Skip to main content
  • Research article
  • Open access
  • Published:

The associations between screen time-based sedentary behavior and depression: a systematic review and meta-analysis



The use of computers/TV has become increasingly common worldwide after entering the twenty-first century and depression represents a growing public health burden. Understanding the association between screen time-based sedentary behavior (ST-SB) and the risk of depression is important to the development of prevention and intervention strategies.


We searched the electronic databases of Medline, Embase and the Cochrane Library. The odds ratio (OR) with corresponding 95% confidence intervals (CIs) was adopted as the pooled measurement. Subgroup analyses were investigated by stratified meta-analyses based on age, gender and reference group (reference category of screen time, e.g. 2 h/day, 4 h/day).


There were 12 cross-sectional studies and 7 longitudinal studies met the inclusion criteria. Overall, the pooled OR was 1.28 with high heterogeneity (I2 = 89%). Compared to those who reported less SB, persons reporting more SB had a significantly higher risk of depression. When the gender was stratified, the pooled OR was 1.18 in female groups while no significant association was observed in males. Among the 19 studies, 5 studies used a reference group with ST = 2 h/days (pooled OR = 1.46), 9 studies used ≥4 h as a reference group (pooled OR = 1.38), 2 studies used 1 h as a reference group (pooled OR = 1.07) and for the remaining 3 studies, hours of ST were calculated as a continuous variable (pooled OR = 1.04).


ST-SB is associated with depression risk and the effects vary in different populations. In addition, valid objective measures of SB should be developed in future studies.

Peer Review reports


The use of computers/TV has become increasingly common worldwide after entering the twenty-first century [1], and there has been a large increase in the number of workers whose major job is computer-related [2, 3]. Moreover, both adolescents and adults also spent a large amount of time on the computer or smartphone or watching television. With advances in technology, screen time (ST), including watching television, using a computer and playing video games, is becoming a central component of the daily lives [4] and the most common sedentary behavior [5] (i.e., activities that require minimal body movement resulting in low energy expenditure similar to that at resting level [1.0 to 1.5 metabolic equivalents (METs)] [6]). Previous studies have shown that screen time-based sedentary behavior (ST-SB) is associated with increased risk for a variety of physical diseases, such as cardiovascular disease [7], obesity [8], and diabetes [9]. Moreover, ST-SB also influences mental health, such as sleep problems [10], anxiety disorders [11] and depression [12].

Currently, mental disorders are widely recognized as a major contributor (14%) to the global burden of disease, and depression is one of the most prevalent mental disorders [13]. Indeed, the World Health Organization (WHO) ranked major depression as one of the most burdensome diseases in the world [14]. Major depression has increased from the 15th-leading cause of adult disease burden in 2000 to the 11th-leading cause in 2010 [15]. According to new estimates of depression released by the WHO, the number of people living with depression increased by 18% between 2005 and 2015. Depressive disorders are ranked as the single largest contributor to nonfatal health loss (7.5% of all years lived with disability). The prevalence varies across the world, from a low incidence of 2.6% among males in the Western Pacific Region to 5.9% among females in the African Region. Furthermore, depressive disorders are projected to be the second leading cause of disease burden worldwide by 2030 and are the leading cause in high-income countries [16]. In addition, the onset of depression is common in adolescents and young adults [17,18,19], who may spend much more time on computers than older persons, coinciding with a pivotal period of physical and psychological development, and can lead to poorer psychosocial functioning, lower life and career satisfaction, more interpersonal difficulty, higher need for social support, more comorbid psychiatric conditions, and increased risk of suicide.

The median age of onset (50th percentile on the age-of-onset distribution) was approximately 30 for major depressive disorders [20]. Currently, many adults study or work in front of computers, and ST-SB has become a common and important issue not only for adolescents but also for adults. Therefore, understanding the association between ST-SB and the risk of depression among adults is also important to the development of prevention and intervention strategies. Many studies have investigated the association among different populations; however, the results were inconsistent. Some studies showed that longer ST might lead to a higher prevalence of depressive-related problems, while some studies thought this association was not significant. Thus, this systematic review was conducted to explore whether ST-SB influenced the risk of depression.


