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Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy
Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4462097/ https://www.ncbi.nlm.nih.gov/pubmed/26113819 http://dx.doi.org/10.3389/fnagi.2015.00102 |
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author | Lyons, Bayard E. Austin, Daniel Seelye, Adriana Petersen, Johanna Yeargers, Jonathan Riley, Thomas Sharma, Nicole Mattek, Nora Wild, Katherine Dodge, Hiroko Kaye, Jeffrey A. |
author_facet | Lyons, Bayard E. Austin, Daniel Seelye, Adriana Petersen, Johanna Yeargers, Jonathan Riley, Thomas Sharma, Nicole Mattek, Nora Wild, Katherine Dodge, Hiroko Kaye, Jeffrey A. |
author_sort | Lyons, Bayard E. |
collection | PubMed |
description | Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies. |
format | Online Article Text |
id | pubmed-4462097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44620972015-06-25 Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy Lyons, Bayard E. Austin, Daniel Seelye, Adriana Petersen, Johanna Yeargers, Jonathan Riley, Thomas Sharma, Nicole Mattek, Nora Wild, Katherine Dodge, Hiroko Kaye, Jeffrey A. Front Aging Neurosci Neuroscience Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies. Frontiers Media S.A. 2015-06-10 /pmc/articles/PMC4462097/ /pubmed/26113819 http://dx.doi.org/10.3389/fnagi.2015.00102 Text en Copyright © 2015 Lyons, Austin, Seelye, Petersen, Yeargers, Riley, Sharma, Mattek, Wild, Dodge and Kaye. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Lyons, Bayard E. Austin, Daniel Seelye, Adriana Petersen, Johanna Yeargers, Jonathan Riley, Thomas Sharma, Nicole Mattek, Nora Wild, Katherine Dodge, Hiroko Kaye, Jeffrey A. Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title | Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title_full | Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title_fullStr | Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title_full_unstemmed | Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title_short | Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy |
title_sort | pervasive computing technologies to continuously assess alzheimer’s disease progression and intervention efficacy |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4462097/ https://www.ncbi.nlm.nih.gov/pubmed/26113819 http://dx.doi.org/10.3389/fnagi.2015.00102 |
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