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.

Background Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. Objective Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies. Methods We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure. Results In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device–based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time. Conclusions We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

Background/Objectives: Mobility is a meaningful aspect of individual health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. Our objective was to characterize the accuracy and reliability performance of a suite of digital measures of walking behaviors, as critical aspects in the practical implementation of digital measures into clinical studies. Methods: We collected data from a wrist-worn device (the Verily Study Watch) worn by a cohort of volunteer participants for multiple days (1-10 days) in a real world setting. Based on step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, daily ambulatory time, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, peak 30-minute walking pace. To characterize accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: Intraclass Correlation Coefficient (ICC), Pearson R, Mean Error (ME), Mean Absolute Error (MAE). To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time-to-reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1-30 days, and analyzing test-retest reliability based on ICCs between adjacent (non-overlapping) time windows for each measure. Results: In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (N=35 participants; median observation time, 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the eight measurements under evaluation, as reflected by ICCs, ranged between 0.7-0.9; Pearson R values were all greater than 0.75. For the time-to-reliability characterization, we collected data for a total of 15,120 participant days (overall cohort N=234; median observation time, 119 days). Here, all digital measures achieved an ICC between adjacent readouts > 0.75 by 16 days of wear time. Conclusions: We characterized accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

BACKGROUND Measuring physical activity amounts and patterns using wearable sensor technology in real-world settings can provide critical insights into health status. OBJECTIVE We trained an algorithm that classifies binary ambulatory status (yes or no) on accelerometer signal from a wrist-worn Biometric Monitoring Technology (BioMeT) and tested its analytical validity and generalizability. METHODS BioMeT 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 two 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 dataset, measuring performance in multiple held-out testing datasets, overall and in demographically-stratified subgroups. RESULTS The algorithm was accurate classifying ambulatory status on 10-second epochs (AUC = 0.938; 95% CI, 0.921-0.958) and on daily-aggregate metrics (daily Mean Absolute Percentage Error [MAPE] = 18%; 95% CI, 15-20%), without significant performance differences across subgroups. CONCLUSIONS Our algorithm can accurately classify ambulatory status using a wrist-worn device in real-world settings, with generalizability across demographic subgroups. CLINICALTRIAL NA

BACKGROUND Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. OBJECTIVE Our objective was to characterize the accuracy and reliability performance of a suite of digital measures of walking behaviors, as critical aspects in the practical implementation of digital measures into clinical studies. METHODS We collected data from a wrist-worn device (the Verily Study Watch) worn by a cohort of volunteer participants for multiple days (1-10 days) in a real world setting. Based on step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, daily ambulatory time, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, peak 30-minute walking pace. To characterize accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4™) with known low error, calculating the following metrics: Intraclass Correlation Coefficient (ICC), Pearson R, Mean Error (ME), Mean Absolute Error (MAE). To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time-to-reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1-30 days, and analyzing test-retest reliability based on ICCs between adjacent (non-overlapping) time windows for each measure. RESULTS In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (N=35 participants; median observation time, 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the eight measurements under evaluation, as reflected by ICCs, ranged between 0.7-0.9; Pearson R values were all greater than 0.75. For the time-to-reliability characterization, we collected data for a total of 15,120 participant days (overall cohort N=234; median observation time, 119 days). Here, all digital measures achieved an ICC between adjacent readouts > 0.75 by 16 days of wear time. CONCLUSIONS We characterized accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions. CLINICALTRIAL NA