Literature search strategy

A structured electronic search of publications from 2000 to 2018 was conducted, since the 2000’s saw an increase in sedentary behavior levels in the population with the widespread use of online technology [18]. Databases included Medline, Embase and the Cochrane Library. The following search strings were used: (depression OR depressive OR dysthymia OR mental health OR mental illness OR Psychinfo) AND (sedentary behav* OR sitting OR TV OR television OR computer OR screen OR smartphones OR tablets OR iPads). These strings were further limited to peer-reviewed publications written in English. First, titles and abstracts of articles identified in the search process were assessed for suitability. Second, the studies listed in the references of the articles were reviewed. The retrieval was conducted in Feb 2019. The full texts of the studies that met our criteria were downloaded after primary selection by reading the titles and abstracts.

Study selection criteria

The risk of depression was defined as either diagnosed depression disorders (including major depressive disorder, dysthymic disorder and depressive disorder not otherwise specified) or the likelihood of developing or experiencing nonclinical depressive symptoms. Studies were considered eligible if they: (1) were observational studies, including cohort, case-control, and cross-sectional studies; (2) examined the risk of depression specifically; (3) assessed screen-time-based sedentary behavior; (4) concluded OR and 95% CI/se/p values; and (5) included participants aged 18 years or over.

Data extraction

The following study characteristics of the identified studies were extracted: the first author, year of publication, country of origin, size of study population, study design, sample size, age, measures used of depression and ST-SB, analysis method and study results in terms of the association between ST-SB and risk of depression.

Quality assessment

A modified version of an eight-component rating scale [21] was used to evaluate the methodological quality of the included studies. Because only observational studies were included in this review, six methodological components were included in the modified version: selection bias (e.g., response rate, representativeness), study design (e.g., cross-sectional, cohort, etc.), confounders (e.g., controlling for age, socioeconomic position, etc.), data collection methods (e.g., valid, reliable), withdrawals and dropouts (e.g., percent providing full data) and analyses (e.g., appropriateness of study design). Each of the components was given an overall section rating (weak, moderate, or strong). If one of these components was not described in the study included, for example, it said ‘more detail was described elsewhere’, we would try to find other papers that used the same database to provide this information. After all components were rated, a global rating for this paper of weak (if ≥2 of the components were scored weak), moderate (if < 3 components were scored strong with no more than one weak score), or strong (if ≥3 components were scored strong and ≤ 1 component was scored weak) was given to each study. Two reviewers (Wang and Li) independently assessed the methodological quality of these studies. Scoring discrepancies were resolved via consensus.

Statistical analyses

The odds ratio (OR) with corresponding 95% confidence intervals (CIs) was used as a measurement to evaluate the association between ST-SB and depression. Adjusted effect sizes were used if available. Reports stratified by gender were treated as separate reports. Finally, because most of the studies included in this meta-analysis were not functionally identical, a DerSimonian and Laird random effects model was used to attain an overall OR and 95% CI. The combined effect size was evaluated using the inverse variance method. Heterogeneity between studies was tested using Cochran’s χ2 statistic and the I2 statistic. Generally, an I2 value of < 25%, corresponds to low heterogeneity, a value of 25–50% corresponds to moderate heterogeneity, and a value > 50% corresponds to strong heterogeneity between studies. Publication bias was assessed using funnel plots. Subgroup analyses were used to identify sources of heterogeneity. Based on the literature, the prevalence of depression differs by gender [22, 23] and age [24, 25]. In addition, the reference group (reference category of screen time, e.g., 2 h/day, 4 h/day) and study design also influenced ORs. Therefore, subgroup analyses were investigated by stratified meta-analyses based on age, gender, reference group, and study design. When an individual study reported effect sizes by gender, it would be divided into two parts in the subgroup analyses of gender. All P values were two-sided analyses, and 0.05 was considered statistically significant. All these analyses were conducted using R5.3 software (meta package and metagen package).


