Depression, psychological distress and Internet use among community-based Australian adolescents: a cross-sectional study
© The Author(s). 2017
Received: 6 May 2016
Accepted: 14 April 2017
Published: 27 April 2017
There has been rapid increase in time spent using Internet as a platform for entertainment, socialising and information sourcing. This study aimed to evaluate the relationship between duration of time spent using Internet for leisure, depressive symptoms, and psychological distress among Australian adolescents.
Depressive symptoms were indicated by the youth self-report module from the Diagnostic and Statistical Manual of Mental Disorders Version IV criteria, and psychological distress was measured by Kessler Psychological Distress scale. Internet use was self-reported based on use on an average weekday, and an average weekend day. Multivariate logistic regression models were used to examine the relationship between Internet use and mental health outcomes. Models were adjusted for potential confounders: age; relative level of socio-economic disadvantage, and body mass index.
Adolescents were aged 11–17 years (M = 14.5 years, SD = 2.04 years). Greatest time spent using internet (≥7 h a day) was significantly associated with experiencing depressive symptoms among females (OR = 2.09, 95% CI = 1.16, 3.76, p < 0.05), and high/very high levels of psychological distress for male (OR = 2.23, 95% CI = 1.36, 3.65, p < 0.01) and female (OR = 2.38, 95% CI = 1.55, 3.67, p < 0.01) adolescents.
With current initiatives to improve health behaviours among adolescents to improve physical health outcomes such as overweight or obesity, it is imperative that the reciprocal relationship with mental health is known and included in such public health developments. Internet use may interact with mental health and therefore could be a modifiable risk factor to reach and improve mental health outcomes for this age group. Caution is advised in interpretation of findings, with some inconsistencies emerging from this evidence.
KeywordsAdolescents depression psychological distress Internet use community-based
A recent examination of sedentary behaviour among Australian young people reported that most youth exceeded the daily recommendation of ≤2 h of screen time per day, and stated that limiting screen-based media use among children and adolescents was ‘virtually impossible’ . Internet use represents one component of the broader category leisure screen – time. It has been suggested that screen-use, typically measured as specifically for leisure (not for school or work-related purposes) may interact with emotional and mental health [2–4]. With a large proportion of adolescents reporting mental health disorders or associated symptomatology , and increasing popularity for online activities for entertainment, socialising and information sourcing, quantifying the strength of any possible associations between Internet use and mental health is critical.
The screen-time recommendation of two hours or less, advocated in Australia  and abroad , has been criticised as unrealistic, due to screens being deeply integrated into modern life. The health impact of using screens has also been questioned, with some researchers suggesting such recommendations being offered despite being counterproductive based on inconsistent research findings to date [8, 9]. In addition, screen use has been suggested to provide opportunity for individuals to experience meaningful entertainment . Inconsistencies in the impact of Internet use specifically was highlighted by the clinical report from the American Academy of Pediatrics , and subsequent contradictory research findings . The report identified ‘Facebook Depression’ as a potential outcome of overuse of social media, however subsequent evidence found no direct link between social media use and symptoms of depression. The continued examination of the health correlates of screen-use through epidemiological studies is imperative.
Previous systematic reviews have predominately focused on the relationship between adolescent sedentary behaviour and physical health outcomes such as body composition, physical activity levels and general well-being indicators [13–15]; however mental health outcomes have mostly been overlooked. Our recent systematic review examined evidence for the relationship between sedentary behaviour and mental health problems among adolescents . We identified a range of cross-sectional and prospective studies and our findings showed consistent evidence for the relationship between sedentary behaviour (most commonly operationalized as hours of screen-time for leisure) and both depressive symptomatology and psychological distress, with significant gender specific associations. However, studies were of poor quality, often using derived mental health indicators based on a single item and therefore vulnerable to validity concerns. Sampling methods often lacked randomisation and representativeness, and weaknesses were identified in statistical methods including failing to account for important confounding variables. The evidence to date is significantly limited by such methodological weaknesses.
We identified the need for a comprehensive examination of mental health status of a large, representative cohort of adolescents, and associations with time spent using Internet for leisure. The impact of sedentary behaviours on health is developing as an important area in research for physical health, and it is critical that the mental health related evidence base is at the same time strengthened. Evidence exists for the unique experiences of mental health for male and females and defining such differences is another critical aspect of this research. The associations between problematic Internet use (addiction) and mental disorders among Australian adolescents has recently been reported . However the relationship between duration of time spent online (spanning low to high users) and mental health status requires further investigation.
The aim of this study was to determine the cross-sectional associations between depressive symptoms, psychological distress and time spent using Internet among a community-based, representative Australian adolescent group. This study aimed to answer the research question; what is the relationship between time spent using Internet for leisure and mental health outcomes; depressive symptoms and psychological distress, in a large, representative Australian adolescent group?
