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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159390


    Title: 應用生成式 AI 聊天機器人建構糖尿病患者的飲食管理意識
    Using Generative AI Chatbots to Build Awareness of Dietary Management for Patients with Diabetes
    Authors: 揭宜蓁
    Jie, Yi-Jhen
    Contributors: 鄭霈絨
    廖峻鋒
    Cheng, Pei-Jung
    Liao, Chun-Feng
    揭宜蓁
    Jie, Yi-Jhen
    Keywords: 生成式 AI
    聊天機器人
    糖尿病
    自我飲食管理
    數位健康
    Generative AI
    Chatbot
    Type 2 diabetes
    Self-dietary management
    Digital health
    Date: 2025
    Issue Date: 2025-09-01 16:51:21 (UTC+8)
    Abstract: 隨著第二型糖尿病盛行率持續上升，該族群的自我飲食管理能力成為疾病控制與併發症預防的關鍵。近年來，生成式人工智慧 (Generative AI) 技術在數位健康領域逐漸受到重視，然而其介入成效及實際行為改變機制尚待深入驗證。因此，本研究旨在評估生成式 AI 聊天機器人介入對第二型糖尿病族群自我飲食管理行為與知識態度的影響。
    本研究採隨機分組實驗設計，招募 43 位自我管理意識較弱且無嚴重併發症的第二型糖尿病參與者，分為生成式 AI 聊天機器人實驗組與靜態衛教對照組，進行為期 18 天的實驗。期間每日早上需與生成式 AI 聊天機器人討論當日飲食計劃，晚上則記錄實際飲食內容並獲得AI 依據紀錄所提供的回應與建議；對照組則於早上接收系統預設之靜態飲食建議，晚上同樣記錄飲食內容。兩組皆於實驗前、後填寫糖尿病自我管理問卷及糖尿病飲食知識與態度量表。
    研究結果顯示，生成式 AI 聊天機器人在提升自我飲食管理行為上的成效優於靜態衛教，可以增強行動意識與反思能力，有助於健康飲食的實踐率；然而，兩種介入方式雖然都能在短期內提升他們的飲食管理知識與態度，但是靜態衛教對於知識提升的成效較爲顯著；在滿意度方面，兩者皆獲得高度肯定，但生成式 AI 在需求理解與互動回饋面向展現出潛在優勢。此外，質性分析結果顯示，生成式 AI 可促進使用者產生「計畫—行動—反思」的循環，有助於健康行為的持續內化。整體結果可看出，生成式 AI 聊天機器人確實有助於強化糖尿病族群自我飲食管理意識，但短期的介入下在飲食管理知識的提升效果有限。建議未來研究可延長追蹤期程，並強化個人化設計與回饋機制，提升生成式AI 輔助工具對慢性病自我飲食管理與健康知識建立的應用成效。
    With the rising prevalence of type 2 diabetes, patients’ self-management of diet has become a critical factor in disease control and the prevention of complications. In recent years, generative artificial intelligence (Generative AI) has gained increasing attention in the field of digital health; however, its effectiveness and the mechanisms through which it influences behavioral change remain insufficiently explored. This study therefore aimed to evaluate the impact of a generative AI–based chatbot intervention on dietary self-management behaviors and knowledge-attitude outcomes among individuals with type 2 diabetes.
    This research employed a randomized controlled experimental design and recruited 43 participants with type 2 diabetes who exhibited relatively low dietary self-management awareness and no severe complications. Participants were randomly assigned to either the experimental group (Generative AI chatbot) or the control group (static education). The 18-day intervention required the experimental group to engage with the chatbot each morning to discuss daily meal plans and to record actual dietary intake each evening, followed by personalized AI-generated responses and suggestions. In contrast, the control group received preset static dietary recommendations in the morning and similarly logged daily food intake at night. Both groups completed the Diabetes Self-Management Questionnaire (DSMQ) and a dietary knowledge and attitude scale before and after the intervention.
    The results indicated that the Generative AI chatbot was more effective than static education in enhancing dietary self-management behaviors, particularly in raising action awareness and reflective capacity, thereby improving adherence to healthy eating practices. While both interventions led to short-term improvements in dietary knowledge and attitudes, static education produced greater gains in knowledge. In terms of user satisfaction, both approaches were highly rated, though the chatbot demonstrated potential advantages in understanding user needs and providing interactive feedback. Qualitative analysis further revealed that the chatbot fostered a “plan–act–reflect” cycle, facilitating the internalization of healthy behaviors. Overall, the findings suggest that Generative AI chatbots can strengthen dietary self-management awareness among individuals with type 2 diabetes; however, short-term interventions appear limited in enhancing knowledge. Future studies are recommended to extend the follow-up duration and to refine personalization and feedback mechanisms, in order to maximize the utility of Generative AI tools for chronic disease dietary self-management and health knowledge building.
