Sleep · 2022
Development and testing of methods for detecting off-wrist in actimetry recordings.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pilz Luísa K, de Oliveira Melissa A B, Steibel Eduardo G, Policarpo Lucas M, Carissimi Alicia, Carvalho Felipe G, Constantino Débora B, Tonon André Comiran, Xavier Nicóli B, da Rosa Righi Rodrigo, Hidalgo Maria Paz
Аннотация
STUDY OBJECTIVES: In field studies using wrist-actimetry, not identifying/handling off-wrist intervals may result in their misclassification as immobility/sleep and biased estimations of rhythmic patterns. By comparing different solutions for detecting off-wrist, our goal was to ascertain how accurately they detect nonwear in different contexts and identify variables that are useful in the process.
METHODS: We developed algorithms using heuristic (HA) and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol were considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses.
RESULTS: All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially < 2 h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods.
CONCLUSIONS: Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters' attention/experience. In our study, detecting short intervals was a limitation across methods.
METHODS: We developed algorithms using heuristic (HA) and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol were considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses.
RESULTS: All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially < 2 h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods.
CONCLUSIONS: Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters' attention/experience. In our study, detecting short intervals was a limitation across methods.