sleep duration, sleep–wake schedule regularity, and body weight in hong kong chinese adolescents

Sleep duration, sleep–wake schedule regularity, and body weight in Hong

Kong Chinese adolescents

Ka-Fai Chung*, Katherine Ka-Ki Kan and Wing-Fai Yeung

Department of Psychiatry, University of Hong Kong, Hong Kong SAR, China

(Received 7 November 2011; final version received 19 December 2011)

The relationship between sleep duration and sleep–wake disturbances and bodyweight has been under-researched in adolescents. This is a cross-sectional school-based study of 327 adolescents with an average age of 14.5 years. Between-group,correlational, and hierarchical regression analyses were performed to investigatethe relationship between body mass index (BMI) and sleep duration, sleepquality, daytime sleepiness, and sleep–wake schedule regularity. There was nosignificant difference between overweight/obese and normal weight adolescents insleep quality, daytime sleepiness, sleep duration during the weekdays andweekend, and mean sleep duration; however, overweight/obese adolescents hadsignificantly later bedtimes (average 25 min) during weekends than did normalweight subjects. After controlling for depression and sociodemographic andlifestyle factors, shorter mean time in bed and greater weekend delay in bedtimewere independent predictors of higher BMI z-scores. The contribution ofchronotype to sedentary lifestyle, eating behaviour, and body weight is worthfurther investigation.

Keywords: adolescents; chronotype; obesity; overweight; sleep disturbance; sleepduration

Introduction

Being overweight is a worldwide health problem, reaching epidemic proportionsin all age groups. A parallel trend that has occurred is a decline in sleep duration.Adolescence is an important developmental period during which overweight andinsufficient sleep can have long-term implications for physical and mental health.In a 2007–2008 survey, one in three US adolescents was found to be overweight(Ogden et al. 2010); in Hong Kong, the recent estimate of overweight adolescentswas around 17% (Ko et al. 2008). Insufficient sleep and irregular sleep–wakeschedules are also common problems in adolescents. Self-report studies showedthat many adolescents do not obtain adequate sleep (Olds et al. 2010), defined bysome researchers as at least 9 h (Carskadon et al. 1980), and tend to stay up lateduring school nights and ‘‘sleep in’’ on weekends (Gradisar et al. 2011).Insufficient sleep and abrupt changes in bedtime and wake-up time betweenweekdays and weekends are associated with fatigue, irritability, difficulty in

*Corresponding author. Email: [email protected]

Biological Rhythm Research

Vol. 44, No. 2, April 2013, 169–179

ISSN 0929-1016 print/ISSN 1744-4179 online

� 2013 Taylor & Francis

http://dx.doi.org/10.1080/09291016.2012.656247

http://www.tandfonline.com

concentration, daytime sleepiness, behavioural symptom, and academic problems(Dahl and Lewin 2002; Chung and Cheung 2008). Other sleep–wake disturbances,such as poor sleep quality which is estimated to affect 10% to 30% ofadolescents, can also lead to health problems and impaired academic andfunctional performances (Roane and Taylor 2008; Yeung et al. 2008). During thetime of puberty, there is a tendency for adolescents to delay their sleep phase,with a shift from morningness in childhood to eveningness, and the phase shiftingback towards morningness at the end of adolescence (Gaina et al. 2006); however,only 0.2% of adolescents will develop delayed sleep phase syndrome (Okawa andUchiyama 2007). The preference of morningness or eveningness, termed as theindividual’s chronotype, is influenced by endogenous and environmental factorsand has been found to be associated with eating behaviour (Schubert andRandler 2008).

The recent decades have shown a rapid development of interest in the possiblerole of sleep in weight regulation. Previous studies have examined the associationsbetween short sleep duration and poor sleep quality and weight gain and eventualdevelopment of being overweight. The relationship between insufficient sleep andbecoming overweight is likely to be mediated by multiple biological andbehavioural pathways (Beccuti and Pannain 2011). In the majority of studieson adolescents, an inverse association between sleep duration and body weighthas been found; however, the effects were often small and might differ by sex,age, and the method used to assess sleep duration. Knutson (2005) andEisenmann et al. (2006) showed that shorter sleep duration was associated withhigher body mass index (BMI), but among male adolescents only. Eisenmannet al. (2006) found that short sleep duration had the strongest association withbeing overweight in 14–16.5-year-old males from a sample of 7.5–16.5-year-oldchildren and adolescents. On the other hand, Lytle et al. (2011) did not detectany association between sleep duration and body weight in 9th–12th grade males,but significant associations were observed in both middle-school and high-school female students. Most of the previous studies have relied on self-reportsof sleep duration, which was either defined as total sleep time or time in bed (orthe mean of these two variables), making it difficult to compare results acrossstudies.

