Background: Adolescence, a volatile period of growth between the ages of 10 and 19, is associated with increased vulnerability to mental health problems. Factors such as academic pressure can contribute to these challenges. Objectives: The current study aimed to evaluate the factors and prevalence of depression, anxiety, and stress among adolescents in the urban and rural areas of Mysuru district. Materials and Methods: A cross-sectional study was conducted in private high schools in both urban and rural regions. Prior permission and informed consent were obtained from participants and their legal guardians aged 18 years and older. Results: The gender distribution in urban areas was 60.2% female and 39.8% male, while in rural areas, it was 51% male and 49% female. The prevalence of depression was higher in rural (39.3%) than in urban areas (24.2%), while anxiety was more prevalent in urban (50.6%) than in rural areas (49%). Stress was also more common in rural (16.6%) than urban adolescents (14.6%). Factors significantly associated with mental health outcomes included monthly family income, parenting practices, academic pressures, and self-esteem. Key needs identified were mobile mental health applications, online counseling services, and access to school counselors. Conclusion: This study provides insights into the prevalence and correlates of common mental health issues among adolescents in this region of South India. The findings emphasize the necessity of providing mobile applications and offline counseling services to effectively support and meet the needs of adolescents in these settings.

S: Introduction: In India in 2020, there were an estimated 1.39 million cancers present in the country. Chemotherapy patients experience several problems such as ADRs (adverse drug reactions), and because of this, many dropouts have been happening. Also, there is a lack of communication between the patient and care providers (doctors). Objectives: Development and validation of a comprehensive needs assessment tool to assess the burden of chemotherapy on patients attending tertiary level health care facilities. Materials and Methods: Development and validation of comprehensive needs assessment in cancer chemotherapy involve several steps, including problem statement and literature review regarding the problem, domain generation, development of the preliminary questionnaire, face validation, statistical validation, and final draft of the tool. Results: A total of 10 experts are involved in face validation. The majority (80%) of the experts agreed with the grammar, clarity, and content of the tool. A few experts (20%) disagreed regarding the construction of the questionnaire, the appropriate level of understanding for the participants, and the content of the tool and suggested changes in the physical and psychological domains. The questionnaire has been re-structured according to the expert’s suggestion before going for statistical validation. Internal consistency of the CNAT-CC was optimal, with a satisfactory Cronbach’s alpha of 0.7 for the total scale. Discussion: The current study was focused on the development and validation of needs assessment in cancer chemotherapy patients. The CNAT-CC promises to be a comprehensive needs assessment tool that applies to a comparatively vast majority of patients undergoing cancer chemotherapy.

Introduction: Diabetes is a chronic medical condition with severe complications mainly caused due to unhealthy lifestyles in genetically susceptible individuals. This study attempts to develop a non-invasive risk assessment tool to identify patients with uncontrolled type 2 diabetes mellitus (T2DM) in southern India. Methodology: An exploratory study was conducted among 545 patients with T2DM in the Mysuru district, South India for 6 months. A prevalidated questionnaire was used to collect data. Univariate and multivariate logistic regression analysis was performed to develop the risk score. Receiver-operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the cut-off for the risk score. The risk score is specifically designed for the population of South Karnataka, India. Results: Out of the 545 study participants, the prevalence of uncontrolled diabetes was 59.9%. Physical activity, duration of diabetes, diabetic diet, regular health check-ups, history of hypertension, smoking history and alcohol consumption were factors significantly associated with uncontrolled diabetes (p13.50, the risk assessment model showed a moderate sensitivity of 71.3%, specificity of 61%, positive predictive value of 73.2% and negative predictive value of 58.3%. The ROC curve was plotted for the model with an AUC of 0.726 (95% CI 0.683 to 0.769). Conclusion: This study developed ‘Diabetes Care’, a simple web-based, non-invasive and inexpensive tool for identifying individuals at risk of developing uncontrolled T2DM in the future.

Background: Diabetes is a chronic medical condition with severe complications, mainly caused by unhealthy lifestyles in genetically susceptible individuals. There has been a growing interest in the role of mobile health technologies in achieving better self-efficacy in managing diabetes. This study attempts to assess the impact of a web-based model on improving the diabetes status among Type 2 diabetic patients attending a tertiary care hospital in southern India. Methods: A longitudinal study was conducted among patients with type 2 diabetes attending the outpatient department of a tertiary care hospital in Mysuru, southern India, for 6 months. Diabetes Care (https://www.diabetes-care.co.in/), which is an online website that can be used as a risk prediction tool for uncontrolled diabetes and recommends lifestyle changes, was used by 456 diabetes patients for 6 months. We assessed the change in glycosylated haemoglobin levels at the beginning and after 6 months of using the software. Results: The mean HbA1c value at the start of the study was 8.039% ± 1.981. The HbA1c value assessed after 6 months post-intervention showed an improvement of 7.794% ± 1.853 with a mean difference of 0.245. A paired T-test showed a statistically significant association with a P value of 0.049. Conclusions: Evidence from this study suggests that intervention using a web-based model focusing on risk prediction and educational intervention showed an improvement in the diabetic status of the patients with T2DM.

Background: Adolescent mental health disorders affect 7.3% of Indian youth, yet treatment remains inaccessible for over 80%. While digital interventions show promise, their economic feasibility in resource-constrained settings remains unexplored. This study conducted a micro-costing analysis of AikyaMind, a comprehensive mHealth application for adolescent mental health in Karnataka, India. Methods: Using the activity-based costing-ingredients methodology, we analyzed costs from a provider’s perspective over a one-year implementation period (2023-24). Data collection employed structured micro-costing tools capturing direct development, meeting, and training costs. Capital items were annualized using a 3% discount rate. Per-user costs were calculated across utilization scenarios (100-10,000 users). Fiscal impact was assessed against Karnataka’s District Mental Health Program budget. One-way sensitivity analysis identified cost drivers. Results: Total costs were ₹74,548 (US$896), comprising direct development (65.73%), meetings (26.84%), and training (7.43%). App development represented the largest component (₹30,000). Per-user costs demonstrated substantial economies of scale, decreasing from ₹745.48 (100 users) to ₹7.45 (10,000 users)- a 99% reduction. Implementation required only 0.07% of the state mental health budget. Sensitivity analysis revealed that app development costs were the primary driver, causing a variation of ±₹6,000 with a 20% change. Conclusions: The AikyaMind application demonstrates exceptional economic feasibility for addressing adolescent mental health gaps in LMICs. With a total development cost of less than US$900 and a cost of US$0.09 per user at scale, digital mental health interventions offer financially viable solutions for resource-constrained settings. These findings support the integration of mHealth within existing public health programs to achieve universal health coverage goals.