Development of a Digital Survey Platform for Human Biomonitoring Health Effect Surveys

Background: In human biomonitoring (HBM), health effect surveys provide contextual information needed to interpret biomarker results and their data quality is critical.The health effect questionnaire of the Korean Human Exposure Safety Survey (KoHESS) comprises 113~142 response items per respondent, with numerous complex matrix questions and multi-level conditional branching.Its first cycle, which was conducted on paper, showed structural limitations on data quality.Objectives: This study aimed to develop a dedicated digital survey platform, the Korean Human Biomonitoring Very Efficient Survey Tool (K-HBM VEST), to digitize this complex questionnaire while preserving its structure.Methods: Six paper questionnaires were analyzed to quantify their items and structural complexity.After identifying complex question types that can be difficult to implement with general-purpose tools, the platform was built with automatic conditional branching, input validation, unit standardization, and response progress visualization.Paper, Google Forms, and REDCap were compared based on expert consensus by the first-cycle research team, with criteria in four aspects referencing the ISO/IEC 25010 and 25012 quality models. Results:The six paper questionnaires comprised 549 response items, 81 matrix questions, and 82 conditional branching items, confirming their large-scale, complex structure.While preserving the paper questionnaire structure, K-HBM VEST implemented complex matrix questions within a single structure and is designed to support data completeness and accuracy by implementing automatic non-response identification, conditional branching, blocking of invalid input, embedded coded units, and auto-saving of responses.In the functional comparison, K-HBM VEST provided additional functionalities specifically designed for complex HBM questionnaires across all four aspects. Conclusions:The complex question structures of HBM health effect surveys exceed the limits of generalpurpose tools, requiring a dedicated platform to preserve structure and assure data quality.This study provides a foundational model for the digital transition of health effect surveys.The platform will be introduced from the second survey cycle (2026~2028), with empirical validation left to future research.