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 unobtrusive sensor-based solution to trigger automatic alerts in case of an acute event or subtle changes over time. It could facilitate older adults' follow-up in their own homes, and thus support independent living.
Objective: The primary objective of this 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 adults, caregivers, and professional support workers. The secondary objective is to evaluate its ability to detect subtle functional and cognitive decline and major medical events.
Methods: The primary outcome will be evaluated for each successive 2-month follow-up period to estimate the progression of our learning algorithm performance over time. In total, 25 frail or disabled participants, aged 75 years and above and living alone in their own homes, will be enrolled for a 6-month follow-up period.
Results: The first phase with 5 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 real-life project lies in the choice of the primary outcome and in our user-centered evaluation. We will evaluate the relevance of the alerts and the algorithm performance over time according to the end users. The first-line recipients of the information are the older adults and their care partners rather than health care professionals. Despite the fast pace of electronic health devices development, few studies have addressed the specific everyday needs of older adults and their families.
Trial registration: ClinicalTrials.gov NCT03484156; https://clinicaltrials.gov/ct2/show/NCT03484156.
International registered report identifier (irrid): PRR1-10.2196/14245.
Keywords: artificial intelligence; frailty; monitoring; older adults; participatory design; sensors.
©Antoine Piau, Benoit Lepage, Carole Bernon, Marie-Pierre Gleizes, Fati Nourhashemi. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 18.11.2019.