Health Information Adoption Among Patients With Chronic Disease in China: Qualitative Interview Study of Patient-Platform Coshaping.

Background: Chronic disease management increasingly relies on digital health information. Traditional adoption models conceptualize adoption as an individual decision determined by cognitive evaluations. However, in contemporary platform environments, health information exposure and interaction are shaped by algorithmic curation and platform governance. Understanding how patients with chronic disease engage with these platform-mediated spaces requires examining adoption as an ongoing process rather than a discrete outcome. Such process-oriented understanding remains limited. Objective: This study aims to construct a theoretical model that captures the dynamic, processual nature of health information adoption among patients with chronic disease in platform-mediated environments, with particular attention to the reciprocal interactions between patient practices and platform structures. Methods: This study used grounded theory methodology and conducted in-depth semistructured interviews with 32 patients with chronic disease in Chengdu, Sichuan, China, from December 2023 to July 2025. Participants included individuals with various chronic conditions such as hypertension, rhinitis, lumbar disc herniation, urticaria, and other persistent health conditions requiring sustained self-management. The sample was diverse in age, gender, and educational background. Theoretical sampling was used across 3 stages to ensure diversity and category saturation. Data were systematically analyzed through 3 levels of coding: open coding generated 22 categories, axial coding organized these into 7 major categories, and selective coding integrated these into 3 core categories. Particular attention was paid to how patients interpreted, evaluated, and integrated digital health information through continuous interaction with platform technologies and governance structures. Results: Analysis identified 3 core categories of health information adoption-adoption propensity, platform context, and intervening conditions-which were integrated into a process model. Adoption Propensity comprises 3 interconnected layers, including motivational (disease learning, information verification, and comfort seeking), behavioral (information seeking, analysis, keeping, and adoption), and attitudinal (cognitive, emotional, and contextual resonance). Platform Context includes both information context (information content, information format, and information source) and institutional context (platform technology, platform characteristics, and platform governance). Intervening Conditions consist of individual circumstances (foundational characteristics, information literacy, and illness experience) and environmental conditions (emergency situations, policy changes, and social relationships). The analysis further revealed a cyclical co-construction process, in which patient behaviors and platform environments continuously shape each other through reciprocal interactions. Conclusions: This study proposes a theoretical model that explores how the interaction between digital platforms and patients influences health information adoption. By adopting an interpretive approach and introducing the patient-platform coconstruction perspective, this research extends existing health information behavior theories to account for platform-era dynamics. The model highlights the crucial role of platforms in structuring patients' ongoing health information practices, providing insights for the design, governance, and optimization of digital health platforms to better support chronic disease self-management.