Participants age 46–77 were instructed to wear a device on their dominant wrists for 7 days. Photo: Shutterstock.
An algorithm written by researchers from Neuroscience Research Australia (NeuRA) and University of New South Wales (UNSW), Sydney, Australia, could help promote health in older people and at-risk populations. The Watch Walk algorithm works by measuring gait with a smart watch’s built in accelerometer. It provides an accurate measurement of walking steadiness and speed and may be used to provide real-time feedback on how to improve individual walking stability to reduce the risk of falls.
In a 2-stage study, 101 participants between 19–81 years wore a wrist sensor and were recorded performing specific movements in their homes in addition to walking and running in a lab setting. The data was used to create a digital gait biomarker algorithm that could measure gait quality with greater accuracy. In the study’s second stage, the validity of the biomarkers was tested on 78,822. Participants aged 46–77 years were instructed to wear a device on their dominant wrist for 7 days and a total of 11,646 4-second recordings of movement were then classified into ‘Walking, Running, Stationary, or Unspecified Arm’ activities. The algorithm measured these activities with high precision (93%; 98%; 86%; and 74%, respectively).
According to the researchers, this measurement tool will allow individuals to gain reliable feedback on their gait and track their improvement over time, it would allow healthcare professionals to analyze how people walk and predict their risk of disease or mortality, and will allow researchers to obtain validated gait quality and quantity measures by uploading their participants’ smartwatch raw accelerometry data to a NeuRA server.
