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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
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Predicting physical activity from self-reported mobility in individuals with transtibial amputation: A validation
Kyle R Leister1,2, Sara E Burke3, Kevin Carroll4
1Department of Clinical and Rehabilitative Sciences, East Tennessee State University, Johnson City, TN, USA.
Journal of Sports Sciences
|June 16, 2025
Summary
This study explored using the Prosthetic Limb User's Survey of Mobility (PLUS-M) to predict accelerometer-measured steps in individuals with transtibial amputation. While a correlation was found, the prediction model requires further refinement for clinical use.
Area of Science:
- Rehabilitation Engineering
- Biomedical Engineering
- Wearable Technology
Background:
- Patient-reported outcome measures (PROMs) like the Prosthetic Limb User's Survey of Mobility (PLUS-M) offer insights into mobility but lack objective measurement.
- Accelerometry provides objective, free-living physical activity data, complementing subjective PROMs.
Purpose of the Study:
- To investigate the relationship between the PLUS-M PROM and accelerometer-measured steps in individuals with transtibial amputation.
- To develop and validate a predictive model for estimating daily steps using PLUS-M scores.
Main Methods:
- Participants with transtibial amputation completed the PLUS-M and wore an activPAL accelerometer for seven days.
- LASSO regression was employed to develop a prediction model using training data (n=80) and validated on holdout data (n=26).
Main Results:
- A moderately high correlation (r=0.77) was observed between predicted and actual steps in the holdout data.
- The model overestimated steps and did not meet the predefined equivalence threshold (±10% of mean step count).
- A root mean square error of 1,380 steps (approx. 33% of mean actual steps) was found.
Conclusions:
- Combining PROMs with accelerometer data shows potential for approximating physical activity in prosthetic users.
- Current model limitations, including overestimation and significant error, preclude its use in clinical decision-making.
- Further research and model refinement are necessary to improve accuracy and clinical applicability.

