.168*** Model R² PSQI, Pittsburgh Sleep Quality Index. Exercise = h week 1. Gaming volume = h day 1. *P > 0.05, **P > 0.01, ***P < Figure 1. Relationship between gaming volume (h day) and sleep variables, controlling for gender, age, educational level, level of exercise and perceived stress (n = 844). Error bars represent 95% confidence intervals. First, regarding sleep latency, respondents are more likely to need more time to fall asleep when their video gaming volume increases (Table 3). Compared with those who do not play computer games, those who played <1 h of video games per day were 1.5 times more likely to have a sleep latency score of 2, although this was only marginally significant (P = 0.098). Those who played more than 1 h day were 3.4 times more likely to have a sleep latency score of 2. Second, video gaming volume positively predicted the sleep efficiency score. Sleep efficiency refers to the proportion of hours slept versus the hours spent in bed. A high sleep efficiency score means that this balance is optimal: time

6 194 L. Exelmans and J. Van den Bulck Table 3 Multinomial regression analyses for PSQI components and gaming volume (categorical data) Sleep latency Sleep efficiency Exp(B) 95% CI Exp(B) 95% CI Exp(B) 95% CI Exp(B) 95% CI Gaming volume (versus 0 h day) Gaming 1 h day Gaming >1 h day ** ** ** Gender male Age ** ** Educational level * ** Excercise Perceived stress 1.040** *** Model sleep latency: R² = (Cox and Snell), (Nagelkerke); Model v² (16) = , P < Model sleep efficiency: R² = (Cox and Snell), (Nagelkerke); Model v² (16) = , P < CI, confidence interval. Gaming volume = h day. Exercise = h week. *P < 0.05, **P < 0.01, ***P < spent in bed is almost completely spent sleeping. Table 3 shows that compared with those who do not play video games, the odds of having a sleep efficiency score of 1 (rather than 0) are 2.7 times higher for those who played less than 1 h day. The odds of having a sleep efficiency score of 2 (rather than 0) were 2.8 times higher for respondents who played video games more than 1 h day, compared with those who never played video games. Third, the results showed that video gaming volume significantly predicted the use of sleep medication. Respondents who played more than 1 h day were 2.2 times more likely to use sleep medication at least once or twice a week (rather than not during the past month), compared with respondents who never played video games, although this was only marginally significant (P = 0.098). The analysis with categorical data for gaming volume could not be conducted for respondents who had a score of 1 on use of sleep medication, because some subcategories of gaming volume and use of sleep medication contained no respondents. Finally, gaming volume appeared to be related to subjective sleep quality. Those who played video games for more than 1 h day were 2.3 times more likely to perceive their sleep quality as rather bad than very good, compared with those who do not play video games [Exp (B) = 2.290, P < 0.05]. When examining these results, it is noteworthy to point out that: (1) there was often only a significant result when comparing the lowest and highest scores on the components; (2) there were almost no significant differences when comparing those who did not play video games and those who played them 1 h day overall. DISCUSSION The present study explored the relationship between gaming volume and sleep quality in a sample of 844 Flemish adults, ranging between 18 and 94 years old. In this adult sample, one in three (34.4%) respondents identified themselves as a gamer, which is a bit lower than the proportion (45.1%) that was found in a study by Weaver et al. (2009), who examined adults of the same age range and used the same dichotomization as this study did. Those who played video games in our sample played 67 min (1.11 h) per day on average, versus 0.87 h in the study by Weaver et al. (2009). The findings from the current study suggest two conclusions. First, gaming volume is significantly and negatively related to fatigue, insomnia, bedtime and rise time in adults: the more adults play video games, the higher their reported levels of fatigue and insomnia, and the later their bedtime and rise time. Gaming volume was a significant predictor of sleep quality in adults: per additional hour of video gaming per day respondents had a significantly greater risk (31%) of having a poor sleep quality, characterized by a PSQI score >5. Attributable risk calculation further suggested that 30% of the prevalence of poor sleep quality among respondents who play more than 1 h of video gaming per day was attributable to their being in that game volume category. Although the observed relationships were small, gaming volume significantly increased the explained variance in each dependent variable. The reported delay in bedtime and rise time associated with video game play confirm previous research demonstrating that media use among adults coincides with later bedtimes, but also with later rise times, a process called time shifting (Custers and Van den Bulck, 2012). This can be considered surprising when taking into account adults daytime commitments. An interesting hypothesis for future research may be to examine whether adults who engage in heavier media use also have a lifestyle that is less restrained by daytime commitments that allow them to delay their sleep.

