Effects of Augmented Reality Dual-Task Training with ChatGPT-Assisted Development on Strength, Balance, Spatiotemporal Gait Parameters, and Fall-Related Self-Efficacy in Patients with Chronic Stroke: A Randomized Controlled Trial.
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 invest...
| Publicado en: | NeuroRehabilitation Vol. 59; no. 1; pp. 85 - 99 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196086092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196086092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10538135 3RE jtl: NeuroRehabilitation issn: 10538135 maglogo: N pubinfo: dt: Aug2026 vid: 59 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 196086092 193830855 196086092 196086092 10.1177/10538135261449483 196086092 ppf: 85 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effects of Augmented Reality Dual-Task Training with ChatGPT-Assisted Development on Strength, Balance, Spatiotemporal Gait Parameters, and Fall-Related Self-Efficacy in Patients with Chronic Stroke: A Randomized Controlled Trial. aug: au: Han, Sangyong Yoon, Jeongeun Park, Donghwan affil: Department of Physical Therapy, Kyungnam University, Changwon, Republic of Korea sug: subj: Chronic Disease Rehabilitation Stroke Rehabilitation Augmented Reality Artificial Intelligence, Generative Therapeutic Exercise Methods Lunge Cognition Motor Skills Gait Analysis Accidental Falls Prevention and Control Self-Efficacy Evaluation Lower Extremity Physiology Muscle Strength Evaluation Balance, Postural Evaluation Treatment Outcomes Human Randomized Controlled Trials Random Assignment Comparative Studies Multitasking Behavior Male Female Adult Middle Age Aged Stroke Patients Rehabilitation Patients Pilot Studies Throwing Walking Speed Step Analysis of Variance Paralysis Rehabilitation Clinical Assessment Tools Physical Therapy Virtual Reality Resistance Training Dual-Task Tests Quadriceps Muscles Ankle Joint Plantarflexion Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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