Data-Driven Risk Prediction of Alzheimer's Disease Using Device-Measured Physical Activity and Robust Variable Selection

Music City Center 

The increasing availability of wearable sensor data presents new opportunities for population-scale risk prediction, which is particularly difficult for rare diseases. In this study, we leverage UK Biobank data (42,157 controls, 157 cases; 264,988 person-years of follow-up) to compare 12 traditional risk factors and 8 accelerometer-based physical measures for predicting incident Alzheimer's disease diagnoses in adults 65 years and older.

Cox proportional hazard models and a robust, data-driven variable selection procedure revealed that moderate-to-vigorous physical activity (MVPA) was the most informative modifiable predictor, improving model concordance beyond age and comorbidities such as diabetes. Additional sensitivity analyses were conducted to account for potential reverse causality. Results suggest that each 14.5-minute increase in daily MVPA was associated with a substantially lower hazard of Alzheimer's disease diagnosis (p = 0.0001), comparable to a two-year reduction in age.

While grounded in classical survival analysis, this methodology reflects core elements of AI frameworks, including algorithmic model selection and prediction-oriented evaluation. These approaches highlight how statistical methods contribute to the development of scalable, data-driven tools for disease monitoring and prevention.