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Introduction Given that chronic, long-term conditions are increasingly common in older patients, the impact of telesurveillance program on clinical outcomes is uncertain. This study aimed to evaluate the feasibility and effectiveness of a 12-month remote monitoring program in preventing rehospitalizations in older patients with two or more chronic diseases returning home after hospitalization. Methods We conducted a multicenter randomized controlled trial in two parallel groups to evaluate the remote monitoring system. Elderly patients with chronic diseases (at least two comorbidities) aged 65 years or older and discharged home after acute hospital care for a chronic disease were randomized to receive a home telemonitoring program (intervention group, n = 267) or conventional care (control group, n = 267). The remote home monitoring program was an online biometric home life analysis technology (e-COBAHLT) with tele-homecare/automation and biometric sensors. The eCOBALTH intervention group received the automation sensors containing chronic disease clinical factor trackers to monitor their biometric parameters and detect any abnormal prodromal disease decompensation by remote monitoring and providing geriatric expertise to general practitioners. The usual care group received no eCOBALTH program. In both groups, baseline visits were conducted at baseline and the final visit at 12 months. The primary outcome was the incidence of unplanned hospitalizations for decompensation during the 12-month period. Results Among 534 randomized participants (mean [SD] age, 80.3 [8.1] years; 280 [52.4%] women), 492 (92.1%) completed the 12-month follow-up; 182 (34.1) had chronic heart failure, 115 (21.5%) had stroke, and 77 (14.4%) had diabetes. During the 12-month follow-up period, 238 patients had at least one unplanned hospitalization for decompensation of a chronic disease: 108 (40.4%) in the intervention group versus 130 (48.7%) in the control group (P = 0.04). The risk of rehospitalization was significantly reduced in the intervention group (age- and sex-adjusted relative risk: 0.72, 95% 95% confidence intervals 0.51-0.94). Conclusion A 12-month home telemonitoring program with online biometric analysis using Home life technology combining telecare and biometric sensors is feasible and effective in preventing unplanned hospitalizations for chronic disease decompensation in elderly patients with chronic diseases at high risk for hospitalizations.

Purpose To examine the impact of discontinuing the use of assistive technology for mobility (ATM) devices on the 6-months incidence of falls in older adults (OA) living at home. Materials and methods A medico-socioeconomic survey was performed to collect information on the quality of life and well-being of older adults, before and 6 months after being loaned an ATM device. Personal data (medical, social, and economic) were collected via a geriatric survey. Results In all, 102 OA participated in the study. Over the 6-months observation period, 17 ( n = 81) serious falls were recorded among participants who were using their ATM device optimally; in those who discontinued device use, 12 falls ( n = 21) were recorded (57.1%; p = 0.001). Factors significantly associated with falls at home were living in an urban area (odds ratio [OR]: 11.46; 95% confidence interval [CI]: 1.48; 88.98; p = 0.020), an Instrumental Activities of Daily Living Scale score > 4 (OR: 34.04; 95% CI: 1.59; 727.86; p = 0.024), and discontinuation of ATM device use (OR: 17.41; 95% CI: 2.59; 117.02; p = 0.003). Conclusion Discontinuation of ATM device use was associated with an increased risk for serious falls.