Studies in adults have shown that worse sleep quality was associated withhigher BMI, and that the risk was higher in women and those with morefrequent and chronic sleep disturbances (Beccuti and Pannain 2011). Therelationship between poor sleep quality and body weight in adolescentshas been under-researched. Decreased growth hormone or increased cortisollevels, emotional eating, and issues associated with insufficient sleep may be themediating factors for weight gain in individuals with poor sleep quality(Beccuti and Pannain 2011). Hence, the relationship between sleepduration and body weight is complicated by a number of possible confoundinginfluences, including regularity of the sleep–wake schedule, sleep quality, anddepression.

The aim of this study was to clarify the position and evaluate the relationship ofsleep duration and sleep–wake disturbances in relation to body weight inadolescents. We hypothesized that there was a significant association between sleepduration and body weight and that this association was independent of the regularityof the sleep–wake schedule, sleep quality, and depression.

170 K.F. Chung et al.

Methods

Study design and subjects

The study was approved by the Institutional Review Board of the University ofHong Kong. This was a secondary analysis of a school-based study conducted in2008 (Chung et al. 2011). Three schools with different academic achievements werepreselected. All 7th–10th and 12th grade students at each school were invited toparticipate in a questionnaire survey. Eleventh-grade students were preparing forpublic examination during the study period, so were not recruited. Written informedconsent was obtained from students and their parents prior to participation in thestudy. Out of 2122 eligible subjects, a total of 1521 students completed the survey,which corresponds to an overall response rate of 71.7%.

Questionnaires were distributed to the students in their classroom by theinvestigators with the help of the teachers. The investigators were available to answerthe students’ questions regarding the study and questionnaires; however, thestudents completed the questionnaires on their own. Since our resources werelimited, a quarter of those who returned the questionnaires were randomly selectedand invited to complete the scales once again 2 weeks after the first administration,to examine the scales’ test–retest reliability. One investigator (KKK) measuredstudents’ weight and height either immediately before or after they filled out thequestionnaires. There was no significant difference in demographics and self-reported BMI (in kg/m2) between the 327 participants and 78 nonparticipants of theretest. This study analyzed the self-report questionnaires data of the 327 studentswhose weight and height had been measured.

Measures

Self-administered questionnaires were presented sequentially in the following order:Socio-demographic characteristics, sleep–wake habits, Insomnia Severity Index(ISI), Epworth Sleepiness Scale (ESS), 12-item General Health Questionnaire (GHQ-12), number and duration of naps per week, time spent on Internet activities andwatching television, alcohol use, and smoking habits.

The items of the sleep–wake habit questionnaire originated from a Chineseversion of the Pittsburgh Sleep Quality Index (Chung and Tang 2006). Students wereasked to fill out their usual bedtime, rise time, and total sleep time on both schooldays and weekends during the past month. Sleep duration was assessed as total sleeptime and time in bed (the period between bedtime and rise time) during both schooldays and at weekends. The mean total sleep time and mean time in bed werecalculated as [(weekday sleep duration/time in bed 6 5) þ (weekend sleep duration/time in bed 6 2)]/7. The students estimated the time to fall asleep (‘‘sleep onsetlatency’’) and the time they spent after waking up early and being unable to get backto sleep (‘‘early morning awakening’’). In addition, two measures of sleep–wakeschedule regularity were derived: weekend–weekday difference in sleep time (thedifference between weekend and school night total sleep time) and weekend delay inbedtime (the difference between weekend and school night bedtime). Our previousstudy in adolescents had shown that the test–retest reliability coefficient for self-reported bedtime, rise time, and total sleep time during school days over a period of1 month was 0.79–0.92; for weekend variables, the coefficients ranged between 0.68and 0.80 (Chung and Cheung 2008).

