Improving Cardiorespiratory and Muscular Function of Korean Firefighters: A Kinect-Based Mixed Reality Device Exercise Intervention Randomized Control Trial.

OBJECTIVE: This study addresses declining physical fitness among Korean firefighters due to a low exercise participation rate caused by the absence of exercise instructors. The research explores Virtual Mate (VM), a Kinect-based mixed reality device, as a potential solution, but its capacity to replace training instructors in enhancing physical fitness remains uncertain. METHODS: Twenty Korean male firefighters were divided into two groups: the exercise group received an 8-week intervention using VM, while the control group maintained regular physical activity. Cardiopulmonary function and muscle function were measured. RESULTS: After 8 weeks, the exercise group demonstrated a significant improvement in VO 2peak . Furthermore, there were notable enhancements in the strength and endurance of most knee, shoulder, and trunk flexors and extensors. CONCLUSIONS: Integrating VM into an exercise program can effectively improve cardiorespiratory and muscular function in Korean firefighters.

An Artificial Intelligence Exercise Coaching Mobile App: Development and Randomized Controlled Trial to Verify Its Effectiveness in Posture Correction.

BACKGROUND: Insufficient physical activity due to social distancing and suppressed outdoor activities increases vulnerability to diseases like cardiovascular diseases, sarcopenia, and severe COVID-19. While bodyweight exercises, such as squats, effectively boost physical activity, incorrect postures risk abnormal muscle activation joint strain, leading to ineffective sessions or even injuries. Avoiding incorrect postures is challenging for novices without expert guidance. Existing solutions for remote coaching and computer-assisted posture correction often prove costly or inefficient. OBJECTIVE: This study aimed to use deep neural networks to develop a personal workout assistant that offers feedback on squat postures using only mobile devices-smartphones and tablets. Deep learning mimicked experts' visual assessments of proper exercise postures. The effectiveness of the mobile app was evaluated by comparing it with exercise videos, a popular at-home workout choice. METHODS: Twenty participants were recruited without squat exercise experience and divided into an experimental group (EXP) with 10 individuals aged 21.90 (SD 2.18) years and a mean BMI of 20.75 (SD 2.11) and a control group (CTL) with 10 individuals aged 22.60 (SD 1.95) years and a mean BMI of 18.72 (SD 1.23) using randomized controlled trials. A data set with over 20,000 squat videos annotated by experts was created and a deep learning model was trained using pose estimation and video classification to analyze the workout postures. Subsequently, a mobile workout assistant app, Home Alone Exercise, was developed, and a 2-week interventional study, in which the EXP used the app while the CTL only followed workout videos, showed how the app helps people improve squat exercise. RESULTS: The EXP significantly improved their squat postures evaluated by the app after 2 weeks (Pre: 0.20 vs Mid: 4.20 vs Post: 8.00, P=.001), whereas the CTL (without the app) showed no significant change in squat posture (Pre: 0.70 vs Mid: 1.30 vs Post: 3.80, P=.13). Significant differences were observed in the left (Pre: 75.06 vs Mid: 76.24 vs Post: 63.13, P=.02) and right (Pre: 71.99 vs Mid: 76.68 vs Post: 62.82, P=.03) knee joint angles in the EXP before and after exercise, with no significant effect found for the CTL in the left (Pre: 73.27 vs Mid: 74.05 vs Post: 70.70, P=.68) and right (Pre: 70.82 vs Mid: 74.02 vs Post: 70.23, P=.61) knee joint angles. CONCLUSIONS: EXP participants trained with the app experienced faster improvement and learned more nuanced details of the squat exercise. The proposed mobile app, offering cost-effective self-discovery feedback, effectively taught users about squat exercises without expensive in-person trainer sessions. TRIAL REGISTRATION: Clinical Research Information Service KCT0008178 (retrospectively registered); https://cris.nih.go.kr/cris/search/detailSearch.do/24006.

Development of the Korean Firefighter Performance Battery: Job-Related Physical Fitness Assessment.

OBJECTIVE: The Candidate Physical Capability Test (CPAT) has been used in many countries to assess the fitness levels of firefighters, but it requires a special facility with a space of at least 900 square m 2 and expensive equipment. Because it can be difficult to have both money and space at smaller gas stations, we have developed the Korean Firefighter Performance Battery (KFPB). METHODS: To verify the validity and reliability of the developed KFPB, this study consists of three parts, a validation test, reliability test, and evaluation criterion. RESULTS: As a result of the analysis, the KFPB met all of the criteria as a valid evaluation. Furthermore, the KFPB seems to compensate for the shortcomings of CPAT. CONCLUSIONS: In locations where the CPAT is difficult to administer, the KFPB is expected to be used as a firefighting job-related physical fitness evaluation.