The article “Actigraphic recording of motor activity in depressed inpatients: A novel computational approach to prediction of clinical course and hospital discharge” by Ignacio Peis, Javier-David Lopez-
Title: Actigraphic recording of motor activity in depressed inpatients: A novel computational approach to prediction of clinical course and hospital discharge
Authors: Ignacio Peis, Javier-David Lopez-
Abstract: Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphs. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants came from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain), who wrist-wore miniature wireless inertial sensors (actigraphs). We modeled activity levels against the normalized time of admission – ‘Progress Towards Discharge’ (PTD) – using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of activity and the real hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age:50.17±12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12±0.13). For n=18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 (the best case) was 0.231±22.98 days (95%CI:14.222-14.684), while PTD was 0.53. These n=18 patients were predicted to need, on average, 7 more days (hence, 14 days) in hospital. Actigraphically recorded motor activity increased during the admission in this sample of depressed patients and early patterns of activity allowed for prediction of hospital discharge date.