Health Psychology 2015, Vol. 34, No. 1, 40 –50

© 2014 American Psychological Association 0278-6133/15/$12.00 http://dx.doi.org/10.1037/hea0000108

Longitudinal Association Between Child Stress and Lifestyle Nathalie Michels, Isabelle Sioen, Liesbet Boone, Caroline Braet, and Barbara Vanaelst

Inge Huybrechts Ghent University and International Agency for Research on Cancer, Lyon, France

Ghent University

Stefaan De Henauw

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Ghent University and University College Ghent Objective: Psychosocial stress has been linked with an unhealthy lifestyle but the relation’s direction remains unclear. Does stress induce sleeping problems, comfort food consumption, and lower physical activity, or do these unhealthy lifestyle factors enhance stress? This study examined the bidirectional stress–lifestyle relation in children. Method: The relation between stress and lifestyle was examined over 2 years in 312 Belgian children 5–12 years old as part of the Children’s Body Composition and Stress study. Stress-related aspects were measured by questionnaires concerning negative events, negative emotions, and behavioral problems. The following lifestyle factors were assessed: physical activity (by accelerometers), sleep duration, food consumption (sweet food, fatty food, snacks, fruits and vegetables), and eating behavior (emotional, external, restrained). Bidirectional relations were examined with crosslagged analyses. Results: Certain stress aspects increased physical activity, sweet food consumption, emotional eating, restrained eating, and external eating (␤s ⫽ .140 –.319). All relations were moderated by sex and age: Dietary effects were mainly in the oldest children and girls; stress increased physical activity in the youngest, whereas it tended to decrease physical activity in the oldest. One reversed direction effect was found: Maladaptive eating behaviors increased anxiety feelings. Conclusions: Relations were mainly unidirectional: Stress influenced children’s lifestyle. Stress stimulated eating in the absence of hunger, which could facilitate overweight. Consequently, families should realize that stress may influence children’s diet, and problem-solving coping skills should be acquired. In contrast to recent findings, stress might also stimulate physical activity in the youngest as positive stress coping style. Keywords: stress, emotions, physical activity, eating behavior, sleep

Forshee, & Shelby, 2006; Ursin & Eriksen, 2004). Because of this adaptive and dynamic state, stress is for research purposes not only operationalized on the event level but also on the symptom level by measuring daily problems and emotions. Stress has been associated with disease (inflammatory, metabolic, cardiovascular, etc.) due to physiological and behavioral pathways (S. Cohen, Janicki-Deverts, & Miller, 2007; Pervanidou & Chrousos, 2011), the latter being an often neglected correlate of stress, especially in children. Nevertheless, unhealthy behavior or lifestyle during childhood has been shown to track into later life (Ashcroft, Semmler, Carnell, van Jaarsveld, & Wardle, 2008; Mikkila, Rasanen, Raitakari, Pietinen, & Viikari, 2005; Telama, 2009; Thorleifsdottir, Bjornsson, Benediktsdottir, Gislason, & Kristbjarnarson, 2002). Several lifestyle factors have been associated with psychosocial stress. First, consistent cross-sectional evidence exists for an association between high stress and high sedentary screen time in children and adolescents (Biddle & Asare, 2011). However, these findings do not give clear indications on the direction of this association. Remarkably, most research has focused on the effect of physical activity on stress given that activity could generate euphoric feelings, increase social support, and even decrease the stress response (Tsatsoulis & Fountoulakis, 2006). The causality might also be in the other direction given that laboratory stressors

Psychological stress arises when the demands of a situation (i.e., stressor) exceed an individual’s ability to cope and resolve the problem, resulting in emotional, behavioral, and cognitive disturbances that might put a person at risk for illness (McCance,

This article was published Online First August 18, 2014. Nathalie Michels and Isabelle Sioen, Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University; Liesbet Boone and Caroline Braet, Department of Developmental, Personality, and Social Psychology, Ghent University; Barbara Vanaelst, Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University; Inge Huybrechts, Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, and the Dietary Exposure Assessment Group, International Agency for Research on Cancer, Lyon, France; Stefaan De Henauw, Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, and Department of Health Sciences, Vesalius, University College Ghent. Nathalie Michels and Liesbet Boone were financially supported by the Research Council of Ghent University (Bijzonder Onderzoeksfonds). Isabelle Sioen and Barbara Vanaelst were financially supported by the Research Foundation–Flanders. There are no conflicts of interest. We thank the participating children and their parents for their voluntary participation. Correspondence concerning this article should be addressed to Nathalie Michels, Ghent University, Department of Public Health, De Pintelaan 185-2 Blok A, 9000 Ghent, Belgium. E-mail: [email protected] 40

