New technologies for managing type 1 diabetes (T1D) offer health care providers (HCPs) a means to minimize face-to-face visits and, consequently, COVID-19 exposure and missed time from work and school. 1 To align with COVID-19 pandemic restrictions, a remote training program was implemented in March 2020 at the Federico II University Hospital diabetes center, to onboard T1D patients to a hybrid closed-loop system (MiniMed 670G). The training program was performed by a Medtronic Training Therapy Specialist along with a diabetologist and a registered dietitian conducting a remote nutritional educational course. 2 Technical training was delivered in three steps: (1) Pump and Continuous Glucose Monitoring (CGM) components of the system, (2) Sensor Augmented Pump (SAP) function (Manual Mode), and (3) hybrid closed-loop Auto-mode function. Two-to-four remote individual training sessions (median duration 60 minutes, range 50-90 minutes) were completed using the Zoom video-conferencing platform. Follow-up remote visits with the diabetes team were scheduled one week, one month, and every second month after Auto-mode initiation.
In the remote trained patients, we evaluated the changes in CGM metrics 3 between before training and one month after initiation of Auto-mode. These changes were compared with those observed in patients trained face-to-face. Participants signed an informed consent for the treatment of their data.
One-month follow-up after Auto-mode initiation was available for 16 patients attending remote training and 28 trained face-to-face (44 and 46% men, age 42 ± 14 and 39 ± 11 years, duration of diabetes 25 ± 12 and 23 ± 9 years, respectively). Time in range (TIR) significantly improved by shifting to Auto-mode to a mean value >70% in both groups. Accordingly, statistically significant decreases in Glucose Management Indicator (GMI) and time above range (TAR > 180 mg/dL) were observed in both groups (Table 1). The coefficient of variation significantly improved only in the remote training group. The changes in outcomes were not significantly different between groups (Table 1).
Table 1.
CGM Metrics Captured Over a Two-Week Period Before Onboarding to 670G and After One Month in Auto-mode in the Remote and Face-to-Face Training Groups.
| Remote training (n = 16) |
Face-to-face training (n = 28) |
P between groups | |||||
|---|---|---|---|---|---|---|---|
| Before onboarding to 670G | After one month in Auto-mode | P | Before onboarding to 670G | After one-month in Auto-mode | P | ||
| Auto-mode, % | — | 90 ± 9 | — | — | 92 ± 6 | — | .364 |
| GMI, % | 7.6 ± 0.6 | 7.0 ± 0.3 | <.001 | 7.5 ± 0.4 | 6.9 ± 0.3 | <.001 | .806 |
| CV, % | 36.0 ± 5.2 | 33.6 ± 4.6 | .028 | 34.9 ± 3.9 | 34.0 ± 4.1 | .323 | .224 |
| Time spent at glucose ranges | |||||||
| 70-180 mg/dL, % | 53.9 ± 11.7 | 71.1 ± 9.0 | <.001 | 56.4 ± 10.0 | 72.6 ± 8.0 | <.001 | .748 |
| <70 mg/dL, % | 1.7 ± 2.1 | 1.5 ± 1.3 | .705 | 1.3 ± 1.3 | 2.0 ± 1.7 | .007 | .068 |
| <54 mg/dL, % | 1.0 ± 2.0 | 0.4 ± 0.6 | .199 | 0.4 ± 0.7 | 0.3 ± 0.5 | .573 | .156 |
| >180 mg/dL, % | 29.1 ± 5.5 | 20.9 ± 6.1 | <.001 | 29.7 ± 8.0 | 19.7 ± 5.5 | <.001 | .453 |
| >250 mg/dL, % | 14.3 ± 9.5 | 5.3 ± 4.0 | <.001 | 12.3 ± 7.1 | 5.4 ± 3.3 | <.001 | .370 |
Data are mean ± SD.
Abbreviations: CGM, continuous glucose monitoring; CV, coefficient of variation; GMI, glucose management indicator; SD, standard deviation.
This study shows that remote training for closed-loop onboarding was as effective as face-to-face training in achieving optimal blood glucose control, with a similar increase in TIR and a consistent improvement in GMI and TAR. Similar good outcomes of remote training were reported by Vigersky et al, 4 with a 2.1% reduction in TIR compared with face-to-face training. Gómez et al 5 reported that virtual training for MiniMed 670G onboarding led to relevant improvements of CGM metrics. The lack of a control group, the high TIR already in manual mode (77%), and the very short timeframe of the study for a large number of patients implying intensive organizational features prevent directly translating their findings to ordinary clinical settings.
In conclusion, the positive clinical outcomes achieved with remote training under COVID-19 restrictions represent evidence of feasibility and effectiveness of virtual health care for advanced technologies. 6 In the future, remote training could be employed on a stand-alone basis or integrated in a hybrid approach including face-to-face training. The choice will depend on the needs of the patient, his attitudes and wishes, his digital skills, and the potentiality and organization of the health care facility, also in relation to access to reimbursement.
Footnotes
Abbreviations: CGM, continuous glucose monitoring; CSII, continuous subcutaneous insulin infusion; GMI, Glucose Management Indicator; HCP, health care provider; SAP, sensor augmented pump; TAR, time above range; TIR, time in range.
Author Contributions: LB made a substantial contribution to the concept and design of the work and to the analysis and interpretation of data and drafted the article. RDA made a substantial contribution to the acquisition, analysis, and interpretation of data and drafted the article. CG and IC made a substantial contribution to the acquisition, analysis, and interpretation of data. GA made a substantial contribution to the concept and design of the work and to the interpretation of data, and critically revised the article for important intellectual content. Each author approved the version to be published.
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Giovanni Annuzzi 
https://orcid.org/0000-0002-9324-6047
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