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      Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study

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          Abstract

          Background

          Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors.

          Objective

          The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities.

          Methods

          Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time.

          Results

          The residents of the +CP and –CP communities exhibit no statistical difference in age ( P=.64), sex ( P=.63), and staff service hours per resident ( P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate ( P=.02), a 69% lower fall rate ( P=.01), and a 67% greater length of stay ( P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% ( P=.02) and 40% ( P=.02), respectively.

          Conclusions

          The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

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          Most cited references 52

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          Fear of falling and restriction of mobility in elderly fallers.

          To identify the characteristics of elderly persons who develop a fear of falling after experiencing a fall and to investigate the association of this fear with changes in health status over time. A prospective study of falls over a 2-year period (1991-92). Falls were ascertained using bimonthly postcards plus telephone interview with a standardized (World Health Organisation) questionnaire for circumstances, fear of falling and consequences of each reported fall. Each participant underwent a physical exam and subjective health assessment each year form 1990 to 1993. New-Mexico Aging Process Study, USA. 487 elderly subjects (> 60 years) living independently in the community. Fear of falling after experiencing a fall. 70 (32%) of 219 subjects who experienced a fall during the 2 year study period reported a fear of falling. Women were more likely than men to report fear of falling (74% vs 26%). Fallers who were afraid of falling again had significantly ore balance (31.9% vs 12.8%) and gait disorders (31.9% vs 7.4%) at entry in the study in 1990. Among sex, age, mental status, balance and gait abnormalities, economic resource and physical health, logistic regression analysis show gait abnormalities and poor self-perception of physical health, cognitive status and economic resources to be significantly associated with fear of falling. Subjects who reported a fear of falling experienced a greater increase in balance (P = 0.08), gait (P < 0.01) and cognitive disorders (P = 0.09) over time, resulting in a decrease in mobility level. The study indicated that about one-third of elderly people develop a fear of falling after an incident fall and this issue should be specifically addressed in any rehabilitation programme.
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            Dementia as a risk factor for falls and fall injuries among nursing home residents.

            To compare rates of falling between nursing home residents with and without dementia and to examine dementia as an independent risk factor for falls and fall injuries. Prospective cohort study with 2 years of follow-up. Fifty-nine randomly selected nursing homes in Maryland, stratified by geographic region and facility size. Two thousand fifteen newly admitted residents aged 65 and older. During 2 years after nursing home admission, fall data were collected from nursing home charts and hospital discharge summaries. The unadjusted fall rate for residents in the nursing home with dementia was 4.05 per year, compared with 2.33 falls per year for residents without dementia (P<.0001). The effect of dementia on the rate of falling persisted when known risk factors were taken into account. Among fall events, those occurring to residents with dementia were no more likely to result in injury than falls of residents without dementia, but, given the markedly higher rates of falling by residents with dementia, their rate of injurious falls was higher than for residents without dementia. Dementia is an independent risk factor for falling. Although most falls do not result in injury, the fact that residents with dementia fall more often than their counterparts without dementia leaves them with a higher overall risk of sustaining injurious falls over time. Nursing home residents with dementia should be considered important candidates for fall-prevention and fall-injury-prevention strategies.
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              Lifespan and Healthspan: Past, Present, and Promise.

              The past century was a period of increasing life expectancy throughout the age range. This resulted in more people living to old age and to spending more years at the older ages. It is likely that increases in life expectancy at older ages will continue, but life expectancy at birth is unlikely to reach levels above 95 unless there is a fundamental change in our ability to delay the aging process. We have yet to experience much compression of morbidity as the age of onset of most health problems has not increased markedly. In recent decades, there have been some reductions in the prevalence of physical disability and dementia. At the same time, the prevalence of disease has increased markedly, in large part due to treatment which extends life for those with disease. Compressing morbidity or increasing the relative healthspan will require "delaying aging" or delaying the physiological change that results in disease and disability. While moving to life expectancies above age 95 and compressing morbidity substantially may require significant scientific breakthroughs; significant improvement in health and increases in life expectancy in the United States could be achieved with behavioral, life style, and policy changes that reduce socioeconomic disparities and allow us to reach the levels of health and life expectancy achieved in peer societies.
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                Author and article information

                Contributors
                Journal
                JMIR Aging
                JMIR Aging
                JA
                JMIR Aging
                JMIR Publications (Toronto, Canada )
                2561-7605
                Jul-Dec 2020
                10 September 2020
                : 3
                : 2
                Affiliations
                [1 ] CarePredict Plantation, FL United States
                [2 ] Lifewell Senior Living Corporation Houston, TX United States
                [3 ] Department of Geriatric Medicine and Palliative Care Icahn School of Medicine Mount Sinai New York, NY United States
                [4 ] Alzheimer’s Drug Discovery Foundation New York, NY United States
                Author notes
                Corresponding Author: Gerald Wilmink jerry.wilmink@ 123456gmail.com
                Article
                v3i2e19554
                10.2196/19554
                7516685
                32723711
                ©Gerald Wilmink, Katherine Dupey, Schon Alkire, Jeffrey Grote, Gregory Zobel, Howard M Fillit, Satish Movva. Originally published in JMIR Aging (http://aging.jmir.org), 10.09.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on http://aging.jmir.org, as well as this copyright and license information must be included.

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