using gout buddy to enhance gout management

Conditions: Gout

Summary: background: dietary management is crucial for gout, but patients often lack adequate dietary literacy. however, patients often lack adequate dietary literacy and struggle to navigate complex dietary recommendations. gout buddy is an autonomous ai agent to offer personalized gout education and awareness tailored to individual needs. this study aims to evaluate the effectiveness and user experience of gout buddy, in improving dietary literacy and gout management. materials and methods: a two-arm rct will randomize patients with gout to either the intervention (gout buddy) or control (standard care). two study visits within 3-6 months will assess changes in dietary literacy and gout management behaviours. qualitative interviews will be conducted with intervention arm participants and multidisciplinary care team members to explore their experiences with gout buddy till the point of data saturation. expected outcomes: the current trial is expected to demonstrate the effectiveness of gout buddy in improving dietary literacy and gout management compared to standard care. qualitative data will provide rich insights into user engagement, perceived benefits, challenges, and the feasibility of integrating the chatbot into routine gout management. significance: this study will provide evidence on the potential of ai chatbots to enhance gout self-management. the findings will inform the development and implementation of digital health tools for chronic disease management, potentially improving patient outcomes and reducing the burden of gout.

background and rationale: gout is a chronic and progressive form of inflammatory arthritis characterized by the accumulation of urate crystals in the joints, leading to recurrent episodes of severe pain, redness, and swelling. it is primarily caused by hyperuricemia, a condition in which there is an excess of uric acid in the blood. gout is more common in men and older adults and is often associated with comorbidities such as hypertension, diabetes, and cardiovascular disease. a 2015 meta-analysis of global gout studies estimated a pooled prevalence of 0.6%, though with significant variation. \[1\] in mainland china, the prevalence of gout from 1998 to 2019 was 1.6%, and a local study from singapore among chinese individuals found that 4.1% had a history of physician-diagnosed gout. \[2,3\] despite the availability of effective treatments, about one-third patients struggle with managing their condition.\[4\] poor control can be attributed due to lack of understanding of gout's etiology, progression, and the importance of adherence to prescribed therapies. \[5\] poor management of gout can result in frequent flare-ups, joint damage, and decreased quality of life. research indicates that patient education significantly impacts the successful management of gout. \[6\] however, traditional methods of patient education-such as pamphlets, infrequent consultations, and group sessions-often fall short in engaging patients and providing timely information tailored to their individual needs. \[7,8\] additionally, these methods do not accommodate the continuous need for information that patients may have outside of clinic hours. therefore, there is a need for innovative, accessible, and patient-centered educational tools that can bridge these gaps and empower patients to manage their gout more effectively. digital health interventions have emerged as a promising solution to these challenges. by leveraging technology, these interventions can provide continuous support, personalized information, and interactive educational content to patients. \[9\] chatbots, in particular, have gained attention for their efficacy of health behaviour change among large and diverse population. \[10\] chatbots can be particularly beneficial in chronic disease management, where ongoing patient education and engagement are critical. by providing instant access to reliable information and guidance, chatbots can help patients better understand their condition, adhere to treatment plans, and make informed decisions about their health. health expert language framework (helf) is a state-of-the-art ai-powered healthcare platform dedicated to advancing health and wellness education. tailored for learners, educators, healthcare professionals, and researchers, helf ai offers an immersive learning environment enriched by direct access to pubmed, a vast repository of the latest health literature. the platform elevates the educational experience by presenting detailed information and resources in a dynamic question-answer format, fostering a deeper understanding of health topics. building on this foundation, helf ai can be further developed into a sophisticated, autonomous ai agent ("gout buddy") to offer personalized gout education and awareness tailored to individual needs. given the challenges associated with gout management and the potential of digital health interventions, this study seeks to evaluate the effectiveness of the gout buddy as an intelligent patient companion, in improving dietary literacy of gout and adherence to its management, leveraging its ai capabilities to offer tailored educational support. study objective: 1. to assess the effectiveness of the gout buddy in improving patients' dietary literacy of gout, adherence, and overall health outcomes 2. to explore the views, acceptance, and potential challenges associated with using the gout buddy among the participants and the multidisciplinary care team, including nurses, dietitians, and pharmacists. the study will utilize the re-aim framework (reach, effectiveness, adoption, implementation, and maintenance) to comprehensively evaluate the gout buddy intervention. this study will employ a sequential explanatory mixed-methods design, incorporating both quantitative and qualitative approaches. quantitative phase: two-arm randomized controlled trial (rct) arm 1: usual care + gout buddy arm 2: usual care qualitative phase: in-depth interviews will be conducted with participants who used the gout buddy, as well as the multidisciplinary care team (nurses, dietitians, and pharmacists) involved in patient care. these interviews will explore their experiences, views on the effectiveness of the chatbot, and any challenges they encountered. this will provide insights into the user experience and identify any barriers to effective implementation of gout buddy in managing patient care. recruitment and consent taking participant recruitment eligible patient participants will be identified through either screening from electronic medical records (emr) or referred by the attending physicians to the study team. the study team will contact potential participants in-person or over phone to invite them to participate in the study and arrange a screening visit following which they will be recruited in-person during their regular clinic visits. the study team will verify eligibility using a screening tool and written informed consent will be obtained in-person at the study site, in a quiet and separate room or space, ensuring privacy. adequate time will be given to read the documents and questions, emphasizing the voluntary nature of participation and its independence from routine clinical care. participant withdrawal participants may choose to withdraw from the study at any time without providing an explanation and this will not result in any punitive consequences. randomization patient participants from each study site will be randomly allocated in a 1:1 ratio to one of the two arms in an open-label fashion, using computer-generated random numbers for simple randomization of subjects. the nature of the intervention makes impossible to blind patients and research team to participant allocation. the randomization sequence is written and kept in an opaque sealed envelope, which will be labelled with a serial number. the study team will open the sealed envelope once the patient has consented to participate and then will be assigned to the study arms accordingly. qualitative: in-depth interviews a semi-structured interview with retrospective probing, using an interview guide will be conducted among the participants who used the gout buddy, as well as with members of the multidisciplinary care team (nurses, dietitians, and pharmacists) involved in patient care till point of data saturation. maximum variation sampling will be employed based on their age, ethnicity, and socioeconomic status. the session will be audio-recorded. follow-up study participants will be followed up at their subsequent clinic visit, scheduled 3-6 months after enrolment, by a second assessor who is blinded to the intervention. if a participant does not return to the clinic within the study period, the follow up survey will be conducted via phone at a time convenient for them.

Eligibility criteria:
inclusion criteria: * age \> 21 years * at least one serum uric acid \> 360 μmol/l or an episode of acute gout attack within the last 18 months * able to read and speak english * access to an internet-enabled device compatible with gout buddy exclusion criteria: * not a singapore citizen or permanent resident * pregnant * end-stage kidney disease or on renal replacement therapy * known terminal illness * visual and/or hearing impairment * cognitive impairment or mental illness * unable to provide informed consent * individuals who do not have a personal mobile device with internet access

Keywords: gout dietary literacy artificial intelligence chatbot primary care