Study design and study sample
We used cross-sectional data from the Young-HUNT study collected between 1995 and 1997 (Young-HUNT1) and between 2006 and 008 (Young-HUNT3). The Young-HUNT study is the adolescent (13–19 years) arm of the Nord-Trøndelag Health (HUNT) study, a longitudinal health study in Norway . It was designed to cover several topics related to major public health issues, including respiratory and allergic disease, subjective health problems, eating habits, and overweight and obesity. Schools have been the main study site in the Young-HUNT surveys, with all 66 schools in the county of Nord-Trøndelag participating. All adolescents, and parents of adolescents under the age of 16, gave written consent to participate. For practical reasons, data were collected in one school at a time and within one municipality, before moving on to the next. In the present study we only included grades 8–10 in junior high schools, where the response rate was 92% (n = 4596) in 1995–1997 and 82% (n = 4615) in 2006–2008 .
To evaluate the NSFS, the study used a natural experimental design characterised by exposure to the intervention having not been manipulated by the researcher . We exploited data collection for the Young-HUNT3 survey being conducted in the same period as NSFS implementation. The NSFS was implemented in all secondary schools (grades 8–10) and combined schools (grades 1–10) during autumn 2007. On each school day, students who were part of the NSFS were offered one kind of fruit or vegetable. Apples, pears, bananas, oranges, clementines, kiwis, carrots and nectarines were most frequently offered.
We defined 1 September 2007 as the start date of the NSFS initiative. The date when participants answered the study questionnaire was used to identify affiliation to either the intervention group or the control group. Adolescents in grades 8–10 who answered the Young-HUNT3 questionnaire after the implementation of the program were classified as the intervention group (September 2007 to July 2008, n = 1892) and those who answered the questionnaire before the implementation (Spring 2006 to 31 August 2007, n = 2855) as the control group.
We aggregated data from the two Young-HUNT surveys to explore possible changes in dietary habits between 1995 and 2008 according to educational intentions, gender and school type. During the period between 1995 to 2008 some schools had been closed (n = 9) and new schools (n = 9) had been opened. To explore the change over time, we only included adolescents attending schools (n = 35) operating in both surveys. Thus, 4113 and 4137 were included from Young-HUNT3 and Young-HUNT1, respectively.
We further explored secular changes in dietary habits according to schools’ NSFS status. Intervention schools and control schools within Young-HUNT3 (2008) were compared to the same schools within Young-HUNT1 (1997). Thus, within the young-HUNT1 sample, we defined the group “intervention schools” (n = 1791) and the group “control schools” (n = 2346). The majority of schools in 2006–08 either had pupils in the intervention group or the control group. However, five schools in the 2006–08 study had pupils in both groups, but most students in these schools were in the control group (only 20 students were part of the intervention group). For secular analysis according to NSFS status, these five schools were categorised as “control schools” within Young-HUNT1, as most students in these schools were part of the control group within Young-HUNT3.
Adolescents’ dietary habits were measured by the same food frequency questionnaire (FFQ) used in Young-HUNT1 and Young-HUNT3. Consumption of fruit, vegetables, potato chips and candy were measured by the question: “How often do you eat the items listed below?” The reply options were: several times a day, once a day, every week but not every day, less than once a week and never. Consumption of SSB and ASB (only included in Young-HUNT3) was measured by the wording: “How often do you drink the items listed below?” The reply options were: seldom/never, 1–6 glasses a week, 1 glass a day, 2–3 glasses a day and 4 or more glasses a day. Consumption was dichotomised into daily consumption (more than once a day and once a day) and less than daily consumption (every week but not every day, seldom and never) of fruit, vegetables, potato chips, candy, SSB and ASB. Participants completed the questionnaire during a school hour, in an exam-like setting.
The dietary questions in the Young-HUNT surveys were based on the FFQ used in the Health Behaviour in School-aged Children (HBSC) study. The validity of the HBSC FFQ has previously been reported for adolescents . Reliability analysis (interval: 6–15 days) resulted in Spearman correlation values ranging from 0.45 to 0.82 among 11–12-year-olds and from 0.57 to 0.78 among 13–14-year-olds, with an overall mean correlation of 0.70 and 0.67, respectively.
