Introduction: This study aimed to compare different algorithms in the implementation of an event-based analysis method of the daily walking pattern using wearable monitors with the specific aim of quantifying walking pain manifestation (WPM) and stop-induced by walking pain (SIWP) in people with lower extremity peripheral artery disease (PAD).

Methods: During a 7-day period, 23 PAD participants wore a wGT3x+ accelerometer and a watch on which they were asked to press an event marker button to record WPM or SIWP. We compared different algorithms of first removing short (≤15s) walking or stopping bouts and assigning SIWPs within 15s or 60s after the end of bouts. Then, event markers were assigned to the detected walking bouts from the wGT3X+ and the pain-free walking time (PFWT) and maximal walking time (MWT) were computed.

Results: Removing short walking bouts first yielded lower WPM/week and higher SIWP/week compared to our original algorithm and to removing short stopping bouts first (P<0.05; Figure1.A). Consequently, it provided significantly lower PFWT (P<0.01; Figure1.B) and MWT (P<0.05; Figure1.C) than both algorithms. It also provided higher maximal PFWT than treadmill tests and higher MWT than Strandness test only (P<0.05).

Discussion: When implementing an event-based algorithm to detect intermittent daily walking bouts, removing short bouts first of either stopping or walking significantly impacted clinical estimation of walking capacity in PAD. Although there was no direct observation of actual WPM or SIWP, we consider that removing short walking bouts first would provide the most consistent assessment of walking capacity because this algorithm significantly increased the ischemic events, had a more consistent difference between the mean and the maximal PFWT and MWT, and previously provided better detection of walking bouts in outdoor walking sessions. This automated event-based method will be implemented on our free-access web platform (mapam.ens-rennes.fr).