Characteristics of the included studies

Our literature search yielded 439 studies (see Fig. 1). A total of 238 studies were screened by title. After a further screening of abstracts (n = 160) and full papers (n = 78), a total of 19 studies were included in the review. There were 12 cross-sectional studies and 7 longitudinal studies that met the inclusion criteria, including a total of 232,581 participants (118,991 in cross-sectional studies and 113,590 in longitudinal studies). The characteristics of the included studies are summarized in Table 1, including the author, year of publication, country, type of study, sample size, mean age, measures of depression and ST-SB, and quality scoring. The sample sizes ranged from 397 to 49,821. Fifteen studies involved both male and female participants, while 4 studies [29, 31, 33, 39] involved only female participants. Among these 15 studies, gender groups were analyzed separately in 2 studies. Several reference categories were used in the 19 analyzed studies. Nine studies used 4 h/day or over (cumulative) as the reference category, five used 2 h/day (cumulative), two used 1 h/day and three analyzed continuous ST. The risk of depression (depression symptoms or depression disorders) was measured using various measures, including the General Health Questionnaire (GHQ-12), Centers for Epidemiologic Studies–Depression Scale (CES-D), Patient Health Questionnaire (PHQ), Self-rating Depression Scale (SDS), Self-reported symptoms of depression, World Mental Health Composite International Diagnostic Interview (WMHCIDI), clinically diagnosed depression, and Edinburgh Postnatal Depression Scale (EPDS) (Table 1). For more details, see Additional file 1.

Fig. 1
figure 1

Flowchart of the article screening process

Table 1 Characteristics of the included studies

Methodological quality

Methodological quality scores are provided in Additional file 2. We classified the overall quality of evidence (strong, moderate and weak) based on the modified version of an eight-component rating scale. Three longitudinal studies demonstrated a moderate methodological quality, and two studies (one cross-sectional, one longitudinal) received a weak methodological quality rating.

ST-SB and depression risk

To analyze the association between ST-SB and depression, we used a random effect model to calculate the total OR and analyze the heterogeneity. As presented in Fig. 1, the overall pooled OR was 1.28 (95% CI 1.17 to 1.39; p < 0.01) with high heterogeneity (I2 = 89%) (Fig. 2). Persons reporting more SB had a significantly higher risk of depression than those who reported less SB.

Fig. 2
figure 2

Forest plot of the association between depression risk and ST-SB

To find the potential sources of heterogeneity, we conducted a group of subgroups analysis of gender, age, reference group and study design. When the gender was stratified, in female groups, the pooled OR was 1.18 (95% CI 1.03 to 1.35; p = 0.09) with moderate heterogeneity (I2 = 48%), and in male groups, the pooled OR was 0.96 (95% CI 0.63 to 1.47; p = 0.51) with low heterogeneity (I2 = 0%). No significant associations were observed in males. However, in studies that did not consider gender, the pooled OR was 1.32 (95% CI 1.18 to 1.48; p < 0.01) with high heterogeneity (I2 = 93%) (Additional file 3: Figure S1). In addition, when the age was presented into 2 groups (young adults and all adults), in the young adult groups, the pooled OR was 1.36 (95% CI 1.05 to 1.77; p < 0.01) with high heterogeneity (I2 = 90%), and in the all adults groups, the pooled OR was 1.25 (95% CI 1.11 to 1.41; p < 0.01) with high heterogeneity (I2 = 89%) (Additional file 3: Figure S2). To take the reference group into consideration, 5 studies used a reference group with ST = 2 h/days, and the pooled OR was 1.46 (95% CI 1.25 to 1.71; p = 0.06) with high heterogeneity (I2 = 52%); 9 studies used ≥4 h as a reference group, and the pooled OR was 1.38 (95% CI 1.08 to 1.77; p < 0.01) with high heterogeneity (I2 = 88%). Two studies used 1 h as a reference group, and the pooled OR was 1.07 (95% CI 0.97 to 1.18; p = 0.57) with low heterogeneity (I2 = 0%). For the remaining 3 studies, ST was calculated as a continuous variable, and the pooled OR was 1.04 (95% CI 1.00 to 1.08; p = 0.12) with high heterogeneity (I2 = 54%) (Additional file 3: Figure S3). Finally, to take the study design into consideration, 7 studies were cohort studies, and the pooled OR was 1.02 (95% CI 1.01 to 1.03; p = 0.41) with low heterogeneity (I2 = 3%), while the remaining 12 studies were cross-sectional studies, and the pooled OR was 1.48 (95% CI 1.25 to 1.74; p < 0.01) with high heterogeneity (I2 = 82%) (Additional file 3: Figure S4).