This study examined data from the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing, conducted during 2013–2014 . The methods summarised in this section have previously been reported . The national household survey aimed to: capture the mental health and well-being of young people across Australia; provide updated national prevalence estimates of common mental disorders; and report current behavioural patterns such as mental health service use and health risk behaviours.
The survey comprised of two parts; face-to-face interviews with the primary carer, and a self-report questionnaire completed by young people aged 11–17 years within participating households. The current study examined data from the adolescents’ self-reported questionnaire responses, and included some demographic information from the carer-reported data.
The national survey involved a random probability-based sample of 5500 young people aged 4–17 years . This sample size was chosen to meet reliability estimates for prevalence of mental disorders and behavioural patterns for males and females aged 4–11 years and 12–17 years. Areas eligible for participation were selected using a multi-stage, area-based sample selection procedure to ensure social and economic proportional representation across Australia. Participation was voluntary and all participants gave informed written consent prior to survey completion.
Participating young people completed the questionnaire privately on a tablet computer. Questionnaires comprised various modules, with those relevant to this study reported below. Details on other information contained in the questionnaire have been reported elsewhere .
Ethics and consent to participate
Ethical approval for this study was granted by Deakin University Human Research Ethics Committee (2016–035). Original ethics for the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing, was provided by the Australian Government Department of Health Ethics Committee and the University of Western Australia Human Research Ethics Committee.
Participation in the survey was voluntary and written consent was required from all participants. Initial verbal consent was obtained from parents or carers for their child’s participation, which was then followed up with paper consent forms from both parents/carers and the young person. Participation was voluntary and all young people were informed that they had the right to withdraw their consent for study participation at any time. Specific protocols were developed to ensure that if any issues arose for participants in response to the survey, there were services available to assist in receiving information and support they may require.
Depressive symptoms were measured by the youth self-report module from the Diagnostic and Statistical Manual of Mental Disorders Version IV (DISC-IV) criteria . The DSM-IV criteria specify that: at least five symptoms of depression must be present for a minimum of a two-week period; the symptoms cause clinically significant distress; and the symptoms interfere with the child or adolescent’s normal functioning at school, at home or in social settings. Symptoms of depression measured included low mood, loss of pleasure in daily activities, irritable, significant weight loss or gain, loss of appetite, insomnia or hypersomnia, restlessness, fatigue and loss of energy, feelings of worthlessness and inability to concentrate. The major depressive module of the DSM-IV has shown moderate to good diagnostic reliability, and moderate to very good validity in relation to clinically meaningful symptomatology . Items used to detect depressive disorder showed high internal consistency (Cronbach’s α > 0.70).
Kessler Psychological Distress scale (K10) [20, 21] was used to measure distress based on symptoms of anxiety or depressive symptoms experienced in the previous month. Ten items asking about an individual’s emotional experiences such as ‘during the last 30 days, about how often did you feel tired out for no good reason?’, with the following response options; ‘none of the time’, ‘a little of the time’, ‘some of the time’, ‘most of the time’ or ‘all of the time’. Responses were scored 0 to 4, where 0 represented lowest severity (none of the time) and 4 represented highest severity (all of the time). Participants received a score out of 40 with higher scores representing higher levels of psychological distress. Scores of psychological distress were categorised as; 0–5 = low, 6–11 = moderate, 12–19 = high, and 20–40 = very high, as adopted by the Australian Bureau of Statistics based on previous population-based research .
The K10 has been shown to be comparative to other mental health instruments such as mental health component of the General Health Questionnaire, Short Form 12 questionnaire, and the Composite International Diagnostic Interview . The K10 has shown moderate reliability in the general Australian population . In this survey data, the K10 Psychological Distress scale showed high internal consistency (Cronbach’s α > 0.70).
Internet use for leisure
Internet use indicators were included in the survey in response to developments in technology as a common means for young peoples’ communication, entertainment and for information sourcing. Internet use excluded time spent on school or work related activities. Internet use was described as Internet accessed on a computer, mobile phone or tablet, and including using social media such as Facebook or Twitter, emailing, looking at websites or chatting online. Questions were focused on time using Internet ‘not related to schoolwork or for work purposes’. One item asked ‘on an average weekday approximately how much time do you spend on the internet?’ and another item asked ‘On an average day on the weekend approximately how much time do you spend on the internet?’ Response categories for both items were; less than 1 h, 1–2 h, 3–4 h, 5–6 h, 7–8 h, 9–10 h, 11 h or more. Responses were categorised into 2 h or less, 3 to 6 h, and 7 h or more, for weekday and weekend responses separately. This was based on Australian guidelines for daily screen-time recommendations (two hours or less per day)  and the growing prevalence of Internet related disorders that are characterised by a large proportion of the day spent online .