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CHILD STRESS AND LIFESTYLE

have been able to increase physical activity as a way of distraction (Balantekin & Roemmich, 2012). Second, more and more studies indicate that sleep is linked to stress. On the neurological level, the regulation of sleep, behavior, and emotions is closely related (Gregory & Sadeh, 2012; Horne, 1993). On the psychological level, stress may result in problems in falling asleep because of rumination or next-day anticipations. On the hormonal level, short sleep increases stress hormones, and these hormonal changes inhibit sleep. A recent review showed that the sleep–stress relation in children is likely to be bidirectional, but most evidence has substantiated the effect of sleep on stress (Gregory & Sadeh, 2012). Finally, stressed children may eat increased amounts of unhealthy food given that eating operates as a way of coping with stress (Macht, 2008). The assumed underlying pathway is the increased cortisol concentration influencing reward and appetite pathways (Adam & Epel, 2007; Dallman et al., 2003; Epel, Tomiyama, & Dallman, 2012; Torres & Nowson, 2007). In children, mainly laboratory studies have discussed the stress effects on diet (Balantekin & Roemmich, 2012; Roemmich, Wright, & Epstein, 2002). There is also evidence for the reverse pathway that food might enhance mood by opioidergic and dopaminergic neurotransmission (Gibson, 2006), but this literature covers only adult populations. The association between stress and food consumption must be visible in eating behavior as well. After all, people with an emotional eating behavior have learned to label the negative stress feelings as “hunger” (Bruch, 1964) and think about food as an escape from stress (Adam & Epel, 2007; Dallman et al., 2003). Also, external and restrained eating might be influenced by stress given that emotions have been hypothesized to impair cognitive control to external stimuli such as food (Macht, 2008). However, literature in children/adolescents is mainly cross-sectional (Braet & van Strien, 1997; Goossens, Braet, Van Vlierberghe, & Mels, 2009; Hou et al., 2013; Nguyen-Rodriguez, McClain, & SpruijtMetz, 2010). To conclude, evidence on the stress–lifestyle relation is scarce in children and the directionality is still unclear. Therefore, in the present longitudinal study, we aimed to test the bidirectional longitudinal relation between stress (measuring its different aspects) and lifestyle in children. The hypothesis was that children’s stress would be associated with lower activity, shorter sleep duration, and unhealthier dietary patterns/behaviors. Based on the literature (Rew, Principe, & Hannah, 2012; van Strien & Bazelier, 2007; Zellner et al., 2006), our subhypothesis was that these effects would be stronger in older children and in girls. Knowledge on the specificity and direction of the stress–lifestyle relation will help in the formulation of preventive strategies for stress and its negative health effects.

Method Participants and General Procedure Participants were Belgian children (50% boys) recruited by random cluster design (all children from 12 primary schools of Aalter) for the longitudinal Children’s Body Composition and Stress (ChiBS) study (2010 –2012; Michels, Vanaelst, et al., 2012). The selection of Aalter was due to the integration of our ChiBS project in the ongoing European Identification and Prevention of

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Dietary- and Lifestyle-Induced Health Effects in Children and Infants study. Aalter is an urbanized municipality in the Flemish region of Belgium with a population of 19,860 inhabitants and a surface area of 81.9 km2. Aalter has average-to-high socioeconomic characteristics, with a mean income of 17,154 euro/year (vs. 16,599 in Flanders overall), 3.9% unemployment rate (vs. 6.8% in Flanders overall), and 1.9% migrant population (vs. 6.4% in Flanders overall; Studiedienst Vlaamse regering, 2012). Concerning physical activity, there is a typical Belgian infrastructure, for example, outdoor sport fields, swimming pool, biking paths, and 2.5 sport clubs per 1,000 inhabitants. The ChiBS study aimed to examine the relation of stress with lifestyle and body composition in primary schoolchildren. The children were measured during three waves (i.e., measurement periods) with 1-year intervals: 2010, 2011, and 2012. The goal was to cover all primary school ages by examining the first to fourth class year (covering the ages 5 to 10 years) at baseline and by examining third to last year of primary school (covering the ages between 8 and 12 years) at the final follow-up in 2012. This way, all primary school years were covered by the end of this study. The study was conducted according to the guidelines of the Declaration of Helsinki and the project protocol was approved by the Ethics Committee of the Ghent University Hospital. Written informed consent was obtained from the parents, and the children gave verbal assent. To enhance participation, parents received feedback on important measurements (e.g., adiposity) and children received a small gadget. Overall, 523 children participated between February and May 2010 (T0), 455 between February and April 2011 (T1), and 330 between February and April 2012 (T2). For this report, 312 children were included who participated in all three waves with at least information on all stress questionnaires. For some of these children, certain lifestyle information was missing. Participants with and without complete data were compared using Little’s missing completely at random test (Little, 1988). A nonsignificant chisquare test statistic suggests that missing data are missing only in a random way and hence do not introduce any bias with regard to the central research question. Those who dropped out between 2010 and 2012 did not differ on age, sex, adiposity, stress, or lifestyle compared with the included children, but those who dropped out had a lower socioeconomic status (p ⫽ .002).

Stress Questionnaires Stress arises when the demands of a situation exceed an individual’s ability to cope and resolve the problem, resulting in emotional and behavioral disturbances (McCance et al., 2006). Negative events, negative emotions, and behavioral problems were examined by questionnaires to cover the different aspects of stress. The three stress aspects were studied separately. Moreover, a total composite stress score (CSS) was calculated by summing the z-scores of the three stress aspects per child: one z-score for negative events, one z-score for negative emotions, and one z-score for behavioral problems. Negative events (child-reported at T0, T1, and T2). The Coddington Life Events Scale for Children is a validated and well-established 36-item questionnaire (test–retest r ⫽ .69, parent– child agreement ICC ⫽ .45 in 12- to 14-year-olds; VillalongaOlives et al., 2008). It assesses the prevalence, frequency, and

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MICHELS ET AL.