Publication bias analysis

Begg’s rank correlation test (p = 0.5459) was conducted for publication bias evaluation. The result indicated that no significant publication bias existed in the meta-analysis. The above results indicated that the conclusions of our study were stable and credible (see Additional file 4: Figure S5).


This study aimed to investigate the association between ST-SB and depression with a meta-analysis, as previous studies showed inconsistent results. The results of the meta-analysis showed that most of the subjects with more than 2 h/d ST-SB were more likely to have depression. When ST was considered as a continuous variable, the associations between ST and depression became small yet remained statistically significant. Some mechanisms may explain the relationship between SB and the risk of depression. First, long-term SB might give rise to biological pathway disturbances including central nervous system arousal or sleep disturbances [45, 46]. Second, physical activity has been shown to be beneficial for reducing depressive symptoms [47]. However, some studies showed that even when controlling for physical activity and other demographic variables, the populations that reported high levels of screen time were more likely to be depressed than those who did not, suggesting that the effects of screen time are independent of physical activity [31]. Another explanation refers to social interaction: prolonged sedentary behaviors, such as television viewing, may lead to social solitude and withdrawal from interpersonal relationships, which have been linked to increased feelings of social anxiety [48]. Furthermore, these studies also showed a positive association between SB and obesity, which is explained by the mechanism through which SB is associated with energy-dense snack consumption and snacking behavior [49], and depression has been shown to be associated with obesity [50, 51].

In addition, according to the results of the subgroup analysis, there were significant differences between these associations in females and males. In the female population, the association was significant, while in the male population, it was not. This might be because of the increasing prevalence of mental health problems among females [52]. Furthermore, men and women use different coping mechanisms when dealing with depression. Women are more likely to internalize and ruminate on their condition, whereas men are more likely to engage in externalizing or distracting activities [53]. Thus, when screen time increases, females would likely have less time to communicate with others and would become more introverted, whereas males may shift their attention to other affairs. Thus, excessive time devoted to media may affect female users more substantially [54]. Moreover, using different reference categories led to different results. There was a week association between SB and depression risk in studies using 0–1 h/day as the reference category, while the association became stronger when using 2 h/day or more as the reference category. This finding provides better clarification of the association between ST-SB and depression risk, indicating that ST in moderation may not be associated with higher levels of depression. One hypothesis was that there was a curvilinear dose-response association between ST and the risk of depression. Some guidelines and recommendations [55] emphasized an overall positive association between ST-SB and morbidity risk. However, studies have shown that when ST is limited to 0–2 h/day, ST-SB is associated with a lower risk of depression, and the lowest risk is detected at ST of 1 h/day [4]. The selection of reference categories should be considered in future studies on SB. The results of the subgroup study by study design showed a consistent association in cohort and cross-sectional studies, but the heterogeneities were different, potentially because of the methodological limitations of cross-sectional studies. To demonstrate the association, cohort studies could provide higher grade evidence than cross-sectional studies [56].

