Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

Abstract

Background: Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data.

Methods: Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403.

Findings: Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred.

Interpretation: AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia.

Funding: JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Figure 1:. iPancreas system

The system comprises a Dexcom G6 CGM, an Insulet Omnipod along with a relay PDM, a Polar M-600 smartwatch with heart rate and accelerometer sensors, and a Samsung smartphone running the exAPD or exMPC control algorithms. AWS=Amazon Web Services. exAPD=exercise-aware adaptive proportional derivative. exMPC=exercise-aware model predictive control. PDM=personal diabetes manager.

Figure 2:. Trial profile

exAPD=exercise-aware adaptive proportional derivative. exMPC=exercise-aware model predictive control.

Figure 3:. CGM data, insulin, and METs during the primary in-clinic session (0700–1900 h)

Middle plots show how the exMPC tended to reduce insulin earlier in the day when activities of daily living were taking place, whereas, the exAPD only shut off insulin when the structured exercise took place later in the day. CGM=continuous glucose monitor. exAPD=exercise-aware adaptive proportional derivative. exMPC=exercise-aware model predictive control. MET=metabolic equivalent to task.

Figure 4:. CGM data, insulin, and METs during the 2 h after the start of the primary in-clinic session structured exercise

The exercise-aware model predictive control (exMPC) algorithm data are shown on the right panel and the exercise-aware adaptive proportional derivative (exAPD) algorithm data are shown on the left panel. Notice that the exAPD shuts off insulin completely once structured exercise is detected while the exMPC will only shut off insulin completely if necessary. CGM=continuous glucose monitor. MET=metabolic equivalent to task.