Previously submitted to: JMIR Formative Research (no longer under consideration since Jun 11, 2024)
Date Submitted: Mar 14, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin
ABSTRACT
Background:
Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patients’ outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses’ expertise to achieve optimal care.
Objective:
The objective of this study is to evaluate the effectiveness of an online nurse-in-the-loop predictive control (ONLC) model, utilizing a predictive digital twin in providing personalized, AI-generated feedback to T2D patients. Additionally, we assess the impact of this individualized feedback on improving patients' adherence to healthy lifestyle behaviors, thereby potentially enhancing their health outcomes.
Methods:
This is a 6-month ancillary study among T2D patients (n = 20, age = 57 ± 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an ONLC model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning based Artificial Neural Network. The PDT was trained on participants' self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient’s glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance.
Results:
The trained ONLC model achieved ≥80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended. The intervention group exhibited significant weight loss (p-value 4.731e-8, average:5.871 lbs.) and maintained glucose levels over time (p-value 0.661) compared to baseline.
Conclusions:
Using a digital twin approach, nurse-in-the-loop AI can potentially improve adherence to recommended healthy lifestyle behaviors. Further studies with larger sample sizes and long-term follow-ups are warranted. Clinical Trial: This study was conducted in strict accordance with ethical standards. This study was approved by the University of Texas Health Science Center at San Antonio (UTHSCSA) institutional review board (IRB) (Protocol Number: HSC20190528H) and registered at http://ClinicialTrials.gov (NCT05071287). This study protocol version number is 4, dated March 9, 2021.
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