Comparison and Validation of Actigraphy Algorithms Using a Large Community Dataset: Algorithm Validation Study
Bibliographic record
Abstract
Background: For decades, the measurement of sleep and wake has relied upon watch-based actigraphy as an alternative to expensive, obtrusive clinical monitoring. At the time of this publication, we have relied upon a handful of algorithms to score actigraphy data as sleep or wake. However, these algorithms have largely been tested and validated with only small samples of young, healthy individuals. Objective: This study aimed to establish the accuracy and agreement of conventional and traditional actigraphy algorithms against polysomnography, the clinical standard, using the diverse Multi-Ethnic Study of Atherosclerosis (MESA) sleep dataset. As a secondary objective, we examined algorithm and polysomnography agreement for key sleep metrics including total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO). Methods: We assessed 5 well-established algorithms, including Cole-Kripke, University of California San Diego (UCSD) scoring, Kripke 2010, Philips-Respironics, and Sadeh, with and without rescoring across 1440 individuals (Mage=mean 69.36, SD 8.97) from the MESA sleep dataset. We conducted epoch-by-epoch comparisons assessing accuracy, confusion matrix analyses, receiver operator characteristic curves (ROC), area under the curve (AUC), and Bland-Altman analyses for agreement. Results: Primary results indicated all algorithms demonstrated accuracy between 78%-80% with the highest accuracy by the Kripke 2010 (80%) algorithm followed closely by the Cole-Kripke (80%) and Philips-Respironics (80%-79%) algorithms. In addition, moderate Cohen κ agreement and moderate positive Matthews correlations were demonstrated by all algorithms. Further, all algorithms demonstrated significant mean difference across sleep metrics. Conclusions: The findings of this study establish that these traditional actigraphy algorithms can, with high accuracy, detect sleep and wake in large, diverse population samples, including older adults or populations at risk of health conditions. However, these algorithms may carry difficulty for precise assessment of sleep metrics, especially in cases of sleep disorders or irregular sleep.
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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".