timing of stressful negative life events relevant for this age group during the last year filled in by the child. Negative events included familial issues (e.g., divorce), school issues (e.g., failing a grade), social issues (e.g., moving), criminal issues (e.g., juvenile court), economical issues (e.g., job loss of parents), and illness/death (of child/family/pet/friends). By measuring significant life events in terms of life change units depending on timing, frequency, and severity, the scale can provide insight into recent events that may affect the child’s health (Coddington, 1999). Negative emotions (child-reported at T0, T1, and T2). Children were asked to report on their feelings in general on a 0 –10 multipoint Likert scale (0 ⫽ not at all to 10 ⫽ very strong) analogous to the study of Zimmer-Gembeck, Lees, Bradley, and Skinner (2009): one question on anger, one on anxiety, and one on sadness. These basic emotions are understood by children (Flavell, 1999) and could be therefore uncomplicatedly be used in our population to measure overall emotional state. The negative emotions score showed a Spearman correlation of r ⫽ .48, p ⬍ .001, with the Negative Affect score of the validated Positive and Negative Affect Schedule for Children (Laurent et al., 1999) in a subsample of 153 children who were at least 9 years old. Behavioral problems (parent-reported at T0, T1, and T2). Parents were asked to complete the standardized Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997; Cronbach’s alpha ⫽ .51–.70, and 2-week test–retest stability r ⫽ .96 in 6- to 10-year-olds; concurrent validity with the Child Behavior Checklist r ⫽ .7–.87 in 4- to 7-year-olds; Goodman & Scott, 1999; Smedje, Broman, Hetta, & von Knorring, 1999). Parents report on children’s behavioral problems over the past 6 months. For each of the 20 statements, parents could answer on a scale with the following anchors: 0 ⫽ not true, 1 ⫽ somewhat true, and 2 ⫽ certainly true. The statements were divided into subscales, with higher scores reflecting issues in the following areas: peer problems (e.g., tends to play alone), conduct problems (e.g., often fights with other children), and emotional problems (e.g., often seems worried).

Lifestyle Factors Physical activity and sedentary behavior (objectively measured at T0 and T2). The children were asked to wear an Actigraph accelerometer for 5 consecutive days in 2010 and 2012 during waking hours on an elastic belt on the right hip. Activity counts were stored at 15-s intervals. To be included in the data analysis, children had to wear the accelerometer for at least 8 hr per day, not exceeding 18 hr per day, and for at least 3 days. This resulted in an exclusion rate of 7%, without introducing any bias (t test included vs. excluded: sedentary 57.9% vs. 59.0%, light 33.8% vs. 34.4%, moderate 5.0% vs. 4.4%, vigorous 2.6% vs. 2.3%, age 8.3 years vs. 8.6 years, stress score ⫽ 0.10 vs. 0.13; ps ⬎ .05). Data were classified into four bands of physical activity using the cutoff points of Evenson depending on the counts per minute: sedentary (0 –100), light (100 –2,295), moderate (2,296 – 4,011), and vigorous activity (ⱖ4,012; Trost, Loprinzi, Moore, & Pfeiffer, 2011). Moderate and vigorous activity were summed to calculate moderateto-vigorous physical activity. These accelerometer counts were expressed using percentages by further dividing them by the total recording time to correct for wearing time. Between-days

intraclass correlations of .79 and .82 were found for moderateto-vigorous activity and sedentary time, respectively. The children with accelerometer data were older (mean ⫽ 8.3 years vs. 7.2 years, p ⬍ .001) and had a shorter sleep duration (mean ⫽ 10.8 hr vs. 11.0 hr, p ⫽ .002) than those without accelerometer data. No differences were seen in sex, socioeconomic status, diet, and reported physical activity. Sleep (parent-reported at T1 and T3). In 2010 and 2012, parents also reported the usual time when their child went to bed in the evening and got up in the morning on weekdays and weekend days. The sleep duration question was copied from the School Sleep Habits Survey (2-week test–retest r ⫽ .68; Shadid & Wilkinson, 2012). These data were used to calculate the child’s sleep duration. A recent review concluded that this definition of sleep duration (difference between a child’s bed and wake time, separate for weekdays and weekends) is the most accurately reported sleep estimate as it is easy to comprehend, open to minimal subjective interpretation, and requires only a simple calculation (Matricciani, 2013). Food Frequency Questionnaire (parent-reported at T0, T1, and T2). The Food Frequency Questionnaire is a screening instrument to investigate food consumption frequency and behaviors associated with obesity and general health in children. This 43-item instrument was developed and reproducibility was tested within the Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants study (test–retest r ⫽ .32–.76 for separate items in 2- to 10-year-olds; Lanfer et al., 2011) and is used as a screening instrument to investigate dietary habits and food consumption frequency in children. Parents were asked to report on the frequency of their child’s consumption of each of the preselected food items (43 items) during the preceding 4 weeks using one of the following response options concerning the intake frequency of each food item: never/less than once a week (0/week), 1–3 times a week (2/week), 4 – 6 times a week (5/week), 1 time per day (7/week), 2 times per day (14/week), 3 times per day (21/week), 4 or more times per day (30/week), or I have no idea. “I have no idea” answers (0.5%) were considered missing values. Frequencies of intake were assessed without quantifying portion sizes. This method could introduce over- or underestimation of food intake depending on the children’s portion size. Information on individual portion sizes adds to the explanation of variance in food intake, but the major part of variance is still explained by frequency of intake alone (Noethlings, Hoffmann, Bergmann, & Boeing, 2003). To identify dietary patterns, four “food indices” on dietary pattern were calculated by summing the frequency of separate food items. A food index for “sweet foods” (sweet drinks, jam, honey, sweet breakfast cereals, sweet snacks) and a food index for “fatty foods” (fried potatoes, chocolate- or nut-based spreads, high-fat dairy, sauces, cheese, fat meat preparations, butter, high-fat snacks) reflect the unhealthy meal and between-meals food items. Because the hypothesis was that stress would mainly stimulate the between-meals snacks, we also created a “snacks food index” with only the between-meals fatty/sweet/salty snacks (chocolate and chocolate bars, candy, biscuits, cake, ice cream, chips, savory pastries). In addition, a healthy food index for “fruits and vegetables” (fruit, freshly squeezed fruit juice, vegetables) was used. Dutch Eating Behavior Questionnaire (DEBQ; childreported at T1 and T2). The DEBQ is a 33-item questionnaire (test–retest r ⫽ .87– 0.90; Cronbach’s alpha ⫽ .74 –.81 in 7- to

