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.

A validation study of two wrist worn wearable devices for remote assessment of exercise capacity

“…In agreement with Rens and colleagues' findings, we observed better accuracy in step count compared to distance. [8] The Garmin device performed better than the Fitbit with a median MAPE of <2.5% compared with ~10% for Fitbit. This finding likely also relates to the turning requirement in 6MWT-S and the small pivot steps required to do so which may be underestimated by the devices.…”

“…Their work used the Apple watch and iPhone and found in clinic agreement of walked distance and agreement in the community to be good (better correlation seen with steps rather than GPS distance). [8] A formal assessment of agreement between a wearablebased assessment of submaximal exercise capacity and a clinic-based assessment (that considers heart rate in addition to distance covered/step count) would be a useful addition to both the literature and the development of a framework for remote, unsupervised, 6MWT. The aim of this study was to determine wearable device accuracy in assessing exercise capacity to develop a framework for a remote, unsupervised, 6MWT.…”

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

“…The home-based 6MWT assessed frailty with 83% sensitivity and 60% specificity. Hence, functional capacity and frailty could be monitored in cardiovascular patients safely and with a higher resolution by using wearable devices [ 22 ]. Another study by Teo et al tracked sleep and collected multi-modal phenotypic data and questionnaire responses from normal volunteers.…”

“…It was able to detect COVID-19 in the pre-symptomatic period as well as the symptomatic phase of the patients, with a precision score of 0.91 (CI: 0.854–0.967) [ 10 ]. Cho et al proposed a one-class SVM method that can detect COVID-19 23.5–40% earlier compared to the method of Mishra et al [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ,…”

The association between heart rate behavior and gait performance: The moderating effect of frailty

“…In the LINK-HF study cohort, wearable sensors on the chest were utilized to collect HR and activity (e.g., walking) in combination with machine learning approaches to predict risk of re-hospitalization in older adults with heart failure [ 30 , 31 ]. More recently, a 6-minute walk test app has been validated for the Apple Watch that incorporates both motor and HR behaviors for use in patients with cardiovascular disease [ 32 ]. Nevertheless, the association between HR measures and deterioration in physical activity, such as gait performance, across frailty groups is not clear.…”