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Associations between obesogenic risk and depressive symptomatology in Australian adolescents: a cross-sectional study
  1. E Hoare1,
  2. L Millar,
  3. M Fuller-Tyszkiewicz2,3,
  4. H Skouteris3,
  5. M Nichols2,
  6. F Jacka4,
  7. B Swinburn2,5,
  8. C Chikwendu6,
  9. S Allender2
  1. 1Faculty of Health, School of Health and Social Development, Deakin University, Geelong, Victoria, Australia
  2. 2Faculty of Health, WHO Collaborating Centre for Obesity Prevention, Population Health Strategic Research Centre, Deakin University, Geelong, Victoria, Australia
  3. 3Faculty of Health, School of Psychology, Deakin University, Geelong, Victoria, Australia
  4. 4Faculty of Health, School of Medicine, Deakin University, Geelong, Victoria, Australia
  5. 5Population Nutrition and Global Health, University of Auckland, Auckland, New Zealand
  6. 6Health Directorate, Australia Capital Territory Government, Canberra, Australian Capital Territory, Australia
  1. Correspondence to Erin Hoare, WHO Collaborating Centre for Obesity Prevention, Faculty of Health, Deakin University, 1 Gheringhap St Geelong, VIC 3220, Australia; ejhoa{at}


Background Depression and obesity are significant health concerns currently facing adolescents worldwide. This paper investigates the associations between obesity and related risk behaviours and depressive symptomatology in an Australian adolescent population.

Methods Data from the Australian Capital Territory It's Your Move project, an Australian community-based intervention project were used. In 2012, 800 students (440 females, 360 males) aged 11–14 years (M=13.11 years, SD=0.62 years), from 6 secondary schools were weighed and measured and completed a questionnaire which included physical activity, sedentary behaviour and dietary intake. Weight status was defined by WHO criteria. A cut-off score ≥10 on the Short Mood and Feelings Questionnaire indicated symptomatic depression. Logistic regression was used to test associations.

Results After controlling for potential confounders, results showed significantly higher odds of depressive symptomatology in males (OR=1.22, p<0.05) and females (OR=1.12, p<0.05) who exceeded guidelines for daily screen-time leisure sedentary activities. Higher odds of depressive symptoms were seen in females who consumed greater amounts of sweet drink (OR=1.18, p<0.05), compared to lower female consumers of sweet drinks, and males who were overweight/obese also had greater odds of depressive symptoms (OR=1.83, p<0.05) compared to male normal weight adolescents.

Conclusions This study demonstrates the associations between obesogenic risks and depression in adolescents. Further research should explore the direction of these associations and identify common determinants of obesity and depression. Mental health outcomes need to be included in the rationale and evaluation for diet and activity interventions.

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Obesity is a growing public health issue, and adolescence appears to be a significant period for targeting behaviours that contribute to development of unhealthy weight.1 Currently in Australia, approximately 25% of children and adolescents suffer from overweight or obesity.2 Obesity in adolescence is linked to a range of negative health consequences including risk for metabolic syndrome, diabetes and cardiovascular diseases, and some forms of cancer.3 Importantly, it is now understood that mental health disorders and obesity often co-occur.4 In light of this co-occurrence, it is possible that obesity and mental health share common underlying mechanisms, such as sedentary lifestyles, lack of physical exercise and poor diet, and these variables may provide valuable insight into the development and maintenance of comorbidity of these two health conditions.5 An understanding of the common predictors of Body Mass Index (BMI) (indicating unhealthy weight) and mental health disorders can be used to develop specific interventions, and inform prevention strategies for both health issues.

Previous adolescent obesity interventions have targeted the aforementioned common risk factors to prevent unhealthy weight gain, with weight reductions commonly observed postintervention.1 ,6 Interventions involving these predictors have also demonstrated significant improvements in mental health postintervention.7–10 Although improvements have been demonstrated, significant effects are inconsistent for weight11 ,12 and mental health,9 ,13 and more research is needed to define best prevention strategies for both health issues.

