Views and conditional acceptance of telehealth and artificial intelligence for lifestyle modification among individuals with prediabetes: A qualitative descriptive study.

Background: Prediabetes is associated with an increased risk of progression to type 2 diabetes, yet sustained lifestyle modification remains difficult in everyday life. Although telehealth and artificial intelligence may extend lifestyle support through remote access, monitoring, reminders and personalised feedback, little is known about how people with prediabetes perceive these technologies or the conditions under which they would consider them acceptable. Objective: To explore how adults with prediabetes understand and manage lifestyle modification and to identify their perceived support needs and the conditions shaping acceptance of telehealth and artificial intelligence as adjuncts to lifestyle support. Design: A qualitative descriptive study. Settings: Hong Kong. Participants: 10 adults with prediabetes Methods: Semi-structured interviews were conducted between September and October 2025. Interviews were conducted in Cantonese, audio-recorded, transcribed verbatim, translated into English, and analysed using thematic analysis. Demographic, clinical, and digital health-related information was collected to contextualise participants' accounts. Results: Five interrelated themes were identified. Participants' perceived risk of prediabetes was shaped less by the diagnosis itself than by family history, bodily symptoms, and personal health biographies. Although participants generally understood the need for dietary change, physical activity, and weight control, they found it difficult to translate knowledge into sustained action because of work demands, food environments, fatigue, emotional distress, and limited control over daily routines. Lifestyle modification was, therefore, approached pragmatically through modest, incremental, and sustainable adjustments rather than strict behavioural transformation. Meaningful support was perceived as ongoing, human-centred, and responsive to individual circumstances. Within this context, telehealth and artificial intelligence were viewed as potentially-acceptable extensions of care, particularly for accessible information access, reminders, self-monitoring, and personalised feedback. However, acceptability was conditional on accuracy, privacy protection, cultural relevance, professional credibility, and continued clinician involvement. Conclusions: Telehealth and artificial intelligence may help support lifestyle modification among individuals with prediabetes, but their value depends on their ability to complement rather than human care. Artificial intelligence-enabled telehealth interventions should be designed as hybrid, clinician-integrated models that translate health data into locally relevant and actionable guidance while preserving trust, accountability, and human reassurance. Such approaches may help bridge the persistent gap between knowing what lifestyle changes are needed and sustaining those changes in everyday life for individuals with prediabetes in Hong Kong. Registration: Not registered.