Evaluation of Patient Willingness to Adopt Remote Digital Monitoring for Diabetes Management.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
04 01 2021
Historique:
entrez: 13 1 2021
pubmed: 14 1 2021
medline: 16 3 2021
Statut: epublish

Résumé

Patients will decide whether to adopt remote digital monitoring (RDM) for diabetes by weighing its health benefits against the inconvenience it may cause. To identify the minimum effectiveness patients report they require to adopt 36 different RDM scenarios. This survey study was conducted among adults with type 1 or type 2 diabetes living in 30 countries from February to July 2019. 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. 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). 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. 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.

Identifiants

pubmed: 33439263
pii: 2774901
doi: 10.1001/jamanetworkopen.2020.33115
pmc: PMC7807289
doi:

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2033115

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Auteurs

Theodora Oikonomidi (T)

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.
Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.

Philippe Ravaud (P)

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.
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, New York.

Emmanuel Cosson (E)

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.
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.

Victor Montori (V)

Department of Health and Human Services, Center for Evidence and Practice Improvement of the Agency for Healthcare Research and Quality, Rockville, Maryland.
Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota.

Viet Thi Tran (VT)

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.
Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.

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