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Page 638 – Stanford Medicine X

VascTrac: Studying peripheral artery disease via smartphones to improve remote monitoring and postoperative surveillance

Raheel Ata raheel@stanford.edu
Neil Gandhi nrgandhi@stanford.edu


Background Peripheral Artery Disease (PAD) affects more than 12 million people in the U.S. and over 200 million people worldwide. A key symptom of PAD is claudication: leg pain experienced when walking. Endovascular therapies (i.e. stents and angioplasty balloons) can alleviate the pain and improve mobility, but duration of efficacy is unclear and long-term treatment failure is common. There is a need for better postoperative surveillance. The prevalence of smartphones and the advent of software frameworks like Apple HealthKit and ResearchKit presents the opportunity for remote monitoring for functional outcomes. VascTrac is the first smartphone-based study that tracks the progression of PAD and monitors outcomes of surgical and medical interventions. By remotely monitoring physical activity of patients, VascTrac aims to introduce precision medicine into the management of PAD. Methods We are running a two-year study through the VascTrac iPhone ResearchKit app. We aim to enroll 3,000 participants, both with PAD and controls. VascTrac collects two main types of data: medical history and physical activity. Medical history is obtained through brief surveys to gather cardiovascular risk factors, symptoms, medications, and prior surgeries. Physical activity data consists of the clinically validated 6-minute walk tests (6MWT) that users perform. We have also developed a new algorithm based on the physiology of PAD called “Max Steps without Stopping” (MSWS), which is collected completely passively without user input. MSWS will serve as a surrogate marker for disease progression. We will also study user engagement by collecting app usage, retention rate, and event completion. See Figure 1 for a summary. We are conducting validation studies in PAD patients from June - August 2017. Results We ran a feasibility study on Version 1.0 of VascTrac from November 2016 to March 2016 with 76 participants. When analyzing the population with long-term data, preliminary results show than participants with PAD (n=3) have fewer MSWS than participants without PAD (n=24). See Table 1 for preliminary results. Conclusion Preliminary data shows that MSWS is smaller for PAD group compared to non-PAD, which is expected as PAD limits continuous walking. More data is being collected for statistical validation. If validated, VascTrac could represent a personalized medicine approach to PAD surveillance and help identify patients at risk for treatment failure. A digital platform like VascTrac will also empower patients to track their own health. This requires designing high quality user interfaces and presenting data in a way that maximizes engagement. Moreover, with over two billion smartphone users worldwide, a large pool of participants in potential can engage in future mHealth studies. Although VascTrac targets PAD, this platform’s insights will be generalizable to future mobile healthcare studies for other conditions.
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