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| ID | Type | Description | Link |
|---|---|---|---|
| P30AG024827 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute on Aging (NIA) | NIH |
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The instrumental activities of daily living (IADL) refer to complex daily activities required for adult independence, such as preparing a meal or taking medications. This study will assess the efficacy of sensing technologies (smartwatch, computer vision, eye tracking) for recognizing IADL activities in naturalistic settings and score performance relative to ratings from occupational therapists. If successful in assessing the efficiency of IADL, the sensing technologies will be a valuable addition to geriatric assessment.
With declines in motor and cognitive function, even older adults living independently may be less efficient in performing daily activities, such as cooking and light housekeeping, which may signal an impending need for caregiver support and healthcare services. Clinicians currently lack automated tools for detecting early declines in daily activity. This research will assess the utility of motion sensors and computer vision assessment in detecting early deficits in the instrumental activities of daily living (IADL), in this case structured cooking and cleaning tasks performed in a standardized kitchen. Older adults with normal cognitive status and those with mild cognitive impairment will be recruited from a research registry to assess differences in performance.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Older adults with normal cognition | Memory Impairment Screen >= 5 and MOCA >= 26 | ||
| Older adults with mild cognitive impairment | Memory Impairment Screen >= 5 and MOCA < 26 |
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| Measure | Description | Time Frame |
|---|---|---|
| Efficiency of IADL Performance | Machine learning composite based on candidate sensing metrics, such as time to complete each element of kitchen task, pacing of activity, corrections, repetition of movement, adjustments of posture, and need to review directions. | 15-minute telephone screening, 90-minute in-person assessment |
| Measure | Description | Time Frame |
|---|---|---|
| Concordance with Occupational Therapist Rating | Agreement between machine-learning sensor categorization and occupational therapist assessment of standardized kitchen tasks. | One 90-min in-person assessment |
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Inclusion Criteria:
Exclusion Criteria:
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Eligible participants from the Community Research Connection registry of the University of Pittsburgh Claude D. Pepper Older Americans Independence Center.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Steven M. Albert, PhD | Contact | 412-383-8693 | smalbert@pitt.edu | |
| Andrea L Rosso, PhD | Contact | 412-624-3060 | ALR143@pitt.edu |
| Name | Affiliation | Role |
|---|---|---|
| Steven M. Albert, PhD | University of Pittsburgh | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Pittsburgh | Pittsburgh | Pennsylvania | 15261 | United States |
De-identified data may be shared for future research with the appropriate sharing agreements in place. IPD includes demograhy, cognitive, sensor measures, and occupational therapist ratings.
June 1, 2027-May 31, 2028
Qualified investigators can contact the PIs to request data. Deidentified data will be provided via password protected data exchange.
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