Activity data from wearables as an indicator of functional capacity in patients with cardiovascular disease

Abstract

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.

Fig 1. Frequency of clinic-based (A) and home-based (B) six-minute walk tests.

Patients averaged 4 clinic-based walk tests, with most patients completing at least 6 (A). Only 15% completed two or fewer. Patients averaged 20 home-based walk tests, with 82% of patients completing at least 10 home-based walk tests (B).

Fig 2. Accuracy of smartphone based six-minute walk test.

For clinic-based six-minute walk tests, the smartphone step counts were more highly correlated with ground truth distance than was the smartphone-measured distance. When used to detect frailty as measured by ground truth distance <300m, the smartphone performed best when incorporating both step and distance data.

Fig 3. Receiver operating characteristic for predictive frailty on in-clinic walk tests.

This figure represents the ability of A) at-home 6MWT data to predict in-clinic 6MWT and B) passive activity data to predict in-clinic 6MWT using a logistic regression model. The AUC of A) is 0.704 while the AUC of B) is 0.643 suggesting that passive data is nearly as accurate as an at-home 6MWT in predicting frailty as measured by an in-clinic 6MWT.