Alzheimer Disease, Alzheimer Disease, Early Onset, Cognitive Impairment, Cognitive Impairment, Mild, Dementia, Frailty
Conditions
Keywords
frailty, remote patient monitoring, mild cognitive impairement, early diagnosis, Telemedicine
Brief summary
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
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.
Interventions
None listed
Sponsors
BioSensics
Baylor College of Medicine
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
50 Years to 95 Years
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Change in cognitive frailty performance every other month from baseline to 12 months | baseline, every 2 months, up to 12 months | Frailty will be evaluated using the Frailty Meter, which will calculate a frailty score based on four frailty phenotypes collected during an upper extremity test that includes a cognitive task of counting backwards. The phenotypes include slowness, exhaustion, weakness, rigidity, and dual-task cost. The cognitive frailty score, which ranges from 0 to 1, indicates the severity of cognitive-frailty with higher values signifying a more advanced stage of frailty |
| Change in cognitive function from baseline to 6 months and 12 months | baseline, every 2 months, up to 12 months | Cognitive performance will be assessed using Montreal Cognitive Assessment (MoCA). Scores on the MoCA range from zero to 30, with a score of 26 and higher generally considered normal. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in Gait speed from baseline to 6 months and 12 months | baseline, 6 month, 12 month | Change from baseline in Gait speed at 6 months and 12 months. Gait speed will be measured using a validated wearable platform (LEGSys) during habitual walking speed. The unit is meter per second (m/s) |
| Change in physical frailty from baseline to 6 months and 12 months | baseline, 6 months, 12 months | The Fried Frailty Questionnaire will be administered to assess frailty based on five phenotypes: slowness, exhaustion, weakness, inactivity, and weight loss. Participants will be classified as robust, pre-frail, or frail based on the presence or absence of each phenotype. |
| Change in Balance from baseline to 6 months and 12 months | baseline, 6 month, 12 month | Change in balance from baseline to 6 months and 12 months will be measured. Balance will be assessed by measuring center of mass sway. The investigator will use a validated wearable platform (BalanSens) to measure body sway. The unit is cm/s2 |
| Change in Physical activity from baseline to 6 months and 12 months | baseline, 6 month, 12 month | Assessed by a validated wearable device called PAMSys (Biosensics LLC, MA, USA). We will use daily number of steps to determine physical activities. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Acceptability of tele-cognitive frailty protocol from baseline to 6 months and 12 months | baseline, 6 month, 12 month | Acceptability will be assessed using Technology Acceptance Model questionnaire (TAM) adopted for telehealth applications. Likert scale is used to quantify perceived benefit, perceived ease of use, and attitude toward use. The scale is ranged from 0 (strongly disagree) to 7 (strongly agree). |
Countries
United States
Outcome results
None listed