The Role of Actigraphy in the Assessment of Central Disorders of Hypersomnolence: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Actigraphy provides an objective measure of sleepiness and is recommended by the American Academy of Sleep Medicine for use 7-14 days prior to multiple sleep latency testing. It plays a valuable role in the differential diagnosis of hypersomnolence. OBJECTIVE: Our aim was to provide a comprehensive summary of actigraphy features in central disorders of hypersomnolence (CDH). METHODS: Data were sourced from six bibliographic databases. Fixed- or random-effects models were applied to compare patients with narcolepsy type 1 (NT1) to controls. RESULTS: Of the 1,737 publications identified in our search, 8 studies met the inclusion criteria. The total sample consisted of 473 participants, encompassing patients with NT1, idiopathic hypersomnia (IH), hypersomnolence with normal CSF hypocretin-1 levels, Kleine-Levin syndrome (KLS), traumatic brain injury (TBI), major depressive disorder (MDD), myotonic dystrophy (MD), primary insomnia and healthy controls. Actigraphy devices varied across studies. Compared to control subjects, NT1 patients had lower total sleep time (TST), sleep efficiency and daytime motor activity, with increased wake after sleep onset, awakenings, nocturnal motor activity and longest nap duration. In KLS, TST was higher during hypersomnia episodes than during asymptomatic phases. TBI and MDD patients had a higher TST than the control group, while MD patients had a lower TST than patients with IH. CONCLUSIONS: Actigraphy is a valuable tool for objectively assessing sleep and can assist in detecting CDH. However, the absence of standardized guidelines limits their broader implementation in clinical practice.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".