Chronic Diseases

This study aims to examine the effects of an AI-based mobile application delivering Tai Chi and Baduanjin exercises on balance and fall risk in adults with chronic diseases. Participants will be randomly assigned to Tai Chi, Baduanjin, or a control group over a 12-week period. The AI application will provide exercise guidance and basic feedback. Outcomes will focus on balance and, fall risk, along with user interaction with the technology. The results are expected to support the use of AI-assisted exercise programs in improving balance and reducing fall risk in individuals with chronic conditions.

This study aims to understand the health status, work stress, and occupational exposure in the work environment among nurses in Gansu Province, China. We will collect information through questionnaires and conduct follow-up surveys every two years. The results of this study will help improve working conditions for nurses in the future and provide scientific evidence for developing health protection policies for the nursing workforce.

This mixed-methods study aims to examine the impact of exposure to online medical misinformation on medication adherence, treatment decision-making, and healthcare-seeking behavior among patients with chronic diseases. The study targets patients with hypertension, diabetes mellitus, and dyslipidemia and evaluates how misinformation influences real-world health behaviors. Quantitative and qualitative data will be collected concurrently to provide a comprehensive understanding of misinformation-related health risks.

This study aims to address the lack of intelligent governance tools in clinical data management to promote efficient governance and secure sharing of real-world health data. To achieve this, a self-adaptive, automated governance intelligent agent will be developed based on a High-Order Programming (HOP) architecture, integrating Large Language Models (LLMs) and deep learning techniques. The agent will continuously monitor and correct data quality issues in real time, improving data accuracy and usability. In parallel, the project will establish a trusted data-sharing framework by integrating AI Confidential Computing (AICC) with Trusted Data Matrix (TDM) technologies. This framework will enable secure, real-time cross-institutional data exchange and collaborative computation while protecting sensitive information. Overall, the study aims to transform fragmented clinical data into high-quality, standardized, and securely accessible resources, thereby facilitating the circulation of data value and advancing collaborative medical research.

This study aims to codesign and implement a physical health improvement behaviour change intervention for people being treated for a mental illness in a mental health inpatient unit of a regional hospital.

Objective: To evaluate and compare the effectiveness and cost-effectiveness of a Virtual Community of Practice (VCoP) via a web-based application versus individual and self-administered online education to improve the activation of middle-aged people with multimorbidity. Design: Randomized controlled pragmatic clinical trial with two parallel arms and 18 months follow-up. Setting: Primary health centres and hospitals (Catalonia, Madrid, and the Canary Islands). Population: Middleaged people (30-60 years old) with multimorbidity (≥=2 chronic diseases). Sample size: 240 patients. Randomization: all participants will be randomly assigned to the intervention (VCoP) or the active control group. Data analysis will be blinded to intervention allocation. Intervention: The intervention group will be offered participation for 12 months in a VCoP based on a gamified web-based application. The control group will receive individual, content-focused education through a web platform that will cover the same topics as the VCoP but will be self-administered and without social interaction within the platform. Measurements: The main variable will be measured using the Patient Activation Measure (PAM) questionnaire at baseline, 6, 12 and 18 months. Secondary variables: participant sociodemographics, depression (PHQ-9), anxiety (HADS-A), treatment burden (TBQ), quality of life (EQ-5D-5L), variables related to the use of health resources and to the use of the VCoP. Analysis: Mixed-effects linear regression will be used to determine the effects of the VCoP on the changes in patient activation. Scores at baseline, 6, 12 and 18 months will be included as a fixed effect variable, and the patient and health professional as random effect variables. Analyses will be performed on an intention-to-treat basis. An economic evaluation will be carried out to analyze the cost-effectiveness of the VCoP compared to active control, from the National Health System and social perspectives.