Some caveats must be discussed. As the heterogeneity was quite high (approximately 90%), the factors that mainly explained this heterogeneity must be explored. Based on the results of the subgroup analysis, we found that gender, reference group and study design influenced the heterogeneity of the overall meta-analysis. In addition, distinct from chronic diseases such as hypertension, which could be diagnosed by objective indicators, information about depression disorders or depressive symptoms was often collected according to self-reported respondent answers to questions. The fieldwork of different studies was carried out by different interviewers, and the diagnoses could vary even though the instruments were the same. Moreover, there were several limitations to this review. First, most studies employed a cross-sectional study design, so these studies were limited by several methodological weaknesses. The cross-sectional character of these studies does not allow causal inferences to be made because relationships were unable to be determined. Second, SB was measured using retrospective self-report measures in most of the studies, which is subject to recall bias. In addition, mental health was possibly underestimated by respondents because of the stigma associated with psychological questions. Third, uncontrolled variables may have influenced the results. In this review, some studies controlled only social demographic variables such as age and gender, while physical activity and weight were also included as covariates in some studies. Further studies with proper controls for relevant covariates are needed to clarify this issue.

In future studies, valid objective measures of sedentary behavior are needed. Not only the dose (e.g., frequency, duration) but also the context (e.g., TV viewing, computer use, smartphone use) should be included in a structured or semistructured questionnaire. Additionally, some objective measures of sedentary behavior (e.g., accelerometers and posture monitors) are recommended. Moreover, some studies have focused on the linear or nonlinear relationships between ST-SB and depression [57, 58]. Further studies should be carried out to estimate the dose-response relationship between ST-SB and depression, exploring the appropriate time limit for ST-SB.


ST-SB is associated with a higher risk of depression, especially when it exceeds 2 h/day. In the female population, the association between SB and risk of depression is significant, while in the male population, no significant associations were observed. Our review supports the current recommendations of limiting ST to promote mental health, especially in women. In addition, valid objective measures of sedentary behavior should be developed in future studies to explore appropriate time limits for ST-SB.

Availability of data and materials

The materials used in the present study is available by request from all academic based researchers by a contact to the corresponding author.



Confidence Interval


Odds Ratio


Sedentary Behavior


Screen Time


screen time-based sedentary behavior


Years Lived with Disability


  1. de Araujo L, Turi BC, Locci B, Mesquita C, Fonsati NB, Monteiro HL. Patterns of physical activity and screen time among Brazilian children. J Phys Act Health. 2018;15(6):457–61.

    Article  PubMed  Google Scholar 

  2. Yang Y, An R, Zhu W. Physical activity and prolonged sedentary behavior in US working adults. Arch Environ Occup Health. 2016;71(6):362–5.

    Article  PubMed  Google Scholar 

  3. Hadgraft N, Dunstan D, Lynch B, Owen N. From the office chair to the couch: correlates of high workplace sitting plus high non-work screen-time. J Sci Med Sport. 2014;18:e126.

    Article  Google Scholar 

  4. Liu M, Wu L, Yao S. Dose-response association of screen time-based sedentary behaviour in children and adolescents and depression: a meta-analysis of observational studies. Br J Sports Med. 2016;50(20):1252–8.

    Article  PubMed  Google Scholar 

  5. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, Goldfield G, Connor GS. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Pate RR, Stevens J, Webber LS, Dowda M, Murray DM, Young DR, Going S. Age-related change in physical activity in adolescent girls. J Adolesc Health. 2009;44(3):275–82.

    Article  PubMed  Google Scholar 

  7. Ford ES, Caspersen CJ. Sedentary behaviour and cardiovascular disease: a review of prospective studies. Int J Epidemiol. 2012;41(5):1338–53.

    Article  PubMed  Google Scholar 

  8. Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Greater screen time is associated with adolescent obesity: a longitudinal study of the BMI distribution from ages 14 to 18. Obesity (Silver Spring). 2013;21(3):572–5.

    Article  Google Scholar 

  9. An R, Yang Y. Diabetes diagnosis and screen-based sedentary behavior among US adults. Am J Lifestyle Med. 2016.

  10. Aadahl M, Andreasen AH, Hammer-Helmich L, Buhelt L, Jorgensen T, Glumer C. Recent temporal trends in sleep duration, domain-specific sedentary behaviour and physical activity. A survey among 25-79-year-old Danish adults. Scand J Public Health. 2013;41(7):706–11.