Spearman correlation between the FFQ and the seven-day food diary (reference method) was 0.34 for fruit, 0.48 for vegetables, 0.46 for SSB, 0.15 for ASB, 0.10 for potato chips and 0.25 for candy. Comparison between the two methods showed that the FFQ overestimated all food items except SSB and potato chips.
Adolescents’ own educational intention was the best proxy for SES available in the Young-HUNT dataset. A previously published study used both educational intentions and parental education as proxies for SES, and the results showed almost identical results when comparing the association between beverage consumption and the two proxies . Therefore, we have reason to believe that educational intention is an acceptable proxy for SES when assessing dietary consumption among adolescents.
Adolescents’ educational intention was measured by the question: “What plans for further education do you have?” The reply options in Young-HUNT3 were: university or university college four years or more, university or university college less than four years, other vocational education, no plans, or don’t know. Young-HUNT1 included two additional reply options: high school general education and secondary vocational education (no plans was not listed as a reply option). As it was possible to choose more than one reply option, we used the highest level of education registered by the participant. The variable was further dichotomised into “higher educational intentions” (college or university) and “lower educational intentions”.
The county of Nord-Trøndelag included 24 municipalities in which six of the municipalities included villages with city status. The variable “municipality urbanity” used in the present study had two categories: rural and urban. Being categorised as urban reflected that the participant lived in one of the six municipalities that included villages with city status. In Young-HUNT1, participants reported their own gender, and which grade they attended. In Young-HUNT3, however, this information was collected by using participants’ personal identification numbers. In both surveys, sociodemographic data (urbanity of the municipality and which school they attended) were registered by using personal identification number and linking this to the school register and national population register.
Evaluation of the NSFS (using data from young-HUNT3)
Descriptive statistics were presented using independent t-tests for continuous variables and chi-square for categorical variables. To evaluate the NSFS, the outcomes (fruit, vegetables, candy, SSB, ASB and potato chips) were analysed separately by multilevel logistic regression models. Schools were included as a random intercept. The models included the covariates gender, grade, educational intentions and municipality urbanity. We tested a priori for interactions between adolescents’ exposure to the NSFS and (i) gender, (ii) educational intentions and (iii) grade level.
Change of dietary patterns over time in relation to SES, gender and school type
We investigated change in dietary habits (fruit, vegetables, candy, potato chips and SSB) between 1995 (Young-HUNT1) and 2008 (Young-HUNT3) by multilevel logistic regression. School was included as a random intercept. The six outcomes were analysed separately, by two models. To examine possible changes in dietary patterns, a binary variable “time” was constructed (1995 [HUNT1] =0; 2008 [HUNT3] =1). In model 1, we analysed the main effect of time, educational intentions, gender, grade and municipality urbanity. In model 2, we included the interactions between time and gender and time and educational intentions.
Secondly, to explore secular trends according to school type, an additional analysis was conducted. The binary variable “school type” was constructed, in which schools in the intervention group in Young-HUNT3 and the same schools in the Young-HUNT1 (intervention schools) were treated as one group “NSFS schools” (coded 1); the schools in the control group in Young-HUNT3 and the same schools in Young-HUNT1 (control schools) were treated as another group “control schools” (coded 0). To explore whether the secular change was different between NSFS schools and control schools from 1995 to 2008, the interaction term between school type and time (HUNT1 vs HUNT3) was tested in a multilevel logistic regression model with a random intercept for school, for all six dietary outcomes. The models included the covariates gender, grade, educational intentions and urbanity. If the interaction term (school type* time) was significant for a dietary outcome, stratification was performed by school type.
In all analysis, we used p < 0.05 to indicate statistically significant associations. For interaction terms p < 0.1 was used, as interaction terms are a multiplication of two variables that include measurement error . All models reported odds ratio (OR) with a robust 95% confidence interval (CI). All statistical analyses were conducted using Stata 15.1.