{"doi":"10.3390/s24051526","title":"Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors","abstract":"<jats:p>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 (p &lt; 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00–01:00), and in step count, more at measurement days 6 and 7 (p &lt; 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.</jats:p>","journal":"Sensors","year":2024,"id":635514,"datarank":0.42498200160843247,"base_score":2.833213344056216,"endowment":2.833213344056216,"self_citation_contribution":0.42498200160843247,"citation_network_contribution":0.0,"self_endowment_contribution":0.42498200160843247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":800925,"name":"Utku Ş. Yavuz","orcid":"0000-0002-6968-8064","position":1,"is_corresponding":false},{"id":70357,"name":"Hermie Hermens","orcid":"0000-0002-3065-3876","position":2,"is_corresponding":false},{"id":778532,"name":"Petrus H. Veltink","orcid":"0000-0002-1796-9999","position":3,"is_corresponding":false},{"id":1648821,"name":"Carlijn I. R. Braem","orcid":"0000-0003-2900-9861","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors","abstract":"<jats:p>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 (p &lt; 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00–01:00), and in step count, more at measurement days 6 and 7 (p &lt; 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.</jats:p>","is_dataset_classified":null,"base_score":2.70805020110221,"endowment":2.70805020110221,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38475061","pmcid":null,"openalex_id":"https://openalex.org/W4392198371","authors":[],"funders":[{"funder_name":"Dutch Research Council (NWO)","grant_id":"628.011.021","title":"EDIC: Exceptional and Deep Intelligent Coach"}],"total_grants":1,"fwci":4.1455,"citation_percentile":0.94929018,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":9},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/1424-8220/24/5/1526/pdf?version=1709023797","host_type":"journal"},{"url":"https://www.mdpi.com/1424-8220/24/5/1526/pdf?version=1709023797","host_type":"publisher"},{"url":"https://www.mdpi.com/1424-8220/24/5/1526/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/s24051526","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38475061","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10935383","host_type":"repository"},{"url":"https://research.utwente.nl/en/publications/2356ade0-a007-4b62-ad27-392ee889ba5b","host_type":"repository"},{"url":"https://doaj.org/article/fa2f90fd64134c029f2547dbebca0280","host_type":"repository"},{"url":"https://research.utwente.nl/files/356708076/sensors-24-01526.pdf","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10935383/pdf/sensors-24-01526.pdf","host_type":"repository"},{"url":"https://ris.utwente.nl/ws/files/356708076/sensors-24-01526.pdf","host_type":"repository"},{"url":"http://dx.doi.org/10.3390/s24051526","host_type":""},{"url":"https://dx.doi.org/10.3390/s24051526","host_type":""}],"fields_of_study":["Heart Rate Variability and Autonomic Control","Non-Invasive Vital Sign Monitoring","Diabetes Management and Research","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":["Fitness Trackers","Blood Glucose","Diabetes Mellitus, Type 2","Glucose","Heart Rate","Humans"],"keywords":["Missing data","Statistics","Count data","Wearable computer","Statistical hypothesis testing","Medicine","Computer science","Mathematics","Signal processing","Heart rate","Continuous glucose monitoring","Vital Signs","Health Monitoring","Wearable Sensors","Biomedical Sensors","Activity Trackers","Blood Glucose","Chemical technology","TP1-1185","Fitness Trackers","Article","Glucose","SDG 3 - Good Health and Well-being","Diabetes Mellitus, Type 2","Humans"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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