532-P: Artificial Intelligence Identifies a Modest Influence of Character and Emotions on Blood Glucose in Type 1 Diabetes

Introduction: People with T1DM and their healthcare providers perceive that mood and sleep quality influence blood glucose (BG). Artificial pancreas algorithms use only BG estimates to predict future BG and determine insulin requirements. We aimed to improve the understanding of the effect of emotional states and personality on temporal BG changes using machine learning.

Methods: Patients with T1DM using continuous glucose monitoring (CGM) completed questionnaires on demographic, socioeconomic and medical information and personality characteristics. For several days, using a mobile app, patients recorded their present valence, arousal state and sleep quality. The data was synced with patient CGM readings and evaluated using gradient boosting decision tree (GBDT) over 5-fold cross-validation. Area under the receiver operating characteristic curve (AUROC) and marginal contribution were used to determine model feature importance. BG was divided into three classes: normal (70-180), high (>180), low (<70). For normal BG values, models were trained to predict BG class in the next 15, 30, and 45 minutes.

Results: A total of 64 participants (age 48±15 years SEM, years of T1D 18±13, 59% women) from two medical centers participated for 17.3±15.2 SEM days, each reporting valence and arousal 56±53 times. Models using personality and emotional features to predict future BG outperformed models using only BG, albeit by a small margin. Following BG and time-of-day features, both neuroticism and arousal were found to be predictive. Neuroticism levels and sleep were significantly different between participants with HbA1c values under 7.6% and over 9.5%. (p=0.001, p=0.02, respectively).

Discussion: Our results show that personality and present arousal are associated with BG fluctuations, while the effect of valence, while detectible, is minute. Integrating personality trait information using standard questionnaires may improve artificial pancreas algorithms.