Metabolic Energy Expenditure and Accelerometer-Determined Physical Activity Levels in Post-Stroke Hemiparetic Patients.

BACKGROUND: Assessment of metabolic energy expenditure in post-stroke patients using accelerometers is clinically important. However, understanding of the best placement of accelerometers on the body and methods for calculating activity counts are limited. METHODS: Thirty hemiparetic post-stroke patients participated in this cross-sectional study. Four triaxial accelerometers were attached to the hemiplegic and contralateral sides of the waist and ankles during various activities: lying, sitting, standing, stepping in place, and walking on a treadmill (1-5 kmh-1). Activity counts and metabolic energy expenditure of the patients were recorded simultaneously. Simple linear regression analyses were performed between the activity counts and energy expenditure. Activities were classified according to their intensity, using the definition of energetic sedentary behavior of post-stroke patients and a low fitness level group. RESULTS: The best estimate of energy expenditure was obtained when the accelerometer was worn on the contralateral ankle and the activity counts was calculated using the vertical and anteroposterior axes (R2=0.812). Six classes of activity intensity (sedentary: ≤1.5 METs, very light: 1.51-1.79, light: 1.80-2.59, moderate: 2.60-3.39, hard: 3.40-4.39, and very hard: ≥4.40) and corresponding activity counts cut-off points are presented. CONCLUSION: A triaxial accelerometer worn on the contralateral ankle and a method of calculating activity counts that includes at least the vertical and anteroposterior axes are recommended for estimating metabolic energy expenditure in post-stroke patients. The new activity counts cut-off points provide a significant advance in the interpretation of post-stroke monitoring in patients outside the hospital or rehabilitation center.

High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents.

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents' body center-of-mass kinematics in view of improving TUG's predictive accuracy. The aim is to find out which kinematic variables and residents' characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.