Theodora Oikonomidi1, Philippe Ravaud2, Arthur James3, Emmanuel Cosson4, Victor Montori5, Viet-Thi Tran3. 1. Université de Paris, CRESS, INSERM, INRA, Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. Electronic address: theodora.oikonomidi@inserm.fr. 2. Université de Paris, CRESS, INSERM, INRA, Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. 3. Université de Paris, CRESS, INSERM, INRA, Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. 4. Sorbonne Paris Nord, Sorbonne Paris Cité, AP-HP, Avicenne Hospital, Department of Endocrinology, CRNH-IdF, CINFO, Bobigny, France; Sorbonne Paris Nord, CRESS, UMR 1153 INSERM/U1125 INRA/CNAM, Unité de Recherche Epidémiologique Nutritionnelle, Bobigny, France. 5. Department of Health and Human Services, Center for Evidence and Practice Improvement of the Agency for Healthcare Research and Quality, Rockville, MD; Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN.
Abstract
OBJECTIVE: To assess the relationship between remote digital monitoring (RDM) modalities for diabetes and intrusiveness in patients' lives. PATIENTS AND METHODS: Online vignette-based survey (February 1 through July 1, 2019). Adults with diabetes (type 1, 2, or subtypes such as latent autoimmune diabetes of adulthood) assessed three randomly selected vignettes among 36 that combined different modalities for monitoring tools (three options: glucose- and physical activity [PA]-monitoring only, or glucose- and PA-monitoring with occasional or regular food monitoring), duration/feedback loops (six options: monitoring for a week before all vs before specific consultations with feedback given in consultation, vs monitoring permanently, with real-time feedback by one's physician vs by anoter caregiver, vs monitoring permanently, with real-time, artificial intelligence-generated treatment feedback vs treatment and lifestyle feedback), and data handling (two options: by the public vs private sector). We compared intrusiveness (assessed on a 5-point scale) across vignettes and used linear mixed models to identify intrusiveness determinants. We collected qualitative data to identify aspects that drove participants' perception of intrusiveness. RESULTS: Overall, 1010 participants from 30 countries provided 2860 vignette-assessments (52% were type 1 diabetes). The monitoring modalities associated with increased intrusiveness were food monitoring compared with glucose- and PA-monitoring alone (β=0.34; 95% CI, 0.26 to 0.42; P<.001) and permanent monitoring with real-time physician-generated feedback compared with monitoring for a week with feedback in consultation (β=0.25; 95% CI, 0.16 to 0.34, P<.001). Public-sector data handling was associated with decreased intrusiveness as compared with private-sector (β=-0.15; 95% CI, -0.22 to -0.09; P<.001). Four drivers of intrusiveness emerged from the qualitative analysis: practical/psychosocial burden (eg, RDM attracting attention in public), control, data safety/misuse, and dehumanization of care. CONCLUSION: RDM is intrusive when it includes food monitoring, real-time human feedback, and private-sector data handling.
OBJECTIVE: To assess the relationship between remote digital monitoring (RDM) modalities for diabetes and intrusiveness in patients' lives. PATIENTS AND METHODS: Online vignette-based survey (February 1 through July 1, 2019). Adults with diabetes (type 1, 2, or subtypes such as latent autoimmune diabetes of adulthood) assessed three randomly selected vignettes among 36 that combined different modalities for monitoring tools (three options: glucose- and physical activity [PA]-monitoring only, or glucose- and PA-monitoring with occasional or regular food monitoring), duration/feedback loops (six options: monitoring for a week before all vs before specific consultations with feedback given in consultation, vs monitoring permanently, with real-time feedback by one's physician vs by anoter caregiver, vs monitoring permanently, with real-time, artificial intelligence-generated treatment feedback vs treatment and lifestyle feedback), and data handling (two options: by the public vs private sector). We compared intrusiveness (assessed on a 5-point scale) across vignettes and used linear mixed models to identify intrusiveness determinants. We collected qualitative data to identify aspects that drove participants' perception of intrusiveness. RESULTS: Overall, 1010 participants from 30 countries provided 2860 vignette-assessments (52% were type 1 diabetes). The monitoring modalities associated with increased intrusiveness were food monitoring compared with glucose- and PA-monitoring alone (β=0.34; 95% CI, 0.26 to 0.42; P<.001) and permanent monitoring with real-time physician-generated feedback compared with monitoring for a week with feedback in consultation (β=0.25; 95% CI, 0.16 to 0.34, P<.001). Public-sector data handling was associated with decreased intrusiveness as compared with private-sector (β=-0.15; 95% CI, -0.22 to -0.09; P<.001). Four drivers of intrusiveness emerged from the qualitative analysis: practical/psychosocial burden (eg, RDM attracting attention in public), control, data safety/misuse, and dehumanization of care. CONCLUSION: RDM is intrusive when it includes food monitoring, real-time human feedback, and private-sector data handling.
Authors: Barbara Kimbell; David Rankin; Ruth I Hart; Janet M Allen; Charlotte K Boughton; Fiona Campbell; Elke Fröhlich-Reiterer; Sabine E Hofer; Thomas M Kapellen; Birgit Rami-Merhar; Ulrike Schierloh; Ajay Thankamony; Julia Ware; Roman Hovorka; Julia Lawton Journal: Pediatr Diabetes Date: 2022-05-25 Impact factor: 3.409