Studies including adolescents and young people (AYP) enhance the relevance of research results, benefit stakeholders, and inform future research. There exists a mental health gap in services for AYP living in low and middle-income countries. This study aims to identify mental health challenges faced by adolescents and young people in Kenya, develop practical recommendations to mitigate these issues, and reduce the mental health burden among this population. We convened an AYP-led meeting that involved 41 participants. The meeting objectives were to (1) identify efforts to support existing national and regional strategic priorities and review goals for addressing mental health needs among AYPs, (2) develop immediate action plans for strengthened mental health services, (3) review and strengthen country-level coordination mechanisms, and (4) identify how participating county experiences can inform mental health services in Kenya. Ministry of Health (MoH) officials from national and county levels, academic experts, and implementing partner agencies involved in mental health services participated in the meeting. The team, including AYP representatives, identified various mental health challenges among the AYA and recommended interventions aimed towards improving their mental health situation in the country. The challenges were clustered into three themes and comprehensively reviewed to establish the precipitating factors to mental health outcomes among AYPs in Kenya and provide recommendations. The themes included (1) legislative, (2) service provider/Ministry of Health, and (3) adolescent/individual-level factors. To bridge the mental health gap in the country and scale up mental health outcomes, the stakeholders recommended interventions within the context of the three clusters. The key suggestions included an increase in insurance financing, acceleration of community health interventions, the establishment of adolescent-friendly spaces, the training of adolescent youth champions, interactive service provision models, implementation of the existing mental health policies and structures, the development of comprehensive assessment tools, well equipped mental health departments in health facilities, the enhancement of telehealth services and digital villages, the mobilization of a functional mental health response team, and the development of a mental health database.

Background: Peripartum adolescents experience significant interpersonal transitions in their lives. Depression and emotional distress are often exacerbated by adolescents’ responses to these interpersonal changes. Improved understanding of pregnancy-related social changes and maladaptive responses to these shifts may inform novel approaches to addressing the mental health needs of adolescents during the perinatal period. The paper aims to understand the sources of psychological distress in peripartum adolescents and map these to Interpersonal Psychotherapy’s (IPT) problem areas as a framework to understand depression. Method: We conducted interviews in two Nairobi primary care clinics with peripartum adolescents ages 16–18 years (n = 23) with experiences of depression, keeping interpersonal psychotherapy framework of problem areas in mind. We explored the nature of their distress, triggers, antecedents of distress associated with an unplanned pregnancy, quality of their relationships with their partner, parents, and other family members, perceived needs, and sources of support. Results: We found that the interpersonal psychotherapy (IPT) framework of interpersonal problems covering grief and loss, role transitions, interpersonal disputes, and social isolation was instrumental in conceptualizing adolescent depression, anxiety, and stress in the perinatal period. Conclusion: Our interviews deepened understanding of peripartum adolescent mental health focusing on four IPT problem areas. The interpersonal framework yields meaningful information about adolescent depression and could help in identifying strategies for addressing their distress.

INTRODUCTION: COVID-19 global pandemic exposed mental health gaps that have been salient among adolescents and young people. The glaring gaps include lack of information, undefined referral systems, and inadequate human resources for mental health. We aimed to develop a digital web-based platform to support young people´s mental health and psychosocial needs during the COVID-19 pandemic. METHODS: we adopted a mixed methods action research approach to design and pilot a digital mental health intervention for young people during the pandemic. We piloted the platform between Oct 2020 and July 2021, and recruited a sample of 150 young people through the community gathering spaces in three informal settlements in Nairobi City County using a convenient sampling method. RESULTS: overall, participants reported that information provided to them via the platform was pertinent and beneficial, and that the self-assessments improved their mental health and psychosocial awareness and signposted them to available support and services. The support services consisted of informative weekly bulk emails and short motivational messages related to mental health, with a total of, 1343 short messages distributed through the short message service (SMS). Moreover, more than half the participants completed online self-assessments and participated in 19 online sessions. CONCLUSION: findings from this pilot study demonstrate that digital mental health systems are feasible and have the potential to improve access to mental health and psychosocial services. This is especially crucial during times of public health crisis, such as the COVID-19 pandemic. The realization of such systems requires swift multistakeholder partnership approach and investment.

