Chatbot

To evaluate the impact of AI-powered chatbot interactions versus traditional educational handouts on increasing participants' knowledge of oral cancer and its prevention

Pneumonia is a leading cause of death and hospitalization among the elderly in Taiwan. High-quality home care is essential to recovery and reducing readmission, yet primary caregivers often lack the specific skills needed, such as airway clearance and safe feeding techniques. Traditional education, consisting of one-time verbal instructions and paper brochures, often lacks interactivity and real-time support. This study introduces "Pneumonia Care Helper," an interactive LINE chatbot designed to provide digital health education. The goal is to evaluate whether this digital tool is more effective than traditional paper-based education in improving the knowledge, attitudes, and caregiving practices of primary caregivers of elderly pneumonia patients. The study will compare the outcomes of caregivers using the chatbot versus those receiving standard paper-based instructions over a 5-day intervention period.

The goal of this clinical trial is to learn whether a postpartum chatbot powered by generative artificial intelligence (genAI) can help new mothers get better pelvic floor health information and feel less lonely after childbirth. The main questions this study aims to answer are: * Does using the chatbot improve postpartum pelvic floor health knowledge? * Does using the chatbot help reduce feelings of loneliness during the postpartum period? * Does using the chatbot impact pelvic floor symptoms? Researchers will compare standard postpartum care to standard care plus the chatbot. Participants will: Be assigned by chance (like flipping a coin) to standard postpartum care with or without access to the chatbot. If in the chatbot group, participants will receive education and support via the chatbot over a 4-week period. Both groups will complete questionnaires to measure their pelvic floor knowledge, pelvic floor symptoms, feelings of loneliness, depression, infant bonding, perceived social support, adverse childhood experiences, and peri-traumatic distress. The chatbot was created by urogynecology experts in collaboration with UC San Diego computer science and biomedical informatics researchers. The chatbot is designed to give new mothers personalized, evidence-based information and support in real time.

Caregivers play a key role in the provision of day-to-day care and the coordination of care services. Caregivers of stroke patients use dysfunctional coping strategies to cope with the stressors and burden of care they encounter during this long caregiving process. In this study, it will be tried to improve the stress coping skills of caregivers by using an application with chatbot support based on the COM-B model.In the study, introductory information form, stress coping styles scale, depression, anxiety, stress (DASS 21) and burden of care measurement tool will be used. The study will be conducted in a randomized controlled manner. Chatbot will be applied to the experimental group and the control group will be exposed to routine practice. The study group will consist of individuals who care for those who are discharged home from Atatürk University stroke center. Experiments and controls will be accessed by searching the hospital records. The fact that nursing education is given through a chatbot and that the chatbot is designed according to a stress training model (behavior change wheel) reflects the originality of the study. With this study, caregivers are expected to be able to manage stress effectively by teaching them how to cope with stress.

The purpose of this study is to develop a Smart mHealth Strategy that delivers behavior change techniques through wearable physical activity trackers and social media chatbots, including self-monitoring, real-time feedback and reminders, goal-setting, competition and rewards, social support, and health coaching. This study also aims to explore the effect of the Smart mHealth Strategy on the behavioral outcomes and psychological factors of physical activity, and physical and mental health. The study design is a three-stage randomized controlled trial. In each stage, 120 are recruited and randomly assigned to control and experimental groups. Participants are adults with insufficient physical activity and a sedentary lifestyle. The Smart mHealth Strategy uses smartwatches and self-developed chatbots. The constrained dialogue content is designed to finally deliver the six behavior change techniques. Data are collected in the pre-, mid-, and post-tests. The measurement includes self-administered questionnaires, Actigraphy GT9X, Inbody 270S, OMRON HEM-7130, and heart rate variability monitors.