In addition, research has indicated both positive and negative mental health impact of screen-use, dependent on time and frequency of use . We therefore recognised the need to uniquely examine the group reporting 7 h or more to gauge differences between low users, those exceeding screen-time based recommendations currently endorsed by the Australian Government, and those considered very high users. We examined Internet use separately for an average weekday and for an average weekend day as done in previous research examining physical activity and sedentary behavioural research among adolescents , and for increased potential for future practical applications of findings (e.g, school or home based interventions).
Demographics and confounding variables
Participants were asked basic demographics including age and gender as a part of the questionnaire completion. Socio-economic status was taken from the 2011 Australian Bureau of Statistics (ABS) Index of Relative Socio-Economic Disadvantage for the Statistical Area in which the family was living at the time of the survey . The ABS defines socio-economic advantage and disadvantage as access to material and social resources, and their ability to participate in society . The index forms a collection of variables designed to assess level of advantage/disadvantage, including; deprivation, poverty, human capital, and economic, social and political opportunities .
Participants were asked to self-report height in metres and weight in kilograms, from which body mass index (BMI) was derived (divided kilograms by height in metres squared). Weight status (thinness/normal weight, overweight/obesity) was calculated using criteria detailed by Cole et al. (2000) . Cole et al. define age and sex specific cut-points that derive from adult BMI equivalent of 25 kg/m2 for overweight and 30 kg/m2 for obesity. Thinness was combined with normal weight due to the low proportion of adolescents categorised as thin (<6%).
Analyses were conducted using STATA release V.14.1 (Stata Corp., College Station, Texas, USA, 2015). All variables were checked for missing data. In all cases there were little missing data and case-wise deletion was used where relevant. Descriptive statistics were used to analyse differences between males and females using independent sample Student t-tests and Pearson’s χ2 test where appropriate. Separate multivariate logistic regression models were used to examine the relationship between Internet use and dichotomous mental health outcomes; depressive symptoms (symptoms suggesting depression compared to no depression) and psychological distress (high/very high levels of distress compared to low/moderate levels), stratified by gender. Models were adjusted for potential confounders: age; relative level of socio-economic disadvantage (henceforth referred to as socio-economic status [SES]); and body mass index (BMI). Results were considered statistically significant at p < 0.05.
Characteristics of survey sample
Mean age in years (SD)
Index of relative socio-economic disadvantage
Lowest quintile (least advantaged)
Highest quintile (most advantaged)
Parent or carer education
Bachelor degree of higher
Diploma or certificate
BMI Mean SD
Proportion of male and female adolescents with major depressive disorder, depressive symptomatology and psychological distress, and time spent using Internet
Major depressive disordera
With depression n (%)
p < 0.05
Total depressive symptomatology score Mean (SD)
p < 0.05
p < 0.05
Total psychological distress score Mean (SD)
p < 0.05
Uses Internet n (%) Yes
Internet use on an average weekday n (%)
2 h or less
7 h or more
Internet use on an average weekend day n (%)
2 h or less
7 h or more
Almost all adolescents (99.2%) reported using the Internet. Over half (57.6%) of the adolescent group reported using the Internet three or more hours on an average weekday, and 65.5% reported three or more hours of Internet use an average weekend day. Close to one fifth reported 7 or more hours of Internet use on an average weekday (17.8%) and on an average weekend day (22.9%). No significant differences existed between Internet usage patterns between male and female subgroups.
Unadjusted (Model 1) and adjusted (Model 2) associations for Internet use for major depressive disorder, depressive symptomatology, and psychological distress (adjusted for age, SES and BMI)
Major depressive disorderb
Weekday internet use
≤ 2 h
≥ 7 h
Weekend internet use
≤ 2 h
≥ 7 h
Weekday internet use
≤ 2 h
≥ 7 h
Weekend internet use
≤ 2 h
≥ 7 h
After adjusting for potential confounders (age, SES, and BMI), the relationship between Internet use and depressive symptoms remained significant for female adolescents reporting 3–6 h (OR = 1.87, 95% CI = 1.15–3.02, p < 0.05) and seven or more hours (OR = 2.09, 95% CI = 1.16–3.76, p < 0.05) on an average weekday. Psychological distress appeared more frequent among female adolescents reporting seven or more hours Internet use on a weekday, however this relationship did not reach significance (p = 0.051). Seven or more hours of Internet use on the weekend doubled the odds of experiencing high/very high psychological distress for males (OR = 2.23, 95% CI = 1.36–3.65, p < 0.05) and females (OR = 2.38, 95% CI = 1.55–3.67, p < 0.05) compared to adolescents reporting two hours or less.
Age increased likelihood of depressive symptoms in both males (OR = 1.20, 95% CI = 1.04–1.37, p < 0.05) and females (OR = 1.38, 95% CI = 1.24–1.53, p < 0.05), and psychological distress in females (OR = 1.20, 95% CI = 1.11–1.29, p < 0.05). BMI was positively associated with depressive symptoms among males (OR = 1.06, 95% CI = 1.00–1.11, p < 0.05) and psychological distress in both males (OR = 1.03, 95% CI = 1.00–1.07, p < 0.05) and females (OR = 1.04, 95% CI = 1.01–1.07, p < 0.05).
