Sedentary versus active leisure activities and their relationship with sleeping habits and body mass index in children of 9 and 10 years of age

Abstract

A random sample of 291 9- and 10-year-old schoolchildren from Asturias (Spain) was taken. Using path analysis, a model was tested in which bedtime, the number of hours spent sleeping and leisure activities were the independent variables and the body mass index was the dependent variable. The results show that sedentary and active leisure time and hours spent sleeping are predictors of the body mass index in children. Those children who go to bed late and who use that extra time to watch the television or play with the computer tend to have a greater body mass index, while those children who go to bed earlier and have spent more time reading or playing in the park or at home have a lower body mass index. Encouraging active leisure activities can have an extremely positive effect on their body mass index.

Keywords

active leisure childhood body mass index sedentary leisure sleep

Introduction

In some countries in the developed world, the percentage of children who are overweight or obese is calculated to be over 30 per cent (Amigo et al., 2013; Ng et al., 2014; Ogden et al., 2012; Toselli et al., 2014; Zapico et al., 2014). Childhood obesity can lead to numerous medical problems. To mention just one example, insulin resistance and the metabolic syndrome, which until a few decades ago were rare in children, are becoming increasingly common and are linked to the rise in childhood overweight and obesity (Yi et al., 2014). Furthermore, it has been calculated that up to two-thirds of obese children will become obese adults (Stovitz et al., 2008).

The reason for the growth of this problem is to be found in the obesogenic lifestyle of the developed world, which includes, among other habits, a generalization of sedentary leisure activities (Tremblay et al., 2011), a decrease in active leisure activities and a lack of sleep (Chaput et al., 2011). A significant relationship has been found to exist between children’s body mass index (BMI) and sedentary leisure time in which the basic forms of entertainment consist of television, games consoles and computers. Of these, TV would appear to be the one which is most closely associated with an increase in BMI (Falbe et al., 2013).

A close correlation has also been found between the amount of physical activity and BMI in children. Laguna et al. (2013) observed that the physical activity carried out in the day-to-day activities (e.g. playing) of 9-year-old children who are of a normal weight is more intense than in children who are overweight or obese. However, less than 50 per cent of children carry out moderately intense or vigorous physical activity in line with current recommendations for children’s health (Kettner et al., 2013).

The importance of sleep in controlling weight has been shown. Sleeping less than 9 hours a day is associated with an increase in BMI in children of 7 years of age, although this increase cannot be explained directly by a lesser degree of physical activity (Planinsec and Matejek, 2004). It appears, therefore, that an inverse relationship exists between time spent sleeping and the risk of being overweight or obese during childhood. This relationship can be explained by the fact that the lack of sleep causes a disruption of hormones, resulting in decreased tolerance to glucose, decreased sensitivity to insulin, an increase in the concentration of cortisol in the latter part of the day, an increase in levels of ghrelin and a decrease in the levels of leptin, all of which lead to an increase in hunger and a decrease in the ability to satisfy that hunger (Leproult and Van Cauter, 2010). In this sense, Hart et al., (2014) examined the effect of experimental changes in children’s sleep duration on self-reported food intake, appetite-regulating hormones and weight. When the duration of their sleep was increased, children reported a significant reduction in their consumption (kcal/day), lower fasting leptin values and lower weight. For all these reasons, it could be affirmed that the decrease in the number of hours that children and adolescents sleep could be playing an important role in the current high prevalence of child-juvenile obesity (Chaput et al., 2011).

Knowledge regarding the influence of each of these variables on children’s BMI and, in particular, the relationships between them could have important implications for the field of education. To date, most research has analysed the particular influence which each of the aforementioned variables has on childhood obesity. However, using path analysis, it is possible to show how this set of habits interacts with each other to promote an obesogenic lifestyle, and this, ultimately, is the key to understanding this problem. For this reason, path analysis was used to test a model in which sedentary leisure activities would be related to a higher BMI because they would predict less hours of sleep, and active leisure activities (playing in the park, reading or playing at home) would be associated with a lower BMI because they would predict going to bed earlier.

Method

Participants

The sample was taken at random from the schools of the Principality of Asturias. A random cluster sample was used, thus making it possible to obtain results which would be representative of the whole of the population of Asturias in this age group.

