PurposeAccess to adequate mental health treatment remains a challenge, especially in low and middle-income countries (LMIC). The WHO has declared schizophrenia to be a priority condition for its mental health gap action program (mhGAP) because of the lack of available services. Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the mhGAP. Technology now affords opportunities to capture real time and longitudinal profiles of symptoms and physiology. This novel data makes it possible to explore relationships between activity and symptomology that may yield personalized relapse signals. SHARP will guide the development and adaptation of LAMP towards predicting and preventing relapse in populations with markedly different lived experiences. |
MethodSHARP is an international, multi-site study that involves patients, family members, and clinicians in adapting LAMP and assessing its real-world use in predicting and preventing relapse. The observational study will be conducted by teams at 3 research sites - the Beth Israel Deaconess Medical Center in Boston, MA, the All India Institute of Medical Sciences (AIIMS) and Sangath in Bhopal, India, and the National Institute of Mental Health and Neuroscience (NIMHANS) in Bangalore, India. Spanning rural and urban settings and low to middle income patient populations, these institutions are a microcosm for the global population living with schizophrenia. At each site, 25 patients with early course schizophrenia will be offered use of LAMP to assess mood, cognition and symptoms over the course of a year. |
Collection Our study will engage patients, family members, and clinicians in co-designing an updated version of LAMP. Phase 1 of the study will consist of focus groups intended to gather information on smartphone use and collect feedback on the features, visualizations, and content mindLAMP offers. Phase 1 will help us build a global digital mental health platform that attracts and retains users to support regular engagement and participant activity.
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InterpretationInsights collected will be analyzed by each site’s research team and converted into a technical specification for app improvements. Subsequent changes to LAMP will be presented back to participants for them to evaluate and test in light of their initial impressions and suggestions. Our hope is to improve usability and ensure cultural relevance across sites by engaging users in ongoing iterations of the app’s build.
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ImplementationPhase 2 will assess the real-world use of LAMP via a 12-month study, which will be conducted independently in Bhopal, Bangalore, and Boston. At each site, 25 patients with schizophrenia and 25 controls will use the LAMP app to collect a combination of active and passive data. This will help answer questions around whether digital phenotyping captures universal or regional and cultural signals of mental illness.
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Relevant Reading
- Torous J, Firth J, Mueller N, Onnela JP,Baker JT. Methodology and Reporting of Mobile Heath and Smartphone Application Studies for Schizophrenia. Harvard review of psychiatry. 2017 May1;25(3):146-54.
- Firth J, Torous J, Nicholas J, Carney R,Pratap A, Rosenbaum S, Sarris J. The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials. World Psychiatry. 2017 Oct;16(3):287-98.
- Ben-Zeev D, Brian R, Wang R, Wang W, CampbellA T, Aung MS, Merrill M, Tseng VW, Choudhury T, Hauser M, Kane JM. Cross Check:Integrating Self-Report, Behavioral Sensing, and Smartphone Use to Identify Digital Indicators of Psychotic Relapse.
- Naslund JA, Aschbrenner KA, Bartels SJ.Wearable devices and smartphones for activity tracking among people with serious mental illness. Mental health and physical activity. 2016 Mar31;10:10-7.
- WHO Mental Health Gap Action Programme (mhGAP)
