Participants
The participants in the present study were part of wave 1 and wave 2 of the “G(F)OOD together” research project, a longitudinal study on Dutch adolescents’ and their parents’ health behaviour. Data for the first three waves were collected in fall 2017, spring 2018, spring 2019 respectively. Parents of 1,657 adolescents were invited to participate, and mothers or fathers provided consent for themselves and their adolescents to participate in the study. Parental consent was provided for 718 children. Moreover, 777 parents also provided consent for themselves. In the first wave 667 adolescents from six secondary schools in the South and the East of the Netherlands participated and in the second wave 688 adolescents participated (95.8% participated in both waves). Because some adolescents were absent at wave 1 due to illness or other appointments, more adolescents actually participated in wave 2. Moreover, 593 mothers and fathers took part in Wave 1 and 586 parents took part in wave 2 (N =480; 80,9% in both waves). Details of the study procedures can be found elsewhere [47].
For this study, we only included mothers, as they are still the most important caregivers in the family, and are more prone to stress and depression [16, 48, 49] than fathers. In total, 442 mothers took part in Wave 1, 438 in wave 2 (as an extra school was recruited at Wave 2), and 358 (81%) in both waves. Of the N = 358 with complete maternal data on both waves, we excluded data from non-biological mothers for this study (n =3) and data from mothers of whom there was no anthropometric and questionnaire data available of their adolescent child (n=22), leaving a final sample of 336 biological mother child dyads who participated in both waves.
Most mothers (97.0%) were born in the Netherlands. Mean age of mothers at the first wave was 44.6 years (SDage = 4.2; age range = 29.8 to 55.5). Most mothers finished higher professional education (39.9%) or secondary vocational education (39.4%) and performed a payed job of less than 32 hours per week (52.5%) or 32 hours per week or more (19.4%).
Adolescent boys (n=161) and girls (n=175) were approximately equally represented. Most adolescents were born in the Netherlands (%). All participants attended regular secondary education (Mage = 12.9 years; SDage = 0.6; age range = 11.3 to 14.8). In the Netherlands, children in secondary schools follow education based on their academic level and interests. Dutch secondary schools are divided into three streams which represent different educational paths: one to prepare students for vocational training, another to prepare students for university, and a middle stream to prepare students to study at universities of applied sciences (higher vocational education). More than half of the participants (57.6%) were in pre-university education, 8.2% of the participants was in higher general secondary education, and 34.2% of the participants was in pre-vocational education.
Procedures
Adolescents and their parents were recruited through secondary schools. We randomly invited 40 secondary schools in the South and the East of the Netherlands to participate in the study. Six secondary schools agreed to participate in wave 1, and all adolescents attending the first and second grade and their parents were invited to participate in this study by means of an active parental consent procedure. A letter describing the four-wave study was mailed to the parents and they were asked to return a (paper or online) consent form indicating whether they agreed to their child participating in the study and if they agreed to participate in the study themselves. Children were rewarded with a small incentive, if at least one of their parents’ forms was returned, regardless of whether permission was given. Before participation, adolescents and parents were informed that participation was voluntary, that answers would be processed anonymously, and that they could withdraw from the study at any moment. Inclusion criteria for participants were being enrolled in a high school, being in the first and second grade of this high school, being proficient in the Dutch language and parents and children both having given active informed consent. Exclusion criteria for participants were not being proficient in the Dutch language, attending special education and not having given active (parental) consent.
Adolescents completed an online survey at school during one classroom hour (approximately 45 minutes), and height and weight were measured outside the classroom by trained research assistants. Parents completed an online survey, which took approximately 20 minutes to complete. The questionnaires were administered through Qualtrics Survey Software (Qualtrics, Provo, UT, USA) and were in Dutch language. Children received a small present after completing the survey, and several prizes were raffled among participating parents. The Institutional Review Board of the Faculty of Social Sciences of the Radboud University, Nijmegen, The Netherlands approved the study protocol (reference number ECSW20170805-516) in 2017.
Measures
Depressive symptoms
Maternal depressive symptoms were assessed with the 10-item short version of the Center for Epidemiological Studies-Depression (CES-D) scale. Although the (shortened) CESD is used as a self-reported measure of depressive symptoms, it is a reliable and valid instrument to screen for the presence or absence of a depressive disorder [50]. The CES-D is widely used and has adequate internal reliability [50]. Respondents rated items on a 4-point Likert scale (rarely or none, to most or all the time). The scale includes positive (I was happy) and negative (I could not get going) items. Higher total CES-D scores reflect greater maternal depressive symptomology. In the current study, Cronbach’s alpha for the CES-D was .77 at T1 and .80 at T2.