    Reference: 一、中文部分
    余佳馨、林佳霈、林士祥 (2022) 。探討糖尿病衛教介入對門診第2型糖尿病患血糖控制與認知功能之影響。台灣營養學會雜誌，46(4)，112-125。https://doi.org/10.6691/nsj.202212_46(4).0001
    社團法人中華民國糖尿病衛教學會 (2015)。糖尿病餐後血糖衛教手冊。臺北市：作者。
    社團法人中華民國糖尿病衛教學會 (2023)。糖尿病衛教白皮書。臺北市：作者。
    洪萱容、洪意華 (2022) 。運用自我管理於一位糖尿病患者合併多重症狀之照護經驗。高雄護理雜誌，39(3)，155-168。https://doi.org/10.6692/KJN.202212_39(3).0012
    曾耀賢 (2024) 。ChatGPT在糖尿病照護中的應用：潛力、挑戰與展望。中華民國糖尿病衛教學會會訊，20(1)，38-43。https://www.airitilibrary.com/Article/Detail?DocID=P20141022001-N202404250017-00010
    黃喬欣、林碧珠、張媚、李碧霞 (2017) 。糖尿病病人自我照護行為及相關因素研究。護理雜誌，64(1)，61-69。https://doi.org/10.6224/JN.000009
    楊睿彬 (2019) 。認識糖尿病。中華自然醫學研究，2(1)，29-34。https://www.airitilibrary.com/Article/Detail?DocID=P20200401001-201906-202004010026-202004010026-29-34
    詹紹華、林宜璇、沈依璇、鄭巧雯、王靜枝 (2024) 。健康識能版衛教文宣於糖尿病病人健康識能及血糖照護指標之成效。嘉基護理，24(1)，16-33。https://www.airitilibrary.com/Article/Detail?DocID=1816661x-N202406140004-00002
    劉育隆、陳淑茹 (2024) 。生成式AI在糖尿病衛教飲食管理與運動指南之應用。中華民國糖尿病衛教學會會訊，20(1)，20-25。https://www.airitilibrary.com/Article/Detail?DocID=P20141022001-N202404250017-00007

    二、英文部分
    Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006
    Allegrante, J. P., Wells, M. T., & Peterson, J. C. (2019). Interventions to Support Behavioral Self-Management of Chronic Diseases. Annual Review of Public Health, 40(1), 127–146. https://doi.org/10.1146/annurev-publhealth-040218-044008
    American Diabetes Association (2014). Diagnosis and classification of diabetes mellitus. Diabetes care, 37 Suppl 1, S81–S90. https://doi.org/10.2337/dc14-S081
    Bond, A., Mccay, K., & Lal, S. (2023). Artificial intelligence & clinical nutrition: What the future might have in store. Clinical nutrition ESPEN, 57, 542–549. https://doi.org/10.1016/j.clnesp.2023.07.082
    Bul, K., Holliday, N., Bhuiyan, M. R. A., Clark, C. C. T., Allen, J., & Wark, P. A. (2023). Usability and Preliminary efficacy of an Artificial Intelligence–Driven platform supporting dietary management in Diabetes: Mixed Methods study. JMIR Human Factors, 10, e43959. https://doi.org/10.2196/43959
    Cheng, L., Leung, D., Sit, J., Li, X., Wu, Y., Yang, M., Gao, C., & Hui, R. (2016). Factors associated with diet barriers in patients with poorly controlled type 2 diabetes. Patient Preference and Adherence, 37. https://doi.org/10.2147/ppa.s94275
    Colombani, P. C. (2004). Glycemic index and load—dynamic dietary guidelines in the context of diseases. Physiology & Behavior, 83(4), 603–610. https://doi.org/10.1016/j.physbeh.2004.07.029
    D, H., & Suresh, N. V. (2024). Generative AI in healthcare. In Advances in medical technologies and clinical practice book series (pp. 79–90). https://doi.org/10.4018/979-8-3693-3731-8.ch004
    Euchner, J. (2023). Generative AI. Research-Technology Management, 66(3), 71–74. https://doi.org/10.1080/08956308.2023.2188861
    Forouhi, N. G., & Wareham, N. J. (2014). Epidemiology of diabetes. Medicine (Abingdon, England : UK ed.), 42(12), 698–702. https://doi.org/10.1016/j.mpmed.2014.09.007
    Gardner, C. D., Vadiveloo, M. K., Petersen, K. S., Anderson, C. A., Springfield, S., Van Horn, L., Khera, A., Lamendola, C., Mayo, S. M., & Joseph, J. J. (2023). Popular Dietary Patterns: Alignment with American Heart Association 2021 Dietary Guidance: A scientific statement from the American Heart Association. Circulation, 147(22), 1715–1730. https://doi.org/10.1161/cir.0000000000001146
