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Nutzung sozialer Medien und depressive Symptome bei Jugendlichen in Indien

von NFI Redaktion

In a recent study published in BMC Public Health, researchers examined the potential bidirectional relationship between social media use and depressive symptoms in adolescents in India.

For this study, they utilized cohort data from the years 2015–16 (Wave 1) to 2018–19 (Wave 2) of the Understanding the Lives of Adolescents and Young Adults (UDAYA) project survey, which was conducted in Uttar Pradesh and Bihar.

Study: Bidirectional and time-lagged relationship between social media use and mental well-being: Insights from an Indian adolescent cohort study. Image source: 1. Shutterstock.com

Background

Social media has a significant influence on adolescents and young adults. However, there is a critical gap in existing research on the impact of social media use on the mental health of young people, which mainly consists of cross-sectional studies from Western countries.

There is a lack of understanding of how this trend is evolving over time and manifesting in developing countries like India, where there were 518 million social media users in 2020, a number expected to rise to approximately 1.5 billion by 2040.

About the Study

In the present study, researchers longitudinally tracked changes in social media use and its effects on the mental health of Indian adolescent boys and girls over a three-year period.

They first assessed the immediate impact in inter- and intra-subject analyses; next, they examined the bidirectional relationship across their stages of development, allowing for a comprehensive examination of the relationship between social media use and depression.

The study population included younger and older adolescents aged 10–14 and 15–19, respectively. They were initially surveyed at Wave 1 and followed up in 2018/19 (Wave 2) when they reached the ages of 13–17 and 18–22, respectively, to illuminate the factors that determine successful transitions into adulthood and identify the levels, patterns, and trends in their condition.

The team evaluated depressive symptoms in adolescents over the past two weeks using nine questions rated on a four-point scale, generating a total score of 27 using STATA 14, which helped classify depressive symptoms into four categories: none, mild, moderate, and severe. They also assessed the frequency and duration of social media use.

Predictor variables measured in Wave 1 included age, gender, mother’s education, and asset index. Variables measured in Waves 1 and 2 were current schooling, paid work, substance use, and social media use.

Additionally, the team analyzed the relationship between depression and these variables, including social media use, using binary logistic regression. They also conducted longitudinal cross-lagged path analysis to examine the bidirectional relationships between social media use and depressive symptoms in adolescents.

The team applied five models to assess the bidirectional influence of social media use and mental well-being over time. Finally, they identified the best-fitting model based on various criteria such as chi-square value, Akaike Information Criterion (AIC), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and chi-square difference tests, for example, indicating a CFI of 0.95 or more and an RMSEA of 0.05 or less as the best fit.

Results

The sample size for the present study comprised 4,428 boys and 7,607 girls (12,035 adolescents) aged 10–19 in Wave 1 and 4,428 boys and 11,864 girls (16,292 adolescents) aged 13–23 in Wave 2.

The study’s results showed that internet access for adolescent boys and girls increased from Wave 1 to Wave 2 (25.3% to 70.2% and 6.6% to 38.5%, respectively), which, in turn, increased their social media use, with a significant difference between boys and girls (13.9% to 57.6% and 3.8% to 26.6%, respectively).

From Wave 1 to Wave 2, a higher percentage of adolescent girls experienced mild and severe depressive symptoms, while mild depressive symptoms in boys increased slightly from Wave 1 to Wave 2 compared to girls (5.9% to 7.3% vs. 12.6% to 18.4%). This vulnerability of adolescent girls is attributed to the social pressures they face in the digital world.

In logistic regression analysis, age emerged as a significant predictor of depressive symptoms in adolescents; adolescents aged 15–19 were twice as likely to have depressive symptoms as their younger counterparts, OR = 2.762.

Education acted as a barrier against depression; educated adolescents were less likely to be depressed than those with lower levels of education. Similarly, wealthier adolescents were more likely to be depressed than their less affluent counterparts.

Additionally, those engaged in paid work and substance abuse in the past year had an 18% and 57% higher risk of experiencing depressive symptoms, respectively. Furthermore, dropping out of school was associated with an increased risk of depression.

Furthermore, the likelihood of depression was higher among frequent social media users (three or more hours daily) compared to non-users of social media.

Conclusion

Overall, the present study identified a significant cross-sectional association between social media use and depression in adolescent boys and girls in India. Factors such as age, gender, and education showed a significant association with this relationship.

Therefore, this study underscores the need for future research to examine this relationship based on the time, purpose, and type of social media used with other mental health issues apart from depression.

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