Key points are not available for this paper at this time.
Background Population aging has heightened concerns regarding social participation and active aging among older adults. Chatbot-based interventions delivered through familiar messaging platforms have increasingly been adopted to support engagement with community activity programs; however, prior research has often treated chatbots as homogeneous interventions, with limited attention to how specific functional components shape user experiences among older adults. Objective This study aimed to examine older adults’ functional experiences with a LINE-based chatbot designed to support engagement with community activity programs and to investigate how different chatbot functions are associated with overall system use experience. Methods A cross-sectional survey was conducted among 299 older adults who used a LINE-based chatbot implemented in community-based programs. Functional experiences were assessed across three chatbot modules: information inquiry, photo-based check-in interaction, and task achievement. Composite mean scores were calculated for each functional module and for the overall system use experience scale. Descriptive statistics, Pearson correlation analyses, and multiple linear regression analyses were performed using IBM SPSS Statistics. Results Participants reported moderate to moderately high levels of functional experience across all chatbot modules. Pearson correlation analyses showed significant positive associations among all functional experience variables. Multiple linear regression analysis indicated that the overall model explained a large proportion of variance in overall system use experience ( R 2 = 0.760, adjusted R 2 = 0.757, F (3,295) = 310.72, p 0.001). Task achievement experience was the strongest predictor of overall system use experience ( β = 0.595, p 0.001), followed by information inquiry experience ( β = 0.252, p 0.001), whereas photo-based check-in experience was not significantly associated with overall system use experience ( β = 0.066, p = 0.139). Conclusion The findings suggest that different chatbot functions contribute unequally to older adults’ system use experience. Task-oriented and goal-focused functions may play a central role in shaping engagement with chatbot systems, whereas interactive features such as photo-based check-ins may serve a supplementary role. These results underscore the importance of function-specific design when developing chatbot-based interventions intended to support engagement with digital systems designed for community activity programs among older adults.
Chung et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: