Using technology to improve longitudinal studies: self‐reporting with ChronoRecord in bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Longitudinal studies are an optimal approach to investigating the highly variable course and outcome associated with bipolar disorder, but are expensive and often have missing data. This study validates patient self-reported mood ratings using a home computer-based system (ChronoRecord) with clinician mood ratings on the Hamilton Depression Rating scale (HAMD) and Young Mania Rating scale (YMRS), and investigates the patient acceptance of the technology. METHODS: After brief training, outpatients with bipolar disorder were given the software version of an established paper based self-reporting form (ChronoSheet) to install on a home computer. Every day for 3 months, patients entered mood, medications, sleep, life events, and menstrual data. Weight was entered weekly. RESULTS: Eighty of 96 (83%) patients returned 8662 days of data. The mean days of data returned was 114.7 +/- 32.3 SD The mean percentage of days missing for mood data was 6.1% +/- 9.3 SD, equivalent to missing 7.3 day of the 114.7 days. Self-reported ratings were strongly correlated with clinician HAMD ratings (-0.683, p < 0.001). CONCLUSIONS: This study demonstrates concurrent validity between ChronoRecord and HAMD. Patients with bipolar disorder showed high acceptance of a computer-based system for self-reporting of daily data. Automation of data collection can reduce missing data and eliminate errors associated with data entry. This technology also enables on-going feedback for both patient and researcher during a long-term study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".