| Sumario: | Background: Dual-task and augmented reality (AR)–based interventions have been used to improve balance and gait in patients with stroke; however, evidence for AR programs developed using accessible approaches such as ChatGPT-assisted programming remains limited. Objective: This study aimed to investigate the effects of ChatGPT-assisted AR dual-task training on muscle strength, balance, gait parameters, and fall-related self-efficacy in patients with chronic stroke. Methods: Twenty-eight patients with chronic stroke were randomly assigned to an experimental group receiving ChatGPT-assisted AR dual-task training or a control group receiving lunge training combined with a throwing task. Both groups trained for 10 min per session, five times per week for four weeks. Primary outcomes were lower-limb strength and balance (static and dynamic). Secondary outcomes included gait parameters and fall-related self-efficacy. Gait speed and step length were measured using the GAITRite system, and step-length symmetry was calculated using a symmetry index derived from paretic and nonparetic step lengths. Group × time interactions were analyzed using mixed-design ANOVA. Results: Significant group × time interactions were observed for most primary and secondary outcomes (p < 0.05), in favor of the experimental group; however, the interaction for paretic step length was not statistically significant. Notably, improvement in dynamic balance, as assessed by the timed up and go test, exceeded the minimal clinically important difference in the experimental group. Conclusions: ChatGPT-assisted AR dual-task training improved muscle strength, balance, gait performance, and fall-related self-efficacy more than conventional dual-task training, providing preliminary evidence of short-term benefits for ambulatory patients with chronic stroke.
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