Smart Environment Technology for Longitudinal Behavior Analysis and Intervention
| Status: | Recruiting |
|---|---|
| Conditions: | Neurology |
| Therapuetic Areas: | Neurology |
| Healthy: | No |
| Age Range: | 75 - Any |
| Updated: | 4/21/2016 |
| Start Date: | January 2013 |
| End Date: | December 2017 |
| Contact: | Maureen Schmitter-Edgecombe, PhD |
| Email: | schmitter-e@wsu.edu |
| Phone: | 509-335-3587 |
The world's population is aging and the resulting prevalence of chronic illnesses is a
challenge that our society must address. The vision is to address this challenge by
designing smart environment technologies that keep older adults functioning independently in
their own homes as long as possible. Smart environments have been used as the basis of
monitoring activities for residents with health conditions. However, there is currently a
lack of large scale, longitudinal research to identify early markers of dementia and other
health status changes and to predict functional decline. The objective of this project is to
perform a 5-year longitudinal study of older adults performing daily activities in their own
smart homes.
challenge that our society must address. The vision is to address this challenge by
designing smart environment technologies that keep older adults functioning independently in
their own homes as long as possible. Smart environments have been used as the basis of
monitoring activities for residents with health conditions. However, there is currently a
lack of large scale, longitudinal research to identify early markers of dementia and other
health status changes and to predict functional decline. The objective of this project is to
perform a 5-year longitudinal study of older adults performing daily activities in their own
smart homes.
By tracking residents' daily behavior over a long period of time our intelligent software
can perform automated functional assessment and identify trends that are indicators of acute
health changes and slower progressive decline (e.g., dementia). By implementing prompt-based
interventions that support functional independence and promote healthy lifestyle behaviors
(e.g., social contact, exercise, regular sleep), the investigators can improve overall
health and well-being. The investigators hypothesize that smart home technologies can be
used to detect and predict functional change, to slow functional change and extend
functional independence, and to improve quality of life in elderly individuals who are at
risk of transitioning to mild cognitive impairment and to dementia. This hypothesis has been
formulated on the basis of preliminary data produced by the applicants which supports the
efficacy of using smart home technologies for both functional status assessment and for
prompting the initiation and completion of activities in individuals with mild cognitive
impairment and dementia. The rationale of the proposed work is that understanding the
natural history of functional change between aging and dementia will lead to early
prevention and proactive interventions that will slow functional change, thereby delaying
nursing home placement and cost of care to society. The investigators plan to pursue the
following specific aims: (1) Characterize the daily lifestyle of smart environment residents
through minimal-supervision activity recognition and activity discovery, (2) Design software
algorithms that detect trends in behavioral data, and (3) Evaluate the efficacy of
activity-aware automated prompting technology for extending functional independence and
improving quality of life. The proposed work is innovative because it will track a large
number of individuals longitudinal in their own homes and determine whether this technology
can be used to promote healthy lifestyle behaviors and detect health care changes that may
lead to early interventions, improved quality of life, and decreased health care
utilization. The project is significant because it will introduce new technologies for
activity discovery and tracking that require minimal-supervision, contribute algorithms that
predict cognitive decline and signal more acute health status change, and demonstrate for
the first time that activity-aware automated prompting technologies can be used to support
and/or slow functional change and to increase quality of life in elderly individuals.
can perform automated functional assessment and identify trends that are indicators of acute
health changes and slower progressive decline (e.g., dementia). By implementing prompt-based
interventions that support functional independence and promote healthy lifestyle behaviors
(e.g., social contact, exercise, regular sleep), the investigators can improve overall
health and well-being. The investigators hypothesize that smart home technologies can be
used to detect and predict functional change, to slow functional change and extend
functional independence, and to improve quality of life in elderly individuals who are at
risk of transitioning to mild cognitive impairment and to dementia. This hypothesis has been
formulated on the basis of preliminary data produced by the applicants which supports the
efficacy of using smart home technologies for both functional status assessment and for
prompting the initiation and completion of activities in individuals with mild cognitive
impairment and dementia. The rationale of the proposed work is that understanding the
natural history of functional change between aging and dementia will lead to early
prevention and proactive interventions that will slow functional change, thereby delaying
nursing home placement and cost of care to society. The investigators plan to pursue the
following specific aims: (1) Characterize the daily lifestyle of smart environment residents
through minimal-supervision activity recognition and activity discovery, (2) Design software
algorithms that detect trends in behavioral data, and (3) Evaluate the efficacy of
activity-aware automated prompting technology for extending functional independence and
improving quality of life. The proposed work is innovative because it will track a large
number of individuals longitudinal in their own homes and determine whether this technology
can be used to promote healthy lifestyle behaviors and detect health care changes that may
lead to early interventions, improved quality of life, and decreased health care
utilization. The project is significant because it will introduce new technologies for
activity discovery and tracking that require minimal-supervision, contribute algorithms that
predict cognitive decline and signal more acute health status change, and demonstrate for
the first time that activity-aware automated prompting technologies can be used to support
and/or slow functional change and to increase quality of life in elderly individuals.
Inclusion Criteria:
- fluent in English
- cognitively health adults and adults who meet criteria for Mild Cognitive Impairment
(CDR score >= 0.5)
Exclusion Criteria:
- current or recent (past year) psychoactive substance abuse
- history of cerebrovascular accidents
- other known medical, neurological or psychiatric causes of cognitive dysfunction
(Parkinson's schizophrenia, TBI)
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