TY - GEN
T1 - Using passively collected sedentary behavior to predict hospital readmission
AU - Bae, Sangwon
AU - Dey, Anind K.
AU - Low, Carissa A.
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/9/12
Y1 - 2016/9/12
N2 - Hospital readmissions are a major problem facing health care systems today, costing Medicare alone US$26 billion each year. Being readmitted is associated with significantly shorter survival, and is often preventable. Predictors of readmission are still not well understood, particularly those under the patient's control: behavioral risk factors. Our work evaluates the ability of behavioral risk factors, specifically Fitbit-assessed behavior, to predict readmission for 25 postsurgical cancer inpatients. Our results show that sum of steps, maximum sedentary bouts, frequency, and low breaks in sedentary times during waking hours are strong predictors of readmission. We built two models for predicting readmissions: Steps-only and Behavioral model that adds information about sedentary behaviors. The Behavioral model (88.3%) outperforms the Steps-only model (67.1%), illustrating the value of passively collected information about sedentary behaviors. Indeed, passive monitoring of behavior data, i.e., mobility, after major surgery creates an opportunity for early risk assessment and timely interventions.
AB - Hospital readmissions are a major problem facing health care systems today, costing Medicare alone US$26 billion each year. Being readmitted is associated with significantly shorter survival, and is often preventable. Predictors of readmission are still not well understood, particularly those under the patient's control: behavioral risk factors. Our work evaluates the ability of behavioral risk factors, specifically Fitbit-assessed behavior, to predict readmission for 25 postsurgical cancer inpatients. Our results show that sum of steps, maximum sedentary bouts, frequency, and low breaks in sedentary times during waking hours are strong predictors of readmission. We built two models for predicting readmissions: Steps-only and Behavioral model that adds information about sedentary behaviors. The Behavioral model (88.3%) outperforms the Steps-only model (67.1%), illustrating the value of passively collected information about sedentary behaviors. Indeed, passive monitoring of behavior data, i.e., mobility, after major surgery creates an opportunity for early risk assessment and timely interventions.
KW - Colorectal cancer surgery
KW - Healthcare outcomes
KW - Physical activity
KW - Sedentary behavior
KW - Wearable tracker
UR - http://www.scopus.com/inward/record.url?scp=84991517872&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991517872&partnerID=8YFLogxK
U2 - 10.1145/2971648.2971750
DO - 10.1145/2971648.2971750
M3 - Conference contribution
AN - SCOPUS:84991517872
T3 - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 616
EP - 621
BT - UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
T2 - 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Y2 - 12 September 2016 through 16 September 2016
ER -