Clinical Research Directory

AI-Supported E-Learning for Pressure Injury Prevention

Brief Summary Pressure injuries are damage to the skin and underlying tissue that may occur in patients who stay in bed for long periods, have limited mobility or use medical devices. These injuries are often preventable. They may cause pain, infection, longer hospital stays and increased care costs. For this reason, it is important that nurses and nursing students have accurate knowledge, confidence and positive attitudes about preventing pressure injuries. The aim of this study is to compare two different methods of teaching pressure injury prevention to nursing students. One group of students received the education in a face-to-face classroom session. The other group completed the same topic through an artificial intelligence-supported modular e-learning programme. The e-learning programme included short videos, clinical examples, question-and-answer activities, short quizzes and feedback. The educational materials prepared with artificial intelligence support were checked by experts before being used with students. Undergraduate nursing students took part in the study. The students were randomly assigned to one of two groups. In both groups, measurements were taken before the education, immediately after the education and four weeks later. The study assessed students' knowledge of pressure injury prevention, their confidence in managing pressure injury prevention and their attitudes towards prevention. This study does not provide a treatment or direct intervention to patients. However, its findings may help improve how nursing students and future healthcare professionals are educated about pressure injury prevention. In the long term, better education may support safer patient care, help prevent pressure injuries and improve the quality of care.

Increasing Germline Genetic Testing for Patients With Cancer

Germline testing for hereditary cancer syndromes is underutilized across most health care settings. Using a learning health care approach, the Genomics-enabled Learning Health Systems (gLHS) network aims to evaluate the impact of a suite of implementation strategies to increase germline test ordering by oncology care teams (i.e., mainstreaming) for eligible patients with breast, pancreatic or colorectal cancer. Secondarily, the study will investigate completion of testing by eligible patients, as well as impact on overall rates of germline test ordering in patients with cancer. The network will bundle and deploy different implementation strategies across the clinical sites in three 6-month phases. A maintenance phase after the implementation periods will measure genetic testing rates without any additional implementation strategies to determine persistence of effects. The implementation strategies address clinician-level factors, and thus oncologists and their team members (e.g. advanced practice providers, nurse navigators, case managers) will be the focus of evaluating the impact of implementation strategies. Strategies that will be considered include provider education, audit and feedback reports, facilitation, peer support, and electronic health record (EHR) system optimization to support germline testing. Using the RE-AIM QuEST framework, outcomes will be assessed using mixed methods separately for each eligible cancer type. Data collection from the EHR, other relevant data sources, and qualitative provider feedback will be used to assess ordering and completion of tests and the effect of the implementation strategies on germline testing rates in oncology clinics.

Neuromuscular Strength Factors Influencing Vertical Jump Performance

This cross-sectional observational study is designed to comprehensively analyze the relationship between high-volume isokinetic muscle performance and explosive vertical jump capacity in healthy, recreationally active young adult males. While traditional neuromuscular assessments frequently emphasize momentary peak force (Peak Torque), this study investigates the hypothesis that sustained mechanical work capacity (Total Work) and fatigue resistance (Fatigue Index) are more robust predictors of complex explosive tasks such as the countermovement jump (CMJ). Methodology and Testing Protocol: Participants will complete all assessments in a single laboratory session under controlled environmental conditions. The standardized testing sequence is as follows: 1. Anthropometric and Body Composition Assessment: Participants' height, body mass, and body fat percentage will be measured using a multi-frequency bioelectrical impedance analyzer (InBody 270) following standard manufacturer protocols. 2. Countermovement Jump (CMJ) Test: Following a standardized dynamic warm-up, participants will perform the CMJ test on a validated contact mat system (SmartJump). Participants will start from a standing position with hands placed on the hips to eliminate the influence of arm swing. They will perform a quick downward countermovement to approximately 90 degrees of knee flexion and execute a maximal vertical jump. Three trials will be conducted with a 30-second rest interval between attempts, and the highest jump will be recorded for analysis. 3. Isokinetic Muscle Strength and Fatigue Test: After a 5-minute cycle ergometer warm-up, participants will undergo isokinetic testing on a computerized dynamometer (Humac Norm). Following familiarization trials, participants will execute a high-volume fatigue protocol consisting of 50 continuous, maximal concentric knee flexion and extension repetitions at an angular velocity of 180°/sec for both legs. Primary Variables Analyzed: From the isokinetic protocol, the primary variables of interest include Peak Torque (Nm), Average Power (W), Total Work (Nm), and the Isokinetic Fatigue Index (%). These mechanical metrics will be statistically analyzed against the maximum CMJ height (cm) to evaluate the predictive strength of sustained mechanical work capacity versus momentary force production on explosive motor performance.