    Article  PubMed  Google Scholar 

  11. Teychenne M, Costigan SA, Parker K. The association between sedentary behaviour and risk of anxiety: a systematic review. BMC Public Health. 2015;15:513.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hamer M, Stamatakis E. Prospective study of sedentary behavior, risk of depression, and cognitive impairment. Med Sci Sports Exerc. 2014;46(4):718–23.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. LANCET. 2007;370(9590):859–77.

    Article  PubMed  Google Scholar 

  14. Guilbert JJ. The world health report 2002 - reducing risks, promoting healthy life. Educ Health (Abingdon). 2003;16(2):230.

    Article  CAS  Google Scholar 

  15. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010. LANCET. 2012;380(9859):2197–223.

    Article  PubMed  Google Scholar 

  16. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11):e442.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sorenson SB, Rutter CM, Aneshensel CS. Depression in the community: an investigation into age of onset. J Consult Clin Psychol. 1991;59(4):541–6.

    Article  CAS  PubMed  Google Scholar 

  18. Owen N, Salmon J, Koohsari MJ, Turrell G, Giles-Corti B. Sedentary behaviour and health: mapping environmental and social contexts to underpin chronic disease prevention. Br J Sports Med. 2014;48(3):174–7.

    Article  PubMed  Google Scholar 

  19. Yalin NYAH. The age of onset of unipolar depression: Etiopathogenetic and treatment implications. Age of Onset of Mental Disorders. 2018.

  20. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593–602.

    Article  PubMed  Google Scholar 

  21. Armijo-Olivo S, Stiles CR, Hagen NA, Biondo PD, Cummings GG. Assessment of study quality for systematic reviews: a comparison of the Cochrane collaboration risk of Bias tool and the effective public health practice project quality assessment tool: methodological research. J Eval Clin Pract. 2012;18(1):12–8.

    Article  PubMed  Google Scholar 

  22. Parker G, Fletcher K, Paterson A, Anderson J, Hong M. Gender differences in depression severity and symptoms across depressive sub-types. J Affect Disord. 2014;167:351–7.

    Article  PubMed  Google Scholar 

  23. Park SC, Lee MS, Shinfuku N, Sartorius N, Park YC. Gender differences in depressive symptom profiles and patterns of psychotropic drug usage in Asian patients with depression: findings from the research on Asian psychotropic prescription patterns for antidepressants study. Aust N Z J Psychiatry. 2015;49(9):833–41.

    Article  PubMed  Google Scholar 

  24. Kessler RC, Birnbaum H, Bromet E, Hwang I, Sampson N, Shahly V. Age differences in major depression: results from the National Comorbidity Survey Replication (NCS-R). Psychol Med. 2010;40(2):225–37.

    Article  CAS  PubMed  Google Scholar 

  25. Jorm AF. Sex and age differences in depression: a quantitative synthesis of published research. Aust N Z J Psychiatry. 1987;21(1):46–53.

    Article  CAS  PubMed  Google Scholar 

  26. Primack BA, Swanier B, Georgiopoulos AM, Land SR, Fine MJ. Association between media use in adolescence and depression in young adulthood: a longitudinal study. Arch Gen Psychiatry. 2009;66(2):181–8.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Teychenne M, Ball K, Salmon J. Physical activity, sedentary behavior and depression among disadvantaged women. Health Educ Res. 2010;25(4):632–44.

    Article  PubMed  Google Scholar 

  28. Vallance JK, Winkler EA, Gardiner PA, Healy GN, Lynch BM, Owen N. Associations of objectively-assessed physical activity and sedentary time with depression: NHANES (2005-2006). Prev Med. 2011;53(4–5):284–8.

    Article  PubMed  Google Scholar 

  29. Lucas M, Mekary R, Pan A, Mirzaei F, O'Reilly EJ, Willett WC, Koenen K, Okereke OI, Ascherio A. Relation between clinical depression risk and physical activity and time spent watching television in older women: a 10-year prospective follow-up study. Am J Epidemiol. 2011;174(9):1017–27.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Thomee S, Harenstam A, Hagberg M. Computer use and stress, sleep disturbances, and symptoms of depression among young adults--a prospective cohort study. BMC PSYCHIATRY. 2012;12:176.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Breland JY, Fox AM, Horowitz CR. Screen time, physical activity and depression risk in minority women. Ment Health Phys Act. 2013;6(1):10–5.

