A new study conducted by Apple researchers in partnership with Zimmer Biomet showcases the viability and efficacy of gait quality metrics passively collected by an iPhone in post-surgical care for patients who have had total hip or knee arthroplasty — reports MyHealthyApple.
The study was led by E. Arnold and I. Mance from Apple, as well as M. Anderson and D. Van Andel from Zimmer Biomet, and has been published in the Journal of Orthopedic Proceedings.
Gait is the way an individual walks, and studying a person’s gait can allow medical professionals to learn about their balance, the coordination between their muscles, and the quality of their walking stride.
iOS 14 blessed iPhones with the ability to measure a number of mobility metrics using onboard sensors, gait quality included.
It takes time for a patient to recover after a total hip or knee arthroplasty, and an abnormal gait during recovery is a cause for concern that requires follow-up visits to the doctor and possible rehab. Traditionally, doctors and physiotherapists call patients in post-op to monitor their gait and measure gait quality metrics through questionnaires.
The study explores the possibility of determining gait quality and normality using real-world metrics passively measured by the patient’s iPhone. The tested sample size consisted of 117 females and 81 males with a mean age of 61.4 and BMI of 30.7.
Gait quality for each participant was approximated using walking speed, step length, and timing asymmetry metrics collected from their iPhones.
According to the results of the study, the recovery curves observed when using post-arthroplasty gait quality metrics obtained from a smartphone in real-world patient care are demonstrably similar to those reported by traditional approaches to gait monitoring as well as patient-reported outcome scores.
The approach also improves accuracy by eliminating the possibility of participants exhibiting doctored behaviour due to their awareness of being observed, known as the Hawthorne Effect, which is ever-present during in-clinic observations.