    Gerontiti, E., Shalit, A., Stefanaki, K., Kazakou, P., Karagiannakis, D. S., Peppa, M., Psaltopoulou, T., & Paschou, S. A. (2024). The role of low glycemic index and load diets in medical nutrition therapy for type 2 diabetes: an update. HORMONES, 23(4), 655–665. https://doi.org/10.1007/s42000-024-00566-7
    Hailu, F. B., Moen, A., & Hjortdahl, P. (2019). Diabetes Self-Management Education (DSME) – Effect on Knowledge, Self-Care Behavior, and Self-Efficacy Among Type 2 Diabetes Patients in Ethiopia: A Controlled Clinical Trial Diabetes Metabolic Syndrome and Obesity, Volume 12, 2489–2499. https://doi.org/10.2147/dmso.s223123
    Han, D., Sun, H., Zhang, K., Jiang, G., Yang, X., Qin, W., Gu, Q., & Lan, W. (2023). AI Dietitian for Type 2 Diabetes Mellitus Management Based on Large Language Model and Image Recognition Model: A Preclinical Concept Validation Study (Preprint). Journal of Medical Internet Research. https://doi.org/10.2196/51300
    Isaksson, S. S., Ólafsdóttir, A. F., & Lind, M. (2023). Design of a randomized cross-over study evaluating effects of carbohydrate intake on glycemic control in persons with type 1 diabetes. Frontiers in Nutrition, 10. https://doi.org/10.3389/fnut.2023.1114317
    Javaid, M., Haleem, A., & Singh, R. P. (2023). ChatGPT for healthcare services: An emerging stage for an innovative perspective. BenchCouncil Transactions on Benchmarks Standards and Evaluations, 3(1), 100105. https://doi.org/10.1016/j.tbench.2023.100105
    Jenkins, D. J., Wolever, T. M., Taylor, R. H., Barker, H., Fielden, H., Baldwin, J. M., Bowling, A. C., Newman, H. C., Jenkins, A. L., & Goff, D. V. (1981). Glycemic index of foods: a physiological basis for carbohydrate exchange. American Journal of Clinical Nutrition, 34(3), 362–366. https://doi.org/10.1093/ajcn/34.3.362
    Jiao, W., Wang, W., Huang, J., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? Yes with GPT-4 as the engine. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2301.08745
    Kumar, N. K., Merrill, J. D., Carlson, S., German, J., & Yancy, W. S., Jr. (2022). Adherence to Low-Carbohydrate Diets in Patients with Diabetes: A Narrative Review. Diabetes Metabolic Syndrome and Obesity, Volume 15, 477–498. https://doi.org/10.2147/dmso.s292742
    Kurnia, D. A., & Yulia, Y. (2021). Understanding food selection and dieting patterns: type 2 diabetes mellitus patients and their families. International Journal of Endocrinology (Ukraine), 17(6), 456–458. https://doi.org/10.22141/2224-0721.17.6.2021.243204
    Lally, P., Van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2009). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40(6), 998–1009. https://doi.org/10.1002/ejsp.674
    Lee, Y., Kim, G., Jun, J. E., Park, H., Lee, W. J., Hwang, Y., & Kim, J. H. (2023). An integrated digital health care platform for diabetes management with AI-Based dietary management: 48-Week results from a randomized controlled trial. Diabetes Care, 46(5), 959–966. https://doi.org/10.2337/dc22-1929
    Magliano, D. J., Boyko, E. J., & IDF Diabetes Atlas 10th edition scientific committee . (2021). IDF Diabetes Atlas. (10th ed.). International Diabetes Federation.
