Smartphone Inclinometer–Measured Abdominal Tilt Angles for Classifying Diabetes and Musculoskeletal Pain in Older Adults: A Machine Learning Study

Background Abdominal obesity measures such as waist circumference and waist-to-hip ratio have been shown to relate more closely to diabetes and musculoskeletal pain than body mass index, but conventional tape-based measurements are not easily scalable. A smartphone inclinometer application provides a practical alternative by capturing abdominal tilt angles that are automatically computed and easily reusable. Purpose This study assessed whether abdominal tilt angles measured using a smartphone inclinometer could serve as digital features for classifying diabetes and musculoskeletal pain in older adults using machine learning. Methods In 105 older adults, 12 abdominal inclination features were extracted from upper and lower tilt angles and refined by minimum redundancy maximum relevance selection. Three models (Light Gradient Boosting Machine (LightGBM), Balanced Random Forest (BalancedRF), and Linear Support Vector Classifier with Probability Calibration) were trained and evaluated with five-fold cross-validation. Results BalancedRF achieved the best performance for diabetes classification (accuracy = 0.83, ROC-AUC = 0.93, PR-AUC = 0.84), with sensitivity 87% and specificity 82%. For musculoskeletal pain, LightGBM achieved moderate performance (accuracy = 0.78, ROC-AUC = 0.83, PR-AUC = 0.56) but sensitivity was limited (53%) despite high specificity (87%). SHAP analysis highlighted the squared terms of the lower and total abdominal angles as key features for diabetes, while associations were weaker for pain. Conclusions Abdominal tilt angles measured by a smartphone inclinometer represent feasible, noninvasive digital features for diabetes risk stratification, although utility for musculoskeletal pain classification is limited. Future work should validate these findings in larger and longitudinal cohorts and explore real-world integration of smartphone posture monitoring for digital health applications.