General perceived stress
Maternal general stress levels were assessed using the 4 item Perceived Stress Scale (PSS). The PSS is a self-report questionnaire measuring a person’s evaluation of stressful situations in the previous 1 month of his or her life. It is a global measure of stress that is simple to use, and there are many studies confirming its reliability and validity in a variety of settings and in multiple languages [51-56]. Although it is used as a self-reported measure of general stress, it is a reliable and valid instrument to screen for general perceived stress [51, 53, 54, 56]. The instrument contains 4 statements which measure how unpredictable and uncontrollable respondents feel their lives are, for example: In the last month, how often have you felt confident about your ability to handle your personal problems? Respondents rate how often they experience stressful situations on a 5-point Likert scale ranging from ‘never’ to ‘very often’. Answers of the 4 items were summed into a total PSS score. The higher the score on the PSS, the greater the respondent perceives that their demands exceed their ability to cope. Cronbach’s alpha was calculated to investigate the internal reliability of the Perceived Stress Scale and was .70 at T1 and .69 at T2.
Financial stress, stress at work and at home
Three types of domain specific stress were measured through three items: financial stress (How often did you experience financial stress in the past year?), stress at work (How often have you felt stress at work in the past year?), and stress at home (How often did you experience stress at home in the past year?). These questions have been used in previous studies to measure different types of stress and are worded in the same manner [57, 58]. Respondents rated how often they experienced stress in the different contexts on a 4-point Likert scale (never, sometimes, regularly, all of the time).
Anthropometrics
Adolescents’ height and weight were measured according to protocol [59] by trained research assistants. Body Mass Index (BMI) was calculated as weight in kilograms divided by height in meters squared. Individual age and gender-specific BMI standard deviation scores (z-scores) were calculated using a Dutch representative sample of 0-21-year olds [60, 61]. Mothers reported their own height and weight based on which we calculated maternal BMI.
Covariates
We controlled for parent’s and adolescents’ educational level in our regression analyses, as a higher BMI seems to be more frequent in lower educated youth and in youth with lower educated parents [62-64]. We also controlled for additional covariates showing a potential link with adolescent zBMI [65-70]. As such, the following covariates were added: adolescents’ gender, maternal BMI, maternal single household status, adolescent stress and depressive symptoms, as also the quality of the parent-child relationship rated by parents.
Statistical analyses
Statistical analyses were conducted using the PASW 20.0 and R software package. Descriptive statistics were used (mean, standard deviations and percentages) to describe the study sample and to investigate population characteristics (see table 1).
First, cross-sectional associations between mothers’ wellbeing and adolescent zBMI and the covariates were examined by calculating Pearson's correlation coefficients using SPSS. Second, to test whether maternal stress or depressive symptoms may precede child zBMI over time, multiple linear regression analyses were performed using the R software package (R Core Team, 2018) with depressive symptoms (CES-D score), general stress (PSS score), financial stress, stress at home or stress at work at T1 as the independent variables, and adolescents’ zBMI at T2 as the dependent variable). We examined two models: a multiple linear regression model, adjusted for adolescents’ zBMI at baseline and potentially relevant covariates (i.e., educational level of the mother and of the adolescent, gender, maternal BMI, maternal marital status (single household status), the quality of parent/child relationship as rated by parents, child stress, and child depressive symptoms and an unadjusted model without covariates. We checked normality and distribution assumptions of zBMI before performing our regression analysis with a scatter plot, QQ plot and the Shapiro Wilk test. The plots showed no extreme outliers and a linear association. The Shapiro Wilk test showed a normal distribution (W = .99, p = .53).
A logistic regression analysis comparing those adolescents who participated in the current study (score = 1) with those who could not participate because of lacking maternal data (score = 0) showed no differences in gender, educational level, age and mean zBMI between both groups, we therefore expect no bias. The overall proportion of drop-out from T1 to T2 is relatively low, of the adolescents 95.8% participated in both waves and 80.9% of parents participated in both waves. The proportion of missing data in our study sample is also low (22/358 = 6%). To account for missing values we used the ‘na.exclude’ function in R which does not use the missing values, but maintains their position for the residuals and fitted values.
For the proposed multiple linear regressions, we conducted a power analysis using G*Power 3.1 [10]. With a small effect size (f2) of .15, an alpha of .05, a standard power level of .80, and a total of 14 predictors , the results of the power analysis showed that a minimum of 135 participants would be needed to achieve an appropriate power level for this study.