7 Sleep quality and video games in adults 195 Second, the more adults play video games, the more likely they are of needing more time to fall asleep, of having a lower sleep efficiency and of using sleep medication more frequently. The observation that most results were only significant when comparing those who play video games more than 1 h day is striking. Previous experimental research has documented an effect of video gaming on sleep only for game play of over 60 min (Dworak et al., 2007; King et al., 2013). It therefore seems that video gaming becomes most detrimental to sleep quality when it exceeds a volume of 1 h day. The results of this study may be explained by two mechanisms that have been described to explain the relationship between media use and sleep quality. First, exposure to bright light emanating from electronic screens suppresses the secretion of melatonin, which in turn delays sleep onset (Higuchi et al., 2005). This is particularly applicable to media use where the user is closer to the screen, such as video gaming or computer use (Christakis and Zimmerman, 2006). Second, our findings may support the idea of arousal as an underlying mechanism between video gaming and sleep (Cain and Gradisar, 2010; Zimmerman, 2008). Playing video games can affect arousal parameters, such as respiratory rate, blood pressure and heart rate (Anderson and Bushman, 2001). Increased arousal has been previously associated with difficulties falling asleep and night wakings (Paavonen et al., 2006; Van den Bulck, 2004a). This may be a valid explanation especially for exciting, competitive and violent video games, given that research has reported stronger effects on arousal than when playing non-violent games (Fleming and Rickwood, 2001). Future research should therefore look into the role of gaming genre in the relationship between video gaming and sleep quality. Several limitations need to be acknowledged. First, while we used random sampling methods, these data only apply to Flemish adults, and conclusions might thus not be transferable to other age groups or countries. There was an overrepresentation of highly educated people, which might have led to some bias in our results. Second, while the fact that one in three of the respondents in our sample was identified as a gamer shows that gaming is prevalent among adults, it also means that our findings indicate a relationship between video game volume and sleep for a limited proportion of the adult population. We suspect this is partly explained by the wide age range of our sample. However, possible moderation effects between video gaming volume and age were not significant, and limiting the sample to a smaller age group did not appear to affect the relationships between gaming volume and sleep variables. Studies have nonetheless found that younger adults are significantly more likely than older adults to play video games (Lenhart et al., 2008). Additionally, it has been well documented that age has a significant impact on a multitude of sleep parameters (Ohayon et al., 2004). We thus believe that the possible interactions between age, video gaming and sleep might be an interesting avenue for future scholars. Third, the beta coefficients of our regression analyses were small, ranging between and While we added several important control variables in our final regression model, we also suspect that our gaming measure may partly account for these small coefficients. Our measure of gaming volume probed the hours of video gaming on a regular weekday, Friday and weekend day, which did not allow us to: (1) measure gaming before bedtime; and (2) make a distinction between Saturday and Sunday. First, it can be expected that the likelihood of an effect of video gaming on sleeping variables is greater when the gaming activity approximates bedtime, considering processes or arousal and screen light. Delineating the gaming activity prior to bedtime or even after bedtime may result in additional insights on its relationship with sleep. Second, research between media use and sleep might benefit from measuring video gaming volume during weekdays (Sun Thurs) and weekend days (Friday and Saturday). While Sunday is part of the weekend, Sunday night ought, perhaps, to be considered a week night and thus adult gamers may be less likely to play video games until late at night. Finally, and most importantly, our findings are limited by a cross-sectional design. We can make no causal inferences and cannot conclude that gaming volume reduces overall sleep quality. The reversed hypothesis that respondents who experience sleep difficulties may be selftreating such a problem by playing video games (e.g. to distract themselves), cannot be ruled out and is a valid suggestion for future research. In particular, some studies have outlined a sleep-facilitating role for media use (Eggermont and Van den Bulck, 2006; Gooneratne et al., 2011), although support for such a hypothesis remains mixed. Taken together, the present study corroborates previous research and appears to provide additional evidence that playing video games can be considered to be part of bad sleep hygiene. While results indicated a significant negative relationship, the coefficients of the regression analyses suggest that the possible impact of video gaming volume on sleep is smaller for adults than for children and adolescents. Although there is a significant subsection of the adult population that plays a lot of computer games, this number is still considerably smaller than the number of teen gamers (Lenhart et al., 2008). Our observation that adults can compensate for lost sleep by time shifting may also explain the discrepancy in effects among adults versus teens. Results from research on media use and sleep in children and adolescents should therefore not be extrapolated to adult populations without further scrutiny, as other patterns, processes and problems are likely to occur (Custers and Van den Bulck, 2012). Finally, although sleep deficiency remains a problem that affects all age groups, we tend to be particularly alarmed about effects at a young age. It is indeed crucial to initiate adoption of good sleep hygiene practices at a young age, yet the importance of the maintance of healthy sleep habits as we become responsible for our own sleep schedule should not be undervalued by sleep scholars.

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