Literature DB >> 20066886

Feasibility of machine-based prompting to assist persons with dementia.

Megan Witte Bewernitz1, William C Mann, Patricia Dasler, Patrícia Belchior.   

Abstract

Nearly 14% of people over age 71 have some form of dementia, with prevalence increasing to nearly 40% of those over age 90. As dimentia progresses, it impacts a person's independent functions and can increase the burden on caregivers. The use of assistive devices can help individuals with dementia live more independently. However, older individuals with cognitive impairment have difficulties using assistive technology devices because the devices are not designed to address their needs. The development of "smart devices" has potential in assisting older adults with cognitive impairment. Eleven community-dwelling seniors with moderate cognitive impairment (Mini-Mental State Examination scores ranging from 12-20) participated in this study. The Functional Independence Measure scores of participants were also collected to determine participants' current level of independence on selected tasks. Three tasks were selected to represent three levels of complexity: drinking water, brushing teeth, and upper body dressing. Participants were prompted through these tasks with simulated smart machine-based prompting. The need for prompts was highly individual, but given appropriate machine-delivered messages, participants completed the tasks an average of 86% of the time across the three self-care tasks. Machine-based prompting devices could aid caregivers as well as increase independence in some tasks.

Entities:  

Mesh:

Year:  2009        PMID: 20066886     DOI: 10.1080/10400430903246050

Source DB:  PubMed          Journal:  Assist Technol        ISSN: 1040-0435


  12 in total

1.  Subjective cognitive complaints and objective memory performance influence prompt preference for instrumental activities of daily living.

Authors:  Emily J Van Etten; Alyssa Weakley; Maureen Schmitter-Edgecombe; Diane Cook
Journal:  Gerontechnology       Date:  2016

2.  Understanding How Sensory Changes Experienced by Individuals with a Range of Age-Related Cognitive Changes Can Effect Technology Use.

Authors:  Emma Dixon; Jesse Anderson; Amanda Lazar
Journal:  ACM Trans Access Comput       Date:  2022

3.  Verbal prompting to improve everyday cognition in MCI and unimpaired older adults.

Authors:  Kelsey R Thomas; Michael Marsiske
Journal:  Neuropsychology       Date:  2013-11-11       Impact factor: 3.295

4.  The Role of Sensory Changes in Everyday Technology use by People with Mild to Moderate Dementia.

Authors:  Emma Dixon; Amanda Lazar
Journal:  ASSETS       Date:  2020-10

5.  Prompting technologies: A comparison of time-based and context-aware transition-based prompting.

Authors:  Kayela Robertson; Cody Rosasco; Kyle Feuz; Maureen Schmitter-Edgecombe; Diane Cook
Journal:  Technol Health Care       Date:  2015       Impact factor: 1.285

6.  "Taking care of myself as long as I can": How People with Dementia Configure Self-Management Systems.

Authors:  Emma Dixon; Anne Marie Piper; Amanda Lazar
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2021-05

7.  Prompting Technology and Persons With Dementia: The Significance of Context and Communication.

Authors:  Rachel Braley; Rochelle Fritz; Catherine R Van Son; Maureen Schmitter-Edgecombe
Journal:  Gerontologist       Date:  2019-01-09

Review 8.  How technology in care at home affects patient self-care and self-management: a scoping review.

Authors:  José M Peeters; Therese A Wiegers; Roland D Friele
Journal:  Int J Environ Res Public Health       Date:  2013-10-29       Impact factor: 3.390

9.  Technologies to Support Community-Dwelling Persons With Dementia: A Position Paper on Issues Regarding Development, Usability, Effectiveness and Cost-Effectiveness, Deployment, and Ethics.

Authors:  Franka Meiland; Anthea Innes; Gail Mountain; Louise Robinson; Henriëtte van der Roest; J Antonio García-Casal; Dianne Gove; Jochen René Thyrian; Shirley Evans; Rose-Marie Dröes; Fiona Kelly; Alexander Kurz; Dympna Casey; Dorota Szcześniak; Tom Dening; Michael P Craven; Marijke Span; Heike Felzmann; Magda Tsolaki; Manuel Franco-Martin
Journal:  JMIR Rehabil Assist Technol       Date:  2017-01-16

10.  Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

Authors:  Bryan Minor; Janardhan Rao Doppa; Diane J Cook
Journal:  IEEE Trans Knowl Data Eng       Date:  2017-09-11       Impact factor: 6.977

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.