Development and validation of a Wechat mini app-based screening tool (eR-BFAS) for early detection of Parkinson's disease.

Background: Parkinson's disease (PD), a progressive neurodegenerative disorder, requires early detection to optimize outcomes. However, existing screening tools are often complex and not widely accessible, especially for elderly individuals.

Objective: To develop and evaluate the reliability and validity of a WeChat Mini app-based screening tool (eR-BFAS) that combines motor symptom assessment with non-motor symptom scales for sleep disorders and hyposmia, to effectively identify early-stage PD.

Methods: The eR-BFAS was developed by integrating the Baylor Functional Assessment Screen (BFAS), Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ), and Hyposmia Rating Scale (HRS). The tool was deployed via WeChat, and participants with mild motor impairments, sleep disorders, or olfactory dysfunction completed the self-assessment. The patients' final diagnoses were followed up, and confirmatory factor analysis, internal consistency, test-retest reliability, and diagnostic performance were evaluated.

Results: A total of 132 participants completed the eR-BFAS, which comprises 24 items assessing motor symptoms, sleep disorders, and olfactory dysfunction. The tool demonstrated strong internal consistency (Cronbach's alpha = 0.875) and excellent test-retest reliability (ICC = 0.927). Confirmatory factor analysis indicated good structural validity (χ²/df = 1.573, RMSEA = 0.064, CFI = 0.927). The eR-BFAS achieved a predictive accuracy of 87.9% with an optimal cut-off score of 27 points, yielding sensitivity of 86.7% and specificity of 88.9%.

Conclusions: The eR-BFAS, integrating motor and non-motor symptom assessments, is a reliable and effective tool for screening early-stage PD. Leveraging the accessibility of WeChat, this tool has the potential to facilitate early detection and intervention in community settings.