The aim of this study was to examine depressive symptoms and psychological distress in a large, representative cohort of Australian adolescents, and to identify associations with time spent using the Internet for leisure purposes. This study found that females (13.9%) were more likely to experience depressive symptoms compared to males (5.8%), and this is consistent with previous estimates . High or very high levels of psychological distress were experienced by a greater proportion of females (28.7%) compared to males (15.3%) and this gender difference is also consistent with previous estimates .
Most adolescents reported using the Internet for three or more hours on an average weekday (57.6%) and on an average weekend day (65.5%). A recent examination of Australian adolescents  reported that most (84.6%) exceeded Australian Government guidelines or two hours or less daily hours spent using screens for leisure, and this current finding was therefore not overly surprising. Nevertheless, this finding demonstrates the proportion of adolescents adhering to the current Australian guidelines of two hours of less is low, given the adolescents participating in this study exceed this recommendation based on Internet use alone.
Compared to adolescents reporting Internet use for two hours or less, depressive symptoms were most frequent among higher users of the Internet (3 or more hours per day) among females only. This finding is novel in light of previous literature which has reported higher levels of depression among high internet users in combined male and female adolescent populations [3, 34–36]. This finding is particularly important given significantly higher rates of depression among adolescent females compared to males, and the need for novel treatment and prevention initiatives. Previous research has found associations between body image disturbances and self-objectification with time spent using social media sites among female adolescents . Given the cross-sectional design of the current study, causality cannot be speculated. However, it is possible that negative self-esteem may increase with time using such sites and precede depression. It is also possible that depressed individuals may seek comfort through connection and social support through such sites.
Using the Internet for seven or more hours on an average weekend day doubled the odds of reporting high or very high psychological distress among males and females, compared to those using the internet for two hours or less. Psychological distress is distinguished from depressive symptoms as it includes symptoms of both depression and anxiety. The current study found this relationship significant only among the group reporting the highest level of Internet use (seven or more hours) on an average weekend day, compared to those reporting two hours or less. This finding is novel in light of previous literature which found associations between psychological distress and internet use among community-based adolescents, when internet use exceed two hours a day threshold [38, 39]. This finding may reflect increasingly normalisation of internet use for day-to-day activities, and may also reflect increased opportunity for online mental health support as previously described.
Mental health concerns are known to increase from younger to older adolescents , and this was represented in the current findings with age predicting increased levels of depressive symptoms in both males and females and psychological distress in females. Increased BMI was linked to depressive symptoms among males, and psychological distress in both males and females. Previous research has supported the relationship between increased BMI, overweight/obese weight status, and mental health among young people [33, 41–44].
Asides from the described findings, there were no other significant associations between Internet use and mental health among this sample of Australian adolescents. While the significant findings support the ongoing investigation into the relationship between Internet use and mental health, such relationships were inconsistent. The possibility for positive or null relationship between mental health and Internet use, as described in the background section, remains plausible.
Given the cross-sectional design of this study it is not possible to provide conclusion on causality and therefore mechanism cannot be approximated. The following discussion of potential mechanisms are provided only as possible explanations, and may support future longitudinal research enquiry.
While female adolescents are known to experience increased risk for internalising disorders including depression and anxiety , the finding that 13.9% were suffering depressive symptoms, and close to a third (28.7%) experienced high or very high levels of psychological distress, is cause for great public health concern. It has been reported that during adolescence, females are at greater risk of factors associated with mood and anxiety-related disorders . Females may experience consequential negative feelings towards the self in the development of such disorders, which then exacerbates symptoms and disorder trajectory . Sociological perspectives examine gender roles and practices as contributors to disparities in health, such as social expectations of masculinity and femininity, and suggest that such practices have the potential to be modified to improve mental health outcomes . The finding that increased Internet use was associated with depressive symptoms requires further exploration; however, given that adolescent females experience significant increased risk for depressive symptoms overall, this could be an important intervention point for future research.
While cross-sectional design precluded conclusions on causality, symptoms of depression such as low motivation and worthlessness, may prohibit adolescents engaging in healthy lifestyle behaviours. Physical activity data were not collected and may have been a confounding variable. Being physically active assists with building self-efficacy, resilience and offers social integration, and it has been suggested that such characteristics may protect against depression [47–49]. Importantly, the relationship between physical activity and sedentary behaviour is complex. The findings of a recent systematic review concluded that the association between sedentary behaviour and physical activity in children and young people is negative, but small, suggesting these behaviours do not directly displace one another . The findings reported in this study would have benefitted in controlling for physical activity levels, to determine whether there is unique contribution of Internet use to mental health among adolescents. Furthermore, young people engage in many sedentary behaviours that are widely approved (e.g., reading for leisure, homework/studying) and it is unrealistic to expect all activities considered sedentary to be associated to negative health outcomes.
