# Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2021 · $700,025

## Abstract

PROJECT SUMMARY
Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas
Closed-loop control (CLC) is now transitioning to the clinical practice and one of the most advanced systems to
date–Control-IQ–uses an algorithm designed and tested by the previous research cycle of this project. With the
first generation of our CLC system now translated to the clinic, our objective is to design and test next-generation
CLC solutions, learning from the experience and utilizing the large database accumulated to date.
 Thus, we focus this project on the new concept of Adaptive Biobehavioral Control (ABC) – a first-in-class
 system that will use human-machine co-adaptation of CLC, recognizing both the necessity for the control
 algorithm to adapt to changes in human physiology, and the necessity for the person to adapt to CLC action.
To achieve its objectives, the ABC system will have two new components added to the current state-of-the art
Control-IQ: a Behavioral Adaptation Module (BAM) – a behavioral intervention deployed in a mobile app to assist
a person's adaptation to CLC by information and risk assessment primarily regarding meals and physical activity,
and a Physiologic Adaptation Module (PAM) – an automated procedure tracking risk status and changes in the
user's metabolic profile and acting in real time to adapt the CLC algorithm's insulin control parameters.
Using these technologies, we now propose to compare, in a randomized cross-over trial enrolling 90 participants
with type 1 diabetes, the current CLC (Control-IQ) to three new treatment modalities: ABC and its components
BAM and PAM. To do so, study participants will be randomized to two groups following two different sequences
of treatment modalities: CLCCLC+BAMCLC+PAMABC and ABCCLC+PAMCLC+BAMCLC. Each
treatment modality will continue for 2 months and the treatments will be separated by 2-week washout periods.
This design was used successfully in our previous study and enables four crossover comparisons: CLC vs. ABC
(primary) and CLC+BAM vs. CLC; CLC+PAM vs. ABC; CLC+BAM vs. CLC+PAM (secondary). We expect that:
(1) ABC will be superior to the current CLC in terms of: improved time in the target range 70-180mg/dl measured
 by continuous glucose monitoring (CGM); reduced risk for hypoglycemia, and better technology acceptance;
(2) Behavioral adaptation (CLC+BAM) will be superior to CLC in terms of improved CGM-measured time in the
 target range during the day and reduced CGM-measured incidence of hypoglycemia during/after exercise;
(3) Physiologic adaptation (CLC+PAM) will account for most of the glycemic benefits of ABC overnight, will be
 inferior to BAM in terms of postprandial glucose variability and hypoglycemia during/after exercise, and will
 be superior to BAM in terms of technology acceptance for those who prefer fully-automated control.
Overall, we affirm that reliable technology has been developed and sufficient data accumulated to warrant the
development of next-gen...

## Key facts

- **NIH application ID:** 10200019
- **Project number:** 5R01DK085623-11
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** SUE A BROWN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $700,025
- **Award type:** 5
- **Project period:** 2009-09-28 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200019

## Citation

> US National Institutes of Health, RePORTER application 10200019, Biobehavioral Human-Machine Co-adaptation of the Artificial Pancreas (5R01DK085623-11). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10200019. Licensed CC0.

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