Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge
Autor: | M. Mercedes Perez-Rodriguez, Enrique Baca-García, Marta Ruiz-Gomez, Javier D. López-Moríñigo, María Luisa Barrigón, Ignacio Peis, Antonio Artés-Rodríguez |
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Přispěvatelé: | UAM. Departamento de Medicina, UAM. Departamento de Psiquiatría, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (ISS-FJD), Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Comunidad de Madrid, Ministerio de Ciencia, Innovación y Universidades (España), European Commission |
Rok vydání: | 2020 |
Předmět: |
Adult
Male medicine.medical_specialty Medicina Mathematics and computing lcsh:Medicine Motor Activity Motor activity Article 03 medical and health sciences 0302 clinical medicine Clinical course Hospital discharge medicine Humans Computational approach lcsh:Science Patient discharge Telecomunicaciones Inpatients Multidisciplinary business.industry Depression lcsh:R Actigraphy Actigraphic Length of Stay Middle Aged Psicología Patient Discharge 030227 psychiatry 3. Good health Risk factors Physical therapy Female lcsh:Q business 030217 neurology & neurosurgery Depressed inpatients |
Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020) Biblos-e Archivo: Repositorio Institucional de la UAM Universidad Autónoma de Madrid Biblos-e Archivo. Repositorio Institucional de la UAM instname |
ISSN: | 2045-2322 |
DOI: | 10.1038/s41598-020-74425-x |
Popis: | 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. This work has been partly-funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (TEC2017-92552-EXP, RTI2018-099655-B-I00, FPU18/00516), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICOCM, B2017/BMD-3740 AGES-CM 2CM), ISCIII (PI16/01852), BBVA Foundation (Deep-DARWiN grant) and AFSP (Grant LSRG-1-005-16). JDLM acknowledges funding support from the Universidad Autónoma de Madrid and European Union-European Commission via the Intertalentum Project & Marie Skłodowska-Curie Actions Grant (GA 713366) |
Databáze: | OpenAIRE |
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