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Background Mental health in adolescence is critical in its own right and a predictor of later symptoms of anxiety and depression. To address these mental health challenges, it is crucial to understand the variables linked to anxiety and depression in adolescence. Methods Here, we analyzed data of 278 adolescents that were collected in a nation-wide survey provided via a smartphone-based application during the COVID-19 pandemic. We used an elastic net regression machine-learning approach to classify individuals with clinically relevant self-reported symptoms of depression or anxiety. We then identified the most important variables with a combination of permutation feature importance calculation and sequential logistic regressions. Results 40.30% of participants reported clinically relevant anxiety symptoms, and 37.69% reported depressive symptoms. Both machine-learning models performed well in classifying participants with depressive (AUROC = 0.77) or anxiety (AUROC = 0.83) symptoms and were significantly better than the no-information rate. Feature importance analyses revealed that anxiety and depression in adolescence are commonly related to sleep disturbances (anxiety OR = 2.12, depression OR = 1.80). Differentiating between symptoms, self-reported depression increased with decreasing life satisfaction (OR = 0.43), whereas self-reported anxiety was related to worries about the health of family and friends (OR = 1.98) as well as impulsivity (OR = 2.01). Conclusion Our results show that app-based self-reports provide information that can classify symptoms of anxiety and depression in adolescence and thus offer new insights into symptom patterns related to adolescent mental health issues. These findings underscore the potentials of health apps in reaching large cohorts of adolescence and optimize diagnostic and treatment.
Background Mental health in adolescence is critical in its own right and a predictor of later symptoms of anxiety and depression. To address these mental health challenges, it is crucial to understand the variables linked to anxiety and depression in adolescence. Methods Here, we analyzed data of 278 adolescents that were collected in a nation-wide survey provided via a smartphone-based application during the COVID-19 pandemic. We used an elastic net regression machine-learning approach to classify individuals with clinically relevant self-reported symptoms of depression or anxiety. We then identified the most important variables with a combination of permutation feature importance calculation and sequential logistic regressions. Results 40.30% of participants reported clinically relevant anxiety symptoms, and 37.69% reported depressive symptoms. Both machine-learning models performed well in classifying participants with depressive (AUROC = 0.77) or anxiety (AUROC = 0.83) symptoms and were significantly better than the no-information rate. Feature importance analyses revealed that anxiety and depression in adolescence are commonly related to sleep disturbances (anxiety OR = 2.12, depression OR = 1.80). Differentiating between symptoms, self-reported depression increased with decreasing life satisfaction (OR = 0.43), whereas self-reported anxiety was related to worries about the health of family and friends (OR = 1.98) as well as impulsivity (OR = 2.01). Conclusion Our results show that app-based self-reports provide information that can classify symptoms of anxiety and depression in adolescence and thus offer new insights into symptom patterns related to adolescent mental health issues. These findings underscore the potentials of health apps in reaching large cohorts of adolescence and optimize diagnostic and treatment.
IntroductionBullying victimization is associated with numerous mental health difficulties yet studies from early in the COVID-19 pandemic revealed significant decreases in bullying victimization but significant increases in mental health difficulties for many children and adolescents. It is unclear whether the decrease in bullying victimization early in the pandemic translated to weaker associations between bullying victimization and mental health difficulties.MethodsUsing a population-based design, we examined whether the correlations between bullying victimization and mental health difficulties were significantly weaker in magnitude during the COVID-19 pandemic compared to before the pandemic in a sample of 6,578 Canadian students in grades 4–12. Students were randomly assigned to report on their bullying and mental health experiences either during the school year before the pandemic or the school year during the pandemic. Only students who reported experiences of victimization were included in the present study as questions on mental health were specifically on difficulties experienced due to victimization.ResultsAs expected, overall bullying victimization and mental health difficulties were significantly correlated before and during the pandemic, but correlations were significantly weaker in magnitude during the pandemic for girls and secondary students. Significant decreases in correlation magnitude were also found predominately for general, verbal, and social forms of bullying victimization, but not for physical and cyber victimization. Among students who reported victimization, we also found significantly lower means for mental health difficulties and most forms of bullying victimization during the pandemic compared to pre-pandemic.DiscussionFindings indicate a strong coupling of bullying victimization and mental health difficulties, particularly before the pandemic, and the need to reduce these associations to improve the well-being of children and adolescents.
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