Sensitivity of sensor-based sit-to-stand peak power to the effects of training leg strength, leg power and balance in older adults

- We contribute to the development of sensor-based mobility assessment in elderly.
- We investigate sensitivity to change of sensor-based STS measures in elderly.
- We also examine sensitivity to change of standard clinical measures in elderly.
- Sensor STS measures are more sensitive to changes than standard clinical measures.
- Sensor-based STS measures can be useful for monitoring mobility.

Increasing leg strength, leg power and overall balance can improve mobility and reduce fall risk. Sensor-based assessment of peak power during the sit-to-stand (STS) transfer may be useful for detecting changes in mobility and fall risk. Therefore, this study investigated whether sensor-based STS peak power and related measures are sensitive to the effects of increasing leg strength, leg power and overall balance in older adults. A further aim was to compare sensitivity between sensor-based STS measures and standard clinical measures of leg strength, leg power, balance, mobility and fall risk, following an exercise-based intervention. To achieve these aims, 26 older adults (age: 70-84 years) participated in an eight-week exercise program aimed at improving leg strength, leg power and balance. Before and after the intervention, performance on normal and fast STS transfers was evaluated with a hybrid motion sensor worn on the hip. In addition, standard clinical tests (isometric quadriceps strength, Timed Up and Go test, Berg Balance Scale) were performed. Standard clinical tests as well as sensor-based measures of peak power, maximal velocity and duration of normal and fast STS showed significant improvements. Sensor-based measurement of peak power, maximal velocity and duration of normal STS demonstrated a higher sensitivity (absolute standardized response mean (SRM): ≥0.69) to the effects of training leg strength, leg power and balance than standard clinical measures (absolute SRM: ≤0.61). Therefore, the presented sensor-based method appears to be useful for detecting changes in mobility and fall risk.