    Article  PubMed  Google Scholar 

  32. Sloan RA, Sawada SS, Girdano D, Liu YT, Biddle SJ, Blair SN. Associations of sedentary behavior and physical activity with psychological distress: a cross-sectional study from Singapore. BMC Public Health. 2013;13:885.

    Article  PubMed  PubMed Central  Google Scholar 

  33. van Uffelen JG, van Gellecum YR, Burton NW, Peeters G, Heesch KC, Brown WJ. Sitting-time, physical activity, and depressive symptoms in mid-aged women. Am J Prev Med. 2013;45(3):276–81.

    Article  PubMed  Google Scholar 

  34. Arredondo EM, Lemus H, Elder JP, Molina M, Martinez S, Sumek C, Ayala GX. The relationship between sedentary behavior and depression among Latinos. Mental Health & Physical Activity. 2013;6(1):3–9.

    Article  Google Scholar 

  35. Feng Q, Zhang QL, Du Y, Ye YL, He QQ. Associations of physical activity, screen time with depression, anxiety and sleep quality among Chinese college freshmen. PLoS One. 2014;9(6):e100914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wu X, Tao S, Zhang Y, Zhang S, Tao F. Low physical activity and high screen time can increase the risks of mental health problems and poor sleep quality among Chinese college students. PLoS One. 2015;10(3):e119607.

    Google Scholar 

  37. Sui X, Brown WJ, Lavie CJ, West DS, Pate RR, Payne JP, Blair SN. Associations between television watching and car riding behaviors and development of depressive symptoms: a prospective study. Mayo Clin Proc. 2015;90(2):184–93.

    Article  PubMed  Google Scholar 

  38. Wu X, Tao S, Zhang S, Zhang Y, Chen K, Yang Y, Hao J, Tao F. Impact of screen time on mental health problems progression in youth: a 1-year follow-up study. BMJ Open. 2016;6(11):e11533.

    Article  Google Scholar 

  39. Padmapriya N, Bernard JY, Shen L, Loy SL, Zhe S, Kwek K, Godfrey KM, Gluckman PD, Chong YS, Saw SM. Association of physical activity and sedentary behavior with depression and anxiety symptoms during pregnancy in a multiethnic cohort of Asian women. ARCH WOMEN MENT HLTH. 2016;19(6):1–10.

    Google Scholar 

  40. Madhav KC, Sherchand SP, Sherchan S. Association between screen time and depression among US adults. Prev Med Rep. 2017;8:67–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Barros M, Lima MG, Azevedo R, Medina L, Lopes CS, Menezes PR, Malta DC: Depression and health behaviors in Brazilian adults - PNS 2013. Rev Saude Publica 2017, 51(suppl 1):8s.

  42. Nam JY, Kim J, Cho KH, Choi J, Shin J, Park EC. The impact of sitting time and physical activity on major depressive disorder in south Korean adults: a cross-sectional study. BMC PSYCHIATRY. 2017;17(1):274.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hallgren M, Owen N, Stubbs B, Zeebari Z, Vancampfort D, Schuch F, Bellocco R, Dunstan D, Trolle LY. Passive and mentally-active sedentary behaviors and incident major depressive disorder: a 13-year cohort study. J Affect Disord. 2018;241:579–85.

    Article  PubMed  Google Scholar 

  44. Stubbs B, Vancampfort D, Firth J, Schuch FB, Hallgren M, Smith L, Gardner B, Kahl KG, Veronese N, Solmi M, et al. Relationship between sedentary behavior and depression: a mediation analysis of influential factors across the lifespan among 42,469 people in low- and middle-income countries. J Affect Disord. 2018;229:231–8.

