TY - JOUR
T1 - Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients
AU - van Dijk-Huisman, Hanneke C.
AU - Bijnens, Wouter
AU - Senden, Rachel
AU - Essers, Johannes M. N.
AU - Meijer, Kenneth
AU - Aarts, Jos
AU - Lenssen, Antoine F.
N1 - Funding Information:
This research was funded by a PPP Allowance made available by Health~Holland, Top Sector Life Sciences & Health, grant number LSHM18071. We would like to thank all physical therapists working at the Maastricht University Medical Center for their assistance in this study. We would also like to thank Joelle Thelen of Maastricht University for her assistance in video recording the included patients and H.Q. Chim and Jan Klerkx who provided language editing services.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/3
Y1 - 2021/3
N2 - Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm's performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, +/- 10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.
AB - Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm's performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, +/- 10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.
KW - physical activity
KW - accelerometers
KW - algorithm
KW - validation
KW - hospitalized patients
U2 - 10.3390/s21051652
DO - 10.3390/s21051652
M3 - Article
C2 - 33673447
SN - 1424-8220
VL - 21
JO - Sensors
JF - Sensors
IS - 5
M1 - 1652
ER -