Carlijn I. R. Braem

Background: The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients. Objective: We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making. Methods: The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated. Results: A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss. Conclusions: With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics. Trial Registration: ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293

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 < 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 < 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.

BACKGROUND The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients. OBJECTIVE We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making. METHODS The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated. RESULTS A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss. CONCLUSIONS With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics. CLINICALTRIAL ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293

Background: Studies have shown co‐contraction of jaw and neck muscles in healthy subjects during (sub) maximum voluntary jaw clenching, indicating functional inter‐relation between these muscles during awake bruxism. So far, coherence of jaw and neck muscles has not been evaluated during either awake or sleep bruxism.Objective: The objective of this study was to evaluate the coherence between jaw and neck muscle activity during sleep bruxism.Methods: In a cross‐sectional observational design, the electromyographic activity of jaw (masseter, temporalis) and neck (sternocleidomastoid, trapezius) muscles in individuals with “definite” sleep bruxism was measured using ambulatory polysomnography (PSG). Coherence for masseter‐temporalis, masseter‐sternocleidomastoid and masseter‐trapezius was measured during phasic and mixed rhythmic masticatory muscle activity episodes using coherence‐analysing software. Outcome measures were as follows: presence or absence of significant coherence per episode (in percentages), frequency of peak coherence (FPC) per episode and sleep stage.Results: A total of 632 episodes within 16 PSGs of eight individuals were analysed. Significant coherence was found between the jaw and neck muscles in 84.9% of the episodes. FPCs of masseter‐temporalis were significantly positively correlated with those of masseter‐sternocleidomastoid or masseter‐trapezius (P < .001). Sleep stages did not significantly influence coherence of these muscular couples.Conclusion: During sleep bruxism, jaw and neck muscle activation is significantly coherent. Coherence occurs independently of sleep stage. These results support the hypothesis of bruxism being a centrally regulated phenomenon.