The literature also suggests that adolescents respond differently to such interventions depending on gender. Gender differences found in interventions include higher dropout rates among males,14 and differences in weight gain postinterventions.12 Gender differences exist in the individual predictor variables: two studies15–17 found that female BMI was linked to subsequent risk of depression, and this association was found to be non-significant in males. One study18 found that high levels of sedentary behaviour predicted depressive symptoms in males at one-year follow-up. Additionally, studies have discussed the need for interventions targeting body image and diet-related behaviours in females19 ,20 due to societal pressures precipitating body image issues in female adolescent populations. An important step in this area of research is to comprehensively profile the unique and combined contribution of these predictors for obesity and mental health, with particular emphasis on how these risk factors may differ by gender.

The aim of the current study is to examine the baseline results of adolescents in an Australia-based obesity prevention intervention, the Australian Capital Territory It's Your Move programme (ACT-IYM), to determine the impact of the variables: low physical activity, sedentary lifestyles, poor diet and weight status on outcomes of depressive symptoms. Importantly, this study will evaluate how these interactions differ by gender, and which predictor variables appear to be the most influential on depressive symptoms. A comprehensive understanding of these relationships can be used to inform future prevention interventions so that strategies are tailored to address the unique needs of male and female adolescents.



This cross-sectional study used the baseline data collected in May/June 2012 from the ACT-IYM; an intervention study aimed at preventing obesity in adolescents through comprehensive school-based and community-based approaches to facilitating healthier lifestyles. The project was based on the model used in the successful It's Your Move! intervention in the Australian state of Victoria.1 The project was approved by the Deakin University Human Research Ethics Committee and the ACT Health Human Research Ethics Committee.


Depressive symptomatology was measured by the Short Moods and Feelings Questionnaire (SMFQ), which contains 13 self-report items aimed at rapidly assessing depressive symptomatology for children and adolescents.21 The SMFQ comprises items relating to mood states, asking participants to indicate how they had been feeling or acting in the past 2 weeks. The development of the full questionnaire can be found in Angold et al.21 Statements such as ‘I felt so tired I just sat around and did nothing’ were rated on a 3-point scale, where 0=not true, 1=sometimes and 2=true. This resulted in a possible score range of 0–26.

Prior research shows that the SMFQ has high internal consistency (Cronbach's α frequently >0.85),21 and correlates with other well-validated measures of depression such as the Children's Depression Inventory and the Diagnostic Interview Schedule for Children Depression Scale.21 This study classified participants scoring 10 or higher as having depressive symptoms, consistent with Chipman et al (2007)22 and Kuo et al23 research. The SMFQ items showed high internal reliability in this study (Cronbach's α=0.88).

The Adolescent Behaviours, Attitudes, and Knowledge Questionnaire (ABAKQ)24–26 contained self-report questions about physical activity, sedentary behaviours and diet. Physical activity behaviours were assessed by student's responses of either ‘mostly played active games’, ‘mostly just sat down’ or ‘mostly stood or walked’ during recess and lunch on the last school day. Students also reported whether they participated in ‘sport, dance or active games’ on the last school day with either ‘yes’ or ‘no’. Students were classified as inactive, low physical activity, moderately active and highly active, based on responses to the above questions.

Leisure time screen-based sedentary behaviours were measured by items related to television viewing (including videos and DVDs) and three related to playing video games and using the computer (other than for homework), on a single school day, and Saturday and Sunday, then calculated to provide a daily estimate. This final estimate was then dichotomised according to whether it met (≤2 h per day) or exceeded (>2 h per day) the Physical Activity guideline recommendations for maximum sedentary activities advised by the Australian Government for adolescents.