In addition, sedentary behaviour is characterised as activities involving very low energy expenditure, and pathophysiological processes occurring during time spent using the Internet may have a negative impact upon mood and emotional health outcomes . Our previous systematic review reported some evidence for the relationship between screen-time and negative mental health outcomes, independent of physical activity [38, 52], therefore this mechanism needs further investigation. An additional consideration is that the relationship between types of screen use is uncertain in that young people may displace television viewing as they watch programs online, and may or may not include this as ‘time spent online’. Furthermore, the Internet has been identified as a useful information source for young people experiencing mental health problems and time spent online may be attributable to help-seeking behaviour . While our findings indicated some significant relationships, we advise caution in applying such findings to sedentary behaviour overall, and we recommend that screen use is investigated by type and content of activity, as opposed to an overall composite screen time measure.
Although the relationship between BMI, weight status and mental health has been previously reported , this has historically been most commonly found among female adolescent populations [55, 56]. The findings of this study suggest that this relationship is more pronounced among male adolescents, and this builds on recent trends showing similar vulnerabilities for overweight and obese males [33, 43]. Traditionally, support for weight-based and body image concerns have been directed towards females . It is possible that male adolescents experiencing overweight or obesity experience negative mental health outcomes, however may not receive the same mental health support. Additionally, overweight and obese males may be less likely to recognise symptoms for depression, and seek mental health support .
Finally it is possible that adolescents experiencing depressive symptoms or increased levels of psychological distress may turn to online sources for support, leading to increased time spent online. The opportunity for internet based mental health interventions for young people has previously been supported, with results suggesting significant positive effect for computer-delivered cognitive behavioural therapy for anxiety and depression symptoms . Given high levels of habitual Internet use among Australian young people, initiatives that are delivered online may hold unique opportunity to improve health outcomes by utilising existing common behaviours.
Strengths and limitations
This study was strengthened by a large, community-based Australian adolescent sample that was representative of the Australian population on key demographic characteristics. The measures used for depressive symptoms and psychological distress have both previously been shown to demonstrate good reliability and validity among adolescent populations. Analyses was strengthened by controlling for potential confounders; age, relative level of socio-economic disadvantage, and BMI, which have been suggested to impact on internet use behaviours and mental health among adolescents [59–62]. Despite inclusion of these covariates, this study was limited by the lack of available physical activity data, and the potential confounding impact on the relationship between internet use and mental health. Furthermore, current health status was not included in the data and failure to include this as a covariate may have confounded the results.
This study was limited by cross-sectional design that precluded conclusions on the directional relationship between duration of time spent using Internet, depressive symptoms and psychological distress. Time estimates for Internet usage were derived from single questions on typical self-reported behaviour, and are therefore subject to various methodological weaknesses such as recall bias, or social desirability bias. Furthermore, the internet use item stipulated that estimates were for time spent using internet other than work or school purposes. It is possible that associations between internet use for non-leisure purposes and mental health factors may exist, but were not captured in this study. This study was also limited by the use of self-report measures; however objective measures have been shown to pose feasibility issues for large, community based samples. While our analyses were conducted following stringent statistical methodology, it has been suggested that in some instances odds ratios may be fragile to Type I error and should be interpreted with caution .
Directions for future research
These results suggest that identifying mechanisms driving gender specific risks for mental health is of utmost importance. Time spent using Internet could be included as an indicator for potential mental health concerns, particularly among adolescents spending large proportions of time online on an average day. With the known benefits of healthy lifestyle behaviours for physical and emotional health, further research could examine the impact of reducing time spent online on mental health disorders and associated symptoms – although online activity for young people is ubiquitous and decreasing usage may be unrealistic . An important step in this research is, therefore, to examine the type and duration of online activity, and the behaviours that time spent online replaces, to further identify mechanisms at work in this relationship. Positive mental health outcomes of Internet use need to be further explored, due to important inconsistencies (e.g, non-significant relationships) found in this research, and potential to leverage on common existing lifestyle behaviours.
There is a concerning number of adolescents with depressive symptoms and a large proportion of this current adolescent sample reported experiencing high or very high levels of psychological distress. Generating evidence to inform efforts to prevent and treat mental health disorders is a clear public health priority for this age group. Most adolescents in this cohort exceeded Australian guidelines for daily screen-time recommendations of two hours or less per day, and this was through Internet use alone (recommendations includes other screen-time such as watching television or gaming). With current developments in initiatives to improve health behaviours among adolescents to improve physical health outcomes such as overweight or obesity, it is imperative that the relationships with mental health are known and included. Internet use may interact with mental health and therefore could be a modifiable risk factor to reach and improve mental health outcomes for this age group. Some inconsistencies emerged from this study, and further investigation is needed to determine the prospective relationship between Internet use and mental health, and the possibility of positive or null relationships.
Data was derived from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing to which over 6000 families generously gave their time.
