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Musculoskeletal disease can be a serious condition associated with aging that may lead to fractures and a bedridden state due to decreased motor function. In addition to exercise training to increase muscle mass, increasing muscle function with the intake of functional foods is an effective treatment strategy for musculoskeletal disease. Muscle-specific SOD2-deficient mice (muscle-Sod2-/-) show a severe disturbance in exercise in association with increased mitochondrial reactive oxygen species, as well as mitochondrial dysfunction and muscle damage. In the present study, to develop a therapeutic strategy for musculoskeletal disease, we searched for substances that enhanced motor function among functional compounds by in vivo screening using muscle-Sod2-/- mice as a muscle fatigue model. We administered 96 compounds, including antioxidants, to muscle-Sod2-/- mice and assessed their effects on treadmill performance. Among the administered compounds, gossypin, genistein, kaempferol, taxifolin, fumaric acid, β-hydroxy-β-methylbutyrate Ca, and astaxanthin, which are dietary functional food factors, increased forced running time in muscle-Sod2-/- mice. In addition, troglitazone, tempol, trolox, and MnTE-2-PyP, which are antioxidants, also significantly increased the running ability of muscle-Sod2-/- mice. These results suggest that the intake of functional foods with antioxidant activity can improve motor function. Muscle-Sod2-/- mice, as a muscle fatigue model, are suitable for the in vivo screening of functional substances that promote improvements in exercise and muscle performance.

Running can promote public health. However, the association between running and the built environment, especially in terms of micro street-level factors, has rarely been studied. This study explored the influence of built environments at different scales on running in Inner London. The 5Ds framework (density, diversity, design, destination accessibility, and distance to transit) was used to classify the macro-scale features, and computer vision (CV) and deep learning (DL) were used to measure the micro-scale features. We extracted the accumulated GPS running data of 40,290 sample points from Strava. The spatial autoregressive combined (SAC) model revealed the spatial autocorrelation effect. The result showed that, for macro-scale features: (1) running occurs more frequently on trunk, primary, secondary, and tertiary roads, cycleways, and footways, but runners choose tracks, paths, pedestrian streets, and service streets relatively less; (2) safety, larger open space areas, and longer street lengths promote running; (3) streets with higher accessibility might attract runners (according to a spatial syntactic analysis); and (4) higher job density, POI entropy, canopy density, and high levels of PM 2.5 might impede running. For micro-scale features: (1) wider roads (especially sidewalks), more streetlights, trees, higher sky openness, and proximity to mountains and water facilitate running; and (2) more architectural interfaces, fences, and plants with low branching points might hinder running. The results revealed the linkages between built environments (on the macro- and micro-scale) and running in Inner London, which can provide practical suggestions for creating running-friendly cities.