Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Abstract

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant’s naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients’ comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist- Worn Accelerometer Data: Development and Validation of ElderNet

“…In this regard, the ubiquity and popularity of smartwatches make wrist-worn accelerometers a practical choice for ensuring high compliance in daily living studies. Wrist-worn accelerometers enable the extraction of a wide range of daily living behaviors, including sleep patterns 19 , circadian metrics 20 , and levels of physical activity 21 . While estimated physical activity levels can provide many insights 22,23 , to date, most studies using a wrist-worn accelerometer lacked detailed and high-resolution information about other crucial facets of gait quality 21 .…”

“…Wrist-worn accelerometers enable the extraction of a wide range of daily living behaviors, including sleep patterns 19 , circadian metrics 20 , and levels of physical activity 21 . While estimated physical activity levels can provide many insights 22,23 , to date, most studies using a wrist-worn accelerometer lacked detailed and high-resolution information about other crucial facets of gait quality 21 . Therefore, recent efforts have focused on leveraging these accelerometers to assess walking and gait quality.…”