Fatigue is a major cause of exercise-induced muscle damage (EIMD). Compression garments (CGs) can aid post-exercise recovery, therefore, this study explored the effects of CGs on muscular efficacy, proprioception, and recovery after exercise-induced muscle fatigue in people who exercise regularly. Twelve healthy participants who exercised regularly were enrolled in this study. Each participant completed an exercise-induced muscle fatigue test while wearing a randomly assigned lower-body CG or sports pants (SP); after at least 7 days, the participant repeated the test while wearing the other garment. The dependent variables were muscle efficacy, proprioception (displacements of center of pressure/COP, and absolute error), and fatigue recovery (muscle oxygen saturation/SmO₂, deoxygenation and reoxygenation rate, and subjective muscle soreness). A two-way repeated measure analysis of variance was conducted to determine the effect of garment type. The results indicated that relative to SP use, CG use can promote muscle efficacy, proprioception in ML displacement of COP, and fatigue recovery. Higher deoxygenation and reoxygenation rates were observed with CG use than with SP use. For CG use, SmO₂ quickly returned to baseline value after 10 min of rest and was maintained at a high level until after 1 h of rest, whereas for SP use, SmO₂ increased with time after fatigue onset. ML displacement of COP quickly returned to baseline value after 10 min of rest and subsequently decreased until after 1 hour of rest. Relative to SP use, CG use was associated with a significantly lower ML displacement after 20 min of rest. In conclusion, proprioception and SmO₂ recovery was achieved after 10 min of rest; however, at least 24 h may be required for recovery pertaining to muscle efficacy and soreness regardless of CG or SP use.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications’ objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications—namely healthcare apps, smartwatches, and remote temperature scanners—are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.