Study design and participants
The present study used data of an ongoing longitudinal study examining psychosocial determinants of growth and development in Anhui Province, China. As illustrated in Fig. 1, of a possible 2025 students in grade 1 to 3 from four large elementary schools of Bengbu city, 1974 (1104 boys, 55.9%) agreed to participate. Bengbu, located in north Anhui Province, is a significant traffic hub in east China. Great economic and social achievements have been made in the past decade. The gross domestic product per capita (GDP) increased by over 12% annually, doubling the figure of the former period before 2010. It is one of the representative cities in China in terms of its urbanization and industrial development.
Students were initially aged 7 to 9 years in the first wave of data collection (March 2013). Subsequent wave of data collection took place 2 years (wave 2), 4 years (wave 3) and 5 years (wave 4) later. The study approval from Institutional Review Boards at Anhui Medical University and then obtained written informed consent from parents and school teachers, as well as child assent. which they were requested to complete and return if they did not want their child to participate in the study or if their child did not want to.
Parents of the students completed baseline questionnaire survey at home. In later waves, all the students completed the questionnaires on their own. Students’ data collection was conducted in a class setting at schools with a supervising research assistant available to answer questions following a standard script.
Youth physical activity levels were ascertained from the Youth Risk Behavior Survey 2013 (YRBS;23) at each wave. Parents (wave 1) and adolescents (wave 2-wave 4) were asked “During the past 7 days, on how many days were your child (you) physically active for a total of at least 60 minutes per day?”.
Weekly average screen time was derived for each child based on daily time (hours) spent on screen during weekdays and weekends (including watching television/video, playing computer, iPad and smartphone).
Sugar-sweetened beverage consumption (SSBs)
Sugar-sweetened beverage consumption was assessed though the question ‘During the past 7 days, how many times did your child (you) drink at least 1 serving regular sugar-sweetened sodas, fruit drink, sweetened iced tea, sports /energy drink that contains sugar?’.
Sleep duration was estimated by subtracting self-reported waketime from bedtime, assessed by asking, “On a usual weekday this past week, when did your child (you) go to bed at night?” and “On a usual weekday this past week, when did your child (you) wake up the next morning?”
Baseline Depressive Symptoms Assessment
The Short Mood and Feelings Questionnaire Parent-report (SMFQ-P) has been widely used in children aged 6 to 17 years, including our previous work (24-26). Cronbach’s alphas for our sample were high (0.85). In keeping with previous research, depressive symptoms were defined as having an SMFQ-P score of 11 or greater (26).
Follow-up Depressive Symptoms Assessment
The Mood and Feelings Questionnaire (MFQ) was used at wave 2-4 (27). Adolescent self-reported current or past 2 weeks' depressive symptoms. The MFQ has shown prognostic validity in clinic and non-clinic samples (28), yielding high internal consistency (α=0·91-0·93) in the present sample. Adolescents with total scores of 29 or above on the MFQ was considered depressive (29).
At wave 4 (2018), adolescents were asked a specific question to assess suicide ideation (“Have you ever seriously thought about killing yourself and, if so, have you had these thoughts in the past 12 months?”) from a section of the World Mental Health CIDI (WMH-CIDI) (30).
Non-suicidal self-harm behaviors
At wave 4 (2018), adolescents were asked ‘‘Have you ever harmed yourself in a way that was deliberate, but not intended as a means to take your life in the last (reference period)?” (1). A list of eight non-suicidal self-harm (NSSI) (hitting, pulling hair, banging head, pinching, scratching, biting, firing/burning, cutting) methods was then presented.
Alcohol use at wave 4 (2018) was defined as having at least one drink of alcohol during the past 30 days (31).
Body mass index
At each wave, height and weight was measured and body mass index (BMI) was calculated as weight (in kg) divided by height squared (in m).
Parental education and family monthly income
Parents at baseline reported educational attainment during the consent process and family monthly income from “1” for “<2,000 RMB” (ca. 313 US$) to “5” for “>15,000 RMB” (ca. 2345 US$).
All parents were questioned a 13-item scale adapted by Raudino et al. (2012) (32). from the Child Rearing Practices Report and the Parenting Scale. Cronbach’s alpha in the current study was 0.91.
Adverse Childhood Experiences
At wave 4 (2018), adolescents reported experience with 10 adverse events including abuse and neglect by using the 10-item Adverse Childhood Experiences Questionnaire - Short Form (ACES-SF) (33,34). Eight items from this measure were included. A total score with higher values indicating greater experience of childhood adversity. Cronbach’s alpha in the current study was 0.75.
The study sample was characterized using descriptive statistics and frequency distributions at baseline and across following three waves. Average level of four lifestyle behaviors over time were analyzed by three-way repeated measures analysis of variance (ANOVA) with time, sex, family income, maternal education, and weight status as factors. Multiple comparisons were assessed by Bonferroni post-hoc test.
Group-based multi-trajectory modeling, a generalization of univariate group-based trajectory modeling to multiple outcomes (35), was adopted to examine latent clusters of children with similar lifestyle trajectories across the four lifestyle behaviors: weekly screen time, physical activity, sleep duration, and SSBs consumption. The number of classes that best fit was selected based on model fit comparisons using a series of standard fit indices (i.e., Bayesian Information Criterion [BIC], sample-size-adjusted BIC [SSABIC], Akaike Information Criterion).
After the lifestyle multi-trajectory profiles were identified, preliminary analyses calculated descriptive statistics and tested lifestyle trajectory group differences in terms of depressive symptoms across four waves, as well as suicide ideation, alcohol use and non-suicidal self-harm assessed in Wave 4.
The associations between longitudinal changes in depressive symptoms and lifestyle trajectory groups examined using mixed-effects logistic growth modeling, controlled for age, sex, family monthly income, maternal education, and childhood adverse experiences.
Multiple logistic regression model was performed to examine association between suicide ideation, alcohol use and non-suicidal self-harm with four lifestyle trajectory groups. In these models, the four lifestyle trajectories were treated as nominal variables with the persistent healthy lifestyle class serving as the reference group.
Analyses were performed using STATA Software Version 14 (College Station, TX: StataCorp LP; 2015). A P-value <0.05 was considered statistically significant.