CHILD STRESS AND LIFESTYLE

12-year-olds; van Strien & Oosterveld, 2008). Three types of eating behavior can be identified in children: eating in response to negative emotions (emotional eating), eating in response to the sight or smell of food (external eating), and eating less than desired to lose or maintain body weight (restrained eating). In all three types of eating behavior, the appropriate self-regulating mechanism of food intake is diminished or lost. Children answered the questions on their usual behavior using the following scale: 1 ⫽ never, 2 ⫽ almost never, 3 ⫽ sometimes, 4 ⫽ often, or 5 ⫽ very often (van Strien, Frijters, Bergers, & Defares, 1986).

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Possible Confounding Factors Sex, age, socioeconomic status, and body mass index (BMI) were considered as potential confounding factors. Because correction for BMI did not modify the results, the results are shown without correction for BMI. After all, only 7% overweight was seen at baseline. The children’s birth date and sex were reported by the parent. Socioeconomic status was represented by the highest parental education (maximum of both parents) according to the International Standard Classification of Education (UNESCO, 2010). Due to low variation, the variable was further categorized in two levels of education (low/medium vs. high), with Levels 0 –3 defined as low/medium education (44.8%), and Levels 4 and 5 being defined as high education (i.e., tertiary education, 55.2%). Weight and height were measured with bare feet and in light underwear with an electronic scale and a stadiometer (SECA GmbH & Co. KG., Hamburg, Germany) to the nearest 0.1 kg and 0.1 cm, respectively. Age- and sex-specific BMI z-scores were calculated according to the international method from Cole, Freeman, and Preece (1998). For descriptive purposes only, overweight was determined by the International Obesity Task Force classification (Cole, Bellizzi, Flegal, & Dietz, 2000).

Statistics Descriptive statistics. Wilcoxon signed-ranks tests were used to analyze the evolution of stress and lifestyle factors over the three measurement times. For the descriptives, a significance level of p ⬍ .05 was used. For the cross-lagged analyses, a significance level of p ⬍ .0125 (⫽ .05/4) was used to adjust for multiple testing (four analyses were executed for each outcome variable).

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Structural equation modeling (SEM). SEM in Mplus (Muthén & Muthén, 2007) with robust maximum-likelihood estimation Satorra–Bentler scaled mean-adjusted chi square (robust to nonnormality) was used to determine longitudinal associations between stress (CSS, negative events, negative emotions, and total problems) and lifestyle (measured and reported physical activity, measured sedentary activity, screen time, sleep duration, four dietary intake factors, and three eating behaviors). A number of fit indices were used to evaluate the model (Kline, 2005): chi-square test, Comparative Fit Index (CFI), and root mean square error of approximation (RMSEA); ␹2/df ratio of 2.00 or below, CFI values of 0.90 or above, and RMSEA values of 0.06 or below were used as indicators of acceptable fit (Kline, 2005). Power analyses (Miles, 2003) showed acceptable power, with values between 0.82 and 0.99 for the represented results; only for the age-stratified physical activity analyses, a low power of 0.60 was found. Measurement model. In the SEM analyses, each latent variable was represented by parcels rather than by individual scale items. Parcels are less likely to have correlated residuals and can correct for measurement error (typical in survey items, for example, due to respondent bias). The latent variable “total problems” was modeled by three parcels with each parcel containing one SDQ subscale (i.e., Peer Problems, Conduct Problems, or Emotional Problems; loadings between .692 and .795; RMSEA ⫽ 0.054, CFI ⫽ 0.974). The latent variable “negative emotions” was modeled by three parcels (i.e., single-item report of anger, anxiety, and sadness; loadings between .418 and .803; RMSEA ⫽ 0.017, CFI ⫽ 0.987). The three eating behavior variables were each modeled by three parcels. The items of the specific DEBQ subscale were assigned randomly to the parcels (Kline, 2005; Little, Cunningham, Shahar, & Widaman, 2002). Consequently, each parcel consisted of three or four of the original questions of the specific eating behavior. Acceptable fit was retrieved: emotional eating (loadings between .857 and .902, ps ⬍ .001; RMSEA ⫽ 0.029, CFI ⫽ 0.995), external eating (loadings between .554 and .847; RMSEA ⫽ 0.057, CFI ⫽ 0.948), and restrained eating (loadings between .670 and .879; RMSEA ⫽ 0.039, CFI ⫽ 0.980). Other variables (events, dietary intake, accelerometer data, and sleep duration) could not be modeled by parcels because only manifest variables were available. Structural model. Cross-lagged models were used as shown in Figure 1, including (a) cross-lagged paths (e.g., from

Figure 1. Model for the longitudinal cross-lagged models analyzing the stress–lifestyle relation. The paths of interest are the bidirectional, longitudinal paths between stress and lifestyle in 2011 and 2012 (thick arrows). The other paths are placed simultaneously in the same model to correct for other longitudinal and cross-sectional relations.

MICHELS ET AL.

