Accepted for/Published in: JMIR Research Protocols
Date Submitted: Apr 2, 2019
Open Peer Review Period: Apr 5, 2019 - Apr 19, 2019
Date Accepted: Jul 16, 2019
(closed for review but you can still tweet)
Real-Time Detection of Behavioural Anomalies for Older People, the 3-PEGASE Study: Methods of an Artificial Intelligence Real-life Prospective Trial.
ABSTRACT
Background:
Most frail older persons are living at home and we face difficulties in achieving seamless monitoring to detect adverse health changes. Even more important, this lack of follow-up could have a negative impact on the living choices made by older individuals and their care partners. People could give up their homes for the more reassuring environment of a medicalized living facility. We have developed a low-cost non-obtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate the follow-up of older adults in their own homes, and thus support independent living.
Objective:
The primary objective of our prospective open-label study is to evaluate the relevance of the automatic alerts generated by our artificial intelligence-driven monitoring solution as judged by the recipients: older adult, caregiver, and professional support worker. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events.
Methods:
The primary outcome assessment will be performed for each successive 2-month follow-up period to estimate the progression of our learning algorithm performances over time. Twenty-five frail or disabled participants aged 75 and above and living alone in their own homes, will be enrolled for a 6-month follow-up period.
Results:
The first phase with five participants for a 4-month feasibility period has been completed and the expected completion date for the second phase of the study (20 participants for 6 months) is July 2020.
Conclusions:
The originality of our 6-month real-life project lies in the choice of the primary outcome and in our user-centered design. We will evaluate the relevance of the alerts and the algorithm performances over time according to the end users. The first-line recipients of the information are the older adults and care partners rather than health-care professionals. Despite the fast pace of e-Health device development, no study addressed the specific everyday needs of older adults and their families using such a participatory design and ‘bottom-up’ approach. Clinical Trial: ClinicalTrials.gov NCT03484156
Citation
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