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Eye-hand coordination (EHC) is a critical cognitive-motor function that enables individuals to interact effectively with their environment through visually guided hand movements. It plays an essential role in daily activities such as reaching, grasping, and object manipulation. Previous studies have shown that targeted physical activities and sports can enhance EHC performance. However, aging is commonly associated with declines in EHC, executive function, and postural control, which can negatively affect independence in daily living. These age-related changes are also closely linked to cognitive decline and may contribute to the development of mild cognitive impairment (MCI), dementia, and Alzheimer's disease, thereby increasing the burden on families and healthcare systems.
To mitigate these effects, various cognitive-motor and technology-assisted training approaches have been proposed to improve EHC and cognitive function in older adults. While many existing EHC training systems are computerized and implemented using virtual reality (VR) or mixed reality (MR), accumulating evidence suggests that virtual environments may not fully replicate real-world eye-hand interactions. Limitations in depth perception, haptic feedback, and realism may alter visual fixation strategies, movement execution, and overall task performance, potentially reducing training effectiveness compared with real-world interactions.
Given these limitations, it remains unclear whether real-world EHC training provides greater benefits to executive functions and motor performance than virtual training. Therefore, this study aims to compare the acute effects of EHC exercise performed in a real-world environment and a mixed reality passthrough environment among older adults. The proposed EHC training task involves catching a real three-dimensional (3D) object guided by a physical mini drone, inspired by natural human behaviors such as swatting at flying insects, and its virtual counterpart involving a virtual 3D object and drone. The primary objective is to examine differences in executive functions, task performance, and postural stability between real and virtual EHC conditions. By identifying which training modality better supports cognitive-motor performance, this study seeks to inform the design of effective and engaging interventions for healthy aging and early prevention of cognitive decline.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Underwent the virtual system after the real system | Experimental |
| |
| Underwent the real system after the virtual system | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Real Object-Based Catching System | Other | This condition involves a participant grasping a physical 3D object located beneath the drone in a real-world environment. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Executive Functions via Flanker-ERP Measurement | Each participant underwent Flanker-ERP assessment at three stages: at baseline (pre-intervention) and following both the physical and virtual object-based EHC training sessions. | 2 hours |
| Success Rate (SR) | SR was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems. | 1-1.5 hours |
| Reaction Time (RT) | RT was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems. | 1-1.5 hours |
| Movement Time (MT) | MT was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems. | 1-1.5 hours |
| Peak Hand Velocity (PHV) | PHV was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems. | 1-1.5 hours |
| Time-to-Peak Hand Velocity (TPHV) | TPHV was measured for each participant during object-catching trials across two EHC training modalities: the physical and the virtual 3D object-based drone-catching systems. | 1-1.5 hours |
| Center of Mass (CoM) |
| Measure | Description | Time Frame |
|---|---|---|
| Subjective participant feedback on perceived task difficulty | Subjective participant feedback on perceived task difficulty regarding the physical and virtual object-based EHC training systems was collected using a 5-point Likert scale (1=very easy, 2=easy, 3=neutral, 4=difficult, and 5=very difficult). | 10-15 minutes |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Motion Analysis Laboratory, Dept. of Biomedical Engineeing, National Cheng Kung University | Tainan | 701 | Taiwan |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | R. B. Davis, S. Õunpuu, D. Tyburski, and J. R. Gage, "A gait analysis data collection and reduction technique," Hum Mov Sci, vol. 10, no. 5, pp. 575-587, 1991, doi: https://doi.org/10.1016/0167-9457(91)90046-Z. | ||
| 24782741 | Background | Lopez-Calderon J, Luck SJ. ERPLAB: an open-source toolbox for the analysis of event-related potentials. Front Hum Neurosci. 2014 Apr 14;8:213. doi: 10.3389/fnhum.2014.00213. eCollection 2014. | |
| 15102499 |
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The study is currently under manuscript preparation.
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| Virtual Object-Based Catching System | Other | The condition involves a participant grasping a virtual counterpart of a physical 3D object within a mixed reality (MR) passthrough environment. |
|
The CoM of every participant while performing EHC training tasks was investigated regarding two different EHC training modalities, including physical object-based and virtual object-based drone-catching systems. |
| 1-1.5 hours |
| Center of Pressure (CoP) | The CoP of every participant while performing EHC training tasks was investigated regarding two different EHC training modalities, including physical object-based and virtual object-based drone-catching systems. | 1-1.5 hours |
| Subjective participant feedback on system preference |
Subjective participant feedback regarding preference between the physical and virtual object-based EHC training systems was collected. |
| 10-15 minutes |
| Virtual Reality Sickness Questionnaire (VRSQ) | Adverse effects of the mixed reality environment were evaluated at the end of the experiment using the Virtual Reality Sickness Questionnaire (VRSQ). The VRSQ assesses the severity of nine distinct symptoms on a 4-point scale (none, slight, moderate, and severe). These symptoms include general discomfort, fatigue, headache, eye strain, difficulty focusing, fullness of the head, blurred vision, dizziness with eyes closed, and vertigo. | 10-15 minutes |
| Background |
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