Exploring the clinical utility of rhythmic digital markers for schizophrenia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".