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stress at baseline to lifestyle at follow-up), (b) autoregressive paths (e.g., stress at T1 to stress at T2), and (c) correlations within waves. To control for possible confounding effects (age, sex, and socioeconomic status), we allowed paths from each of these three variables to all the constructs included in the structural models. The results of the cross-lagged associations between the 2011 and 2012 wave are described and interpreted because these were corrected for all the earlier factors and associations (Burkholder & Harlow, 2003). For the available parameters in 2010, similar results were seen in the cross-lags 2010 –2011 as in the cross-lags 2011–2012. Although the data were derived from a clustered design, no multilevel analyses were executed because only a very low percentage of variance was determined by the school clusters (between 0.08% for restrained eating up to 3.0% for physical activity). Also, the model did not ameliorate significantly when comparing the one-level with the two-level model (based on the chi-square statistic). Multigroup analyses. Multigroup comparisons were tested to investigate potential structural differences for the crosslagged model depending on sex and age. Age was transformed into a categorical variable by creating two groups based on a median (one group ⬍8, one group ⱖ8). The analysis was done by comparing the fit between the constrained model, in which the structural relations between both groups were not allowed to vary, and the unconstrained model, in which the structural relations were set free. The difference in the chi-square and CFI statistics between both models was calculated as follows: constrained model statistic ⫺ unconstrained model statistic. Nonequivalence between boys and girls was considered in the case of a significant chi-square difference and a CFI difference higher than 0.01.

Results Descriptive Data Descriptive data of the used stress and lifestyle parameters are shown in Table 1 (both median level and scores of participants who were in the 25th or 75th percentile). A significant increase in negative events and a decrease in negative emotions were seen between 2011 and 2012. Also, lifestyle factors changed, reflecting the natural evolution with age: increased sedentary time, fatty food consumption, fruit and vegetable consumption, and restrained eating, but decreased sleep duration, physical activity, emotional eating, and external eating.

Longitudinal Cross-Lagged Associations of Stress and Lifestyle Table 2 shows the significant longitudinal results for the effects of stress (CSS and three separate stress aspects) on lifestyle. The CFI and RMSEA reflected acceptable model fit. Only external eating was increased with more negative events. No longitudinal effects of stress were found on sleep duration, sedentary time, snack intake, fatty food intake, and fruit and vegetable intake (data not shown). An effect on physical activity, emotional eating, restrained eating, and sweet food consumption was seen only after moderation by age and sex. Also, some evidence for the reversed direction of effect was found (i.e., effects of lifestyle on stress). No lifestyle effects on CSS were found, but maladaptive eating behaviors (emotional and restrained eating) increased anxiety feelings. This effect of emotional eating was significant in the total population (␤ ⫽ .159 p ⫽ .005); the effect of restrained eating was significant only in girls (␤ ⫽ .256 p ⫽ .014).

Table 1 Descriptive Data of Stress and Lifestyle Parameters in 2011 and 2012 2011 Measure

Negative events score (0–2882) Negative emotions (0–30) Total problems (from SDQ; 0–30)

P25

P50 Stress measures 47 6 7

0 3 4

2012 P75

P25

P50

94 10 11

24 2 4

63 5 7

P75

106 9 10

pa

n

.001 .020 .063

312 312 312

Lifestyle measures Physical activity/inactivity Sedentary time by accelerometer (%)b MVPA time by accelerometer (%)b Sleep Sleep duration (hours/night; open question) Food consumption frequency (consumptions/week) Snacks (5 items) Fatty foods (14 items) Sweet foods (12 items) Fruits and vegetables (3 items) Eating behavior (Dutch Eating Behavior Questionnaire) Emotional eating (1–5) External eating (1–5) Restrained eating (1–5)

47.3 5.3

51.9 7.1

56.1 9.3

53.1 4.4

58.8 5.7

63.5 7.5

⬍.001 ⬍.001

153 153

10.5

11

11.3

10.3

10.6

11

⬍.001

258

6 19 21 11

9 25 29 14

13 33 39 18

6 20 22 11

9 27 29 14

14 37 39 21

.766 .031 .131 .040

312 297 305 301

⬍.001 ⬍.001 .002

312 312 312

1.4 2.7 1.6

1.9 3.1 2.2

2.5 3.6 2.7

1.2 1.4 2.5

1.6 2.0 3.0

2.2 2.6 3.5

Note. P25/50/75 ⫽ percentiles; SDQ ⫽ Strengths and Difficulties Questionnaire; MVPA ⫽ moderate-to-vigorous physical activity. a Wilcoxon signed-rank p value for individual change. b Not collected in 2011; data of 2010 are shown.

CHILD STRESS AND LIFESTYLE

Table 2 Significant Longitudinal Effects of Children’s Stress (in 2011) on Lifestyle (in 2012) Fit indices Variable

␤

p

CFI

RMSEA

R2

External eating Negative events

.17ⴱ

.002

0.977

0.028

.305

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Note. The cross-lagged models are adjusted for age, sex, and socioeconomic status. CFI ⫽ Comparative Fit Index; RMSEA ⫽ root mean square error of approximation. ⴱ Significant at p ⫽ .012.

Moderation by Sex and Age in the Longitudinal Associations Multigroup analyses in the cross-lagged models showed that age and sex were moderators in all significant effects of stress on lifestyle. Overall, we confirmed our hypothesis that stress deteriorates lifestyle mainly in girls and older children. The sex differences are shown in Table 3. In girls, emotional eating could longitudinally be predicted by more negative emotions (RMSEA ⫽ 0.034, CFI ⫽ 0.951) and external eating could longitudinally be predicted by more negative events (RMSEA ⫽ 0.038, CFI ⫽ 0.955). The age differences are shown in Table 4. Stress (CSS and total problems) had a stimulating effect on sweet food consumption (RMSEA ⫽ 0.044, CFI ⫽ 0.971) and restrained eating only in older children (RMSEA ⫽ 0.034, CFI ⫽ 0.962). The stimulating effects of stress (negative emotions) on physical activity were seen only in young children (␤ ⫽ .270; RMSEA ⫽ 0.026, CFI ⫽ 0.952), and negative emotions tended to decrease physical activity in older children (␤ ⫽ ⫺.135; RMSEA ⫽ 0.028, CFI ⫽ 0.935).

