Feasibility of Digital Phenotyping and Actigraph-Based Biomarkers for Characterizing Individuals with Major Depressive Disorder
Bibliographic record
Abstract
Major Depressive Disorder (MDD) reduces patients’ quality of life. The heterogeneity of MDD remains underexplored; both clinical and biological insights could improve understanding of MDD. This thesis presents a systematic review of digital phenotyping in MDD and a current study analyzing feasibility and preliminary biomarker data. The review suggests passive data collection offers a promising method to deepen understanding of the disorder without disruptions.The current study aims to characterize patients with MDD through analysis of demographic, behavioral, clinical, and biological factors, including actigraphy-derived data. In this longitudinal study, adults diagnosed with MDD and receiving treatment in the MDD-ICP at CAMH are recruited. Feasibility analysis highlights concerns with missing data and technological barriers. Self-reported sleep metrics were underreported compared to passively tracked metrics. Preliminary findings suggest three subgroups, including one presenting fragmented long-duration sleep. Overall, this early analysis demonstrates the potential of passive monitoring to improve understanding of MDD heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".