The Efficacy of a Home-Based, Augmented Reality Dual-Task Platform for Cognitive-Motor Training in Elderly Patients: A Pilot Observational Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This study introduces a novel home-based dual-task platform incorporating augmented reality (AR), COGNIMO, aimed at simultaneously enhancing cognition and physical abilities. The purpose of this study was to assess the effectiveness of this intervention in enhancing cognitive and physical abilities in elderly individuals with subjective cognitive decline, mild cognitive impairment (MCI), and mild Alzheimer's dementia. METHODS: A 12-week observational study enrolled 57 participants aged 60-85 years. Primary outcomes included changes in cognitive scores (Korean Mini-Mental State Examination, 2nd edition [K-MMSE-2] and Korean-Montreal Cognitive Assessment [K-MoCA]), while secondary outcomes measured physical parameters and depression scores between baseline and week 12 in the active and the control groups. RESULTS: Of 57 participants, 49 completed the study. The active group (≥12 sessions) exhibited significant improvement in K-MoCA compared to the control group (<12 sessions) (p=0.004), while K-MMSE-2 score changes showed no significant difference (p=0.579). Positive correlations between training sessions and K-MoCA changes were observed (r=0.31, p=0.038), emphasizing a dose-response relationship. Subgroup analyses revealed a distinction in cognitive changes, particularly in the MCI group. CONCLUSION: The COGNIMO platform showed positive effects on cognitive function in MCI patients, suggesting potential benefits for this population. The study highlights the potential of AR-integrated home-based interventions for cognitive enhancement in elderly individuals, underlining the need for further trials in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it