    Article  PubMed  Google Scholar 

  45. Liyanarachchi S, Wojcicka A, Li W, Czetwertynska M, Stachlewska E, Nagy R, Hoag K, Wen B, Ploski R, Ringel MD, et al. Cumulative risk impact of five genetic variants associated with papillary thyroid carcinoma. THYROID. 2013;23(12):1532–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Maillard S, Damiola F, Clero E, Pertesi M, Robinot N, Rachedi F, Boissin JL, Sebbag J, Shan L, Bost-Bezeaud F, et al. Common variants at 9q22.33, 14q13.3, and ATM loci, and risk of differentiated thyroid cancer in the French Polynesian population. PLOS ONE. 2015;10(4):e123700.

    Article  CAS  Google Scholar 

  47. Teychenne M, Ball K, Salmon J. Physical activity and likelihood of depression in adults: a review. Prev Med. 2008;46(5):397–411.

    Article  PubMed  Google Scholar 

  48. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W. Internet paradox. A social technology that reduces social involvement and psychological well-being? AM PSYCHOL. 1998;53(9):1017–31.

    Article  CAS  PubMed  Google Scholar 

  49. Thomson M, Spence JC, Raine K, Laing L. The association of television viewing with snacking behavior and body weight of young adults. Am J Health Promot. 2008;22(5):329–35.

    Article  PubMed  Google Scholar 

  50. Gariepy G, Nitka D, Schmitz N. The association between obesity and anxiety disorders in the population: a systematic review and meta-analysis. Int J Obes. 2010;34(3):407–19.

    Article  CAS  Google Scholar 

  51. Proper KI, Picavet HS, Bemelmans WJ, Verschuren WM, Wendel-Vos GC. Sitting behaviors and mental health among workers and nonworkers: the role of weight status. J Obes. 2012;2012:607908.

    Article  PubMed  Google Scholar 

  52. Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–105.

    Article  PubMed  Google Scholar 

  53. Nolen-Hoeksema S. Sex differences in unipolar depression: evidence and theory. Psychol Bull. 1987;101(2):259–82.

    Article  CAS  PubMed  Google Scholar 

  54. Kawachi I, Berkman LF. Social ties and mental health. J Urban Health. 2001;78(3):458–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 65-71.

    Article  PubMed  Google Scholar 

  56. Desrosiers J, Hebert R, Bravo G, Rochette A. Comparison of cross-sectional and longitudinal designs in the study of aging of upper extremity performance. J Gerontol A Biol Sci Med Sci. 1998;53(5):B362–8.

    Article  CAS  PubMed  Google Scholar 

  57. Khouja JN, Munafo MR, Tilling K, Wiles NJ, Joinson C, Etchells PJ, John A, Hayes FM, Gage SH, Cornish RP. Is screen time associated with anxiety or depression in young people? Results from a UK birth cohort. BMC Public Health. 2019;19(1):82.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Przybylski AK, Weinstein N. A large-scale test of the goldilocks hypothesis. Psychol Sci. 2017;28(2):204–15.

    Article  PubMed  Google Scholar 

Download references


We are grateful to all the colleagues involved in this study for their support and help to search the electronic databases and assist with the data analysis.


The views expressed in the submitted article are our own and not an official position of the institution or funder.

Source of support

This study was supported by the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation (QCXM201705).


This study was supported by the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation (QCXM201705). The funding bodies had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript.

Author information

Authors and Affiliations



WX and FHL originally designed the idea of the study. LYX and WX assessed the methodological quality of the studies met the inclusion criteria. WX did the analysis for the study and wrote the initial draft. LYX and FHL contributed to the amendment of the manuscript and suggestions for data analysis. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiao Wang or Haoliang Fan.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1.

Detailed characteristics of the included studies.

Additional file 2.

Methodological quality assessment checklist for observational studies.

Additional file 3.

Forest plot of subgroup analyses the association between depression risk and ST-SB.

Additional file 4.

Test for Publication Bias.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Li, Y. & Fan, H. The associations between screen time-based sedentary behavior and depression: a systematic review and meta-analysis. BMC Public Health 19, 1524 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: