# ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2022 · $599,041

## Abstract

Project Summary
Recent large-scale trials have shown no significant benefit of pharmacological interventions in delirium
patients, and non-pharmacological approaches remain the cornerstone of delirium prevention. Among those
strategies, minimizing patient immobility and circadian desynchrony are particularly difficult to implement, as
their assessment is dependent on sporadic human observations. The overall objective of this application is to
develop ADAPT, the Autonomous Delirium Monitoring and Adaptive Prevention system using novel pervasive
sensing and deep learning techniques. It will autonomously quantify patients’ mobility and circadian
desynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for
integration of these risk factors into a dynamic model for predicting delirium trajectories. It will also enable
adaptive action prompts aimed at increasing patients’ mobility, reducing nightly disruptions, optimizing ambient
light, and reducing noise, based on precise real-time quantification. The rationale is that successful application
of the proposed technology would augment clinical-decision making in the fast-paced ICU environment and
would promote more targeted interventions. The overall objective will be achieved by pursuing three specific
aims. (1) Developing and validating an interpretable deep learning algorithm for precise and dynamic prediction
of the delirium trajectory, to determine if it is more accurate in predicting delirium trajectory transitions
compared to existing tools, while providing interpretable information to the physician. (2) Developing a
pervasive sensing system for autonomous monitoring of mobility and circadian desynchrony, to determine if it
can provide accurate assessments compared to human expert and circadian biomarkers, and if it can enrich
delirium trajectory prediction when combined with clinical data. (3) Developing and evaluating prompts for
adaptive delirium prevention using real-time monitoring system, to determine if the system has acceptable
satisfaction and perceived benefit among ICU physicians. The approach is innovative, because it represents
the first attempt to (1) dynamically predict precise delirium trajectory, (2) autonomously monitor mobility and
circadian desynchrony risk factors in the ICU, and (3) implement adaptive preventions in real time. The
proposed research is significant since it will address several key problems and critical barriers in critical care,
including (1) lack of precise and real-time delirium trajectory prediction models, (2) uncaptured aspects of
mobility and circadian desynchrony, and (3) the need for novel approaches for non-pharmacological
prevention. Ultimately, the results are expected to improve patient outcomes and decrease hospitalization
costs, as well as lifelong complications.

## Key facts

- **NIH application ID:** 10396041
- **Project number:** 5R01NS120924-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Azra Bihorac
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $599,041
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10396041

## Citation

> US National Institutes of Health, RePORTER application 10396041, ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention (5R01NS120924-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10396041. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*