    Masood, S., Bashrawi, M. a. A., Khan, M. A., & Nazir, A. (2024). Exploring ChatGPT applications in healthcare: a comprehensive overview. The Visual Computer. https://doi.org/10.1007/s00371-024-03697-5
    Matpady, P., Maiya, A. G., Saraswat, P. P., Mayya, S. S., Pai, M. S., S, A. D., & Umakanth, S. (2020). Dietary self-management practices among persons with T2DM: An exploratory qualitative study from western-coast of India. Diabetes & Metabolic Syndrome Clinical Research & Reviews, 14(6), 2161–2167. https://doi.org/10.1016/j.dsx.2020.10.033
    Mehra, C. G., Raymond, A. M., & Prabhu, R. (2022). A personalized multi-interventional approach focusing on customized nutrition, progressive fitness, and lifestyle modification resulted in the reduction of HbA1c, fasting blood sugar and weight in type 2 diabetes: a retrospective study. BMC Endocrine Disorders, 22(1). https://doi.org/10.1186/s12902-022-01212-2
    Mehraeen, E., Mehrtak, M., Karimi, A., Aghamohammadi, V., AkbarShekarchi, A., Farzi, J., Nazeri, Z., Hayati, B., & Nasiri, K. (2022). Self-care behaviors in people living with type 2 diabetes. Romanian Journal of Diabetes Nutrition and Metabolic Diseases, 29(1), 115-128. https://rjdnmd.org/index.php/RJDNMD/article/view/987
    Naja, F., Taktouk, M., Matbouli, D., Khaleel, S., Maher, A., Uzun, B., Alameddine, M., & Nasreddine, L. (2024). Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome. European Journal of Clinical Nutrition, 78(10), 887–896. https://doi.org/10.1038/s41430-024-01476-y
    Neuhouser, M. L. (2018). The importance of healthy dietary patterns in chronic disease prevention. Nutrition Research, 70, 3–6. https://doi.org/10.1016/j.nutres.2018.06.002
    Norris, S. L., Engelgau, M. M., & Narayan, K. V. (2001). Effectiveness of Self-Management training in Type 2 diabetes. Diabetes Care, 24(3), 561–587. https://doi.org/10.2337/diacare.24.3.561
    Schmitt, A., Gahr, A., Hermanns, N., Kulzer, B., Huber, J., & Haak, T. (2013). The Diabetes Self-Management Questionnaire (DSMQ): development and evaluation of an instrument to assess diabetes self-care activities associated with glycaemic control. Health and Quality of Life Outcomes, 11(1), 138. https://doi.org/10.1186/1477-7525-11-138
    Shamanna, P., Joshi, S., Thajudeen, M., Shah, L., Poon, T., Mohamed, M., & Mohammed, J. (2024). Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control. Frontiers in Endocrinology, 15. https://doi.org/10.3389/fendo.2024.1485464
    Sukartini, T., Nursalam, N., Pradipta, R. O., & Ubudiyah, M. (2023). Potential Methods to Improve Self-management in Those with Type 2 Diabetes: A Narrative Review. International Journal of Endocrinology and Metabolism, 21(1). https://doi.org/10.5812/ijem-119698
    Thomas, D., & Elliott, E. J. (2009). Low glycaemic index, or low glycaemic load, diets for diabetes mellitus. Cochrane Library. https://doi.org/10.1002/14651858.cd006296.pub2
    Ungersboeck, M., Tang, X., Neeff, V., Steele, D., Grimm, P., & Fenech, M. (2022). Personalised Nutritional Recommendations Based on Individual Post-Prandial Glycaemic Responses Improve Glycaemic Metrics and PROMs in Patients with Type 2 Diabetes: A Real-World Assessment. Nutrients, 14(10), 2123. https://doi.org/10.3390/nu14102123
    Van Vugt, M., De Wit, M., Cleijne, W. H., & Snoek, F. J. (2013). Use of behavioral change techniques in Web-Based Self-Management programs for Type 2 diabetes patients: Systematic review. Journal of Medical Internet Research, 15(12), e279. https://doi.org/10.2196/jmir.2800
    Zanuso, S., Jimenez, A., Pugliese, G., Corigliano, G., & Balducci, S. (2009). Exercise for the management of type 2 diabetes: a review of the evidence. Acta Diabetologica, 47(1), 15–22. https://doi.org/10.1007/s00592-009-0126-3
    Description: 碩士
    國立政治大學
    數位內容碩士學位學程
    112462014
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112462014
    Data Type: thesis
    Appears in Collections:[數位內容碩士學位學程] 學位論文

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