Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study

Abstract

Background While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. Objective The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. Methods In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. Results The overall preprocedural stress level (P=.02), minutes of high stress per day (P=.02), minutes of rest per day (P=.045), and daily heart rate maximum (P=.048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; P=.048; GSC-6MWT AUC 0.671, CI 0.487-0.855; P=.42; EFS-C AUC 0.636, CI 0.254-1.000; P=.44). Conclusions This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments.

Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates

“…By comparison, multimodal biometric data, including heart rate and sleep, have been used to predict surgical complications in other specialties and to guide personalized interventions for conditions such as smoking relapse. 31-33…”

“…By comparison, multimodal biometric data, including heart rate and sleep, have been used to predict surgical complications in other specialties and to guide personalized interventions for conditions such as smoking relapse. [31][32][33] Despite the potential value of wearable trackers, pain and disability remain inherently subjective experiences, as shown by the inconsistent association between heart rate/activity and pain in Figure 3. Consequently, self-report remains essential to measuring disease severity, predicting surgical outcome, and guiding perioperative interventions.…”

Digital interventions in the management of advanced liver disease: Prescription and monitoring of healthy living in homes

“…Telerehabilitation includes exercises for which intensity and duration can be monitored with technology (“telemonitoring”) with a focus on improving exercise‐related outcomes 15 . In the cardiovascular literature, numerous studies exist on the use of telerehabilitation in frail patients with a variety of tools, including smartphone apps, smartwatches, and wearable sensors 16–20 . Recent literature review by Cruz and colleagues 21 failed to identify any studies investigating the role of digital interventions in nutritional management in CLD as compared with studies in chronic kidney disease ( n = 6), cancer ( n = 6), and cardiovascular disease ( n = 3).…”

Digital health in older adults for the prevention and management of cardiovascular diseases and frailty. A clinical consensus statement from the ESC Council for Cardiology Practice/Taskforce on Geriatric Cardiology, the ESC Digital Health Committee and the ESC Working Group on e‐Cardiology

“…Other sensor based digital solutions provide data to populate risk scores for frailty. For example, wrist‐worn fitness trackers used before TAVR have been used to develop a Fitness‐tracker assisted Frailty‐Assessment Score (FIFA score) that has greater predictive performance for in‐hospital mortality compared with that of the 6‐minute walk test and the Edmonton Frail Scale classification 122 …”