Diet-related items from the ABAKQ were used to measure consumption of fruit and vegetables, sweet drinks and takeaway food. Fruit and vegetable consumption was indicated by participant's responses to how many servings of fruit/vegetables they consumed on the last school day, including those eaten at home. Responses were dichotomised into those participants who met the World Health Organisation recommendations of minimum five servings (400 grams) of fruit and vegetables per day, and those who failed to meet this recommendation.27 A single index of sweet drink consumption was indicated by responses to two questions: one asked how many glasses of soft drink or energy drinks did they consume on the last school day, and the other asked how many glasses of fruit drinks or cordial they consumed on the last school day. From these two questions, a single continuous variable of daily glasses consumed of sweet drinks was derived. Takeaway food consumption was measured by one question (‘How often do you usually eat food from a takeaway?’), with the five response options of ‘once a month or less’, ‘2–3 times a month’, ‘once a week’, ‘2–3 times a week’, or ‘most days’ used as response categories.

Participants’ heights and weights were measured in a separate space, screened from other students by gender-matched researchers trained in anthropometrics and body image protection. Measures were taken in light clothing and without shoes. Weight was measured to the nearest 0.5 kg using electronic scales (A&D Personal Precision Scale UC-321) and height was measured using a stadiometer (Charder Portable Stadiometer Height Rod HM200P). Body Mass Index-z score and weight status was calculated using the WHO Reference 2007.28 Given the small number of thin participants (<2%), this category was combined with normal weight.

Statistical analysis

Data were analysed using Stata V.12.0 (StataCorp LP, College Station, Texas, USA). All variables were checked for missing data, and in all cases there were less than 5% missing; accordingly, case-wise deletion was used.29 Univariate outliers were defined as values >3 SD from mean and multivariate outliers were identified using Hadi's method.30 Four cases were deleted using Hadi's method case-wise deletion. The number of univariate outliers varied, depending on the analysis performed, and cases that were outliers were removed for those analyses. Continuous variables were checked for normality through histograms and calculation of skew and kurtosis values; no transformations were required.

Results were considered statistically significant at p<0.05. Descriptive statistics were calculated for the total sample and for male and female subgroups. Differences between males and females in terms of age, ethnicity, BMI, parents’ level of education, weight status, physical activity, sedentary behaviours, diet quality and SMFQ scores were examined using two-tailed independent sample t tests or Pearson χ2 tests, as appropriate (tables 1 and 2).

Table 1

Participant characteristics from 2012 baseline data of the ACT-IYM project

Table 2

SMFQ, physical activity, sedentary behaviour, diet and BMI frequencies

The primary outcome of the study was probable depression indicated by symptomatology at or above the cut point of SMFQ scores. One-tailed logistic regression analyses were used to examine whether each of the obesogenic risks was associated with increased risk of depressive symptoms, with analyses stratified by gender. Crude and adjusted regression models are reported, to evaluate the effects of potential confounders of age, parent's level of education, and school the participant attended.


All students (n=1549) in grade levels 7 and 8 attending the six participating schools were invited to participate in baseline data collection; 800 (440 females, and 360 males) consented and were subsequently included (response rate 52%). Students were aged 11–14 years (M=13.11 years, SD=0.62 years) (table 1), all attended secondary schools in Australian Capital Territory, Australia. The majority of participants were of European Australian decent (67%). Other participants identified themselves as Indigenous Australian (3%), Indian (2%), Chinese (2%) or Other (26%) (table 1). Overall mean BMI was 20.4 (SD=7.40).

Of the adolescent sample, 17% were inactive on the last school day (table 2). Half the adolescents (51%) had low physical activity, reporting that they played active games once during the last school day. Adolescents who were physically activity twice or more on the last school day represented 32% of the sample. Sixty-five per cent of adolescents in the current sample exceeded the Australian Government guidelines for maximum leisure screen-based media use (21). Seventy per cent of males and 60% of females exceeded recommendations (difference between sexes not statistically significant). The majority of adolescents (73%) were classified as normal weight, with approximately a quarter (27%) classified as overweight or obese.