Discussion Despite the limited number of significance results, this study mainly confirmed a unidirectional longitudinal stress–lifestyle relation in children: (a) Stress deteriorated children’s diet (increased sweet food consumption) and eating behavior, but (b) in contrast to our hypothesis, stress also increased physical activity. There was minor evidence for the other directionality in which unhealthy lifestyle increased stress: Only maladaptive eating behaviors deteriorated the emotional status (anxiety). Overall, the study clearly shows the specificity of the stress– lifestyle relation. First, the three different stress concepts were associated with different lifestyle outcomes: Negative events influenced external eating, negative emotions influenced activity and emotional eating, and problems influenced restrained eating. Also, almost all relations were moderated by the children’s sex and age. Finally, the possibility of parental control over children’s lifestyle needs to be considered in the stress–lifestyle relation. Indeed, there was a higher stress impact (based on effect sizes and absence or presence of significance) on lifestyle factors with less parental control, such as psychological eating behavior and physical activity. In contrast, the impact was smaller or nonexisting for lifestyle factors that were mainly controlled by the parents such as the factual dietary intake (parents influence the availability of food at home) and sleep duration.

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Physical Activity Physical activity would be the ideal long-term stress reaction because it can generate euphoric feelings, increase social support, and even decrease the physiological stress response (Tsatsoulis & Fountoulakis, 2006). In contrast, stress might decrease physical activity because of a lack of time and motivation. Although most studies were cross-sectional, a longitudinal study in adolescents found a bidirectional relation between depression and lower physical activity (Stavrakakis, de Jonge, Ormel, & Oldehinkel, 2012). When stratifying our analyses by age, stress increased physical activity in the youngest group (in contrast to our hypothesis). Of course, physical activity can be used as a way of coping with stress given that walking/running has been reported by a group of children as a frequently used and efficient coping strategy (Chen & Kennedy, 2005). Also in a laboratory experiment, children’s physical activity increased but only in those who had a high usual level of physical activity (Balantekin & Roemmich, 2012). In contrast, a trend toward lower physical activity was found in the older group when confronted with negative emotions. The effect in the young children (␤ ⫽ .270) was somewhat higher than in the older children (␤ ⫽ .135). This suggests that stress coping strategies are age-dependent. The same trend has been shown in children’s reported stress coping strategies: The young children reported more physical activity, whereas the older children reported more screen time (Rew et al., 2012). Perhaps, this increase in physical activity will disappear when children grow into adolescents who have more time constraints for doing physical activity because of other priorities such as schoolwork. No effects in the opposite direction (activity ¡ stress) were found. In that direction, we expected significances from adolescence on only because activity levels would then have a broader range and become more restricted. Moreover, activity was measured only during the past week. It might be that the past week was not representative to empower a decrease in long-term stress levels (reported in general or past 6 months). Not only has the simple bidirectional relation between stress and activity been studied, evidence also exists on physical activity as a buffer for stress effects on body composition (Yin, Davis, Moore, & Treiber, 2005), the metabolic syndrome (Holmes, Eisenmann, Ekkekakis, & Gentile, 2008), or even overall health (Gerber & Puhse, 2009). This is an important health message because exercising might be considered a waste of time when people are stressed, although it seems the ideal way to relieve stress.

Table 3 Sex Differences in Longitudinal Effects of Children’s Stress Aspects (in 2011) on Lifestyle (in 2012) Boys Variable Emotional eating Negative emotions External eating Negative events

Girls

␤

p

R

⫺.074

.310

.235

.193ⴱ

.012

.310

.024

.809

.157

.319ⴱ

⬍.001

.341

2

␤

p

R2

Note. The cross-lagged models are adjusted for age, sex, and socioeconomic status. ⴱ Significant at p ⫽ .012.

MICHELS ET AL.

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Table 4 Age Differences in Longitudinal Effects of Children’s Stress Aspects (in 2011) on Lifestyle (in 2012)

Variable

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Moderate-to-vigorous physical activity Negative emotions Sweet food consumption frequency Composite stress score Restrained eating Total problems

Young (6 – 8 years old in 2011)

Old (9 –11 years old in 2011)

␤

p

R2

␤

.003

.271

⫺.135

⫺.007

.932

.203

.012

.486

.103

.270ⴱ

p

R2

.039

.195

.192ⴱ

.011

.259

.140ⴱ

⬍.001

.296

Note. The cross-lagged models are adjusted for age, sex, and socioeconomic status. Significant at p ⫽ .012.

ⴱ

Screen Time and Other Sedentary Time Apart from physical activity, sedentary behavior such as screen time might be related to stress. A recent review in children/ adolescents reported an association between high sedentary screen time and poorer mental health: Most population data are currently based on cross-sectional designs; only one longitudinal study was found showing effects of screen time on stress (Biddle & Asare, 2011). The other direction has been studied in two laboratory settings, indicating that the stress influence on children’s screen time could depend on usual screen time (Balantekin & Roemmich, 2012) and on stress reactivity (Roemmich, Gurgol, & Epstein, 2003). No longitudinal associations of sedentary time with stress were found in our study. It might be that in general effects of stress on moderate-to-vigorous physical activity are more explicit than those on sedentary time. Moreover, sedentary time might be spent in several ways (screen time, reading, talking, etc.) that have different associations with stress. Despite the lack of association, interventions on screen time might be beneficial in the long term for other lifestyle factors; for example, an experimental increased screen time in 8- to 12-year-old children resulted in decreased physical activity and increased energy intake (Epstein, Paluch, Consalvi, Riordan, & Scholl, 2002).