Biological Rhythm Research 171

The ISI is a 7-item global insomnia symptom scale assessing the perceivedseverity of difficulty falling asleep, difficulty staying asleep and problem waking uptoo early, the degree of satisfaction with sleep, interference with daytime functioning,noticeability of impairment, and concern caused by any sleep problems (Bastienet al. 2001). The scale has 58 of severity for each symptom ranging from 0 to 4. TheISI has been widely used in research and our recent study showed that it was areliable and valid measure of insomnia in adolescents (Chung et al. 2011).

The ESS is an 8-item self-reported questionnaire to assess the average daytimesleep propensity (Johns 1991; Chung 2000). It focuses upon the tendency tosleepiness, with 3-point scales rating the likelihood of dozing in eight situationsfound in daily life. The total score ranges from 0 to 24. A recent study in adolescentshas found that the test–retest reliability of ESS over 2 weeks was high (Takegamiet al. 2009).

The 12-item GHQ-12 has been extensively used in different cultures for theassessment of depressive symptoms and psychological distress in adults andadolescents (Goldberg 1972). Subjects were asked to rate their responses using a4-point Likert scale (‘‘less than usual’’, ‘‘no more than usual’’, ‘‘rather more thanusual’’, or ‘‘much more than usual’’). Each question was scored using a binary code,with total score ranging from 0 to 12. The Chinese version of GHQ-12 has beenwidely used in different populations (Pan and Goldberg 1990; Li et al. 2010).

Students also described their age, gender, number and total duration of napstaken per week, total time spent on Internet activities and watching television perweek, and current smoking habits. Alcohol use was assessed using 5-point categories(0: never; 1: rarely; 2: sometimes; 3: almost always; 4: often). Students also reportedtheir parents’ marital status, occupation, and years of education. A 7-point index offamily occupational status, as the higher of the father’s or mother’s occupationalstatus, was derived: 1, professional; 2, manager or administrator; 3, associateprofessional; 4, skilled non-manual worker; 5, skilled or semi-skilled manual worker;6, unskilled manual worker; 7, unemployed or homemaker.

Individuals’ weight and height were measured in school uniform and withoutshoes, using the same scale throughout the study period. BMI was calculated andtransformed into a BMI z-score accordingly (So et al. 2008). ‘‘Overweight’’ wasdefined as a BMI z-score between the 85th and 95th percentiles for gender and age,while students with a BMI z-score above the 95th percentile were considered‘‘obese’’.

Data analysis

All statistical analysis was done by PASW Statistics 18 (SPSS, Chicago, IL). Subjectvariables that were significantly deviated from normality were square root-transformed to obtain a distribution closer to normal. Comparisons of sleep–wakevariables between overweight/obese and normal weight students used unpaired t-tests. Pearson’s correlation coefficient was used to examine the relationship betweenBMI z-score and sleep duration variables for the total sample and for subgroupsdivided by gender and age. Students aged 515 years and �15 years were analyzedseparately with the former group representing students in the first 3 years ofsecondary school education. Three-step hierarchical regression analysis wasperformed to investigate the relative contribution of sleep duration in predictingBMI z-score: socio-demographic and lifestyle variables (including age, gender,


parents’ educational level, marital status and occupation, drinking habits and timespent on the Internet, and watching television) comprised the first step; the amountof napping, sleep–wake schedule regularity, severity of insomnia, daytime sleepiness,and depression comprised the second step; mean total sleep time or mean time in bedcomprised the third step. Due to the significant correlation between mean total sleeptime and mean time in bed, the variable which had the stronger bivariate correlationwith BMI z-score was used in the regression analysis.

Results

Table 1 presents the students’ socio-demographic, lifestyle and body size variables.The participants’ age was 14.5 + 1.5 years (mean + SD), and 57.2% were female.The percentages of overweight and obesity in the whole sample were 9.2% and 8.9%,respectively; for male students, the respective percentages were 8.6% and 10.2% and,for females, 10.0% and 7.1%. On school days, the students’ average bedtime, rise

Table 1. Characteristics of subjects.

