Dejun Su1, Junmin Zhou2, Megan S Kelley3, Tzeyu L Michaud1, Mohammad Siahpush1, Jungyoon Kim1, Fernando Wilson1, Jim P Stimpson4, José A Pagán5. 1. College of Public Health, University of Nebraska Medical Center, Omaha, NE, United States. 2. College of Public Health, University of Nebraska Medical Center, Omaha, NE, United States. Electronic address: junmin.zhou@unmc.edu. 3. College of Education and Human Services, University of Nebraska-Lincoln, Lincoln, NE, United States. 4. School of Public Health, City University of New York, New York, NY, United States. 5. New York Academy of Medicine, New York, NY, United States; Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Abstract
AIMS: To assess the overall effect of telemedicine on diabetes management and to identify features of telemedicine interventions that are associated with better diabetes management outcomes. METHODS: Hedges's g was estimated as the summary measure of mean difference in HbA1c between patients with diabetes who went through telemedicine care and those who went through conventional, non-telemedicine care using a random-effects model. Q statistics were calculated to assess if the effect of telemedicine on diabetes management differs by types of diabetes, age groups of patients, duration of intervention, and primary telemedicine approaches used. RESULTS: The analysis included 55 randomized controlled trials with a total of 9258 patients with diabetes, out of which 4607 were randomized to telemedicine groups and 4651 to conventional, non-telemedicine care groups. The results favored telemedicine over conventional care (Hedges's g=-0.48, p<0.001) in diabetes management. The beneficial effect of telemedicine were more pronounced among patients with type 2 diabetes (Hedges's g=-0.63, p<0.001) than among those with type 1 diabetes (Hedges's g=-0.27, p=0.027) (Q=4.25, p=0.04). CONCLUSIONS: Compared to conventional care, telemedicine is more effective in improving treatment outcomes for diabetes patients, especially for those with type 2 diabetes.
AIMS: To assess the overall effect of telemedicine on diabetes management and to identify features of telemedicine interventions that are associated with better diabetes management outcomes. METHODS: Hedges's g was estimated as the summary measure of mean difference in HbA1c between patients with diabetes who went through telemedicine care and those who went through conventional, non-telemedicine care using a random-effects model. Q statistics were calculated to assess if the effect of telemedicine on diabetes management differs by types of diabetes, age groups of patients, duration of intervention, and primary telemedicine approaches used. RESULTS: The analysis included 55 randomized controlled trials with a total of 9258 patients with diabetes, out of which 4607 were randomized to telemedicine groups and 4651 to conventional, non-telemedicine care groups. The results favored telemedicine over conventional care (Hedges's g=-0.48, p<0.001) in diabetes management. The beneficial effect of telemedicine were more pronounced among patients with type 2 diabetes (Hedges's g=-0.63, p<0.001) than among those with type 1 diabetes (Hedges's g=-0.27, p=0.027) (Q=4.25, p=0.04). CONCLUSIONS: Compared to conventional care, telemedicine is more effective in improving treatment outcomes for diabetespatients, especially for those with type 2 diabetes.
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