The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial
BackgroundChildhood asthma is a prevalent chronic disease that affects millions of children worldwide. Managing this disease demands not only medical treatment but also the long-term self-management efforts of both children and their parents. Conventional self-management education typically depends...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1585702/full |
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| author | Huan-Fang Wang Yun-Hua Li Qiaoling Zhang Lihong Han Lirong Wang Lifang Zhang Xue Bai Mingyue Cheng Ting Zhang Fang Zhao Hui Li Xiao-Yun Wang |
| author_facet | Huan-Fang Wang Yun-Hua Li Qiaoling Zhang Lihong Han Lirong Wang Lifang Zhang Xue Bai Mingyue Cheng Ting Zhang Fang Zhao Hui Li Xiao-Yun Wang |
| author_sort | Huan-Fang Wang |
| collection | DOAJ |
| description | BackgroundChildhood asthma is a prevalent chronic disease that affects millions of children worldwide. Managing this disease demands not only medical treatment but also the long-term self-management efforts of both children and their parents. Conventional self-management education typically depends on face-to-face approaches, often failing to take into account the personalized requirements and ongoing support needed. Nevertheless, with the evolution of artificial intelligence (AI) technology, fresh prospects have emerged to boost the effectiveness of self-management for childhood asthma. Based on it, we have designed an AI Health Education Accurately Linkage System (AI-HEALS) to explore whether AI-driven interventions can improve self-management capabilities of families with asthmatic children, thereby helping them control the disease and reduce medical costs.MethodsThis research is a pilot single-blind randomized controlled trial (RCT) intended to gauge the efficacy of the AI-HEALS intervention delivered via the WeChat platform in enhancing the self-management abilities of families with asthmatic children. Participants will be recruited from eligible families whose children have been diagnosed with asthma and randomly allocated to either the intervention group or the control group. The control group will receive standard treatment, whereas the intervention group will receive both standard treatment and the AI-HEALS intervention. The intervention includes an AI-enabled, voice-activated interactive question-and-answer system, as well as monitoring and recording of physiological indicators, regular reminders, and customized educational articles. All components of the intervention will mainly be provided through a WeChat official account named “Children’s Asthma Health Management Expert.” AI-HEALS will construct its knowledge base according to pediatric asthma treatment guidelines to enhance the accuracy and reliability of the information it offers. The primary outcome measure is the alteration in asthma symptom control levels, while secondary outcomes comprise a variety of other physiological indicators related to asthma, parents’ self-management behaviors, and mental health conditions.DiscussionThis study combines AI and mobile health technology to develop the AI-HEALS system, with the aim of assisting families of children with asthma in controlling the disease symptoms. The primary objective is to evaluate whether the intervention can improve asthma symptom control.Clinical trial registrationThe study is scheduled to begin in April 2025 and is expected to conclude in December 2026. This research protocol is the first version and was registered with the China Clinical Trial Registration Center on February 14, 2025 (Registration Number: ChiCTR2500097233). |
| format | Article |
| id | doaj-art-f0deac0721c441419b15e07cb52101b8 |
| institution | OA Journals |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Psychiatry |
| spelling | doaj-art-f0deac0721c441419b15e07cb52101b82025-08-20T02:03:15ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-06-011610.3389/fpsyt.2025.15857021585702The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trialHuan-Fang Wang0Yun-Hua Li1Qiaoling Zhang2Lihong Han3Lirong Wang4Lifang Zhang5Xue Bai6Mingyue Cheng7Ting Zhang8Fang Zhao9Hui Li10Xiao-Yun Wang11Nursing Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaCollege of Education, Chengdu College of Arts and Sciences, Chengdu, ChinaPediatric Internal Medicine Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaPediatric Internal Medicine Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaPediatric Internal Medicine Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaPediatric Outpatient Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaNursing Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaNursing Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaNursing Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaNursing Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaNursing Department, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaPediatric Department, Inner Mongolia Maternal and Child Health Care Hospital, Hohhot, ChinaBackgroundChildhood asthma is a prevalent chronic disease that affects millions of children worldwide. Managing this disease demands not only medical treatment but also the long-term self-management efforts of both children and their parents. Conventional self-management education typically depends on face-to-face approaches, often failing to take into account the personalized requirements and ongoing support needed. Nevertheless, with the evolution of artificial intelligence (AI) technology, fresh prospects have emerged to boost the effectiveness of self-management for childhood asthma. Based on it, we have designed an AI Health Education Accurately Linkage System (AI-HEALS) to explore whether AI-driven interventions can improve self-management capabilities of families with asthmatic children, thereby helping them control the disease and reduce medical costs.MethodsThis research is a pilot single-blind randomized controlled trial (RCT) intended to gauge the efficacy of the AI-HEALS intervention delivered via the WeChat platform in enhancing the self-management abilities of families with asthmatic children. Participants will be recruited from eligible families whose children have been diagnosed with asthma and randomly allocated to either the intervention group or the control group. The control group will receive standard treatment, whereas the intervention group will receive both standard treatment and the AI-HEALS intervention. The intervention includes an AI-enabled, voice-activated interactive question-and-answer system, as well as monitoring and recording of physiological indicators, regular reminders, and customized educational articles. All components of the intervention will mainly be provided through a WeChat official account named “Children’s Asthma Health Management Expert.” AI-HEALS will construct its knowledge base according to pediatric asthma treatment guidelines to enhance the accuracy and reliability of the information it offers. The primary outcome measure is the alteration in asthma symptom control levels, while secondary outcomes comprise a variety of other physiological indicators related to asthma, parents’ self-management behaviors, and mental health conditions.DiscussionThis study combines AI and mobile health technology to develop the AI-HEALS system, with the aim of assisting families of children with asthma in controlling the disease symptoms. The primary objective is to evaluate whether the intervention can improve asthma symptom control.Clinical trial registrationThe study is scheduled to begin in April 2025 and is expected to conclude in December 2026. This research protocol is the first version and was registered with the China Clinical Trial Registration Center on February 14, 2025 (Registration Number: ChiCTR2500097233).https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1585702/fullartificial intelligencelarge language modelchildhood asthmachronic diseasemobile healthRCT |
| spellingShingle | Huan-Fang Wang Yun-Hua Li Qiaoling Zhang Lihong Han Lirong Wang Lifang Zhang Xue Bai Mingyue Cheng Ting Zhang Fang Zhao Hui Li Xiao-Yun Wang The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial Frontiers in Psychiatry artificial intelligence large language model childhood asthma chronic disease mobile health RCT |
| title | The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial |
| title_full | The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial |
| title_fullStr | The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial |
| title_full_unstemmed | The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial |
| title_short | The effect of Artificial Intelligence Health Education Accurately Linking System on childhood asthma: study protocol for a pilot randomized controlled trial |
| title_sort | effect of artificial intelligence health education accurately linking system on childhood asthma study protocol for a pilot randomized controlled trial |
| topic | artificial intelligence large language model childhood asthma chronic disease mobile health RCT |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1585702/full |
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