  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 patient 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. 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 online
nurse-in-the-loop predictive control (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. Findings: 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.
