Depression and anxiety symptoms, subjective well-being, and happiness among Indian high school students.

Background: Mental health problems cause significant distress and impairment in adolescents worldwide. One-fifth of the world's adolescents live in India, and much remains to be known about their mental health and wellbeing. Aim: In this preregistered study, we aimed to estimate the rates of depressive and anxiety symptoms, examine their relationship with indicators of wellbeing, and identify correlates of mental health among Indian adolescents. Methods: We administered self-report measures of depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), wellbeing (WEMWBS), and happiness (SHS) to 1,213 Indian adolescents (52.0% male; Mage = 14.11, SDage = 1.48). Results: Findings from the PHQ-9 (M = 8.08, SD = 5.01) and GAD-7 (M = 7.42, SD = 4.78) indicated high levels of depressive symptoms and anxiety symptoms. Thirty seven percent of the sample scored above the clinical cutoff for depressive symptoms, and 30.6% scored above the cutoff for anxiety symptoms. Although measures of mental health symptoms (PHQ-9 and GAD-7) were associated with measures of wellbeing and happiness (WEMWBS and SHS), these associations were only modest (Correlation < 0.45). Female students reported higher symptoms (and worse wellbeing) compared to male students, and older students reported higher symptoms (and worse wellbeing and happiness) compared to younger students. Conclusion: This study highlights the high prevalence of depressive symptoms and anxiety symptoms among Indian high school students. Symptom measures correlated only modestly with measures of wellbeing and happiness, suggesting that wellbeing and happiness reflect more than the absence of internalizing symptoms. Future research is needed to identify effective and appropriate ways to promote mental health and wellness among Indian students.

Which symptoms of depression and anxiety are most strongly associated with happiness? A network analysis of Indian and Kenyan adolescents.

BACKGROUND: Network analyses have been applied to understand the relationships between individual symptoms of depression and anxiety. However, little is known about which symptoms are most strongly associated with "positive" indicators of mental health, such as happiness. Furthermore, few studies have examined symptom networks in participants from low- and middle-income countries. METHODS: To address these gaps, we applied network analyses in a sample of Indian adolescents (Study 1; n=1080) and replicated these analyses in a pre-registered study with Kenyan adolescents (Study 2; n=2176). Participants from both samples completed the same measures of depressive symptoms, anxiety symptoms, and happiness. RESULTS: Feeling sad and feeling like a failure had the strongest (negative) associations with happiness items. These two symptoms, as well as worrying and feeling nervous, had the strongest associations with other symptoms of depression and anxiety. Symptoms of depression and anxiety formed a single cluster, which was distinct from a cluster of happiness items. Main findings were consistent across the two samples, suggesting a cross-culturally robust pattern. LIMITATIONS: We used cross-sectional data, and we administered scales assessing a limited subset of symptoms and happiness items. CONCLUSIONS: Our findings support the idea that some symptoms of depression and anxiety are more strongly associated with happiness. These findings contribute to a body of literature emphasizing the advantages of symptom-level analyses. We discuss how efforts to understand associations between individual symptoms and "positive" mental health indicators, like happiness, could have theoretical and practical implications for clinical psychological science.

Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders.

Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

Harnessing single-session interventions to improve adolescent mental health and well-being in India: Development, adaptation, and pilot testing of online single-session interventions in Indian secondary schools.

BACKGROUND: Many evidence-based prevention and intervention approaches for youth mental health exist, but most are time-consuming and expensive to deliver. Innovative approaches are needed to develop and disseminate scalable mental health interventions, especially for youth in low- and middle-income countries. Computerized single-session interventions (SSIs) may be able to overcome many of these implementation barriers, especially if deployed in school settings. METHODS: Guided by this logic, we designed three computerized SSIs (based on behavioral activation, gratitude, and growth mindset exercises) for delivery in Indian high schools. Then, we conducted focus groups with Indian adolescents and school officials to inform adaptations of the interventions. Finally, we designed a Hybrid Type 1 effectiveness-implementation study to evaluate the interventions. RESULTS: In this paper, we present the rationale for selecting the interventions, the results of the focus groups, and the design of a Hybrid Type 1 effectiveness-implementation study. Our initial focus group data showed that the interventions were considered acceptable, feasible, and appropriate by both educators and students. We also describe feedback on the interventions which we incorporated to refine the interventions prior to our randomized controlled trial. DISCUSSION: Computerized SSIs may offer innovative, scalable ways to disseminate evidence-based interventions. By identifying which interventions are most acceptable and effective in this highly scalable format, we may understand which SSIs are best suited for continued development, testing, and, ultimately, widespread dissemination.