![]() ![]() The Ankle-Brachial Index (ABI) correlated poorly with steps (R = 0.365) and distance (R = 0.413). Our correction factor improved distance estimation to 8% ± 32%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. The iPhone CMPedometer step algorithm underestimated steps with a bias of −7.2% ± 13.8% (mean ± SD) and had a mean percent difference with the Actigraph (Actigraph-iPhone) of 5.7% ± 20.5%. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. The distance walked during a 6-min walk test (6MWT) correlates well with patient claudication symptoms, so we developed the VascTrac iPhone app as a platform for monitoring PAD using a digital 6MWT. Clinical validation of smartphone-based activity tracking in peripheral artery disease patients Clinical validation of smartphone-based activity tracking in peripheral artery disease patientsĪta, Raheel Gandhi, Neil Rasmussen, Hannah El-Gabalawy, Osama Gutierrez, Santiago Ahmad, Alizeh Suresh, Siddharth Ravi, Roshini Rothenberg, Kara Aalami, OliverĪRTICLE OPEN Clinical validation of smartphone-based activity tracking in peripheral artery disease patients 1 1 1 1 1 1 2 Raheel Ata, Neil Gandhi, Hannah Rasmussen, Osama El-Gabalawy, Santiago Gutierrez, Alizeh Ahmad, Siddharth Suresh, 1 1 1,2,3 Roshini Ravi, Kara Rothenberg and Oliver Aalami Peripheral artery disease (PAD) is a vascular disease that leads to reduced blood flow to the limbs, often causing claudication symptoms that impair patients’ ability to walk.
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