Task-aware modality configuration in personalized health information systems: Evidence from multi-attribute task contexts
Zhenyu Wang, Jianmin Wang · Information Processing & Management · 2026
This study investigates how multi-attribute health-related tasks shape users’ observed choices among single-modal and multimodal information combinations. It examines modality configuration as a task-triggered, micro-level instantiation of task–technology fit in personalized health information systems. Grounded in task facet classification theory, the study conceptualizes health tasks along three dimensions: task type, task complexity, and topic group. These effects are examined through a quasi-experimental design based on simulated work task scenarios. A total of 120 participants were recruited, yielding 25,000 valid health information records selected and saved in a researcher-supervised controlled live-web SWTS setting. Distributional differences in modality choices across task configurations are examined, and the findings are triangulated using descriptive odds ratios and multinomial logit models to assess the consistency of effect direction and relative strength in task–modality relationships. The results show that users’ observed modality choices are highly concentrated in a small number of dominant text-centered single-modal and multimodal formats, indicating a clear distributional concentration pattern rather than indiscriminate multimodal use. Systematic distributional differences are observed across task configurations in the selection proportions of key modality combinations, including text-only, text–voice, text–image, text–image–video, and text–video formats, suggesting that task attributes are associated with observed modality choices in multimodal information system use. By translating task attributes into implementable modality configuration logic, this study operationalizes modality configuration as a task-triggered, micro-level instantiation of task–technology fit and provides actionable principles for adaptive and risk-sensitive modality design in personalized and generative AI–enabled health information systems.