Disclosures: Rens and Aalami report receiving funding from Apple Inc. and the Precision Health and Integrated Diagnostics Center at Stanford University.
April 07, 2021
3 min read
Save

Q&A: Wearable technology accurately predicts functional capacity in patients with CVD

Disclosures: Rens and Aalami report receiving funding from Apple Inc. and the Precision Health and Integrated Diagnostics Center at Stanford University.
You've successfully added to your alerts. You will receive an email when new content is published.

Click Here to Manage Email Alerts

We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com.

Data generated passively through the VascTrac app on an iPhone and Apple Watch predicted patient performance on a 6-minute walk test as accurately as a home-based 6-minute walk test, according to a longitudinal observational study.

The VascTrac app predicted participant frailty with: Sensitivity, 90%; Specificity, 85%
Data were derived from: Rens N, et al. PLoS One. 2021;doi:10.1371/journal.pone.0247834.
Neil Rens 
Neil Rens
Oliver Aalami 
Oliver Aalami

Neil Rens, a medical student at Stanford University, Oliver O. Aalami, MD, a clinical associate professor of surgery-vascular surgery at the university, and colleagues enrolled 110 participants (99% men; mean age, 68.9 years; never smoked, 11%) who were scheduled for a vascular or cardiac procedure at a Veterans Affairs hospital. The researchers supplied participants with an iPhone and Apple Watch; participants completed supervised 6-minute walk tests (6MWTs) during clinic visits and at-home 6MWTs weekly, while the app continuously collected activity data. The study’s benchmark for frailty was walking fewer than 300 m during the supervised 6MWT.

The researchers found that the wearable technology predicted frailty with 90% sensitivity and 85% specificity, while the at-home walk test predicted frailty with 83% sensitivity and 60% specificity. They also reported that passive data collected at home through the app were “nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT,” with an area under curve of 0.643 and 0.704, respectively.

We spoke with Rens and Aalami to learn more about the technology, how patients responded to it, and more.

Healio Primary Care: What kind of data does this technology collect? What can physicians do with the information?

Rens and Aalami: The VascTrac app uses smartphones and wearables to collect activity data. These data include passive steps walked, distance walked, cadence, pace, heart rate and stairs climbed. During a 6MWT, the app also collects accelerometer data. Importantly, in our study we had study coordinators physically count the steps and distance walked during a 6MWT to establish ground truth.

These data provide physicians with similar information to an in-clinic 6MWT. This means that some aspects of cardiovascular fitness can be tracked without the patient needing to come into clinic. The VascTrac app was able to assess a patient’s frailty using remote data, which could serve as an indicator for when the patients need to come into the clinic.

Healio Primary Care: How is information from the app transmitted back to physicians? Are there any privacy concerns?

Rens and Aalami: The data are stored on a HIPAA-compliant server that can be accessed through a portal. The data itself only includes a patient identifier and the patient’s activity statistics. This study served to test the reliability and repeatability of a home-based 6MWT and was not designed to fit into the workflow of a physician's practice. There are many improvements which can be made to bring this into the daily practice workflow.

Healio Primary Care: Which patients would benefit most from this technology?

Rens and Aalami: Patients with peripheral artery disease and other forms of cardiovascular disease that involve repeated 6MWT measurements would benefit from this technology. We also envision this metric to serve as a "vital sign" — if being measured continuously and passively, it could serve as valuable data when evaluating a patient in any setting. In cardiovascular and pulmonary disease, cardiopulmonary fitness as ascertained via a 6MWT is often the only way to assess treatment success or failure.

Healio Primary Care: How easily did patients adapt to the technology?

Rens and Aalami: Patients adapted well to this technology. The average age of our participants was 69 and 23% of our study participants were smartphone naive. Overall, 75% of enrollees were adherent to the weekly at-home walk test when they received an alert notification on their phone reminding them to do the test. Our vision is to make the technology work in the background and be available to physicians when needed.

Healio Primary Care: What additional research is needed to evaluate this technology?

Rens and Aalami: This research was conducted at a VA hospital, and as a result nearly all of our study participants were men. It is especially important to include women in future studies because they may carry their phones in ways that affect the data collection (eg phones in purses may not be as accurate at counting steps).

The most exciting insight of this research is that passively collected data can provide similar information to the 6MWT. This result should be further explored and validated because it could improve accessibility, affordability, and adherence to care. Now that we have established the accuracy of the smartphone sensors in this population, we need to implement this more widely to establish the "normal range" for different patient populations. More importantly now that we have continuous longitudinal data, we need to determine what change over time should be considered significant and perhaps trigger a clinical encounter. This is the "holy grail" for such remote monitoring.

Healio Primary Care: What efforts are needed to make this technology more accessible to patients?

Rens and Aalami: In order to improve accessibility, the app needs to be tested on other types of devices (eg Android phones). Additionally, low-income patients may not have access to smartphones and wearables, so insurance coverage would improve accessibility.