Presenting a data imputation concept to support the continuous assessment of human vital data and activities

Haescher, Marian; Matthies, Denys J. C.; Krause, Silvio; Bieber, Gerald

doi:10.1145/3316782.3322785

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…Missing values of measured clinical parameters such as heart rates and ECG can be a consequence of lack of charging and misfunctions of the devices due to technical problems [132]. This can be handled using methods such as data interpolation [133] and imputation [134].…”

Section: Utilization Of Smart Devicesmentioning

confidence: 99%

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

et al. 2020

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An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

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Section: Utilization Of Smart Devicesmentioning

confidence: 99%

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

et al. 2020

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“…However, such longitudinal health monitoring will inevitably lead to data loss, caused by the device or sensor [ 10 ], actions by the subjects or a combination of the device and human interaction [ 11 ]. Most of the human-associated causes discussed in previous studies are based on participant adherence [ 12 ] or maintenance of devices (e.g., charging or synchronizing with storage device) [ 13 ]. In all cases, the missing data need to be addressed during data analysis, regardless of how they went missing.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, missing not at random (MNAR) occurs when missing data are related to time and the observed value. All three types of missing data types can be present in long-term health monitoring [ 13 ]. Knowledge of the underlying missing data characteristics and statistics can be used to select an appropriate imputation algorithm or design a missing data simulator to verify imputation algorithms [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, missing data in time series are closely related to behavior in time. Especially in health monitoring, missing data in time are linked to human behavior by, for example, the circadian rhythm, week patterns, and other daily-life patterns [ 13 , 15 ]. Both Lee and also Lee and Gill showed that missing data in activity monitors are related to the time of day [ 24 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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

Braem,

Yavuz,

Hermens

et al. 2024

Sensors

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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.

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Presenting a data imputation concept to support the continuous assessment of human vital data and activities

Cited by 2 publications

References 11 publications

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

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

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