Inactograms and objective sleep measures as means to capture subjective sleep problems in patients with a bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Sleep problems are common in bipolar disorders (BDs). To objectively characterize these problems in BDs, further methodological development is needed to capture subjective insomnia. AIM: To test psychometric properties of the Athens Insomnia Scale (AIS), and associations with actigraphy-derived measures, applying modifications in actigraphy data processing to capture features of perturbed sleep in patients with a BD. METHODS: Seventy-four patients completed the AIS and the Quick Inventory of Depressive Symptomatology, self-report (QIDS-SR-16). Locomotor activity was continuously recorded by wrist actigraphy for ≥10 consecutive days. We computed the sleep onset/offset, the center of daily inactivity (CenDI), as a proxy for chronotype, and the degree of consolidation of daily inactivity (ConDI), as a proxy for sleep-wake rhythm strength. RESULTS: AIS showed good psychometric properties (Cronbach's alpha = 0.84; test-retest correlation = 0.84, P<.001). Subjective sleep problems correlated moderately with a later sleep phase (CenDI with AIS rho = 0.34, P = .003), lower consolidation (ConDI with AIS rho = -0.22, P = .05; with QIDS-SR-16 rho = -0.27, P = .019), later timing of sleep offset (with AIS rho = 0.49, P = ≤.001, with QIDS-SR-16 rho = 0.36, P = .002), and longer total sleep (with AIS rho = 0.29, P = .012, with QIDS-SR-16 rho = 0.41, P = ≤.001). While AIS was psychometrically more solid, correlations with objective sleep were more consistent across time for QIDS-SR-16. CONCLUSIONS: AIS and QIDS-SR-16 are suitable for clinical screening of sleep problems among patients with a BD. Subjective insomnia associated with objective measures. For clinical and research purposes, actigraphy and data visualization on inactograms are useful for accurate longitudinal characterization of sleep patterns.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".