Accuracy of the actibelt(®) accelerometer for measuring walking speed in a controlled environment among persons with multiple sclerosis.

BACKGROUND: Advances in portable sensor technology have opened an era for objective, real-life monitoring of walking speed in persons with multiple sclerosis (MS). PURPOSE: The present study examined the accuracy of the actibelt(®) accelerometer for measuring walking speed during a standard 6-min walk (6MW) and the possibility that disability status influenced the degree of accuracy among persons with MS. METHODS: On a single testing session, 51 persons with MS and Expanded Disability Status Scale scores between 2.0 and 6.5 performed a 6MW while wearing an actibelt(®) in the body's sagittal symmetry plane and close to the body's centre of mass. RESULTS: All 51 participants completed the 6MW without stopping, falling, or any adverse events, and the actibelt(®) provided walking speed data for each of the participants. The actibelt(®) significantly overestimated walking speed (actual minus actibelt(®)) by a mean±standard deviation of -0.12±0.17 m/s for the overall sample (p<0.0001). There was no significant overestimation in the sample with mild disability (-0.02±0.11 m/s), but there was in the samples with moderate (-0.10±0.16 m/s) and severe (-0.26±0.12 m/s) disability. CONCLUSION: The actibelt(®) is ready for real-life monitoring of walking speed in persons with mild MS, but caution is necessary when interpreting the accuracy of the walking speed data for those with MS who have moderate and severe disability.

Association between walking speed and age in healthy, free-living individuals using mobile accelerometry--a cross-sectional study.

CONTEXT: Walking speed is a fundamental parameter of human motion and is increasingly considered as an important indicator of individuals' health status. OBJECTIVE: To evaluate the relationship of gait parameters, and demographic and physical characteristics in healthy men and women. DESIGN, SETTING, AND PARTICIPANTS: Recruitment of a subsample (n = 358) of male and female blood donors taking part in the Cambridge CardioResource study. Collection of demographic data, measurement of physical characteristics (height, weight and blood pressure) and assessment of 7-day, free-living activity parameters using accelerometry and a novel algorithm to measure walking speed. Participants were a median (interquartile range[IQR]) age of 49 (16) years; 45% women; and had a median (IQR) BMI of 26 (5.4). MAIN OUTCOME MEASURE: Walking speed. RESULTS: In this study, the hypothesis that walking speed declines with age was generated using an initial 'open' dataset. This was subsequently validated in a separate 'closed' dataset that showed a decrease of walking speed of -0.0037 m/s per year. This is equivalent to a difference of 1.2 minutes, when walking a distance of 1 km aged 20 compared to 60 years. Associations between walking speed and other participant characteristics (i.e. gender, BMI and blood pressure) were non-significant. BMI was negatively correlated with the number of walking and running steps and longest non-stop distance. CONCLUSION: This is the first study using accelerometry which shows an association between walking speed and age in free-living, healthy individuals. Absolute values of gait speed are comparable to published normal ranges in clinical settings. This study highlights the potential use of mobile accelerometry to assess gait parameters which may be indicative of future health outcomes in healthy individuals.

Development and validation of a new method to measure walking speed in free-living environments using the actibelt® platform.

Walking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92-0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12-0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure.