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

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.

Coherence of jaw and neck muscle activity during sleep bruxism.

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.