Spatial cognition as an early cognitive Marker for Alzheimer's disease: evidence from NavegApp, an innovative serious game for cognitive assessment
Bibliographic record
Abstract
Abstract Background Limited access to diagnostic technologies in low and middle‐income countries hinders the early detection of Alzheimer's Disease (AD). Advances in digital neuropsychology have enabled the development of tools like NavegApp, a serious game‐based platform that evaluates allocentric navigation, mental rotation, and visuospatial memory—domains affected during the preclinical and prodromal stages of AD. While previous studies have confirmed NavegApp's usability and content validity, further evidence of its construct validity and diagnostic accuracy is required to support its potential implementation in primary care settings. The analysis of cognitive changes during these early disease stages, particularly in individuals carrying causative mutations for early‐onset familial AD, is essential to advance the clinical utility of innovative digital cognitive markers. Therefore, the main objective of this study was to evaluate the construct validity and diagnostic accuracy of NavegApp's spatial cognition metrics across the AD spectrum. Method A retrospective observational study was conducted with 226 participants classified into five groups, including presymptomatic and symptomatic PSEN1‐E280A carriers. Construct validity was assessed by examining correlations between NavegApp metrics and standard neuropsychological assessments. Group performance differences were analyzed using effect size estimates, controlling for sex, education, and age, while diagnostic accuracy was evaluated using ROC curve analysis. Result NavegApp's spatial cognition metrics demonstrated moderate associations with general cognitive status, memory, and visuospatial domains. Diagnostic accuracy analysis revealed excellent discriminative capacity for identifying symptomatic PSEN1‐E280A carriers compared to asymptomatic participants, particularly in allocentric navigation (AUC‐ROC = 0.94–0.97). However, diagnostic performance for early preclinical detection was limited (AUC‐ROC = 0.66). In contrast, metrics showed acceptable accuracy for distinguishing sporadic MCI from healthy elder controls (AUC‐ROC = 0.77). Conclusion The findings demonstrate the feasibility of NavegApp as an innovative digital tool with potential applications in cognitive screening, particularly in underserved populations. Further research is needed to validate its clinical utility in broader settings and to explore the variability of cognitive changes across the AD spectrum. This evidence could inform its integration into clinical workflows for the preclinical and prodromal detection of AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".