Clinical Research Directory

Pain Prediction Via Machine Learning: Electrical Thresholds and Interoception

Sponsor: Amasya University

Summary

The goal of this observational study is to predict (retrospective and prospective) musculoskeletal pain in university students using machine learning models based on electrical threshold measurements and interoceptive awareness. The main question it aims to answer is: Can electrical sensory, motor, and pain thresholds, along with interoceptive awareness scores, predict musculoskeletal pain status in young adults using machine learning algorithms? Participants will: Complete demographic, healthy lifestyle (physical activity, sitting time, meal frequency), and substance use (ASSIST v3.0) questionnaires. Complete self-report surveys including the Nordic Musculoskeletal Questionnaire (NMQ) and the Multidimensional Assessment of Interoceptive Awareness (MAIA-2). Undergo quantitative electrical threshold testing on their dominant hand, which includes 3 consecutive measurements of sensory threshold, motor threshold (after a 10-minute rest), and pain threshold (after another 10-minute rest).

Official title: Machine Learning-Based Pain Prediction in University Students: Electrical Threshold Measurements and Interoception

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