Clinical validation of smartphone-based activity tracking in peripheral artery disease patients

Raheel Ata, Neil Gandhi, Hannah Rasmussen, Osama El-Gabalawy, Santiago Gutierrez, Alizeh Ahmad, Roshini Ravi, Kara Rothenberg & Oliver Aalami npj Digital Medicine volume 1, Article number: 66 (2018)

Abstract

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.

Introduction

Peripheral arterial disease (PAD) affects over 10 million people in the United States.1,2 While intermittent claudication (IC) is the classic early symptom for PAD, many patients are asymptomatic or have exertional leg symptoms other than IC.3 Medical management with smoking cessation, exercise, aspirin, and statin therapy are the first line of therapy, however, surgical interventions are employed to improve PAD patient’s walking ability when there is a “disabling” loss of mobility.4

The current standard of care for diagnosis and post-operative surveillance of PAD consists of ankle-brachial indices (ABIs) and/or arterial duplex scans, both conducted in the clinic. Given the association between severity of walking disability and arterial disease burden,5 patient-reported claudication symptoms (i.e., leg cramping) are often monitored during clinic visits as well. However, arterial duplex scans and ABI results do not always correlate with symptoms, and self-reported patient data can be unreliable.

Initially developed as a fitness test for the Air Force,6 the 6-min walk test (6MWT) is an objective tool that is commonly used in clinic to assess functional capacity in chronic obstructive pulmonary disease and congestive heart failure (CHF).7,8 The primary measure of a 6MWT is the 6-min walk distance (6MWD), the distance walked in 6-min on a linear 100-ft course. Though the 6MWT is not traditionally used in the PAD space, it has been shown to correlate with claudication symptoms9,10,11,12 and functional capacity in patients with PAD.3,10,12,13,14 Despite the 6MWT’s simplicity, it is typically administered by trained personnel in a clinical setting.

Smartphones have accelerometers and gyroscopes that can measure physical activity and have been shown to be effective tools for collecting clinical data at high resolution and on a large scale.15,16,17 Brooks et al.18 demonstrated that smartphone apps can effectively and reliably administer a 6MWT both in clinic and at home for patients with CHF or pulmonary hypertension, suggesting that smartphones may be a promising platform for remotely monitoring functional capacity. We hypothesize that a remotely-administered 6MWT could more accurately reflect the day-to-day function of PAD patients and provide a more patient-centric metric for patients’ functional limitations. Interestingly, while studies have assessed the use of Fitbits and the ActiGraph pedometer to remotely monitor “free-living physical activity”,19 to the authors’ knowledge no study to date has assessed the validity of a smartphone-based 6MWT in the PAD population. With over 75% of the general population, and, notably, 46% of participants aged 65+ reporting smartphone ownership,20 we believe that a smartphone-based 6MWT could change the paradigm for PAD surveillance by allowing physicians to track functional limitations in patients with PAD and to measure patient responses to intervention longitudinally.

To explore the use of a smartphone-based monitoring tool in the PAD population, we created an iPhone app that administers a 6MWT as well as PAD-specific survey questionnaires. In this study we aim to assess the feasibility of our 6MWT app, “VascTrac,” to serve as a platform for performing 6MWTs in patients with PAD by (1) evaluating the accuracy of the iPhone’s step and distance tracking algorithms in the PAD population, and (2) assessing the concordance of the iPhone algorithms with the ActiGraph GT9X.

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Stephen Jacobs