Actigraphic recording of motor activity in depressed inpatients: A novel computational approach to prediction of clinical course and hospital discharge
Autor
Peis, Ignacio
López-Morinigo, Javier D.
Perez-Rodriguez, Mercedes
Barrigón-Estévez, María L.
Ruiz, Marta
Artés-Rodríguez, Antonio
Baca-Garcia, Enrique
Fecha
2020Resumen
Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission—‘Progress Towards Discharge’ (PTD)—using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual 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 was 0.231 ± 22.98 days (95% CI 14.222–14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.
Fuente
Scientific Reports, 10, 17286Link de Acceso
Click aquí para ver el documentoIdentificador DOI
doi.org/10.1038/s41598-020-74425-xColecciones
La publicación tiene asociados los siguientes ficheros de licencia: