ArticleSensors (Basel, Switzerland)2024
Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
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Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- Feasibility of Digital Augmentation of Parent-Child Interaction Therapy: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Passive Smart Home Monitoring for Delirium-Relevant Anomaly Detection in People Living With Dementia: Proof-of-Concept Study.JMIR formative research · 2026Article
- Determining a Likely Mechanism of Missingness in Repeated Measures Sleep Data From Wearable Fitness Trackers: Longitudinal Analysis.JMIR mHealth and uHealth · 2026Article
- Overcoming Data Loss in Wearable Disease Detection with GAN-Based Imputation.NPJ digital medicine · 2026Article
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Implementation Framework to Increase Patient Adherence in Clinical Trials Utilizing Wearable Devices.Research square · 2025Article
- Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis.IEEE transactions on knowledge and data engineering · 2025Article
- Participant-Centered Engagement for Sustained Adherence to Smartwatches: A 12-Month Prospective Decentralized Digital Health Study.Clinical and translational science · 2025Article
- Article
- Consequences of Data Loss on Clinical Decision-Making in Continuous Glucose Monitoring: Retrospective Cohort Study.Interactive journal of medical research · 2024Article
- Methods for Wearable-Derived Pulse Rate Measures and Their Application to Modeling the Relationship between Pulse Rate and Motor Activity in Narcolepsy Type 1.Digital biomarkersArticle
- Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundData 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.
methodsTwo-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.
resultsData 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 (
conclusionsOur novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.