Background: Adolescent depression is highly prevalent in low- and middle-income countries (LMICs). Identifying top key risk factors is necessary to inform effective prevention program design. Machine learning (ML) offers a powerful approach to analyze complex multidomain of data to identify the most relevant predictors and estimate risks for mental health problems. This paper applies ML to study risks for adolescent depression to enhance adolescent depression prevention efforts in LMICs. Methods: Six ML approaches (e.g., Explainable Boosting Machine, random forests, and XGBoost) were applied to study the risks of depression. Data were drawn from a digital health intervention study conducted in Kenya (year 2024–2025, n = 269). Multiple domains of childhood and adolescent adversity and stress experiences were used to predict adolescent depression (using PHQ9-A). Findings: We found that ML was a valuable approach in the early identification of adolescents at risk for depression. Among the six ML approaches applied, the random forest approach outperformed other ML approaches, especially when multiple domains of risks were included. We also found that childhood adversity or home adversity alone were not strong predictors for depression. Adding adolescent stress experiences and community school adversity experiences significantly improves the accuracy and predictability of depression. Using the top-15 and top-20 ranking factors, we achieved 74.8% and 75.1% accuracy in depression prediction, which was similar to the accuracy when all 49 adverse/stress factors were included in the predictive model (78.3%). Conclusions: Innovative ML and modern predictive modeling approaches have the potential to transform modern preventive mental health care by better utilizing multidomain data to identify individuals at risk for developing depression early and identify top risk factors (for targeted individuals and/or populations). Findings from ML can inform tailored intervention design to better mitigate risks in order to prevent depression problem development. They can also inform the better utilization of resources to target high-need cases and key determinants, which is particularly useful for LMICs and low-resource settings. This paper illustrates an example of how to move toward this direction. Future research is needed to validate the approach.

Background: Loneliness is emerging as a key risk factor for child and adolescent mental health. Exacerbated by lack of support, busy routines, continuous adversities, and poor social networks, it is a public health concern. Sleep is essential for healthy development and emotional regulation, critical for modulating risk‑taking, and determines optimal learning and mental health. However, the connection between loneliness and sleep and their impact on mental health and educational outcomes is not well known in low‑ and middle‑income countries (LMICs) like Kenya, where a large portion of the population is young.

Background: Group interpersonal psychotherapy (IPT‑G) has universal elements that appeal to non‑Western cultural contexts, while offering flexibility for contextual adaptations, making it an intervention of choice for adolescent populations in low‑ and middle‑income countries (LMICs). We anticipate that a shorter four‑session version, which we are calling IPT‑G Mini, offered to adolescents and young people during the COVID‑19 pandemic, would effectively mitigate psychological distress. Objective: The objective of this study was to test the feasibility of a short version of IPT‑G among adolescents screened for depression and anxiety. Methods: IPT‑G Mini was offered by lay health workers to 40 adolescents aged 10–19 years in Nairobi, Kenya, experiencing depressive, stress, and anxiety symptoms during the COVID‑19 pandemic during June–September 2021. Primary outcomes were a reduction in depression and psychological distress on the Patient Health Questionnaire‑9 (PHQ‑9), Revised Children’s Anxiety and Depression, and Kessler Psychological Distress (K10) scales. Findings: The intervention was effective, with a significant reduction in psychological distress (K10) and depression (PHQ‑9) scores at the end of the intervention (with p < 0.05), Cohen’s d = 0.49 (95% C.I. = 0.04–0.94) and d = 0.51 (95% C.I. = 0.06–0.96), respectively. Younger participants (10–14 years) had better outcomes than older ones on K10 and PHQ‑9 assessments. Conclusions: IPT‑G Mini must be tested in a more controlled setting with a larger and more diverse sample. It does appear to reduce distress and depressive symptoms, and further evaluation may be needed to refine the adapted version. Despite the challenges brought by the pandemic, the timely and effective intervention focused on interpersonal issues proved beneficial to participants. Addressing various problem areas helped provide perspective and support in dealing with multiple losses and socioeconomic hardships experienced by our young participants.