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Introduction: The Zio® XT continuous ambulatory electrocardiographic monitor (Zio) contains an accelerometer that can help quantify an individual's physical activity in the free-living environment. Purpose: (1) To estimate activity cut-points to estimate daily time spent performing very light, light, or moderate to vigorous physical activity (VLIPA, LIPA, MVPA) for the Zio accelerometer. (2) To describe how Zio's 24-h wear protocol affects estimates of daily MVPA relative to a waist-worn accelerometer's waking-wear protocol. Methods: Three hundred eighty one participants from the Atherosclerosis Risk in Communities (ARIC) study wore a waist-mounted ActiGraph GT3X (except while sleeping or water-based activities) and a Zio (24-h) simultaneously for three to seven days. For each person-minute, physical activity was summarized as the Mean Amplitude Deviation (MAD) for the Zio and Vector Magnitude Counts (VMC) for the GT3X. Cut points previously used in ARIC were mapped from GT3X VMC to Zio MAD using a conditional two-sample quantile–quantile approach. Results: Evenson VMC cut-points for the GT3X (VLIPA≥76, LIPA≥903, MVPA≥2075 counts/min) were mapped to MAD for Zio (9.04, 28.2, and 58.1 mili-g). Daily hours spent in each intensity category were each strongly correlated (ICC > 0.7) between summaries produced by Zio and GT3X when restricting estimates to overlapping wear time. Zio and GT3X-estimated MVPA had high agreement (ICC = 0.77) when using device-specific wear time, but Zio measured one hour more of daily LIPA and VLIPA (95% CI = 0.83, 1.07 hrs/day). Conclusions: We recommend the use of our cut-points for clinical research with Zio accelerometry in populations of older adults. [ABSTRACT FROM AUTHOR]

Background: Combination devices to monitor heart rate/rhythms and physical activity are becoming increasingly popular in research and clinical settings. The Zio XT Patch (iRhythm Technologies, San Francisco, CA, USA) is US Food and Drug Administration (FDA)-approved for monitoring heart rhythms, but the validity of its accelerometer for assessing physical activity is unknown. Objective: To validate the accelerometer in the Zio XT Patch for measuring physical activity against the widely-used ActiGraph GT3X. Methods: The Zio XT and ActiGraph wGT3X-BT (Actigraph, Pensacola, FL, USA) were worn simultaneously in two separately-funded ancillary studies to Visit 6 of the Atherosclerosis Risk in Communities (ARIC) Study (2016–2017). Zio XT was worn on the chest and ActiGraph was worn on the hip. Raw accelerometer data were summarized using mean absolute deviation (MAD) for six different epoch lengths (1-min, 5-min, 10-min, 30-min, 1-h, and 2-h). Participants who had ≥3 days of at least 10 h of valid data between 7 a.m–11 p.m were included. Agreement of epoch-level MAD between the two devices was evaluated using correlation and mean squared error (MSE). Results: Among 257 participants (average age: 78.5 ± 4.7 years; 59.1% female), there were strong correlations between MAD values from Zio XT and ActiGraph (average r: 1-min: 0.66, 5-min: 0.90, 10-min: 0.93, 30-min: 0.93, 1-h: 0.89, 2-h: 0.82), with relatively low error values (Average MSE × 106: 1-min: 349.37 g, 5-min: 86.25 g, 10-min: 56.80 g, 30-min: 45.46 g, 1-h: 52.56 g, 2-h: 54.58 g). Conclusions: These findings suggest that Zio XT accelerometry is valid for measuring duration, frequency, and intensity of physical activity within time epochs of 5-min to 2-h. [ABSTRACT FROM AUTHOR]

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time. [ABSTRACT FROM AUTHOR]