Literature DB >> 31668595

Automated sensor-based detection of challenging behaviors in advanced stages of dementia in nursing homes.

Doreen Goerss1,2, Albert Hein3, Sebastian Bader3, Margareta Halek4,5, Sven Kernebeck4,5, Andreas Kutschke6, Christina Heine1, Frank Krueger3, Thomas Kirste3, Stefan Teipel1,2.   

Abstract

INTRODUCTION: 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.
© 2019 The Authors. Alzheimer's & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.

Entities:  

Keywords:  Accelerometry; Challenging behavior; Dementia; Nursing home; Real-world evidence

Year:  2020        PMID: 31668595     DOI: 10.1016/j.jalz.2019.08.193

Source DB:  PubMed          Journal:  Alzheimers Dement        ISSN: 1552-5260            Impact factor:   21.566


  6 in total

1.  A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors.

Authors:  S Spasojevic; J Nogas; A Iaboni; B Ye; A Mihailidis; A Wang; S J Li; L S Martin; K Newman; S S Khan
Journal:  J Healthc Inform Res       Date:  2021-05-01

2.  Exploring Resident Care Information Technology Use and Nursing Home Quality.

Authors:  Kimberly R Powell; Chelsea B Deroche; Ethan J Carnahan; Gregory L Alexander
Journal:  J Gerontol Nurs       Date:  2020-04-01       Impact factor: 1.436

3.  TraMiner: Vision-Based Analysis of Locomotion Traces for Cognitive Assessment in Smart-Homes.

Authors:  Samaneh Zolfaghari; Elham Khodabandehloo; Daniele Riboni
Journal:  Cognit Comput       Date:  2021-02-02       Impact factor: 4.890

4.  Comparison of the Mental Burden on Nursing Care Providers With and Without Mat-Type Sleep State Sensors at a Nursing Home in Tokyo, Japan: Quasi-Experimental Study.

Authors:  Sakiko Itoh; Hwee-Pink Tan; Kenichi Kudo; Yasuko Ogata
Journal:  JMIR Aging       Date:  2022-03-23

5.  Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models.

Authors:  Andrea Iaboni; Sofija Spasojevic; Kristine Newman; Lori Schindel Martin; Angel Wang; Bing Ye; Alex Mihailidis; Shehroz S Khan
Journal:  Alzheimers Dement (Amst)       Date:  2022-04-27

Review 6.  Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review.

Authors:  James Chung-Wai Cheung; Bryan Pak-Hei So; Ken Hok Man Ho; Duo Wai-Chi Wong; Alan Hiu-Fung Lam; Daphne Sze Ki Cheung
Journal:  Front Psychiatry       Date:  2022-09-16       Impact factor: 5.435

  6 in total

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