A Practical Platform for In-Home Remote Monitoring of Cognitive Frailty

NCT ID: NCT05754021

Last Updated: 2025-02-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-02-01

Study Completion Date

2025-01-31

Brief Summary

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Cognitive frailty, characterized by the coexistence of physical frailty and cognitive impairment, is a robust indicator of cognitive decline. Recognizing its significance, the International Association of Gerontology and Geriatrics and the International Academy on Nutrition and Aging have advocated for the use of cognitive frailty assessment as a means of monitoring the progression of mild cognitive impairment towards debilitating conditions like dementia, Alzheimer's disease, and loss of independence. Despite the clear need, a practical and remotely accessible tool for measuring cognitive frailty is currently lacking, especially within the context of telehealth visits. With telehealth video-conferencing becoming increasingly popular, accepted by healthcare payers, and preferred by older adults who may face difficulties traveling to a clinic, there is a pressing need for a software-based solution for remote cognitive frailty assessment that can be easily integrated into existing telehealth systems. This study proposes designing and validating a video-based solution to remotely monitor cognitive-frailty in older adults.

Detailed Description

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The investigators are proposing to evaluate the feasibility and accuracy of the Frailty Meter (FM), a cutting-edge video-based solution for remotely assessing frailty. FM determines frailty phenotypes, such as weakness, slowness, reduced range-of-motion, and exhaustion, by quantifying the results of a 20-second rapid repetitive elbow flexion-extension task captured by a standard video camera. Image processing algorithms are then used to estimate the angular velocity of the elbow, and a previously validated model is employed to calculate frailty phenotypes from the speed of elbow rotation. Furthermore, FM can also be used to assess cognitive impairment when applied during dual-task conditions, such as while performing a working memory task. The objective of this study is to validate the effectiveness of this video-based solution in tracking longitudinal changes in cognitive-motor function among older adults.

Conditions

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Frailty Cognitive Impairment Dementia Cognitive Impairment, Mild Alzheimer Disease Alzheimer Disease, Early Onset

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* 50 years or older
* Ambulatory
* Able and willing to provide consent
* May have a diagnosis of mild dementia or pre-dementia or mild cognitive impairment (MCI), or MoCA score of 26 or lower.

Exclusion Criteria

* Major bilateral upper-limb disorder
* Major hearing/visual impairment
* History of stroke in the last 90 days
* Receiving hospice care
* Immobility or major mobility disorder: We will exclude those who were bedbound or unable to stand or ambulate with or without walking assistance
* inability to use telemedicine (e.g. no internet at home, severe visual or hearing problem, lack of caregiver support, etc)
* inability or unwillingness to participate in bi-monthly tele-medicine assessments or in-clinic visit (e.g., living farther than 30 mills from the clinic, unavailability of caregivers).
* significant cognitive impairment (MoCA score\<16)
* severe dementia
* severe apathy
* severe depression
* in hospice care or palliative care
* history of drug or alcohol abuse over the last six months
* unable to communicate in English or Spanish
Minimum Eligible Age

50 Years

Maximum Eligible Age

95 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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BioSensics

INDUSTRY

Sponsor Role collaborator

Baylor College of Medicine

OTHER

Sponsor Role lead

Responsible Party

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Bijan Najafi, PhD

Professor of Surgery

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Baylor College of Medicine

Houston, Texas, United States

Site Status

Countries

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United States

References

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Zahiri M, Wang C, Gardea M, Nguyen H, Shahbazi M, Sharafkhaneh A, Ruiz IT, Nguyen CK, Bryant MS, Najafi B. Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health. IEEE Access. 2020;8:219391-219399. doi: 10.1109/access.2020.3042451. Epub 2020 Dec 4.

Reference Type BACKGROUND
PMID: 33777594 (View on PubMed)

Wang C, Zahiri M, Vaziri A, Najafi B. Dual-Task Upper Extremity Motor Performance Measured by Video Processing as Cognitive-Motor Markers for Older Adults. Gerontology. 2023;69(5):650-656. doi: 10.1159/000528853. Epub 2023 Jan 13.

Reference Type RESULT
PMID: 36642072 (View on PubMed)

Other Identifiers

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43917

Identifier Type: -

Identifier Source: org_study_id

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