An International, Mixed-Methods Study of the Perceived Intrusiveness of Remote Digital Diabetes Monitoring

Mayo Clin Proc. 2021 May;96(5):1236-1247. doi: 10.1016/j.mayocp.2020.07.040. Epub 2021 Jan 21.


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.

Publication types

  • Comparative Study

MeSH terms

  • Actigraphy / methods*
  • Adult
  • Artificial Intelligence
  • Biomarkers / blood
  • Blood Glucose / metabolism
  • Blood Glucose Self-Monitoring / methods*
  • Combined Modality Therapy
  • Diabetes Mellitus, Type 1 / blood
  • Diabetes Mellitus, Type 1 / diagnosis*
  • Diabetes Mellitus, Type 1 / psychology
  • Diabetes Mellitus, Type 1 / therapy
  • Diabetes Mellitus, Type 2 / blood
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Diabetes Mellitus, Type 2 / psychology
  • Diabetes Mellitus, Type 2 / therapy
  • Diet Records*
  • Diet Therapy
  • Exercise
  • Female
  • Health Care Surveys
  • Healthy Lifestyle
  • Humans
  • Linear Models
  • Male
  • Middle Aged
  • Patient Preference / psychology*
  • Patient Preference / statistics & numerical data
  • Telemedicine / methods*


  • Biomarkers
  • Blood Glucose