The sample size was calculated a priori in order to obtain moderate effect sizes (effect size f2 = .15) (Cohen, 1988) using the GPower 3 program (Faul, 2012) for multiple regression (a type I error (α err prob = .05), a statistical power analysis (1−β err prob = .95) and number of predictors = 3). This procedure for regression models handles cases of tests for an overall effect – that is, the hypothesis that the population value of R² is different from 0 (Faul et al., 2007). The results showed that a sample size of 119 participants was required in order to obtain a moderate effect size. A total of 291 children from 30 state education centres of the Principality of Asturias were evaluated. In all, 142 (49.3% of the sample) were girls and 149 (50.7%) were boys, the mean age being 9.33 years with a standard variation of .55.

Instruments

In order to weigh the participants, electronic scales of the Firstline brand, model FPS4141, were used. To measure their height, a Kóndor brand measuring tape, model CF265, was used. A questionnaire was designed regarding habits related to sedentary and active leisure activities and sleep (Table 1). The questionnaire made it possible to calculate the time that each child dedicated to each of the variables being studied.

Table 1.

Questionnaire regarding physical activity, sedentary leisure activities and sleep.

1. At what time do you get up during the week to go to school?

1.1. At what time do you catch the bus?

2. Where do you have breakfast during the week?

2.1. Do you watch the television while you have breakfast?

2.2. What programmes do you watch?

3. Where do you have breakfast at the weekend?

3.1. Do you watch the television while you have breakfast?

3.2. What programmes do you watch?

4. Where do you have lunch during the week?

4.1. Do you watch television while you have lunch?

4.2. What programmes do you watch?

4.3. What do you normally do when you come out of school at lunchtime?

5. Where do you have tea during the week?

5.1. Do you watch television while you have tea?

5.2. What programmes do you watch?

5.3. What do you normally do when you come out of school in the afternoon?

6. Where do you have supper during the week?

6.1. Do you watch television while you have supper?

6.2. What programmes do you watch?

7. At what time do you normally go to bed during the week?

8. At what time do you get up on Saturday and Sunday?

8.1. What television programmes do you watch?

9. At what time do you go to bed on Friday, Saturday and Sunday?

9.1. What television programmes do you watch in the evening and before you go to bed?

10. Do you watch the television on your own or with your parents?

11. Have you got a television in your bedroom?

12. Have you got a computer in your bedroom?

13. How many hours a day do you play with the computer during the week?

13.1. What games do you usually play on your computer?

13.2. How many times do you play them?

14. Have you got a games console in your bedroom?

14.1. How many hours a day do you play with the console during the week?

14.2. How many hours a day do you play with the console during the weekend?

14.3. What games do you usually play with the console?

15. How many days a week do you read? What about at the weekend?

15.1. How long do you spend reading each day?

15.1. If you start to read at about … what time do you read until?

15.2. Do you read before you go to sleep? For how long? At what time do you start to read and at what time do you turn off the light?

15.3. What book are you reading at the moment? How many pages do you read a day?

16. Do you usually go to the park to play?

16.1. How many days a week? What days?

16.2. At what time do you go to the park? What time do you stay in the park until?

16.3 How long are you in the park?

16.4. What about at the weekend?

17. At home, do you spend time playing on your own or with you brothers and sisters (if they have any)?

17.1. How many days a week do you play? What days? What about at the weekend?

17.2. How long do you spend playing with games which do not involve the computer or the video console?

17.3. At what time do you start to play and at what time do you finish?

17.4. What games do you usually play?

Procedure

Parents were asked to give their signed consent for the children to participate in a study of children’s lifestyles. The study involved an individual interview, lasting between approximately 25 and 30 minutes, which was carried out in an office in the school. Two anthropometric parameters, weight and height, were obtained, and these were subsequently used to calculate the BMI following the criteria of Cole et al. (2000). Each participant was weighed and measured barefoot in an upright position and with their head held up. In order to check the reliability of the height measurement, a series of 50 measurements were taken and then compared with those taken by another evaluator. Both the Kappa concordance index (.75) and the intra-observer concordance (.79) were good.

Having obtained these two parameters, the child completed the questionnaire, which consisted of 14 basic questions. When answering the questions regarding the number of hours spent watching television, the children were shown a television guide in order to check the real duration of the programmes and thus assure a greater degree of accuracy in the answers. Information regarding the time of going to bed and of getting up was provided by parents at the same time as they gave their signed consent for the participation of their children.

