Theodora Oikonomidi1,2, Philippe Ravaud1,2,3, Emmanuel Cosson4,5, Victor Montori6,7, Viet Thi Tran1,2. 1. Université de Paris, Centre of Research in Epidemiology and Statistics, French National Institute of Health and Medical Research, National Institute for Agricultural Research, Paris, France. 2. Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 3. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York. 4. Sorbonne Paris Nord, Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Avicenne Hospital, Department of Endocrinology, Research Centre in Human Nutrition-Ile de France, North Ile-de-France Integrated Obesity Centre, Bobigny, France. 5. Sorbonne Paris Nord, Centre of Research in Epidemiology and Statistics, Research Unit 1153, French National Institute of Health and Medical Research, U1125 National Institute for Agricultural Research, National Conservatory of Arts and Crafts, Bobigny, France. 6. Department of Health and Human Services, Center for Evidence and Practice Improvement of the Agency for Healthcare Research and Quality, Rockville, Maryland. 7. Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota.
Abstract
Importance: Patients will decide whether to adopt remote digital monitoring (RDM) for diabetes by weighing its health benefits against the inconvenience it may cause. Objective: To identify the minimum effectiveness patients report they require to adopt 36 different RDM scenarios. Design, Setting, and Participants: This survey study was conducted among adults with type 1 or type 2 diabetes living in 30 countries from February to July 2019. Exposures: Survey participants assessed 3 randomly selected scenarios from a total of 36. Scenarios described different combinations of digital monitoring tools (glucose, physical activity, food monitoring), duration and feedback loops (feedback in consultation vs real-time telefeedback by a health care professional or by artificial intelligence), and data handling modalities (by a public vs private company), reflecting different degrees of RDM intrusiveness in patients' personal lives. Main Outcomes and Measures: Participants assessed the minimum effectiveness for 2 diabetes-related outcomes (reducing hypoglycemic episodes and preventing ophthalmologic complications) for which they would adopt each RDM (from much less effective to much more effective than their current monitoring). Results: Of 1577 individuals who consented to participate, 1010 (64%; 572 [57%] women, median [interquartile range] age, 51 [37-63] years, 524 [52%] with type 1 diabetes) assessed at least 1 vignette. Overall, 2860 vignette assessments were collected. In 1025 vignette assessments (36%), participants would adopt RDM only if it was much more effective at reducing hypoglycemic episodes compared with their current monitoring; in 1835 assessments (65%), participants would adopt RDM if was just as or somewhat more effective. The main factors associated with required effectiveness were food monitoring (β = 0.32; SE, 0.12; P = .009), real-time telefeedback by a health care professional (β = 0.49; SE, 0.15; P = .001), and perceived intrusiveness (β = 0.36; SE, 0.06; P < .001). Minimum required effectiveness varied among participants; 34 of 36 RDM scenarios (94%) were simultaneously required to be just as or less effective by at least 25% of participants and much more effective by at least 25% of participants. Results were similar for participant assessments of scenarios regarding the prevention of ophthalmologic complications. Conclusions and Relevance: The findings of this study suggest that patients require greater health benefits to adopt more intrusive RDM modalities, food monitoring, and real-time feedback by a health care professional. Patient monitoring devices should be designed to be minimally intrusive. The variability in patients' requirements points to a need for shared decision-making.
Importance: Patients will decide whether to adopt remote digital monitoring (RDM) for diabetes by weighing its health benefits against the inconvenience it may cause. Objective: To identify the minimum effectiveness patients report they require to adopt 36 different RDM scenarios. Design, Setting, and Participants: This survey study was conducted among adults with type 1 or type 2 diabetes living in 30 countries from February to July 2019. Exposures: Survey participants assessed 3 randomly selected scenarios from a total of 36. Scenarios described different combinations of digital monitoring tools (glucose, physical activity, food monitoring), duration and feedback loops (feedback in consultation vs real-time telefeedback by a health care professional or by artificial intelligence), and data handling modalities (by a public vs private company), reflecting different degrees of RDM intrusiveness in patients' personal lives. Main Outcomes and Measures: Participants assessed the minimum effectiveness for 2 diabetes-related outcomes (reducing hypoglycemic episodes and preventing ophthalmologic complications) for which they would adopt each RDM (from much less effective to much more effective than their current monitoring). Results: Of 1577 individuals who consented to participate, 1010 (64%; 572 [57%] women, median [interquartile range] age, 51 [37-63] years, 524 [52%] with type 1 diabetes) assessed at least 1 vignette. Overall, 2860 vignette assessments were collected. In 1025 vignette assessments (36%), participants would adopt RDM only if it was much more effective at reducing hypoglycemic episodes compared with their current monitoring; in 1835 assessments (65%), participants would adopt RDM if was just as or somewhat more effective. The main factors associated with required effectiveness were food monitoring (β = 0.32; SE, 0.12; P = .009), real-time telefeedback by a health care professional (β = 0.49; SE, 0.15; P = .001), and perceived intrusiveness (β = 0.36; SE, 0.06; P < .001). Minimum required effectiveness varied among participants; 34 of 36 RDM scenarios (94%) were simultaneously required to be just as or less effective by at least 25% of participants and much more effective by at least 25% of participants. Results were similar for participant assessments of scenarios regarding the prevention of ophthalmologic complications. Conclusions and Relevance: The findings of this study suggest that patients require greater health benefits to adopt more intrusive RDM modalities, food monitoring, and real-time feedback by a health care professional. Patient monitoring devices should be designed to be minimally intrusive. The variability in patients' requirements points to a need for shared decision-making.
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