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Veterans with disabilities can experience poor quality of life following military service due to the associated negative physical and psychological ramifications. However, participation in physical activities has shown to induce both physical and mental benefits and improve the quality of life of this population. Adaptive sports, an innovative approach to address the unique physical and psychosocial needs of veterans with disabilities, are becoming more widely used as a rehabilitation tool to improve the quality of life for these veterans. This study aimed to determine the acute influence of participation in a single-day, veteran-based, adaptive kayaking and sailing event on the perceived overall health, quality of life, and quality of social life of veterans with varying disabilities. It was hypothesized that all three categories and the sum score of quality of life would reflect a positive acute response after participation in the community-based physical activity event. Veterans responded to three quality of life-related questions using a 5-point Likert scale before and directly after participating in the event. Findings indicated that an adaptive sporting event can have an acute positive influence on the quality of life of veteran participants, with improvements observed in all three categories of perceived quality of life. Therefore, it is advantageous for the whole-health rehabilitation of veterans with disabilities for the Department of Veterans Affairs to continue to provide opportunities for veterans to participate in non-traditional, community-based activities.

Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger's line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.