Assessing the Influence of Sleep-Wake Variables on BodyMass Index ( BMI ) in Adolescents

Recent work has established an association between overweight/obesity and sleep duration, suggesting that short sleep duration and timing of sleeping may lead to overweight. Most of these studies considered sleep-length rather than any other aspects associated with the sleep and wake rhythm, e.g. chronotype, which is a measure of timing of sleeping (‘when to sleep’; based on the midpoint of sleep). The objective of this study was to assess the influence of different factors of the sleep-wake cycle and of co-variates on the Body Mass Index in a cross-sectional questionnaire study. Nine hundred and thirteen pupils (406 boys, 507 girls) from Southwestern Germany participated in this study. Mean age was 13.7 ± 1.5 (SD) years and range was between 11 – 16 years. We found that chronotype (β = .079) and social jetlag (β = .063) showed a significant influence on Body Mass Index (BMI), while sleep duration did not. Social jetlag is the absolute difference between mid-sleep time on workdays and free days. Further, screen time (in front of TV, computer, β = .13) was positively related with BMI. Self-efficacy on nutrition (β = -.11), a psychological variable important in health-behaviour models, showed an influence with high scores on self-efficacy related to lower BMI. A high BMI was correlated with low fast-food consumption (β = -.12) suggesting that adolescents with high BMI may exert some control over their eating.

gical clock when they can awake spontaneous rather than by an alarm clock (Wittmann, Dinich, Merrow, & Roenneberg, 2006).However, there is an on-going discussion about measuring chronotype by questionnaires, either by a clock-based questionnaire or by a circadian preference scale (Adan et al., 2012).
Recent studies showed that evening oriented adolescents (Fleig & Randler, 2009) and adults (Kanerva et al., 2012) showed a more unhealthy diet.In this study, we assessed the relationship between sleep-wake variables and overweight in adolescents, but in addition, we focus also on psychological and social variables (Magee, Iverson, Huang, & Caputi, 2008).More generally, there may be different mechanisms involved in the links between sleep-wake variables and BMI in childhood, adulthood and adolescence, e.g. when considering parental influence on sleep timing (Randler, Bilger, & Díaz-Morales, 2009).
There are some additional mediators or co-variates that might influence BMI, such as parental monitoring of eating behaviour, nutrition self-efficacy (Delahanty, Meigs, Hayden, Williamson, & Nathan, 2002), screen time (Li et al., 2010) and fast food consumption (Fleig & Randler, 2009).Parental monitoring has been found to a significant predictor in sleep duration (Randler et al., 2009), so we expected a similar influence on eating behaviour.
In addition to these well-known aspects self-efficacy has become an emerging topic in obesity research.Studies revealed a large body of evidence for the nutrition self-efficacy to enhance the motivation and the volition to change nutrition behaviour (Contento, Randell, & Basch, 2002;Luszczynska, Tryburcy, & Schwarzer, 2007).Self-efficacy in general mediates whether health-related actions are initiated, sustained or persisted (Schwarzer, 2008;Ziegelmann & Lippke, 2009).Different facets of healthy eating self-efficacy show strong correlations with BMI (Delahanty et al., 2002) and interventions aiming at eating self-efficacy showed effects on the BMI of the participating subjects (AbuSabha & Achterberg, 1997;Bas & Donmez, 2009;Roach et al., 2003).In this study, we focus on the relationship between sleep-wake variables and BMI, but we also take aspects of self-efficacy into account to expand previous work.We expect an influence of sleep-wake variables on BMI, but we further hypothesise that nutrition-related self-efficacy should be inversely correlated to BMI, that parental monitoring/control should be related to a lower BMI and that screen time and fast food consumption should be positively related to BMI.

Participants
Nine hundred and thirteen pupils (406 boys, 507 girls) from Southwestern Germany participated in this study.
Mean age was 13.7 ± 1.5 (SD) years and range was between 11 -16 years.Socioeconomic status was not measured but all participants were from middle schools ('Realschule') with parents from the middle class.Eight schools participated in the study but 44% of the data were from one single school.More than 95% of the participants were Caucasian.Participation was voluntary, unpaid and anonymous.Participants were informed prior to the questionnaire that this study was carried out to seek information about the sleep-wake cycle and different aspects of adolescents' life-habits, including nutrition.The completion of the questionnaire took between 30 and 55 minutes depending on the age of the children and was done during one regular school lesson.The study has been approved by Regierungspräsidium Stuttgart (Az74 -6499.2/101/1).Written consent was obtained from all adolescents and from the parents.

