Use of Telecommunication and Diabetes-Related Technologies in Older Adults With Type 1 Diabetes During a Time of Sudden Isolation: Mixed Methods Study.

COVID-19 continuous glucose monitoring diabetes diabetes technology glucose monitoring health technology older adults older population telehealth type 1 diabetes

Journal

JMIR diabetes
ISSN: 2371-4379
Titre abrégé: JMIR Diabetes
Pays: Canada
ID NLM: 101719410

Informations de publication

Date de publication:
18 Nov 2022
Historique:
received: 19 04 2022
accepted: 15 10 2022
revised: 31 08 2022
pubmed: 19 10 2022
medline: 19 10 2022
entrez: 18 10 2022
Statut: epublish

Résumé

The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections. In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D). Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown. We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown. These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

Sections du résumé

BACKGROUND BACKGROUND
The COVID-19 lockdown imposed a sudden change in lifestyle with self-isolation and a rapid shift to the use of technology to maintain clinical care and social connections.
OBJECTIVE OBJECTIVE
In this mixed methods study, we explored the impact of isolation during the lockdown on the use of technology in older adults with type 1 diabetes (T1D).
METHODS METHODS
Older adults (aged ≥65 years) with T1D using continuous glucose monitoring (CGM) participated in semistructured interviews during the COVID-19 lockdown. A multidisciplinary team coded the interviews. In addition, CGM metrics from a subgroup of participants were collected before and during the lockdown.
RESULTS RESULTS
We evaluated 34 participants (mean age 71, SD 5 years). Three themes related to technology use emerged from the thematic analysis regarding the impact of isolation on (1) insulin pump and CGM use to manage diabetes, including timely access to supplies, and changing Medicare eligibility regulations; (2) technology use for social interaction; and (3) telehealth use to maintain medical care. The CGM data from a subgroup (19/34, 56%; mean age 74, SD 5 years) showed an increase in time in range (mean 57%, SD 17% vs mean 63%, SD 15%; P=.001), a decrease in hyperglycemia (>180 mg/dL; mean 41%, SD 19% vs mean 35%, SD 17%; P<.001), and no change in hypoglycemia (<70 mg/dL; median 0.7%, IQR 0%-2% vs median 1.1%, IQR 0%-4%; P=.40) during the lockdown compared to before the lockdown.
CONCLUSIONS CONCLUSIONS
These findings show that our cohort of older adults successfully used technology during isolation. Participants provided the positive and negative perceptions of technology use. Clinicians can benefit from our findings by identifying barriers to technology use during times of isolation and developing strategies to overcome these barriers.

Identifiants

pubmed: 36256804
pii: v7i4e38869
doi: 10.2196/38869
pmc: PMC9678329
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e38869

Subventions

Organisme : NIDDK NIH HHS
ID : DP3 DK112214
Pays : United States
Organisme : NIDDK NIH HHS
ID : P30 DK036836
Pays : United States

Informations de copyright

©Elena Toschi, Christine Slyne, Katie Weinger, Sarah Sy, Kayla Sifre, Amy Michals, DaiQuann Davis, Rachel Dewar, Astrid Atakov-Castillo, Saira Haque, Stirling Cummings, Stephen Brown, Medha Munshi. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 18.11.2022.

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Auteurs

Elena Toschi (E)

Joslin Diabetes Center, Boston, MA, United States.
Beth Israel Deaconess Medical Center, Boston, MA, United States.
Harvard Medical School, Boston, MA, United States.

Christine Slyne (C)

Joslin Diabetes Center, Boston, MA, United States.

Katie Weinger (K)

Joslin Diabetes Center, Boston, MA, United States.

Sarah Sy (S)

Joslin Diabetes Center, Boston, MA, United States.

Kayla Sifre (K)

Joslin Diabetes Center, Boston, MA, United States.

Amy Michals (A)

Joslin Diabetes Center, Boston, MA, United States.

DaiQuann Davis (D)

Joslin Diabetes Center, Boston, MA, United States.

Rachel Dewar (R)

Joslin Diabetes Center, Boston, MA, United States.

Astrid Atakov-Castillo (A)

Joslin Diabetes Center, Boston, MA, United States.

Saira Haque (S)

Research Triangle Institute International, Research Triangle Park, NC, United States.

Stirling Cummings (S)

Research Triangle Institute International, Research Triangle Park, NC, United States.

Stephen Brown (S)

Research Triangle Institute International, Research Triangle Park, NC, United States.

Medha Munshi (M)

Joslin Diabetes Center, Boston, MA, United States.
Beth Israel Deaconess Medical Center, Boston, MA, United States.
Harvard Medical School, Boston, MA, United States.

Classifications MeSH