Data analysis

The statistical analysis was carried out using path analysis or structural equation modelling with the program Mplus 5 (Muthén and Muthén, 2012). The analyses were fundamentally of a confirmatory nature. The model was evaluated on the basis of the significance of the chi-squared statistical test and also on goodness of fit indexes, namely, the Tucker–Lewis Index (TLI), Comparative Fit Index (CFI) and the Root Mean Square Error Approximation (RMSEA). Previously, Pearson’s Correlation analysis and a multiple regression analysis had been carried out in order to determine the significant relationships between variables in the path analysis.

Results

Descriptive statistics of the variables are shown in Table 2. Analyses were carried out in order to identify any cases of outliers using box-plot diagrams and normality tests in order to check that the skewness and kurtosis statistics were within a range from −1 to 1. The box-plot diagrams did not indicate the presence of atypical values, although in the case of sedentary leisure, one outlier case was found and this was replaced by the mean value. The correlation matrix between the variables studied is shown in Table 3.

Table 2.

Descriptive statistics of the variables.

	Minimum	Maximum	Mean	SD	Skewness		Kurtosis
					Statistic	SE	Statistic	SE
BMI	13.86	32.29	19.59	3.22	.69	.14	.21	.28
Hours of sleep	7.50	11.50	9.85	.68	−.38	.14	.52	.28
Time of going to bed	20.30	24.15	22.06	.03	.26	.14	.92	.28
Active leisure time	60	1710	682.34	388.11	.48	.22	−.57	.44
Sedentary leisure time	95	3060	1335.85	570.17	.24	.14	−.29	.28

SD: standard deviation; SE: standard error; BMI: body mass index.

Table 3.

Correlation matrix between variables.

	Sedentary leisure time	BMI	Hour of sleep	Active leisure time
Sedentary leisure time
BMI	.13*
Hours of sleep	−.29**	−.15*
Active leisure time	0.03	−.15	0.03
Time of going to bed	.35**	.15**	−.76**	−0.05

BMI: body mass index.

p < .01; *p < .05

Different models of linear regression equations were constructed based on the aforementioned variables, using hours of sleep and time of going to bed as criterion variables. Table 3 shows the standardized coefficients, the significance of those coefficients and the percentage of explained variance of each model. First, a simple regression model was elaborated where hours of sleep was the dependent variable and time of going to bed was the independent variable. This was done in order to discover the relationship between these variables, which would then be used as dependent variables in subsequent models in this study. This model explains 4.1 per cent of the total explained variance (adjusted R²) and is significant (F(1, 290) = 13.51; p = 0.001). Time of going to bed was a significant predictor (β = −.211; p < 0.001).

The regression model for time of going to bed explains 8.3 per cent of the total explained variance (adjusted R²) and is significant (F(3, 115) = 9.697; p = 0.000). Sedentary leisure time was a significant variable (p = .000). The regression model for hours of sleep explains 6.7 per cent of the total explained variance (adjusted R²) and is significant (F(3, 115) = 3.806; p = 0.000). Active leisure time was a significant variable (p = .000). Standardized statistics and p-values of the last two models are shown in Table 4.

Table 4.

Multiple regression models of hours of sleep and time of going to bed.

	Hours of sleep		Time of going to bed
	β	p-value	β	p-value
BMI	−.14	.11	.01	.94
Active leisure time	−.25	.006	.05	.59
Sedentary leisure time	.15	.09	−.39	.001

BMI: body mass index; β: standardized coefficients.

The path analysis showed that the fit of the model tested was good. The chi-squared test was not significant (4.244; p = 0.3740); the fit indexes CFI and TLI were 0.995 and 0.987, respectively, and the RMSEA showed a value of 0.014 with confidence intervals from 0.000 to 0.090 (see Figure 1).

Figure 1.

Model of relationships between BMI, sedentary and active leisure, bedtime and sleep.

The model obtained using path analysis indicates that the BMI has a significant positive relationship with sedentary leisure activities (p < 0.05). The time of going to bed also shows a significant positive relationship with BMI (p < 0.001). Time of going to bed is a predictor of a greater degree of number of hours of sleep (p < 0.001). Sedentary leisure activities are a predictor of the number of hours of sleep (p < 0.001), and active leisure time is a predictor of time of going to bed (p < 0.001), there being a significant inverse relationship.