VariablesTotal sample (n ¼ 327)a

n (%)/mean + SD (range)

Gender (male/female) 140/187Age, yr 14.5 + 1.5 (12–19)12–13 86 (26.3)14 86 (26.3)15 75 (22.9)16 54 (16.5)17–19 26 (8.0)

Parents marital statusSingle 7 (2.2)Married/cohabiting 276 (84.9)Divorced/widowed 42 (12.9)

Average parental education inyr

8.8 + 2.8 (1–16)

Parents’ occupationProfessionals, managers,

and administrators54 (16.9)

Associate professionals,clerks, and serviceworkers

70 (21.8)

Skilled and semi-skilledmanual workers

117 (36.6)

Unskilled manual workers 31 (9.7)Unemployed/homemakers 48 (15.0)

Height, cm 162.3 + 8.3 (133.0–187.5)Weight, kg 53.1 + 11.8 (28.2–110.0)Body mass index (BMI), kg/

m220.3 + 3.7 (14.0–35.1)

BMI z-score 0.14 + 1.13 (71.68 to 4.95)Time on Internet and

television per day, h3.5 + 2.2 (0–15)

Alcohol useb 45 (13.8)Current smoker 5 (1.5)

aDifference from total n reflects omissions on report forms.bIncludes ‘‘often’’, ‘‘almost always’’, and ‘‘sometimes’’ responses.


time, time in bed, and total sleep time were 23:23 h, 07:00 h, 7 h 36 min, and 7 h6 min, respectively. There were no significant differences between overweight/obeseand normal weight groups in bedtime, rise time, time in bed, and total sleep time onschool days (Table 2). During weekends, the overweight/obese group had asignificantly later bedtime; on average, they went to bed 25 min later than theadolescents with normal weight. There were no significant differences between thetwo groups in mean time in bed and mean total sleep time. The overweight/obese andnormal weight groups did not differ in sleep onset latency, early morning awakening,ISI score, ESS score, and total amount of napping per week.

Table 3 presents the results of correlational analysis between BMI z-score andsleep duration variables. We found that BMI z-score was more closely related to timein bed than total sleep time. For the total sample, there were significant negativecorrelations between BMI z-score and time in bed during weekends and mean time inbed (r ¼ 70.11, p ¼ 0.049 and r ¼ 70.14, p ¼ 0.015, respectively). Subgroupanalyses showed that there were significant negative correlations between BMI z-score and mean time in bed and mean total sleep time among students aged �15years (r ¼ 70.16, p ¼ 0.042, and r ¼ 70.16, p ¼ 0.048, respectively) and betweenBMI z-score and mean time in bed in male students (r ¼ 70.20, p ¼ 0.021).

Table 4 presents the three-step hierarchical regression analysis predicting BMI z-score. Among the socio-demographic and lifestyle variables entered into the first stepof the regression analysis (see the section ‘‘Methods’’), age, parental occupational

Table 2. Sleep–wake variables compared by weight status.

Variables aTotal sample(n ¼ 327)

Overweight/obese (n ¼ 59)

Normal weight(n ¼ 268)

School night bedtime 23:23+01:12 23:25+01:15 23:22+01:12School night rise time 07:00+00:39 07:00+00:25 07:00+00:42School night time in bed, h 7.6+1.2 7.7+0.9 7.8+1.5School night total

sleep time, h7.1+1.4 7.1+1.3 7.1+1.5

Weekend bedtime 00:20+01:27 00:41+01:31 00:16+01:25*Weekend rise time 09:58+01:46 10:04+01:27 09:56+01:50Weekend time in bed, h 9.6+1.6 9.4+1.6 9.7+1.6Weekend total sleep time, h 9.2+2.0 9.2+2.1 9.2+2.0Mean time in bed, h 8.2+1.0 8.1+1.1 8.2+1.0Mean total sleep time, h 7.7+1.3 7.7+1.2 7.7+1.4Weekend–weekday

difference in sleeptime, minb

124.5+122.0 129.7+122.4 123.3+122.1

Weekend delay inbedtime, min

57.6+71.9 75.9+75.0 53.5+70.7*

Sleep onset latency, minb 17.7+16.6 16.4+14.9 17.9+17.0Early morning awakening, minb 18.4+31.1 15.9+17.0 19.0+33.4Insomnia Severity Index score 7.2+4.7 6.9+4.9 7.3+4.7Epworth Sleepiness Scale score 7.8+4.1 7.6+4.3 7.8+4.1Napping per week, minb 139.5+212.6 103.3+126.0 147.4+226.6

a Values are presented as mean+SD. Bedtime and rise time are expressed in 24-h clock time.b Raw data are presented to aid interpretation, although square root-transformed scores were used inanalyses.

