Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders

Smartphone-based cognitive assessments have emerged as promising tools for frequent and ecologically valid monitoring of cognitive function in real-world settings. These tools enable continuous capture of cognitive and behavioral patterns, including intra-individual variability, practice-related improvement, and contextual influences. Repeated assessments offer a unique opportunity to detect subtle cognitive changes over time. The interpretability and clinical utility of the metadata generated by such assessments, however, remain underexplored. In this review, we consider the current landscape of smartphone-derived cognitive metadata in the context of cognitive and affective disorders. We focus on emerging evidence linking metadata features to functional outcomes and symptom fluctuations across conditions such as schizophrenia, bipolar disorder, and depression. Additionally, we discuss methodological considerations for optimizing metadata analysis, including test design, sampling frequency, and analytical strategies. We propose that cognitive metadata may serve as sensitive indicators of early cognitive change and support personalized mental health monitoring and targeted intervention.