More than half the adolescents (53%) did not meet the WHO guidelines of five servings of fruit and vegetables per day, and 44% of participants ate takeaway foods once a month or less (table 2). The mean sweet drink consumption was almost two glasses per day (SD=1.83). There was a significant difference between males and females in symptomatic depression (p<0.001)—females had greater prevalence of depressive symptoms. Significant differences were also found in depressive prevalence across the schools tested and were dependent on parents’ level of education, in that those children whose parents had a higher level of qualification, were less likely to be classified with symptomatic depression.

Unadjusted logistic regression results show some similarities and differences in the models for males and females. For males and females, exceeding healthy levels of sedentary behaviour was associated with a higher likelihood of depressive symptoms by 15% and 12%, respectively. Furthermore, for males and females, each glass of daily sweet drink consumption increased the odds of symptomatic depression by 16%. Weight status was significantly associated in males to depressive symptoms; males of heavier weight status were 1.62 times more likely than those in the normal weight group to have symptomatic depression.

When the covariates were included, exceeding screen time recommendations remained a significant correlate of symptomatic depression for males and females, with the OR of experiencing depression 22% for males and 12% for females (table 3). Weight status in males remained a significant correlate of symptomatic depression, as did sweet drink consumption in females. For females, each glasses of sweet drink per day was associated with the likelihood of being classified with depressive symptomatology by 18%. After controlling for school, age, and parent's level of education, results indicate that males in the obese/overweight group were 1.83 times (p<0.05) more likely than those in the normal group to have symptomatic depression. The combined influence of the obesogenic risks and weight status explained 15% variance of depressive symptomatology in males, and 6% of the variance in females.

Table 3

Multivariable logistic regression estimates for depressive symptomatology according to obesogenic risk in adolescents, mutually adjusted and further adjusted for age, parents’ level of education, and school


This present study aimed to evaluate the independent relationships between key obesogenic risk factors and depressive symptoms in a cohort of Australian adolescents. These findings will be used to inform intervention strategies that aim to improve healthy weight and mental health outcomes in this vulnerable population.

In this study, the overall prevalence of symptomatic depression was 24%, which is consistent with earlier estimates for community samples.31 A subset of obesity and obesogenic risk variables made significant unique contributions to depressive symptomatology, and some of these patterns differed across gender. Adolescents who had greater sedentary behaviours reported greater symptomatic depression, before and after adjusting for covariates. Sweet drink consumption was significantly correlated to depressive symptoms and related to increased odds of symptomatic depression in females, before and after adjusting for covariates; among males, however, there was no relationship between sweet drink consumption and odds of symptomatic depression in the fully adjusted model. The combined effect of the obesity and obesity risk factors accounted for 15% variance of depressive symptoms in males, and 6% variance in females.

Increased odds for depressive symptoms were found in males classified in overweight/obesity weight status before and after adjusting for covariates. The current findings suggest that the strongest association in males was weight status, which increased the odds of experiencing depressive symptoms after adjusting for covariates. Sweet drink consumption in females increased odds for depressive symptoms after adjusting for covariates, and this was the strongest association in females.

Physical activity did not show a significant association with depressive symptoms in the multivariate context. This finding is unexpected given that this relationship has been found consistently in previous studies.32 The method in our study for estimating physical activity may have led to some measurement error in that the adolescents were categorised into high, medium, low and inactive depending on their self-reported activities on the last school day. Other studies have measured physical activity in terms of weekly participation in sports, number of extracurricular activities they were involved in, or measured by hours of exercise,33 ,34 and differences in measurement may impact the strength of the relationship found. The physical activity measurement tool was particularly crude in that it was based on a student's activity recall from the previous day and may not have been an accurate indication of typical activity levels. Sedentary activity was found to be associated with symptomatic depression before and after adjusting for covariates in males and females, and this is consistent with previous studies in Australian adolescents.24

Previous research examining diet and mental health in adolescence has shown support for the relationship between poorer diet quality and mental health issues,35 however, in the current study, neither fruit and vegetable nor takeaway food consumption were related to depressive symptomatology in multivariate analyses. WHO cut-off for fruit and vegetable intake (did or did not meet five serves per day) may have been too strict to allow for meaningful differences in mental health based on amount of fruit and vegetable consumed. Given the low level of fruit and vegetable consumption among Australian adolescents generally,28 a more conservative measure of fruit and vegetable may have been warranted. Similar to activity levels, the measurement tool was particularly blunt in that it asked participants to recall consumption on a previous day and may have been an inaccurate reflection on typical consumption.

