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Automated sensor‐based detection of challenging behaviors in advanced stages of dementia in nursing homes
Author(s) -
Goerss Doreen,
Hein Albert,
Bader Sebastian,
Halek Margareta,
Kernebeck Sven,
Kutschke Andreas,
Heine Christina,
Krueger Frank,
Kirste Thomas,
Teipel Stefan
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.08.193
Subject(s) - dementia , neuropsychology , accelerometer , psychology , set (abstract data type) , physical medicine and rehabilitation , association (psychology) , clinical psychology , medicine , computer science , psychiatry , cognition , psychotherapist , disease , pathology , programming language , operating system
Sensor‐based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors. Methods We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four‐week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen‐Mansfield Agitation Inventory. Results We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen‐Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level. Discussion We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long‐term measurements in a real‐world setting.