Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 5; p. 1526
• Main Authors: Braem, Carlijn I. R., Yavuz, Utku S., Hermens, Hermie J., Veltink, Peter H.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.02.2024; MDPI
• Subjects: Algorithms; Analysis; biomedical sensors; Chronic illnesses; continuous glucose monitoring; Data analysis; Data integrity; Dextrose; Diabetes; Evaluation; Exercise; Glucose; Glucose monitoring; health monitoring; Heart beat; Heart rate; Information management; Missing data; Probability distribution; Sensors; Signal processing; Statistics; Time series; Type 2 diabetes; wearable sensors; United States > US; heart rate; vital signs; health monitoring; signal processing; activity trackers; wearable sensors; continuous glucose monitoring; biomedical sensors; missing data

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 con...