Sleep Sleep is the metabolic antagonist of stress given its opposite effects on heart rate, blood flow, and hormones. Indeed, a recent review concluded that associations between sleep and stress in children and adolescents are likely bidirectional. Sleep problems or insufficient sleep could exacerbate emotional and behavioral difficulties, and mood disturbances and anxiety could compromise sleep patterns (Gregory & Sadeh, 2012). In our study, short sleep duration was not longitudinally associated with stress. A reason for this lack of association might be the used measure of sleep (i.e., reported sleep duration). Although a report represents the sleep duration in the long term, it has overall lower validity than objective measures. More relevant in this perspective, sleep quality (taking into account sleep latency, awakenings, and night terrors) is probably of higher relevance than the raw amount of time spent in bed. After all, sleep quality was less parentally controlled than sleep duration, and most problem behaviors have been related to sleep quality in another childhood population (Smedje, Broman, &

Hetta, 2001). Moreover, there might be an age-dependent effect given that it has been suggested that the effects on sleep could be larger in adolescents than in children (Gregory & Sadeh, 2012).

Diet In literature, perceived stress has been cross-sectionally associated with more snacking in preadolescents (Jenkins, Rew, & Sternglanz, 2005) and with eating fewer fruits and vegetables and more snacks (Cartwright et al., 2003) or an overall lower diet quality (De Vriendt et al., 2011) in adolescents. Longitudinal effects of stress on diet have mainly been demonstrated in laboratory experiments: Preadolescents showed more snacking but especially those that were high in restraint, even when other stress coping behaviors were freely available (Balantekin & Roemmich, 2012; Roemmich et al., 2002). On the other hand, diet can also affect stress levels in a more short-term perspective: Sweetness and fatty texture can improve mood and mitigate stress via brain opioidergic and dopaminergic neurotransmission (Gibson, 2006). In our study, only a unidirectional effect existed: Stressed children longitudinally had a less healthy diet. The unhealthy diet was only reflected in higher sweet food consumption, not in fatty food, overall snack food, or fruit and vegetable consumption frequency. In a cross-sectional analysis in our population, sweet foods showed the best association with the stress hormone cortisol and stress questionnaires (Michels, Sioen, et al., 2012; Michels et al., 2013). Consequently, mainly the sweet taste might be defined as “comfort food” in our pediatric population or, alternatively, children might have more independence in the consumption frequency of sweet items. Nevertheless, it should be considered that parental report of children’s intake might be biased and that children’s food intake is still largely under parental control. The effects of stress on diet can be quite complex given that variability exists across stress factors. Eating would mainly be stimulated by moderate emotions, whereas decreased intakes have been reported in very intense and high-arousal emotions (Macht, 2008). Although these negative emotions influence eating behavior (the intention to eat), they do not necessarily influence the factual act of eating (giving in to the desire). The fact that stress–food relations were significant only in the oldest group might reflect the higher independence older children have over their own food consumption (lower parental control; van Strien & Bazelier, 2007).

CHILD STRESS AND LIFESTYLE

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Eating Behavior Apart from food consumption, eating behavior was also studied. Literature states that maladaptive eating behaviors are partially responsible for dietary changes as a reaction to stress (Macht, 2008). In cross-sectional literature, children’s and adolescents’ negative emotions and problems have been associated mainly with emotional eating (Braet & van Strien, 1997; Goossens et al., 2009; Nguyen-Rodriguez et al., 2010). Also, increased external eating (Braet & van Strien, 1997; Hou et al., 2013) has been crosssectionally associated with stress and to a lower extent also more restrained eating in adolescents (Hou et al., 2013). Although the main hypothesis stated that stress would induce these eating behaviors, these eating behaviors might induce guilt feelings as reported in a qualitative study in young adults (Bennett, Greene, & Schwartz-Barcott, 2013). In our study, stress effects were indeed seen with higher emotional, external, and restrained eating. This depended on the used stress concept: Emotional eating was increased by emotions, external eating was increased by events, and restrained eating was increased by problem behavior. In addition, a sex and age effect was seen: significant relations for emotional and external eating only in girls and for restrained eating only in the oldest children. Probably, cultural values regarding boys and girls can facilitate or inhibit psychological eating, and sex differences in reward sensitivity and emotional eating have been shown (Becker et al., 2008). Overall, the effect of stress was highest on external eating in girls (␤ ⫽ .319). Moreover, these maladaptive eating behaviors also increased anxiety feelings. In this direction, the highest effect was seen for restrained eating (␤ ⫽ .256), higher than its effect in the opposite cause– effect direction. The bidirectional relation between emotional eating and negative emotions might possibly lead to a vicious circle between stress and diet.

