Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data
Abstract
We compared performance of four popular interpretative algorithms (IAs), i.e., Cole-Kripke, Rescored Cole-Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of 'Rescoring Rules' improved performance of the Cole-Kripke IA. The Sadeh and Rescored Cole-Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen's Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen's kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention.
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An in-home sleep study of 40 healthy people measuring sleep characteristics using Motionlogger® Micro Watch Actigraphy using Cole-Kripke algorithm revealed 94% to 98% sensitivity and 42% to 54% specificity in identifying Sleep epochs.
“An in-home sleep study of 40 healthy people measuring sleep characteristics using Motionlogger® Micro Watch Actigraphy (MMWA) using Cole–Kripke algorithm, revealed 94% to 98% sensitivity and 42% to 54% specificity in identifying Sleep epochs [196].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [196]
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- Authors
- Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J.
- Affiliations
- The University of Texas at Austin (US)
- University of Southern California (US)
- Corresponding author
- Haghayegh, Shahab
- ORCID
- Venue
- Chronobiology International
- Publisher
- Informa UK Limited
- Published
- 2019-10-28
- Volume
- 36
- Issue
- 12
- Pages
- 1752-1760
- ISSN
- 0742-0528 (ISSN-L)
- 1525-6073
- OpenAlex
- W2982385880
- Cited by
- 38
- References
- 23
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 7 · 2023: 6 · 2022: 4 · 2021: 6 · 2020: 6 · 2019: 3
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Chronobiology International (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
- Keywords
- wrist actigraphy, sleep stage classification, sleep latency, wake after sleep onset, total sleep time, sleep efficiency, minimal detectable change
- MeSH terms
- Adolescent, Adult, Algorithms, Circadian Rhythm, Female, Humans, Male, Sleep, Young Adult, Actigraphy
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