OBJECTIVES: Emerging technologies for patient-generated health data (PGHD) may address challenges in assessing real-world functioning in clinical trials. In schizophrenia this could include PGHD-based endpoints such as ecological momentary assessment (EMA) or passive mobile sensing systems. These could be important to show improvements in negative symptoms or cognitive impairment associated with schizophrenia. However, it is unclear whether PGHD-based endpoints are being adopted in schizophrenia clinical trials.

METHODS: A review was conducted of recent Phase 2 and 3 clinical trials (2013-2023) for schizophrenia. Trials were identified from www.clinicaltrials.gov using the terms “schizophrenia” with “negative symptoms” or terms specific to key negative symptom assessment tools (PANSS, NSA-16, NSA-4, CGI-SCH scale, SANS, CAINS, BNSS); and "schizophrenia” with “cognition” or key cognitive assessment instruments (MATRICS, MCCB, BACS, CogState, CANTAB, RBANS, SCIP).

RESULTS: A total of 497 trials were identified and screened. PGHD-based endpoints derived from key emerging technologies were not included in the trials. Only 4% of the trials included patient-reported outcomes related to real-world functioning. The use of clinician-reported outcomes and performance outcomes were the most common evidence approaches to evaluating new treatments for schizophrenia.

CONCLUSIONS: While there is a belief that PGHD is a feasible, reliable, valid, and cost-effective method to assess functioning in schizophrenia, this has not been translated into inclusion in recent clinical trials. Both the challenges associated with traditional measurements, and the value payers place on showing functional improvements when assessing new schizophrenia treatments, drive the need for specific guidance on the development and use of PGHD for functional outcomes in regulatory and reimbursement decision-making.