Discussion

The initial correlational analysis showed that there exists a significant relationship between sedentary leisure and BMI, length of sleep and time of going to bed. Those children who spend more hours looking at the screens of televisions, videogames and computers have a higher BMI, sleep less and go to bed later. These results coincide with those of (Ghavamzadeh et al., 2013; Olds et al., 2011) and underline the degree to which the problem of childhood obesity is affected by leisure activities which are becoming increasingly sedentary and the reduction in the length of sleep during childhood. In relation to this, the model tested using path analysis can explain, to some extent, how a specific type of leisure activity can affect BMI through its relationship with sleep (Figure 1). Sedentary leisure activities have been associated with BMI in two different ways. First, there is a direct, two-way relationship: sedentary leisure activities predict a higher BMI and, in turn, a higher BMI predicts sedentary-type leisure activities. This can give rise to a self-perpetuating relationship in which overweight children tend to do less physical activity (Nixon et al., 2008), and this in turn increases the likelihood of their gaining weight, since physical exercise plays an essential role in preventing weight gain throughout life (Gordon-Larsen et al., 2009). Swinburn and Egger (2004) presented a model which demonstrates how, as gradual weight gain speeds up, there is a reduction in the healthy habits which help to keep that increase in weight under control. These habits include physical activity in general and active leisure activities in particular.

However, in this study, sedentary leisure time is also associated indirectly with an increase in BMI as a result of the reduction in the hours of sleep. Sedentary leisure time was associated with a reduction in the number of hours of sleep, which, in turn, predicted a higher BMI. This relationship can be explained by the fact that the lack of sleep causes a hormonal disruption (Leproult and Van Cauter, 2010). As some authors have shown, an increase in overweight and obesity in childhood and adolescence could be connected with a generalized decrease in hours of sleep in this population group (Matricciani et al., 2012). It would seem, therefore, that these could be the most important stages of life with regard to the acquisition of behavioural patterns capable of increasing well-being throughout a person’s life.

The results also indicate that active leisure time, that is, the sum of the time spent on reading, playing at home and in the park, predicts a lower BMI because this type of leisure time is associated with going to bed earlier. Once again, this coincides with the results of Olds et al. (2011) regarding the relationship between the time of going to bed, physical activity and BMI.

Within active leisure time, the time spent in the park is considered to be an extremely important element in children’s health (Blanck et al., 2012). It is curious to note that in this set of variables, reading is regarded as an active leisure activity. This result does, however, coincide to some degree with that of Sisson et al. (2011). The path analysis carried out in our study indicates that it is one of the elements associated with a lower BMI. This fact is not easy to explain but could be related to certain habits, within the family, where reading is the lead-up to a particular bedtime as opposed to going to bed after watching the television, which would not always guarantee that the children go to bed at a suitable time.

For this reason, it is important to consider measures to ensure proper sleep when designing campaigns aimed at improving children’s health preventing obesity. According to the available data, correcting the existing sleep deficit means correcting another very widespread habit, namely, that of staying up late to watch the television. Lack of sleep and television appear to be an inseparable binomial. Consequently, parents should ensure that their children do not stay up watching the television until the programme finishes, but go to bed at a predetermined time, independently of what is on television. By extension, a television in the child’s bedroom only increases the likelihood of the child going to sleep later (Cameron et al., 2013). If children are to grow up healthily, it is also important to encourage to play at home and in the park and to read on a daily basis.

One of the limitations of this study is its cross-sectional nature, which could cast some doubt on just how significant the relationships between the variables studied would be over a period of time (Bijleveld et al., 1998). Nevertheless, the use of path analysis makes it possible to overcome this problem, at least partially, since this method involves creating ‘a priori’ a theoretical model which is based on a study of the literature and in which the relationships between a series of variables, subsequently confirmed statistically in this study, are determined.

Footnotes

Acknowledgements

This research has been developed within the project d + i + i i+d+i MICINN (Spain) titled ‘Behavioral habits, emotional states and eating style associated with childhood overweight’ (Ref. PSI2010-1608).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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