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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. 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. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. 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%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. Our correction factor improved distance estimation to 8% ± 32%. The Ankle-Brachial Index (ABI) correlated poorly with steps ( R = 0.365) and distance ( R = 0.413). Thus, in PAD patients, the iPhone’s built-in distance algorithm is unable to accurately measure distance, suggesting that custom algorithms are necessary for using iPhones as a platform for monitoring distance walked in PAD patients. Although the iPhone accurately measured steps, more research is necessary to establish step counting as a clinically meaningful metric for PAD.

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Background Smartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease. Methods We enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for “frailty” as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT. Results Under a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess ‘frailty’ with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing “frailty.” Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively. Conclusions In this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.

Background The 6-Mniute-Walk-Test (6MWT) is a validated proxy for frailty and a predictor of clinical outcomes, yet is not widely used due to implementation challenges. This comparative effectiveness study assesses the reliability and repeatability of a home-based 6MWT compared to in-clinic 6MWTs in patients with cardiovascular disease. Methods One hundred and ten (110) patients scheduled for cardiac or vascular surgery were enrolled during a study period from June 2018 to December 2019 at the Palo Alto VA Hospital. Subjects were provided with an Apple iPhone 7 and Apple Watch Series 3 loaded with the VascTrac research study application and performed a supervised in-clinic 6MWT during enrollment, at two weeks, one, three, and six months post-operatively. Subjects also received notifications to perform at-home smartphone-based 6MWTs once a week for a duration of six months. Test-retest reliability of in-clinic measurements and at-home measurements was assessed with an industry standard Cronbach’s alpha reliability test. Results Test-Retest Reliability for in-clinic ground truth 6MWT steps vs. in-clinic iPhone 6MWT steps was 0·99, showing high reliability between the two tested measurements. When comparing for in-clinic ground truth 6MWT steps vs. neighboring at-home iPhone 6MWT steps, reliability was 0·74. Conclusion Running the test-reliability test on both measurements shows that an iPhone 6MWT test is reliable compared to an in-clinic ground truth measurement in patients with cardiovascular disease.