An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

Abstract

Background Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status. Objective This study’s aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology. Methods Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups. Results The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups. Conclusions Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users’ walking activity and help researchers gain insights on users’ health status.

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study

“…This report expands upon prior research [30,31], presenting a comprehensive application of an algorithm that captures step count and other aspects of mobility, such as walking cadence and bouts. We characterized the accuracy and reliability of this comprehensive set of digital walking measures from users wearing a wrist-worn device in real-world environments.…”

“…In a previous study, we developed an algorithm that accurately classifies ambulatory status from data collected from a wrist-worn device, characterizing its performance across diverse demographic groups in a real-world setting [30]. Further, results from a substudy of an interventional randomized phase 2 trial demonstrated that digital measures of physical activity (step count and ambulatory time) could be sensitive to treatment effects in patients with Lewy body dementia [31].…”

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study

“…Here, we propose a protocol to study a wrist-worn device that can measure physical activity and physical capacity passively and remotely while performing assessments at home. The measurement properties of this device regarding physical activity are good but have been obtained in a healthy population [ 32 , 33 ]. We will extend these results and focus on the measurement properties of measuring physical capacity using the 6MWT in people with PD and COPD.…”

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-worn Sensor: Performance Characterization Study

“…In our prior work, we have developed algorithms whose outputs (measures for step counts and ambulatory time) demonstrated sensitivity to treatment effects in patients with Lewy Body Dementia [31]. We have also characterized the accuracy of an iteration of that algorithm in a cohort of diverse individuals in the real world [30].…”

“…In previous work, we developed an algorithm that accurately classifies ambulatory status from data collected from a wrist-worn device, characterizing its performance across diverse demographic groups in a real-world setting [30]. Further, results from a substudy of an interventional randomized phase-2 trial demonstrated that digital measures of physical activity (step count and ambulatory time) could be sensitive to treatment effect in patients with Lewy Body dementia [31].…”