Exploring the clinical utility of rhythmic digital markers for schizophrenia

This study investigates the clinical utility of rhythmic digital markers (RDMs) in schizophrenia. RDMs are digital markers capturing behavioral rhythms over different timescales - within 24 hours span (ultradian), at a span of 24 hours (circadian), or over cycles of more than 24 hours (infradian). While previous research has explored digital markers for schizophrenia, the focus has primarily been on sensor data variability rather than rhythmic patterns. This study introduces two RDMs: an entropy RDM, which quantifies uncertainty in activity distribution over the infradian cycles, and a dynamic RDM, which is derived from models of transitions in entropy and psychotic symptom intensity using Markov chain analysis. Data were ecological momentary assessments (EMAs) of 39 activities collected from 390 individuals diagnosed with schizophrenia (N = 153) or bipolar disorder (N = 192) and controls (N = 45). We assessed associations between RDMs and symptom severity and whether participants could be differentiated based on these RDMs. We found that participants with schizophrenia significantly differed on dynamic RDMs, suggesting a potential diagnostic utility. However, dynamic RDMs were not associated with symptom severity, and entropy RDM had no significant clinical correlate. Our findings contribute to the growing evidence on digital markers in psychiatry and highlight the potential of rhythmic digital markers (RDMs) in characterizing digital phenotypes for schizophrenia.Author summary: In our study, we explored new ways to track schizophrenia using digital tools that measure daily activity patterns. These tools, called rhythmic digital markers (RDMs), analyze behavior over different time periods—short cycles within a day, full-day rhythms, and patterns lasting multiple days. Our study introduces two new RDMs: one that measures the unpredictability of activity patterns over longer cycles and another that tracks how changes in activity relate to psychotic symptoms. To test these markers, we collected data from 390 individuals, including those with schizophrenia, bipolar disorder, and healthy participants. Our results showed that people with schizophrenia had distinct rhythmic patterns, suggesting that these markers could be useful for diagnosis. However, we found that they did not strongly relate to symptom severity. Our findings add to the growing research on digital technology for psychiatry and suggest that rhythmic digital markers could play a role in identifying schizophrenia in the future.