Identifying Accuracy-related Outliers in a 6-Minute Walk Test Application for Monitoring Cardiovascular Disease Patients

The 6-Minute Walk Test (6-MWT) is frequently used to evaluate functional physical capacity of patients with cardiovascular diseases. To determine reliability in remote care, outlier classification of a mobile Global Navigation Satellite System (GNSS) based 6-MWT App had to be investigated. The raw data of 53 measurements were Kalman filtered and afterwards layered with a Butterworth high-pass filter to find correlation between the resulting Root Mean Square value (RMS) outliers to relative walking distance errors using the test. The analysis indicated better performance in noise detection using all 3 GNSS dimensions with a high Pearson correlation of r = 0.77, than sole usage of elevation data with r = 0.62. This approach helps with the identification between accurate and unreliable measurements and opens a path that allows usage of the 6-MWT in remote disease management settings.Clinical Relevance— The 6-MWT is an important assessment tool of walking performance for patients with cardiovascular diseases. Using a sufficiently accurate application would enable unsupervised and easy remote usage, which could potentially reduce the demand for in-clinic visits and facilitate a more convenient and reliable monitoring method in telehealth settings.