Measurement Instruments
Sleep-Wake-Variables -Subjects were asked for rise time and bed time on weekdays and on the weekend (free days) to calculate proxies of sleep length and the midpoint of sleep on free days (adopted from Roenneberg et al., 2004).The mid-sleep time on free days (MSF) is calculated from the two questions sleep-onset time and wake time days on which there are no school or social obligations.MSF is the mid-point between these two times.
The measure differs from that in (Roenneberg et al., 2004) because it uses rise times and bed times.To account for sleep debt acquired during the week, the algorithm proposed in (Roenneberg et al., 2004)  'Social jetlag' can be quantified by calculating the absolute difference between mid-sleep on workdays (MSW) and mid-sleep on free days (MSF) (Wittmann et al., 2006).
Body Mass Index -Weight and height were asked for to calculate Body Mass Index (BMI).To assist pupils, a ruler and a weigh were present in the classroom.Although this was based on self-report, studies showed that these self-report measures are highly reliable in adolescents but generally underestimate overweight (Brener, Mcmanus, Galuska, Lowry, & Wechsler, 2003).However, as we did not classify our subjects into categories (where the underestimation of overweight could be considerable), we used the values for correlational analysis.BMI was transformed to z-scores for each gender and age group separately to remove the effects of age and gender from the relationship.
Psychological Factors -Self-efficacy related to the nutritional behaviour (NSE) was measured with a questionnaire adopted from Lach (2003) and Schaal (2013).Within the nutrition-related self-efficacy subjects responded to five Likert-scaled items dealing with self-efficacy related to internal (e.g.'I am sure to eat healthy even if I have ravenous appetite for something special') or external (e.g.'I am sure to eat healthy even if I am opposed to specific stressors (boredom, loneliness, too much work, conflicts, …)') influences.Responses ranged from 1 ('I completely disagree') to 4 ('I totally agree').The scale showed an acceptable internal consistency of Cronbach's α 0.70 in the present sample (see Schaal, 2013).
Covariates -Screen time was calculated by the mean of two items i) TV, video, DVD as one item and ii) Computer/Internet as the other item scaled from more than 4 hours daily, 3-4 hours daily, 1-2 hours/daily, 30 min daily, no min/day.Fast-food consumption was assessed based on six-point scale from 1 = almost daily to 6 = never.
Parental monitoring was based on a 4-point Likert scale 'My parents take care that I am on a healthy diet' from 1 fully disagree to 4 = fully agree.
Statistical Analyses -All data were treated as continuous data in the calculations based on Pearson's correlations and multiple regressions.Sleep duration, chronotype and social jetlag were inter-correlated and are calculated from the same basic data, thus two statistical approaches were used.In the first, each variable was tested singly and second, a principal component analysis (PCA) with varimax rotation was applied to create one single response variable.The factors scores from (regression) from the PCA were saved and used for further calculations.SPSS 19 was used for analyses.

Results
Descriptive statistics are presented in Table 1.Weight ranged from 27.5 kg to 90.0 kg in girls (mean ± SD: 52.8 ± 10.3 kg) and from 28.8 kg to 100.0 kg in boys (57.2 ± 13.2 kg).In girls, BMI was on average 19.9 ± 3.1 (range 13.0 to 32.2), and 20.1 ± 2.9 (range 14.0 to 33.8) in boys.Less than 1% of the participants had average sleep duration below 6 hours and 2% had sleep duration below 6 hours on weekdays.17.5% of the participants were considered as overweight (11.8% overweight, 5.7% obese).
Short sleep duration, late chronotype and high social jetlag were associated with higher BMI (Table 2).The four different regression models are depicted in Table 3.In all four models, screen time, fast food consumption, and nutrition related self-efficacy had a significant influence.A high amount of screen time was related to a higher BMI.Self-efficacy was negatively related to BMI, thus, children and adolescents that were confident to retain a Europe's Journal of Psychology 2013, Vol.9(2), 339-347 doi:10.5964/ejop.v9i2.558 Sleep and BMI in Adolescents 342 healthy diet even during adverse conditions or circumstances had a lower BMI.The extent to what parents exerted control about healthy eating was not related to BMI, but tended to be significant (p < .1)concerning social jetlag.
Interestingly, fast food consumption was inversely related to BMI.Midpoint of sleep and social jetlag were also significant predictors, but average sleep duration was not.When composing the variables together in a single response variable, this variable also produced a significant effect.

Conclusions
To advance the field further, chronotype should be incorporated in further research in obesity.Further, researchers and clinician may be aware of the bed times as one possible point for interventions to prevent obesity.Recent studies suggest that chronotype could be -at least to some extent -changed to morningness when parents set the bed times (Randler et al., 2009).
was used to calculate the midpoint of sleep on free days corrected for sleep debt.The algorithm is: corrected MSF = MSF -0.5*(SDF -(5*SDW + 2*SDF)/7) with SDF is sleep duration on free days, and SDW is sleep duration on weekdays.Additionally, average sleep duration was measured by calculating (5 * weekday sleep length + 2 * free day sleep length) / 7.
Social jetlag was correlated with midpoint of sleep (r = .847),and with average sleep duration (r = -.246).Midpoint of sleep was correlated with average sleep duration (r = -.510).All three contributed to the factor solution (principal component with varimax rotation, based on the Eigen-value > 1 criterion): midpoint of sleep loaded with .964 on the first component, sleep duration negatively with -.638, and social jetlag with .878.

Table 1
Descriptive Statistics of the Sleep Variables

Table 3
Regressions ofSleep-Wake Variables, Screen Time, Fast-Food Consumption, Self-Efficacy and Parental Control on BMI (z-Scored for Age  and Sex)