In silico modeling of tibial fatigue life in physically active males and females during different exercise protocols. (1st May 2022)

• Record Type: Journal Article
• Authors: Paul, Elliot; Pant, Anup; George, Stephanie; Willson, John; Meardon, Stacey; Vahdati, Ali
• Is Part Of: Biomedical physics & engineering express. Volume 8:Number 3(2022)
• Publication Date: 2022-05-01
• Subjects: bone stress injury -- computer modeling -- musculoskeletal modeling -- gait mechanics -- fatigue life -- stress fracture; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/ac62ff
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: Preventing bone stress injuries (BSI) requires a deep understanding of the condition's underlying causes and risk factors. Subject-specific computer modeling studies of gait mechanics, including the effect of changes in running speed, stride length, and landing patterns on tibial stress injury formation can provide essential insights into BSI prevention. This study aimed to computationally examine the effect of different exercise protocols on tibial fatigue life in male and female runners during prolonged walking and running at three different speeds. To achieve these aims, we combined subject-specific magnetic resonance imaging (MRI), gait data, finite element analysis, and a fatigue life prediction algorithm, including repair and adaptation's influence. The algorithm predicted a steep increase in the likelihood of developing a BSI within the first 40 days of activity. In five of the six subjects simulated, faster running speeds corresponded with higher tibial strains and higher probability of failure. Our simulations also showed that female subjects had a higher mean peak probability of failure in all four gait conditions than the male subjects studied. The approach used in this study could lay the groundwork for studies in larger populations and patient-specific clinical tools and decision support systems to reduce BSIs in athletes, military personnel, and other active individuals.