* Significant difference with p 5 0.05 by unpaired t-test.


status, and alcohol use were the significant predictors of BMI z-score in some of thesamples tested (Table 4, step 1). Taking into account the students’ sleep–wakeschedule regularity, sleep disturbances, and depression together with socio-demographic and lifestyle variables, the significant predictors of BMI z-score thenincluded age, parental occupational status, alcohol use, and the weekend delay inbedtime (Table 4, step 2). After controlling these major confounders, mean time inbed was a significant and independent predictor of BMI z-score and accounted foran additional 3–5% of the total variance (Table 4, step 3). For the total sample, age,parental occupational status, and weekend delay in bedtime were the othersignificant predictors of BMI z-score, with younger age, higher parental occupa-tional status, and longer weekend delay in bedtime associated with higher scores.The weekend delay in bedtime was found to predict BMI z-score in the age �15 yearsgroup, but not in the 515 years group. After controlling for all sleep–wakevariables, severity of insomnia, daytime sleepiness, and depression were notindependently associated with the BMI z-scores.

Discussion

This study extends the understanding of sleep–wake disturbances in overweight/obese adolescents and the relationship between sleep duration and sleep–wakedisturbances and body weight. Overweight/obese adolescents had significantlylonger weekend delays in bedtime. There were significant bivariate correlationsbetween BMI z-score and both total sleep time and time in bed, but time in bedhad the stronger association. After controlling for socio-demographic, lifestyle,sleep–wake, and mental health variables, shorter times in bed and greaterweekend delays in bedtime were independently associated with higher BMI z-scores.

Consistent with the findings in cross-sectional studies, the adolescents’ time spentin bed at night had a weak but significant inverse relationship with BMI z-score. Inthis study, the relationship between sleep duration and body weight was significant inthe analyses of age and gender subgroups. The findings suggested that sleep durationwas a major factor influencing body weight in boys, girls, young adolescents, andlate adolescents. Unexpectedly, body weight was more closely related to time in bedthan to total sleep time. Total sleep time and time in bed have seldom been analyzed

Table 3. Pearson’s correlations between body mass index z-score and aspects of sleepduration, for total sample and subgroups based on age and gender.

Age Gender

Total sample(n ¼ 327)

515 yr(n ¼ 172)

�15 yr(n ¼ 155)

Males(n ¼ 140)

Females(n ¼ 187)

School night time in bed 70.11 70.11 70.12 70.16 70.06School night total sleep time 70.06 70.02 70.13 0.04 70.11Weekend time in bed 70.11* 70.08 70.15 70.16 70.08Weekend total sleep time 0.02 0.13 70.11 0.004 0.03Mean time in bed 70.14* 70.12 70.16* 70.20* 70.08Mean total sleep time 70.04 0.04 70.16* 0.03 70.07

* p 5 0.05.


together in relation to associations with body weight in previous studies; however,the only study with non-significant results had used total sleep time as the measure ofsleep duration (Knutson and Lauderdale 2007). The present findings raise amethodological issue – namely, whether self-reported time in bed is a more accuratemeasure of sleep duration in adolescents than is self-reported total sleep time. It isalso possible that body weight is more closely related to the resting time in bed thanthe duration of actual sleep (Nielsen et al. 2011).

Overweight/obese adolescents had significantly later bedtimes during weekendsand longer weekend delays in bedtime, compared with normal weight adolescents.After controlling for the major confounders, the longer weekend delay in bedtimewas independently associated with higher BMI z-score in the total sample and

Table 4. Three-step hierarchical regression analyses predicting body mass index z-score.

Independent variablesa

Statistics by step Statistics by variableb

Step R2 Fchange p value b t p value

Total sample (n ¼ 327)1 Age 0.05 1.79 0.09 70.14 72.12 0.04

Parents’ occupation 0.17 2.42 0.022 Age 0.09 1.63 0.14 70.14 72.10 0.04

Parents’ occupation 0.16 2.34 0.02GHQ score 0.16 2.13 0.03

3 Age 0.13 10.02 0.002 70.15 72.35 0.02Parents’ occupation 0.17 2.51 0.01Weekend delay in bedtime 0.13 2.11 0.04Mean time in bed 70.21 73.17 0.002

