Using technology to support management of type 1 diabetes during exercise

Background Uptake of exercise in people with type 1 diabetes is low despite significant health benefits. Fear of hypoglycaemia is the main barrier to exercise. Continuous glucose monitoring (CGM) with predictive alarms warning of impending hypoglycaemia may improve self-management of diabetes around exercise. Aim To assess whether an urgent low soon (ULS) alert in a CGM device affects the percentage time spent in hypoglycaemia during and after exercise, and which other variables are associated with hypoglycaemia during and after exercise. Methods After 10 days of blinded run-in (baseline), CGM was unblinded and participants randomised 1:1 to have the "urgent low soon" (ULS) alert switched 'on' or 'off' for 40 days. Participants then switched alerts 'off' or 'on' respectively for a further 40 days. Physical activity, carbohydrate and insulin doses were recorded. Results Twenty-four participants were randomised. In the 12 hours after exercise, there was no between-group difference in change from baseline in %time in hypoglycaemia in the group with ULS alert on compared to ULS off. There was no difference in change from baseline in %time <3.0 and <3.9mmol/L with the ULS on or off during the 24 hours after exercise, but with ULS alert 'on' time spent below 2.8mmol/L compared to baseline was significantly (p=0.04) lower than with ULS 'off'. Throughout the 40 days, there was a significant between-group difference in change in %time <3.0mmol/L and 2.8mmol/L in the group with the ULS alert on compared to the group with ULS alert off, favouring alert on.

Institutions

Publication Details

Journal
Imperial College Research Computing Service Data Repository
Published
2025-01-15
DOI
https://doi.org/10.25560/118762
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using technology to support management of type 1 diabetes during exercise

Imperial College Research Computing Service Data Repository
Diabetes Management and Research
article

Using technology to support management of type 1 diabetes during exercise

article en

Abstract

Background Uptake of exercise in people with type 1 diabetes is low despite significant health benefits. Fear of hypoglycaemia is the main barrier to exercise. Continuous glucose monitoring (CGM) with predictive alarms warning of impending hypoglycaemia may improve self-management of diabetes around exercise. Aim To assess whether an urgent low soon (ULS) alert in a CGM device affects the percentage time spent in hypoglycaemia during and after exercise, and which other variables are associated with hypoglycaemia during and after exercise. Methods After 10 days of blinded run-in (baseline), CGM was unblinded and participants randomised 1:1 to have the "urgent low soon" (ULS) alert switched 'on' or 'off' for 40 days. Participants then switched alerts 'off' or 'on' respectively for a further 40 days. Physical activity, carbohydrate and insulin doses were recorded. Results Twenty-four participants were randomised. In the 12 hours after exercise, there was no between-group difference in change from baseline in %time in hypoglycaemia in the group with ULS alert on compared to ULS off. There was no difference in change from baseline in %time <3.0 and <3.9mmol/L with the ULS on or off during the 24 hours after exercise, but with ULS alert 'on' time spent below 2.8mmol/L compared to baseline was significantly (p=0.04) lower than with ULS 'off'. Throughout the 40 days, there was a significant between-group difference in change in %time <3.0mmol/L and 2.8mmol/L in the group with the ULS alert on compared to the group with ULS alert off, favouring alert on.

Imperial College Research Computing Service Data Repository
Imperial College London (GB)
Good health and well-being
Openalex Percentile: Top 44%
Diabetes Management and Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.