{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:16:16Z","timestamp":1791303376416,"version":"4.1.0"},"reference-count":44,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n With the increasing burden of chronic diseases, self-management education (SME) is crucial. Traditional SME based on face-to-face delivery by clinicians is resource-intensive, and general digital tools such as web-based platforms often provide limited interactivity for patient learning. Although chatbots based on large language models (LLMs) show promise in interactivity, their real-world effectiveness lacks empirical evidence.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to explore the feasibility and preliminary effectiveness of an LLM-based chatbot specifically designed for osteoporosis SME.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A formative randomized controlled trial was conducted in a tertiary hospital from February 2024 to March 2025. Adults aged \u226518 years with osteoporosis were recruited and randomly assigned (1:1) to either the intervention (OPBot) group or a control group receiving traditional health education. The chatbot provided interactive educational content and question-and-answer support, while the control group received face-to-face education and written materials. Osteoporosis knowledge was assessed using the Osteoporosis Knowledge Assessment Tool at baseline and discharge. Nurses\u2019 time spent on health education was self-recorded during each intervention session and aggregated across sessions. Adherence to disease management was assessed at 1, 3, and 6 months after discharge via telephone using Likert-scale questionnaires. The reliability of OPBot responses was evaluated by 2 clinician assessors using a 5-point Likert scale, with interrater agreement calculated using Cohen \u03ba. Group comparisons were conducted using 2-tailed independent t tests, Mann-Whitney U tests, and chi-square tests, and adherence outcomes were analyzed using mixed-effects models.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 100 participants were randomized; 12% (12\/100) were excluded due to loss to follow-up, refusal of the second knowledge assessment, or death, leaving 88% (88\/100) participants for analysis (n=45, 51.1% in the OPBot group and n=43, 48.9% in the control group). The OPBot group showed significantly higher postintervention knowledge scores than the control group (median 80.0, IQR 70.0-89.0 vs median 75.0, IQR 65.5-80.0; P=.01). Nurses in the OPBot group spent lesser time on SME than those in the control group (median 5.0, IQR 2.0-17.0 vs median 23.0, IQR 20.0-25.0 minutes; P<.001). For adherence outcomes, a significant group\u00d7time interaction was observed for calcium supplement intake (odds ratio 1.49, 95% CI 1.08-2.06; P=.02), indicating differing adherence trajectories over time. The OPBot group also showed higher odds of consuming calcium-rich foods across time points (odds ratio 2.87, 95% CI 1.04-7.89; nominal P=.04), although this association did not remain significant after Holm correction. No significant effects were observed for sun exposure (P=.56), exercise (P=.79), or total adherence scores (P=.33). In the question-and-answer module, most OPBot responses were rated as highly reliable 89.4% (76\/85), with high interrater agreement (Cohen \u03ba=0.83).<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n LLM-based chatbots specifically designed for osteoporosis SME may improve patient knowledge, supporting adherence behaviors, and reducing healthcare workload. However, further large-scale studies are needed to confirm these findings.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/85475","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T19:40:05Z","timestamp":1778614805000},"page":"e85475","source":"Crossref","is-referenced-by-count":2,"title":["Preliminary Evaluation of a Large Language Model\u2013Powered Chatbot for Osteoporosis Self-Management Education: Formative Randomized Controlled 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