The survey was funded by the Australian Government Department of Health. EH is supported by an Australian Postgraduate Award and Australian Endeavour Research Fellowship.
Availability of data and materials
All data was derived from the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Data access was granted through the Australian Data Archive. All relevant data for this research is reported within the manuscript and the full data set is accessible through Australian Data Archive (https://www.ada.edu.au/).
EH led the study conception and design, acquired data access, analysed and interpreted findings, and drafted the manuscript. KM contributed to study conception and design, and critically revised the manuscript for intellectual content. CF and SA critically revised the study for intellectual content, read and approved the final version for publication. All authors read and approved the final manuscript.
The authors have no competing interests to declare.
Consent to publication
Ethics approval and consent to participate
Ethical approval for this study was granted by Deakin University Human Research Ethics Committee (2016–035). Original ethics for the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing was provided by the Australian Government Department of Health Ethics Committee and the University of Western Australia Human Research Ethics Committee. Informed consent was obtained prior to survey completion.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Houghton S, Hunter SC, Rosenberg M, Wood L, Zadow C, Martin K, Shilton T. Virtually impossible: limiting Australian children and adolescents daily screen based media use. BMC Public Health. 2015;15(1):1.View ArticleGoogle Scholar
- Teychenne M, Ball K, Salmon J. Sedentary behavior and depression among adults: a review. Int J Behav Med. 2010;17(4):246–54.View ArticlePubMedGoogle Scholar
- Pantic I, Damjanovic A, Todorovic J, Topalovic D, Bojovic-Jovic D, Ristic S, Pantic S. Association between online social networking and depression in high school students: behavioral physiology viewpoint. Psychiatr Danub. 2012;24(1):90–3.PubMedGoogle Scholar
- Teychenne M, Costigan SA, Parker K. The association between sedentary behaviour and risk of anxiety: a systematic review. BMC Public Health. 2015;15(1):1.View ArticleGoogle Scholar
- Lawrence D, Johnson S, Hafekost J, de Haan KB, Sawyer M, Ainley J, Zubrick SR. The Mental Health of Children and Adolescents: Report on the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Canberra: Department of Health; 2015.Google Scholar
- Government A. Health do, Ageing: Australia’s physical activity recommendations for 12–18 year old. Canberra: Australian Government; 2004.Google Scholar
- Tremblay MS, LeBlanc AG, Janssen I, Kho ME, Hicks A, Murumets K, Colley RC, Duggan M. Canadian sedentary behaviour guidelines for children and youth. Appl Physiol Nutr Metab. 2011;36(1):59–64.View ArticlePubMedGoogle Scholar
- Linebarger DL, Barr R, Lapierre MA, Piotrowski JT. Associations Between Parenting, Media Use, Cumulative Risk, and Children's Executive Functioning. J Dev Behav Pediatr. 2014;35(6):367–77.View ArticlePubMedGoogle Scholar
- Ferguson CJ, Donnellan MB. Is the association between children’s baby video viewing and poor language development robust? A reanalysis of Zimmerman, Christakis, and Meltzoff (2007). Dev Psychol. 2014;50(1):129.View ArticlePubMedGoogle Scholar
- Banks J, Bowman ND. Close intimate playthings? Understanding player-avatar relationships as a function of attachment, agency, and intimacy. Sel Pap Internet Res. 2013;3:1–4.Google Scholar
- O'Keeffe GS, Clarke-Pearson K. The impact of social media on children, adolescents, and families. Pediatrics. 2011;127(4):800–4.View ArticlePubMedGoogle Scholar
- Simoncic TE, Kuhlman KR, Vargas I, Houchins S, Lopez-Duran NL. Facebook use and depressive symptomatology: Investigating the role of neuroticism and extraversion in youth. Comput Hum Behav. 2014;40:1–5.View ArticleGoogle Scholar
- Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, Goldfield G, Gorber SC. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8(1):1.View ArticleGoogle Scholar
- Cliff DP, Hesketh K, Vella SA, Hinkley T, Tsiros MD, Ridgers ND, Carver A, Veitch J, Parrish AM, Hardy LL. Objectively measured sedentary behaviour and health and development in children and adolescents: systematic review and meta-analysis. Obes Rev. 2016;17(4):330–44.Google Scholar
- Costigan SA, Barnett L, Plotnikoff RC, Lubans DR. The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. J Adolesc Health. 2013;52(4):382–92.View ArticlePubMedGoogle Scholar
- Hoare E, Milton K, Foster C, Allender S. The associations between sedentary behaviour and mental health among adolescents: a systematic review. J Behav Nutri Phys Act Under Rev. 2016;13(1):108.Google Scholar
- Rikkers W, Lawrence D, Hafekost J, Zubrick SR. Internet use and electronic gaming by children and adolescents with emotional and behavioural problems in Australia–results from the second Child and Adolescent Survey of Mental Health and Wellbeing. BMC Public Health. 2016;16(1):1.View ArticleGoogle Scholar
- Hafekost J, Lawrence D, de Haan KB, Johnson SE, Saw S, Buckingham WJ, Sawyer MG, Ainley J, Zubrick SR: Methodology of Young Minds Matter: The second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 2015:doi:https://doi.org/10.1177/0004867415622270.
- Shaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME. NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. J Am Acad Child Adolesc Psychiatry. 2000;39(1):28–38.View ArticlePubMedGoogle Scholar
- Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, Walters EE, Zaslavsky AM. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(06):959–76.View ArticlePubMedGoogle Scholar
- Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, Howes MJ, Normand S-LT, Manderscheid RW, Walters EE. Screening for serious mental illness in the general population. Arch Gen Psychiatry. 2003;60(2):184–9.View ArticlePubMedGoogle Scholar
- Australian Bureau of Statistics. Information paper: Use of the Kessler Psychological Distress Scale in ABS Health Surveys, Australia, 2007–08. Canberra: Australian Government; 2012.Google Scholar
- Andrews G, Slade T. Interpreting scores on the Kessler psychological distress scale (K10). Aust N Z J Public Health. 2001;25(6):494–7.View ArticlePubMedGoogle Scholar
- Dal Grande E, Taylor A, Wilson D. South Australian Health and Wellbeing Survey. Adelaide: South Australian Department of Human Services; 2002.Google Scholar
- Block JJ. Issues for DSM-V: Internet addiction. Am J Psychiatr. 2008;165(3):306–7.View ArticlePubMedGoogle Scholar
- Przybylski AK. Electronic gaming and psychosocial adjustment. Pediatrics. 2014;134(3):e716–22.View ArticlePubMedGoogle Scholar
- Santos MP, Gomes H, Mota J. Physical activity and sedentary behaviors in adolescents. Ann Behav Med. 2005;30(1):21–4.View ArticlePubMedGoogle Scholar
- Australian Bureau of Statistics. Census of Population and Housing: Socio-economic indicators for areas (SEIFA). Canberra: Australian Government; 2013.Google Scholar
- Australian Bureau of Statistics. Socio-economic indexes for areas (SEIFA)—technical paper. Canberra: Australian Government; 2006.Google Scholar
- Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240.View ArticlePubMedPubMed CentralGoogle Scholar
- Avenevoli S, Swendsen J, He J-P, Burstein M, Merikangas KR. Major depression in the National Comorbidity Survey–Adolescent Supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry. 2015;54(1):37–44.e32.View ArticlePubMedGoogle Scholar
- Mewton L, Kessler RC, Slade T, Hobbs MJ, Brownhill L, Birrell L, Tonks Z, Teesson M, Newton N, Chapman C. The Psychometric Properties of the Kessler Psychological Distress Scale (K6) in a General Population Sample of Adolescents. Psychol Assess. 2016; 28(10):1232–42.Google Scholar
- Hoare E, Millar L, Fuller-Tyszkiewicz M, Skouteris H, Nichols M, Malakellis M, Swinburn B, Allender S. Depressive symptomatology, weight status and obesogenic risk among Australian adolescents: a prospective cohort study. BMJ Open. 2016;6(3):e010072.View ArticlePubMedPubMed CentralGoogle Scholar
- Maras D, Flament MF, Murray M, Buchholz A, Henderson KA, Obeid N, Goldfield GS. Screen time is associated with depression and anxiety in Canadian youth. Prev Med. 2015;73:133–8.View ArticlePubMedGoogle Scholar
- Kremer P, Elshaug C, Leslie E, Toumbourou JW, Patton GC, Williams J. Physical activity, leisure-time screen use and depression among children and young adolescents. J Sci Med Sport. 2014;17(2):183–7.View ArticlePubMedGoogle Scholar
- Katon W, Richardson L, Russo J, McCarty CA, Rockhill C, McCauley E, Richards J, Grossman DC. Depressive symptoms in adolescence: the association with multiple health risk behaviors. Gen Hosp Psychiatry. 2010;32(3):233–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Meier EP, Gray J. Facebook photo activity associated with body image disturbance in adolescent girls. Cyberpsychology Behav Soc Netwo. 2014;17(4):199–206.View ArticleGoogle Scholar
- Trinh L, Wong B, Faulkner GE. The Independent and Interactive Associations of Screen Time and Physical Activity on Mental Health, School Connectedness and Academic Achievement among a Population-Based Sample of Youth. J Can Acad Child Adolesc Psychiatry. 2015;24(1):17.PubMedPubMed CentralGoogle Scholar
- Arbour-Nicitopoulos KP, Faulkner GE, Irving HM. Multiple health-risk behaviour and psychological distress in adolescence. J Can Acad Child Adolesc Psychiatry. 2012;21(3):171.Google Scholar
- Patel V, Flisher AJ, Hetrick S, McGorry P. Mental health of young people: a global public-health challenge. Lancet. 2007;369(9569):1302–13.View ArticlePubMedGoogle Scholar
