Support of Home-Based Structured Walking Training and Prediction of the 6-Minute Walk Test Distance in Patients With Peripheral Arterial Disease Based on Telehealth Data: Prospective Cohort Study.

Background: Telehealth has been effective in managing cardiovascular diseases like stroke and heart failure and has shown promising results in managing patients with peripheral arterial disease. However, more work is needed to fully understand the effect of telehealth-based predictive modeling on the physical fitness of patients with peripheral arterial disease.

Objective: For this work, data from the Keep Pace study were analyzed in depth to gain insights on temporal developments of patients' conditions and to develop models to predict the patients' total walking distance at the study end.

Correlation between timed-up-and-go test and 6-minute walk test in patients with peripheral artery disease.

Peripheral artery disease (PAD) is a widespread condition affecting over 200 million people worldwide. PAD leads to a restricted blood flow to the extremities, causing painful limitations and the risk of amputation in advanced stages. Among medical and surgical interventions, the therapy of PAD involves walking training.

Usability of a telehealth-nurse supported home-based walking training for peripheral arterial disease - The Keep Pace! pilot study.

: Guidelines recommend walking trainings for peripheral arterial disease (PAD) management. Supervised walking training is superior to walking advise to improve the walking distance. Telehealth service with nurse support may close this gap.

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.