Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool

Abstract

Monitoring the real-life mobility of stroke patients could be extremely useful for clinicians. Step counters are a widely accessible, portable, and cheap technology that can be used to monitor patients in different environments. The aim of this study was to validate a low-cost commercial tri-axial accelerometer-based step counter for stroke patients and to determine the best positioning of the step counter (wrists, ankles, and waist). Ten healthy subjects and 43 post-stroke patients were enrolled and performed four validated clinical tests (10 m, 50 m, and 6 min walking tests and timed up and go tests) while wearing five step counters in different positions while a trained operator counted the number of steps executed in each test manually. Data from step counters and those collected manually were compared using the intraclass coefficient correlation and mean average percentage error. The Bland–Altman plot was also used to describe agreement between the two quantitative measurements (step counter vs. manual counting). During walking tests in healthy subjects, the best reliability was found for lower limbs and waist placement (intraclass coefficient correlations (ICCs) from 0.46 to 0.99), and weak reliability was observed for upper limb placement in every test (ICCs from 0.06 to 0.38). On the contrary, in post-stroke patients, moderate reliability was found only for the lower limbs in the 6 min walking test (healthy ankle ICC: 0.69; pathological ankle ICC: 0.70). Furthermore, the Bland–Altman plot highlighted large average discrepancies between methods for the pathological group. However, while the step counter was not able to reliably determine steps for slow patients, when applied to the healthy ankle of patients who walked faster than 0.8 m/s, it counted steps with excellent precision, similar to that seen in the healthy subjects (ICCs from 0.36 to 0.99). These findings show that a low-cost accelerometer-based step counter could be useful for measuring mobility in select high-performance patients and could be used in clinical and real-world settings.

Validity of Hip and Ankle Worn Actigraph Accelerometers for Measuring Steps as a Function of Gait Speed during Steady State Walking and Continuous Turning

“…The strength of the ICC was classified as follows: <0.50 = poor; 0.50–0.75 = moderate; 0.75–0.9 = good and >0.90 = excellent [ 26 , 27 ]. Additionally, Bland–Altmann plots were used to describe the mean percentage bias between the SW and the AG devices [ 28 , 29 ]. We defined the mean percentage bias as: (the difference between the number of steps between AG and SW/mean number of steps between AG and SW) × 100 [ 29 ].…”

“…Additionally, Bland–Altmann plots were used to describe the mean percentage bias between the SW and the AG devices [ 28 , 29 ]. We defined the mean percentage bias as: (the difference between the number of steps between AG and SW/mean number of steps between AG and SW) × 100 [ 29 ]. We also defined a mean percentage bias of <10% to be an acceptable agreement between the SW and AG devices.…”

The feasibility of remotely monitoring physical, cognitive, and psychosocial function in individuals with stroke or chronic obstructive pulmonary disease

“…The study team provided the Fitbit device to all individuals and assisted with set up, including the installation of the Fitbit application onto their smartphones. Participants with a history of stroke wore the device on their unimpaired wrist whenever possible to maximize the accuracy of the device; 37 , 38 however, individuals with stroke who were unable to independently place the device on their unimpaired arm due to hemiparesis and who had limited social support to assist in donning the device wore it on their paretic wrist. All participants with COPD wore the device on their non-dominant wrists.…”

“…All participants with COPD wore the device on their non-dominant wrists. Although the literature suggests that wearing the device on the upper extremity may not be as accurate as wearing it in other locations, 37 – 40 we elected for the wrist wear location to capture heart rate as this metric allows us to better define wear time and provides valuable information about an individual's health. Individuals were instructed to remove the device when showering and when charging the device but to wear it at all other times, including while sleeping.…”

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol

“…Predictors may also differ for different interventions ( 10 ), suggesting clues to personalize rehabilitation and, possibly, improve rehabilitation outcomes ( 11 ). To date, a large representative cohort study focusing on individual variability of response to a standardized, evidence-based treatment is lacking ( 12 ), and predictive factors of individual response to treatment are still largely unexplored ( 13 ).…”