Thai PainPREDICT Validation Study

Overview

Painful diabetic neuropathy (PDN) is one of the most common and disabling complications of diabetes mellitus, substantially affecting quality of life, daily functioning, and health system burden. Early identification of PDN is crucial for timely treatment, prevention of complications such as foot ulcers and amputations, and for reducing healthcare costs. However, in Thailand there are limited culturally adapted and validated tools for screening PDN. PainPREDICT is an internationally validated questionnaire designed to characterize neuropathic pain profiles, but its adaptation for Thai patients has not yet been undertaken. In parallel, the use of mobile health technologies (mHealth) has the potential to expand access to screening and monitoring of chronic conditions, particularly in resource-limited settings.

This study aims to translate, culturally adapt, and validate the PainPREDICT questionnaire for the Thai population, and to integrate it into a mobile application for use in both clinical and community settings. The study is structured into three phases. Phase 1 involves forward-backward translation of PainPREDICT into Thai, with expert panel review, pilot testing, and psychometric validation. Reliability will be assessed using internal consistency (Cronbach's alpha) and test-retest intraclass correlation coefficients, while validity will be examined through correlations with existing neuropathic pain measures (e.g., DN4) and neurophysiological studies (nerve conduction study, quantitative sensory testing). Phase 2 focuses on the design, development, and usability testing of a mobile application embedding the Thai PainPREDICT questionnaire. Clinicians and patients will test the application, and usability will be assessed with the System Usability Scale and qualitative interviews. Phase 3 evaluates diagnostic accuracy and real-world usability in 200-300 patients with type 1 or type 2 diabetes, including subgroup analysis in resource-limited settings. Diagnostic performance (sensitivity, specificity, positive and negative predictive values, ROC AUC) will be compared with physician diagnosis and objective neurophysiological tests. Eligible participants are adults (≥18 years) with type 1 or type 2 diabetes who are able to communicate in Thai and provide informed consent. Patients with neuropathy from non-diabetic causes or significant cognitive impairment will be excluded. Recruitment will take place at Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, and affiliated diabetes clinics. The study anticipates enrolling approximately 277 participants, which provides adequate power for psychometric validation and diagnostic accuracy testing. Both PDN patients and diabetic controls without PDN will be included to enable comparative analysis. The primary outcome is the reliability and validity of the Thai PainPREDICT questionnaire for PDN screening. Secondary outcomes include usability of the mobile application, diagnostic accuracy compared with clinical and neurophysiological standards, and effectiveness in increasing PDN detection in resource-limited settings. The study also seeks to generate evidence on patient and provider satisfaction with the mobile platform. Data will be collected securely, stored in REDCap, and managed in compliance with Thai data protection regulations (PDPA 2019). Ethical approval will be obtained from the Institutional Review Board, Faculty of Medicine, Chulalongkorn University, and written informed consent will be required from all participants. The expected benefit of this project is the development of a reliable and culturally valid Thai version of PainPREDICT, coupled with an accessible mobile application. This tool is anticipated to facilitate earlier detection of PDN, improve patient management pathways, and reduce disparities in access to care across Thailand. The study will also provide a model for adapting digital health screening tools for other populations with high diabetes burden. Ultimately, the project aims to strengthen evidence-based screening and management of neuropathic pain, improve patient quality of life, and contribute to the broader application of mHealth in chronic disease management.