TY - JOUR
T1 - Methods for Step Count Data
T2 - Determining “Valid” Days and Quantifying Fragmentation of Walking Bouts
AU - for METRC OUTLET Study Investigators
AU - Reider, Lisa
AU - Bai, Jiawei
AU - Scharfstein, Daniel O.
AU - Zipunnikov, Vadim
N1 - Funding Information:
This work was supported by the United States Department of Defense , through the Peer Reviewed Orthopaedic Research Program, under award number W81XWH-10-2-0090.
Publisher Copyright:
© 2020
PY - 2020/9
Y1 - 2020/9
N2 - Background: Step count monitors are frequently used in clinical research to measure walking activity. Systematically determining valid days and extracting informative measures of walking beyond total daily step count are among major analytical challenges. Research Question: We introduce a novel data-driven anomaly detection algorithm to determine days representing typical walking activity (valid days) and examine the value of measures of walking fragmentation beyond total daily step count. Methods: StepWatch data were collected on 230 adults with severe foot or ankle fractures. Average steps per minute (SC), average steps per active minute (SCA), active to sedentary transition probability (ASTP) and sedentary to active transition probability (SATP) were computed for each participant. The joint distribution of these measures was used to identify and eliminate invalid days through a multi-step process based on the support vector machine. The value of SCA, ASTP and SATP beyond SC were assessed by regressing Short Musculoskeletal Functional Assessment (SMFA), a measure of self-reported function, on these measures and quantifying the increase in the adjusted R-squared. In an unsupervised comparison, the total joint variability of SCA, ASTP and SATP was decomposed into the variability explained by SC and the unique variability of these three measures. Results: Of the 4,448 days in the original data set, 39% were determined invalid. Individuals with higher average SC had higher SCA, lower ASTP and higher SATP. Measures of fragmentation (SCA, ASTP and SATP) explained 25% more of the variability in SMFA compared with SC alone. Approximately 41% of the variability in SCA, ASTP and SATP could not be explained by SC suggesting that these three measures provide unique information about walking patterns. Significance: Applying SVM and quantifying fragmentation in walking bouts for step count data can help to more precisely assess activity in clinical studies employing this modality.
AB - Background: Step count monitors are frequently used in clinical research to measure walking activity. Systematically determining valid days and extracting informative measures of walking beyond total daily step count are among major analytical challenges. Research Question: We introduce a novel data-driven anomaly detection algorithm to determine days representing typical walking activity (valid days) and examine the value of measures of walking fragmentation beyond total daily step count. Methods: StepWatch data were collected on 230 adults with severe foot or ankle fractures. Average steps per minute (SC), average steps per active minute (SCA), active to sedentary transition probability (ASTP) and sedentary to active transition probability (SATP) were computed for each participant. The joint distribution of these measures was used to identify and eliminate invalid days through a multi-step process based on the support vector machine. The value of SCA, ASTP and SATP beyond SC were assessed by regressing Short Musculoskeletal Functional Assessment (SMFA), a measure of self-reported function, on these measures and quantifying the increase in the adjusted R-squared. In an unsupervised comparison, the total joint variability of SCA, ASTP and SATP was decomposed into the variability explained by SC and the unique variability of these three measures. Results: Of the 4,448 days in the original data set, 39% were determined invalid. Individuals with higher average SC had higher SCA, lower ASTP and higher SATP. Measures of fragmentation (SCA, ASTP and SATP) explained 25% more of the variability in SMFA compared with SC alone. Approximately 41% of the variability in SCA, ASTP and SATP could not be explained by SC suggesting that these three measures provide unique information about walking patterns. Significance: Applying SVM and quantifying fragmentation in walking bouts for step count data can help to more precisely assess activity in clinical studies employing this modality.
KW - fragmentation
KW - orthopaedic trauma
KW - valid days
KW - walking bouts
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U2 - 10.1016/j.gaitpost.2020.07.149
DO - 10.1016/j.gaitpost.2020.07.149
M3 - Article
C2 - 32798809
AN - SCOPUS:85089277527
SN - 0966-6362
VL - 81
SP - 205
EP - 212
JO - Gait and Posture
JF - Gait and Posture
ER -