Effectiveness of a home-based telesurveillance program in reducing hospital readmissions in older patients with chronic disease: The eCOBAHLT randomized controlled trial

Abstract

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.

Digital health intervention effect on older adults with chronic diseases living alone: Systematic literature review and meta-analysis (Preprint)

“…Regarding medical factors, Tchalla (2023) implemented a remote monitoring program for 12 months for older patients with chronic diseases. The rehospitalization rate of the intervention group was 40.4% (108 people), and that of the control group was 48.7% (130 people), which showed a significant difference, suggesting the effectiveness of remote monitoring [26].…”

“…Regarding the general characteristics of the literature, they are randomized controlled trial studies; nine articles were found that were conducted in a single country (one each in France, Canada, the Netherlands, Italy, China, and Hong Kong, and three in the United States) [25][26][27][28][29][30][31][32][33], while two were multinational studies (one in the Netherlands, Finland, and France, and one in Spain, the United Kingdom, Slovenia, Estonia, and Sweden) [34,35]. The mean age of participants was 72.…”

“…The most frequent chronic diseases were hypertension, diabetes, and cardiovascular disease. Among the 11 articles, six were on self-management factors [25,27,29,30,33,35], two were on quality of life factors [32,34], and three were on medical factors [26,28,31 For the 11 selected articles, we presented a summary and graphs of the assessment results (Figure 2). Among the selected articles, ten had at least one component with a high risk of bias (90.9%).…”