- Loth KA, Mond J, Wall M, Neumark-Sztainer D. Weight status and emotional well-being: longitudinal findings from Project EAT. J Pediatr Psychol. 2011;36(2):216–25.View ArticlePubMedGoogle Scholar
- Swallen KC, Reither EN, Haas SA, Meier AM. Overweight, obesity, and health-related quality of life among adolescents: the National Longitudinal Study of Adolescent Health. Pediatrics. 2005;115(2):340–7.View ArticlePubMedGoogle Scholar
- Hoare E, Millar L, Fuller-Tyszkiewicz M, Skouteris H, Nichols M, Jacka F, Swinburn B, Chikwendu C, Allender S. Associations between obesogenic risk and depressive symptomatology in Australian adolescents: a cross-sectional study. J Epidemiol Community Health. 2014;68(8):767–72.View ArticlePubMedGoogle Scholar
- Cornette R. The emotional impact of obesity on children. Worldviews Evid-Based Nurs. 2008;5(3):136–41.View ArticlePubMedGoogle Scholar
- Rosenfield S, Mouzon D. Gender and mental health. In: Handbook of the sociology of mental health. edn.: Netherlands: Springer; 2013. p. 277–96.Google Scholar
- Rogers A, Pilgrim D. A sociology of mental health and illness. London: McGraw-Hill Education; 2014.Google Scholar
- Mason MJ, Schmidt C, Abraham A, Walker L, Tercyak K. Adolescents’ social environment and depression: social networks, extracurricular activity, and family relationship influences. J Clin Psychol Med Settings. 2009;16(4):346–54.View ArticlePubMedGoogle Scholar
- Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191.View ArticlePubMedGoogle Scholar
- Adeniyi AF, Okafor NC, Adeniyi CY. Depression and physical activity in a sample of nigerian adolescents: levels, relationships and predictors. Child Adolesc Psychiatry Mental Health. 2011;5(1):1.View ArticleGoogle Scholar
- Pearson N, Braithwaite R, Biddle SJ, Sluijs E, Atkin AJ. Associations between sedentary behaviour and physical activity in children and adolescents: a meta-analysis. Obes Rev. 2014;15(8):666–75.View ArticlePubMedPubMed CentralGoogle Scholar
- Lopresti AL, Hood SD, Drummond PD. A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. J Affect Disord. 2013;148(1):12–27.View ArticlePubMedGoogle Scholar
- Nihill GFJ, Lubans DR, Plotnikoff RC. Associations between sedentary behavior and self-esteem in adolescent girls from schools in low-income communities. Ment Health and Phys Act. 2013;6(1):30–5.View ArticleGoogle Scholar
- Calear AL, Christensen H, Mackinnon A, Griffiths KM, O’Kearney R. The YouthMood Project: a cluster randomized controlled trial of an online cognitive behavioral program with adolescents. J Consult Clin Psychol. 2009;77(6):1021.View ArticlePubMedGoogle Scholar
- Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BW, Zitman FG. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry. 2010;67(3):220–9.View ArticlePubMedGoogle Scholar
- Needham BL, Crosnoe R. Overweight status and depressive symptoms during adolescence. J Adolesc Health. 2005;36(1):48–55.View ArticlePubMedGoogle Scholar
- Anderson SE, Cohen P, Naumova EN, Jacques PF, Must A. Adolescent obesity and risk for subsequent major depressive disorder and anxiety disorder: prospective evidence. Psychosom Med. 2007;69(8):740–7.View ArticlePubMedGoogle Scholar
- Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health. 2000;90(2):251.View ArticlePubMedPubMed CentralGoogle Scholar
- Clarke AM, Kuosmanen T, Barry MM. A systematic review of online youth mental health promotion and prevention interventions. J Youth Adolesc. 2015;44(1):90–113.View ArticlePubMedGoogle Scholar
- Suchert V, Hanewinkel R, Isensee B. Sedentary behavior and indicators of mental health in school-aged children and adolescents: A systematic review. Prev Med. 2015;76:48–57.View ArticlePubMedGoogle Scholar
- Melchior M, Chollet A, Fombonne E, Surkan PJ, Dray-Spira R. Internet and video game use in relation to overweight in young adults. Am J Health Promot. 2014;28(5):321–4.View ArticlePubMedGoogle Scholar
- Derbyshire KL, Lust KA, Schreiber LR, Odlaug BL, Christenson GA, Golden DJ, Grant JE. Problematic Internet use and associated risks in a college sample. Compr Psychiatry. 2013;54(5):415–22.View ArticlePubMedGoogle Scholar
- Reiss F. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc Sci Med. 2013;90:24–31.View ArticlePubMedGoogle Scholar
- Davies HTO, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ. 1998;316(7136):989–91.View ArticlePubMedPubMed CentralGoogle Scholar