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  3. iSPHYNCS: Enabling long-term actigraphy with vital signals in a comparison between Fitbit Inspire 2/HR and MotionWatch 8 for non-parametric circadian rhythm analysis

iSPHYNCS: Enabling long-term actigraphy with vital signals in a comparison between Fitbit Inspire 2/HR and MotionWatch 8 for non-parametric circadian rhythm analysis

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DOI
10.48620/91982
Publisher DOI
10.1016/j.smhl.2025.100614
Abstract
Actigraphy is a tool to study an individual’s rest-activity rhythm, but its use is limited secondary to both cost and its availability to only specialized medical centers. Consumer-grade activity trackers are widely used by the general population. However, it remains to be determined if these devices provide equivalent information compared to traditional actigraphy and if the additional measurements of vital signals may further facilitate the identification of specific sleep disorders. We propose a new approach to perform long-term actigraphy with consumer-grade activity trackers that provide a broader array of physiological vital signals. We recorded one week of simultaneous actigraphy (MotionWatch 8) and activity tracking (Fitbit Inspire HR/2) in a study population (n = 40) that included individuals with central disorders of hypersomnolence and healthy controls. We leverage information from the individuals’ heart rate, step count, and calorie consumption measured by Fitbit to calculate non-parametric circadian rhythm analysis. Performing Bland-Altman analyses to assess the agreement between the clinical actigraphy and our proposed methods, we show that calorie consumption of Fitbit offers an acceptable alternative to the actigraphy device, superior to step count. Our results suggest that consumer-grade activity trackers have the potential to expand current medical device-based actigraphy by enabling additional signal detection and longer recording times at a lower cost.
Date Issued
2025-09-01
Publication Type
Article
Subject(s)
600 Technology > 610 Medicine & health
Subjects
Actigraphy
•
Rest-activity rhythm
•
NPCRA
•
Wearable
•
Activity tracker
Language(s)
en
Author(s)
Morand, Rafael  
Universitätsklinik für Neurologie - SWEZ  
ARTORG Center for Biomedical Engineering Research, Neuro Robotics  
Gnarra, Oriella  
Clinic of Neurology  
Universitätsklinik für Neurologie - SWEZ  
van der Meer, Julia  
Clinic of Neurology  
Warncke Jan D.  
Clinic of Neurology  
Helmy, Annina  
Clinic of Neurology  
Universitätsklinik für Neurologie - SWEZ  
Inselspital  
Fregolente, Livia G.  
Clinic of Neurology  
Wenz, Elena  
Clinic of Neurology  
Zub, Kseniia  
Clinic of Neurology  
Brigato, Lorenzo  
ARTORG Center for Biomedical Engineering Research  
ARTORG Center - Artificial Intelligence in Health and Nutrition  
Bassetti, Claudio L. A.  
Clinic of Neurology  
Universitätsklinik für Neurologie - SWEZ  
Mougiakakou, Stavroula  
ARTORG Center for Biomedical Engineering Research  
ARTORG Center - Artificial Intelligence in Health and Nutrition  
Schmidt, Markus H.  
Clinic of Neurology  
Universitätsklinik für Neurologie - SWEZ  
Additional Credits
ARTORG Center for Biomedical Engineering Research, Neuro Robotics  
Universitätsklinik für Neurologie - SWEZ  
ARTORG Center for Biomedical Engineering Research  
Clinic of Neurology  
ARTORG Center - Artificial Intelligence in Health and Nutrition  
Inselspital  
Journal
Smart Health
Publisher
Elsevier
ISSN
2352-6483
Access(Rights)
restricted
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