Schizophrenia represents a significant contributor to the global burden of disease, with this burden disproportionately impacting low- and middle-income countries (LMICs). In India, the burden due to schizophrenia is further exacerbated by low access to effective psychosocial interventions aimed at promoting recovery, rehabilitation, and community tenure, as well as inadequate attention to managing co-occurring chronic medical conditions that result in significantly reduced life expectancy among those living with schizophrenia compared to the general population. A major driver of these alarming gaps in access to care for persons with schizophrenia in India is the limited capacity within primary care settings aimed at addressing the complex co-occurring mental health, physical health, and functional needs of this patient population. There now exists strong evidence demonstrating that community programs delivered in primary care and leveraging psychosocial interventions combined with linkage to specialty psychiatric services are effective for supporting treatment and recovery of schizophrenia in low-resource settings. We will leverage our existing collaboration and robust research infrastructure in both rural and urban settings in Madhya Pradesh and Karnataka, India to conduct a hybrid type 1 effectiveness-implementation trial to evaluate whether the use of a digital platform offers added clinical benefit and can support integration of this task shared care for schizophrenia into routine primary care settings. We will address the following aims: 1) evaluate whether the use of the mindLAMP digital platform can enhance the clinical effectiveness of task-shared community-based psychosocial rehabilitation (COPSI) for individuals with schizophrenia, and 2) determine whether the addition of mindLAMP to the delivery of the COPSI program has an impact on implementation metrics when compared to delivery of COPSI alone.

Schizophrenia is one of the leading causes of disability due to mental disorders in low-income and middle-income countries (LMICs), such as India, with this burden disproportionately impacting lower income individuals who primarily access health care services through publicly run facilities. In 2017, it was estimated that there were over 3.5 million people in India living with schizophrenia, with an increasing prevalence of schizophrenia observed from 1990 to 2017 as the population ages and disease burden shifts to chronic conditions experienced in adulthood. Importantly, the burden of disability due to schizophrenia is often underestimated as many epidemiological studies do not adequately account for the added burden of chronic medical conditions, such as hypertension, heart disease, and diabetes that disproportionately impact individuals living with schizophrenia. Globally, the dramatically reduced life expectancy observed among individuals living with schizophrenia is largely due to preventable and treatable medical conditions. Recent epidemiological studies in India have further observed a mortality rate among individuals living with schizophrenia that is twice the rate observed in the general population, with calls for greater efforts to address this significant health disparity. In addition to recognizing the need to address the alarming care gap, where in India it is estimated that upwards of 75% of individuals living with schizophrenia do not have access to essential mental health care, urgent attention is also needed towards responding to the medical and physical health needs of this vulnerable patient population. Psychosocial interventions, focused on rehabilitation and skill-building, engaging in social activities, managing mental health symptoms, and promoting recovery and community reintegration, hold potential to reduce disability and improve mental health and functioning for individuals living with schizophrenia. Furthermore, building on recent compelling evidence from higher-income countries, community-based programs could be augmented with additional content aimed at addressing risk factors for early mortality, such as lifestyle behaviors, tobacco use, and management of co-occurring chronic medical conditions. Therefore, our study seeks to evaluate the use of a digital platform for supporting the clinical effectiveness and integration of task shared delivery of the evidence-based COPSI (Community care for People with Schizophrenia in India) program in primary care. We will build on important preliminary work led by project collaborators to support our aims to evaluate whether a novel digital platform can enhance the clinical effectiveness (Aim 1) and the integration (Aim 2) of an evidence-based psychosocial rehabilitation intervention for patients with schizophrenia in primary care settings in India.

Inclusion Criteria:

• Primary diagnosis of schizophrenia per IDC-10 diagnostic criteria for research and an illness duration of greater than 12 months and overall moderate level of severity on the CGI-SCH scale
• At least one risk factor for early mortality (e.g. hypertension, diabetes, dyslipidemia, etc)
• Willingness to stay in the study area during the trial period
• Ability to operate a smartphone

Exclusion Criteria:

• Major visual impairment or inability to operate a smartphone
• Cognitive impairment or diagnosis of dementia
• Planning to move out of the study area in the next 12 months
• Does not speak Hindi or Kannada

In this trial, the Outcome Assessors administering study assessments at baseline, midpoints and the endpoint will be masked to the intervention arm that participants are allocated to receive. The Study Investigators will also be masked to the intervention arm that participants are allocated to receive. Masking Outcome Assessors and Study Investigators will minimize potential bias due to knowledge of which arm the participant is allocated to, and can ensure unbiased ascertainment of study outcomes is possible. For allocation concealment, the intervention allocation for each participant will not be revealed to the participant until they have been enrolled into the trial, to avoid selection bias.