ArticleFrontiers in psychiatry2026
Sleep quality metrics combined with virtual reality motion parameters enhance early detection of mild cognitive impairment.
Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by cognitive and motor deficits. With its global prevalence increasing rapidly and no effective treatment available, early identification of high-risk individuals is critical. This study investigated the relationship between motor parameters extracted from virtual reality (VR) tasks, combined with sleep-related measures, and cognitive impairment in patients with mild cognitive impairment (MCI). Our goal was to determine whether integrating VR-derived digital markers with sleep quality metrics could provide an objective and clinically applicable tool for early detection. Methods: 66 participants were recruited, including 28 healthy controls (HC) and 38 patients with MCI. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). All participants performed two scenario-based VR tasks, during which task completion time, accuracy, and overall performance scores were recorded. Group differences were evaluated using independent-samples t-tests, and these behavioral features and sleep quality metrics were further incorporated into ROC analyze to assess predictive performance for distinguishing MCI from HC. Results: Compared with HC, patients with MCI reported significantly poorer sleep quality based on the Pittsburgh Sleep Quality Index (PSQI) and subdomains such as sleep latency and habitual sleep efficiency. In the VR tasks, MCI patients required more time and achieved lower accuracy than HC, consistent with MoCA and MMSE scores. Correlation analysis confirmed strong associations between VR performance metrics and cognitive test scores. Importantly, integrating VR-derived digital markers with sleep parameters yielded superior predictive accuracy for MCI (AUC = 0.863; sensitivity = 86.84%; specificity = 71.43%; p < 0.001) compared with single-modality models. Conclusion: VR-based cognitive and sensorimotor tasks, when combined with sleep quality assessments, offer a robust and noninvasive approach for the early identification of prodromal AD. This multimodal strategy holds promise for enhancing clinical decision-making and enabling timely interventions.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.