While the needs of dependent seniors needs are important in Nursing Home (NH), the medical resources are low in residences located in a medical desert. Access to care in these NH is very unequal and can result in a loss of chance for residents who are immediately transferred to emergency with a risk of iatrogeny or inappropriate hospitalization. Objective: We aim to find new models of care organization to optimize management of elderly in NH thanks to new technologies Artificial Intelligence (AI) associated to telemedicine. Method: Intel@Care is a 3 steps program planned over 5 years : 1- faisability study : Intel@Med-Faisa (2019-2020); 2- proof of concept : Intel@Med-POC with socio-environmental study (2020-2022); 3- efficiency study with medico-economic assessment Intel@Med-Efficiency (2022-2024). Results: Intel@Med-Faisa : 18 residents were included in 2 NHs; 20 additional diagnoses were created to adapt the Artificial Intelligence algorithm to the clinical characteristics of the elderly. Intel@Med-POC: (study in progress): 306 residents to be included to validate development of evidence-based decision tool to help physician to make decisions to manage healthcare interventions, verify the acceptability of the new care organization. A medico-economic study is also conducted. Perspectives : This program aims to secure the use of this tool by professionals and to identify the obstacles and levers about use, perception and find an economic model for the deployment on the territory. In parallel, a clinical research will be conducted in order to adapt this organization to elderly people living at home in medical desert areas., Le vieillissement de la population s���accompagne de la probl��matique des pathologies chroniques et de la d��pendance parfois lourde, imposant une admission en Etablissement d���H��bergement pour Personnes ��g��es D��pendantes (EHPAD). En raison d���une d��mographie m��dicale h��t��rog��ne, l���acc��s aux soins dans ces ��tablissements est tr��s in��galitaire au niveau national et surtout en Limousin et en Dordogne. L���arriv��e de l���Intelligence Artificielle (IA) peut ��tre une vraie opportunit�� pour trouver de nouveaux mod��les d���organisation de soins pouvant pallier �� la d��sertification m��dicale. M��thodologie : Nous avons construit un projet de validation en trois ��tapes. 1- La faisabilit�� (2019-2020) : r��alis��e localement dans deux EHPAD ; 2- La preuve de concept (2020-2022): r��alis��e dans 7 EHPAD localis��s dans des d��serts m��dicaux de la Nouvelle Aquitaine avec une ��tude socio-environnementale adoss��e; 3- L���efficience avec une approche m��dico-��conomique et le d��ploiement (2022-2024) : projet qui sera men�� sur le territoire europ��en. R��sultats : Intel@Med-Faisa : 18 r��sidents inclus, cr��ation de 20 diagnostics suppl��mentaires pour adapter l���algorithme d���IA aux caract��ristiques cliniques des personnes ��g��es. Intel@Med-POC : Etude en cours, 306 r��sidents �� inclure pour valider l���outil d���aide �� la d��cision et v��rifier l���acceptabilit�� de la nouvelle organisation de soins. Retomb��es : Le projet Intel@Care est une exp��rimentation utilisant l���IA, pour r��pondre �� la probl��matique d���acc��s aux soins des personnes ��g��es vivant en zone de d��sertification m��dicale sur le territoire en impliquant tous les acteurs du secteur m��dico-social. Le programme Intel@Care inclus ��galement une exp��rimentation aupr��s des personnes ��g��es vivant �� domicile dans les d��serts m��dicaux afin d���adapter une organisation sp��cifique., Trayectorias Humanas Trascontinentales, N�� 12 | 2021

Background Independence is related to the aging process. Loss of independence is defined as the inability to make decisions and participate in activities of daily living (ADLs). Independence is related to physical, psychological, biological, and socioeconomic factors. An enhanced understanding of older people’s independence trajectories and associated risk factors would enable the develop early intervention strategies. Methods Independence trajectory analysis was performed on patients identified in the Unité de Prévention de Suivi et d’Analyse du Vieillissement (UPSAV) database. UPSAV cohort is a prospective observational study. Participants were 221 community-dwelling persons aged ≥75 years followed for 24 months between July 2011–November 2013 and benefits from a prevention strategy. Data were collected prospectively using a questionnaire. Independence was assessed using the “Functional Autonomy Measurement System (Système de Mesure de l’Autonomie Fonctionnelle (SMAF))”. Group-based trajectory modeling (GBTM) was performed to identify independence trajectories, and the results were compared with those of k-means and hierarchical ascending classifications. A multinomial logistic regression was performed to identify predictive factors of the independence trajectory. Results Three distinct trajectories of independence were identified including a “Stable functional autonomy (SFA) trajectory” (53% of patients), a “Stable then decline functional autonomy decline (SDFA) trajectory” (33% of patients) and a “Constantly functional autonomy decline (CFAD) trajectory” (14% of patients). Not being a member of an association, and previous fall were significantly associated of a SDFA trajectory (P P Conclusions Community-living older persons exhibit distinct independence trajectories and the predictive factors. The evidence from this study suggests that the prevention and screening for the loss of independence of the older adults should be anticipated to maintaining autonomy.