Artikel ; Online: Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.
2024 Band 24, Heft 5
Abstract: Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to ... ...
Abstract | Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. Methods: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis. Results: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly ( Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data. |
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Mesh-Begriff(e) | Humans ; Diabetes Mellitus, Type 2 ; Fitness Trackers ; Glucose ; Blood Glucose ; Heart Rate |
Chemische Substanzen | Glucose (IY9XDZ35W2) ; Blood Glucose |
Sprache | Englisch |
Erscheinungsdatum | 2024-02-27 |
Erscheinungsland | Switzerland |
Dokumenttyp | Journal Article |
ZDB-ID | 2052857-7 |
ISSN | 1424-8220 ; 1424-8220 |
ISSN (online) | 1424-8220 |
ISSN | 1424-8220 |
DOI | 10.3390/s24051526 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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