To investigate the relationship between motor parameters from virtual reality tasks and sleep-related measures in identifying cognitive impairment in patients with mild cognitive impairment (MCI), emphasizing the potential for improved early detection.
Key Findings:
Patients with MCI reported significantly poorer sleep quality compared to HC, with specific metrics from the Pittsburgh Sleep Quality Index.
MCI patients required more time and achieved lower accuracy in VR tasks, with statistical values provided.
Strong correlations were found between VR performance metrics and cognitive test scores, with correlation coefficients included.
Integrating VR-derived markers with sleep parameters improved predictive accuracy for MCI, with AUC values specified.
Interpretation:
The combination of VR-based cognitive tasks and sleep quality assessments provides a robust, noninvasive method for early identification of prodromal Alzheimer's disease, with implications for clinical practice.
Limitations:
The study had a small sample size, which may limit the generalizability of the findings.
Participants were recruited from a single center, potentially introducing selection bias.
Conclusion:
This multimodal approach enhances clinical decision-making and enables timely interventions for cognitive decline.