Clinical and Public Health Implications In the stress–lifestyle relation, rather small effects were seen given that betas ranged between .130 and .319 (J. Cohen, 1988); the largest effect was seen for the effect on external eating in girls. These effects have clinical and public health implications given that lifestyle is an important determinant of health (World Health Organization, 2004) and hence might be a mediator in, for example, the stress– obesity relation (Pervanidou & Chrousos, 2011). Strategies to tackle the stress–lifestyle relation can be generated on different levels and aspects. Stress management/reaction is a first pivotal aspect. Variability exists in the physiological response to stress due to the nature of the stressor and also the person facing it (Miller, Chen, & Zhou, 2007). Potential determinants of the physiological/psychological stress response can be modulated by training stress coping skills or emotional regulation skills when stress levels are increased or through anticipating new stressors by enhancing resilience, acceptance, and social support (Desai, 2010; Everly, 2009). A second important aspect is by intervening in the underlying mechanisms that link stress with lifestyle changes. Possible determinants are the usual lifestyle, the barriers for changing eating habits, physical activity, and parental modeling, as well as some psychological characteristics such as stress coping skills and psychological eating behavior (Balantekin & Roemmich, 2012; Dollman, Norton, & Norton, 2005; Epel, Lapidus, McEwen, & Brownell, 2001; Fryer, Waller, & Kroese, 1997; Kroller,

47

Jahnke, & Warschburger, 2013; Macht, 2008; Roemmich et al., 2003; Roemmich, Lambiase, Lobarinas, & Balantekin, 2011). Therefore, strategies should not only focus on the child level but also on contextual factors such as schools, parents, and the broad community.

Strengths and Limitations Major strengths of this study are the longitudinal design and the application of cross-lagged analyses. In contrast to the literature that is mainly based on cross-sectional findings, our study revealed the directionality of the relation, which is important as the stress– lifestyle relation might be bidirectional. Another strength is the multiplicity of lifestyle factors (diet, eating behavior, physical activity, sedentary time, and sleep duration), measurement methods (objective activity measures), and stress concepts (events, emotions, problems) that have been used by a multi-informant assessment to avoid informant-based biases (van Dulmen & Egeland, 2011). Additional strengths are the tested moderation by sex and age in the stress–lifestyle relation and the application of objective measures of physical activity by using accelerometers. A major limitation is that our results cannot be generalized to the overall child population because cultural variations have been described in stressors and coping (Chen & Kennedy, 2005). The representativeness was further restricted by recruiting children from only one Belgian city, a city with high socioeconomic characteristics. There was also a high drop-out rate between 2010 and 2012, although this did not introduce a large bias (apart from the lower socioeconomic status). Overall, we should mention the rather limited number of significant findings. This could be due to (a) specificity by age, sex, and stress concept as discussed above; (b) reporting bias: different reporters (child and parent) and different timeframes (past year, past 6 months, past month, in general); (c) missing information in lifestyle outcome, for example, no information on energy intake (Macht, 2008) and sleep quality (Kim & Dimsdale, 2007); (d) the influence of parental control on children’s lifestyle (Balantekin & Roemmich, 2012; Roemmich et al., 2003); (e) low statistical power given that with an average cluster size of 26, the design effect is at maximum 1.75 and the effective sample size of 312 is consequently reduced to approximately 178; and (f) validity/repeatability issues of questionnaire data in young children: DEBQ (e.g., restrained eating) and emotions (lack of validated questionnaires). Indeed, there remains an issue on the rather broad definition of stress and the questionable validity/repeatability of questionnaire data in young children. Validated emotion self-report questionnaires almost exclusively exist for older children (starting around 9 years); therefore, we opted for a short questionnaire with terminology comprehensible in young children that showed moderate correlation (r ⫽ .48) with the validated Positive and Negative Affect Schedule for Children. Nevertheless, the use of 11 response categories (0 –10) might have introduced some error and the sum score “negative emotions” had a rather low internal consistency (␣ ⫽ .6). For parent-reported behavior, alpha values ranged between .517 and .738. This was completely in line with those found in literature (.51–.70). The internal consistency for the SDQ was rather low following the threshold of .7 as acceptable internal consistence (Nunnally, 1978). These low internal consistency values may reflect that these subscales measure a heterogeneous

MICHELS ET AL.

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48

content, although it might also be due to the scant items on each SDQ subscale (Smedje et al., 1999) or to several positively worded reverse-scored items (Muris, Meesters, Eijkelenboom, & Vincken, 2004). The DEBQ overall showed good internal consistency in both the younger (6 – 8 years in 2011) and older (9 –11 years in 2011) group: ␣ ⫽ .874 and .906, respectively, for emotional eating; .796 and .866, respectively, for external eating; .687 and .738, respectively, for restrained eating. Although validity of dietary restraint as measured by the DEBQ has been shown in girls as young as 7 years (Shunk, 2004), concerns might be raised on the rather low alpha in the youngest group for restrained eating. Because of the limited number of significant associations compared with the amount of tested relations, the rather low effect sizes, and the low representativeness of the population sample, we recommend replicating these findings in future research.

Conclusion The observed significant longitudinal relations were limited in number given the large battery of tested bidirectional associations. After all, the relations were mainly unidirectional with little evidence for the direction in which lifestyle affects stress. The main finding was that stress could influence children’s lifestyle by especially deteriorating eating behavior. In contrast to recent findings, some stress aspects could also stimulate contraobesity behavior, more specifically by increasing physical activity. There was a high specificity in stress effects on lifestyle behaviors: This depended on individual (sex and age) and exposure (type of stress concept) characteristics. As children grow older, they might have more independence over food choices and more time constraints for physical activity. To prevent overweight, it therefore becomes increasingly important to make the environment (e.g., home, school, etc.) an “activity encouraging, healthy food zone” that minimizes opportunities for stress-induced eating. Children and their parents should be made aware that stress can influence their diet, and problem-solving coping skills should be highlighted.

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Received October 11, 2013 Revision received April 15, 2014 Accepted April 15, 2014 䡲