The significant association between weight status and depression observed for males may be due to more support and psycho-education currently being directed at female populations, in response to perceptions that weight status affects females more than males.36 Females may be encouraged to seek help for weight-related concerns, whereas the same encouragement may not be directed to males.37 The current study suggests that gender differences exist in obesogenic risks and associations with depressive symptoms, and these differences need to be explored and considered in future obesity and mental health research.

Several considerations must be made before generalising the current findings. First, although the data support the hypotheses that some obesogenic risks are related to depressive symptoms in adolescents, the cross-sectional design precludes clear conclusions about the direction of the associations. Future longitudinal research is needed to account for time-invariant individual differences, and to establish temporal order between obesogenic risk and symptomatic depression. The ACT-IYM project will address this limitation by following-up the same participants after 2 years.

Second, participants in this study were recruited exclusively from secondary schools in ACT and, consequently, the sample is not a representative cross-section of Australian adolescents. It is possible those individuals with more severe mental health issues may not attend school, and that parents of these children may be less willing to consent for their child to participate in the research. Additionally, considerably more parents of children in the present study had tertiary qualifications than the Australian average (56% vs 24%).38 Parents’ level of education may be a useful indicator of family SES, and previous research has shown an association between SES and depression in adolescents.39

Finally, the self-report methods in this study may not provide precise data from which to measure the variables, given the possibility of recall bias and social desirability bias associated with self-reports.40 However, asking specifically about the last school day strengthened the self-report measures in this study as it reduced the temporal lag between the activity and recall of events, and thus, reduced the potential for recall bias.

Future research should be aimed at investigating the direction of the observed relationships through longitudinal designs. Studies should aim to incorporate samples outside of secondary school populations, to potentially increase generalisability of findings, and include measurement tools that indicate typical behaviours. Specifically, interventions in adolescent mental health may target obesogenic risk factors, namely screen time and weight itself, which appear to be associated with symptomatic depression. Additionally, weight and lifestyle interventions should incorporate the mental health of a young person given the demonstrated associations between these health issues.

What is already known on this subject?

  • Associations exist between obesity and depression, and the period of adolescence poses significant risk for onset of both health issues. Obesogenic risk factors include low physical activity, high sedentary behaviour, and poor diet. Decreased activity levels and poor diet quality have been associated with increased depressive symptoms in adolescents, however, the associations between these obesogenic risks and depressive symptoms has not yet been evaluated in a cohort of Australian adolescents.

What this study adds?

  • This study demonstrates specific strength of associations between each identified obesogenic risk factor and depressive symptoms in a large Australian adolescent sample. Strengthening the evidence base of these associations in a cross-section of Australian adolescents will help with defining potential interventions for obesity and depression.


A thank you to all the staff & students participating in the project. Special thanks to the IYM school coordinators.


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  • Contributors EH participated in the conception and design of this study, analysed and interpreted the data, and drafted the manuscripts. LM participated in the conception and design of this study, analysed and interpreted the data, and critically revised the manuscripts. MFT analysed and interpreted the data, revised the manuscripts and gave final approval to the final version. HS participated in the conception and design of this study, critically revised the manuscripts and approved the final version. MN participated in the conception and design of the ACT IYM project and revised the manuscripts. FJ revised the manuscripts and approved the final version. BS, CC and SA participated in conception and design of the study, revised the manuscripts and approved the final version.

  • Funding This programme is a joint Australian, State and Territory Government initiative under the National Partnership Agreement on Preventive Health.

  • Competing interests None.

  • Ethics approval Deakin University Human Research Ethics Committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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