Age 5 15 years (n ¼ 172)1 Parents’ occupation 0.13 2.48 0.02 0.25 2.72 0.008

Alcohol use 0.23 2.57 0.012 Parents’ occupation 0.14 0.42 0.87 0.24 2.52 0.01

Alcohol use 0.22 2.36 0.023 Age 0.18 5.05 0.03 70.18 72.04 0.04

Parents’ occupation 0.24 2.56 0.01Mean time in bed 70.21 72.25 0.03

Age � 15 years (n ¼ 155)1 – 0.03 0.51 0.83 – – –2 Weekend delay in bedtime 0.10 1.31 0.26 0.20 2.07 0.043 Weekend delay in bedtime 0.15 6.51 0.01 0.27 2.75 0.007

Mean time in bed 70.25 72.55 0.01Males (n ¼ 140)

1 Age 0.06 1.10 0.37 70.21 72.09 0.042 GHQ score 0.12 1.02 0.42 0.26 2.07 0.043 Mean time in bed 0.16 4.51 0.04 70.23 72.12 0.04

Females (n ¼ 187)1 Parents’ occupation 0.07 1.64 0.14 0.19 2.11 0.04

Alcohol use 0.17 1.99 0.0482 – 0.11 1.04 0.40 – – –3 Mean time in bed 0.14 3.97 0.048 70.18 71.99 0.048

aThe variables entered include socio-demographic and lifestyle variables (step 1), amount of napping,weekend delay in bedtime, weekend–weekday difference in sleep time, and severity of insomnia, daytimesleepiness and depression as measured by the Insomnia Severity Index, Epworth Sleepiness Scale and 12-item General Health Questionnaire (GHQ) (step 2), and mean time in bed (step 3).bOnly significant predictors (p 5 0.05) are presented.


subjects aged �15 years. The only study that has examined the relationshipbetween sleep–wake schedule regularity and body weight showed that overweightstatus was significantly associated with a later wake time on weekends among5th–8th grade males, and with an earlier wake-time and a longer sleep onweekends among 9th–12th grade females (Lytle et al. 2011). It is difficult tointerpret these contradictory results regarding different age and gender subgroups.The present findings of delayed weekend bedtime in overweight/obeseadolescents suggest that body weight might be related to chronotype. Previousstudies have shown that eveningness is associated with a lack of dietary restraint,overeating, perceived hunger, sedentary lifestyle, and excessive television viewing(Gaina et al. 2006; Schubert and Randler 2008). Future studies to examine sleepduration and its relation to body weight should include possible effects ofchronotype.

Significant differences in the severity of insomnia and daytime sleepiness betweenthe overweight/obese and normal weight groups was not detected in the presentstudy. A recent study in 60 obese children and adolescents from a weight-management clinic found that the obese group had more disrupted sleep and daytimesleepiness than controls (Beebe et al. 2007). It is possible that the overweight/obeseadolescents in our sample had lower body weights than the participants in a weight-management program (Beebe et al. 2007), and this difference in body weight mightaccount for the different results.

The study has several limitations that should be considered when interpreting thefindings. Besides body weight, other indicators of adiposity – such as waistcircumference and skinfold thickness – were not examined. In addition, chronotypeand parental reports or objective measures of the adolescents’ sleep duration werenot obtained. Although previous studies have shown that the relationship betweensleep duration and body weight remains present after adjusting for energy intake andenergy expenditure (Gupta et al. 2002; Lytle et al. 2011), the lack of data regardingdiet and physical activity should be considered as a study limitation. A possible ageand gender interaction upon the relationship between sleep duration and bodyweight could not be determined, and this might be due to the small sample size.Lastly, because of the cross-sectional research design, it is impossible from our datato infer the direction of causality between short sleep duration and the weekenddelay in bedtime and being overweight.

Despite these limitations, the present findings add to the database on theimportance of sleep in adolescents. Hierarchical regression analysis showed that timein bed and the weekend delay in bedtime were independently associated with BMI z-scores. Given that being overweight can be intractable after puberty, education onhealthy sleep habits should be provided early, even in primary schools. Futurestudies should obtain objective measures of the duration of actual sleep and restingtime in bed, in order to understand better the relationship between sleep durationand body weight. The contribution of chronotype to dietary habit, lifestyle, andbody weight is also worth further investigation.

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sleep duration, sleep–wake schedule regularity, and body weight in hong kong chinese adolescents

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