ArticleSleep2025
Evaluating the performance of open-source and proprietary processing of actigraphy sleep estimation in children with suspected sleep disorders: a comparison with polysomnography.
Article in Sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Universal approach to actigraphic sleep/wake scoring, verified against 5 classic algorithms on 3 datasets.Scientific reports · 2026Article
- Performance of an automated sleep scoring approach for actigraphy data in children and adolescents.Sleep · 2026Article
- The Modular Actigraphy Platform: A Data Science Solution for Processing High-Resolution Time Series Sensor Data for Sleep and Physical Activity Assessment.medRxiv : the preprint server for health sciences · 2025Article
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Abstract
STUDY
objectivesEvaluate the performance of actigraphy-based open-source and proprietary sleep algorithms compared to polysomnography in children with suspected sleep disorders.
methodsIn a sleep clinic, 110 children (5-12 years, 54% female, 50% black, 82% with sleep disorders) wore wrist-placed ActiGraph GT9X during overnight polysomnography. Actigraphy data were scored as sleep or wake using open-source GGIR and proprietary ActiLife software. Discrepancy and epoch-by-epoch analyses were conducted to assess agreement between algorithms and polysomnography, along with equivalence testing.
resultsThe open-source vanHees2015 algorithm showed good accuracy (79.5% ± 12.0%), sensitivity (81.1% ± 13.5%), and specificity (66.0% ± 23.8%) for sleep detection but was outperformed by the proprietary ActiLife algorithms. The magnitude and trend of bias for total sleep time (TST), sleep efficiency (SE), sleep onset latency, and wake after sleep onset were similar between algorithms. TST and SE were statistically equivalent for the Cole-Kripke (Actilife) and vanHees2015 algorithms compared to the Sadeh (Actilife) algorithm. The Cole-Kripke (ActiLife) demonstrated higher sensitivity (90.5%) to detect sleep but lower specificity (61.2%) than Cole-Kripke (GGIR) (sensitivity: 62.7%, specificity: 79.9%). Sadeh and Cole-Kripke estimated sleep outcomes were not statistically equivalent between implementations in ActiLife and GGIR.
conclusionsThe open-source vanHees2015 algorithm performed well but slightly worse than the proprietary ActiLife algorithms in children. The open-source nature vanHees2015 makes it ideal for clinical pediatric use. Implementation of the Sadeh and Cole-Kripke algorithms in the proprietary ActiLife and open-source GGIR software yield different sleep estimates, so comparisons between studies using these different implementations should be avoided.
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