ReviewNeuropsychology review2025
Digital Tools for Mild Cognitive Impairment: A Systematic Review and Meta-analysis of Diagnostic Accuracy and Methodological Challenges.
Review in Neuropsychology review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual reality-based cognitive training in mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2026Pooled it
- Early Detection of Cognitive Impairment Using Time-Frequency Analysis of Fine Motor Accelerometry.Annals of biomedical engineering · 2026Article
- Longitudinal Variability of Wearable-Derived Sleep and Heart Rate as a Digital Biomarker for Early Detection of Mild Cognitive Impairment.Yonsei medical journal · 2026Article
- Evaluating Cognition Across Aging and Traumatic Brain Injury: Integrating Neurological and Neuropsychological Approaches.Journal of clinical medicine · 2026Review
- Editorial: Mild cognitive impairment and cognitive aging.Frontiers in behavioral neuroscience · 2026Article
- Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing.Frontiers in aging neuroscience · 2026Article
- Is the MMSE enough for MCI? A narrative review of the usefulness of the MMSE.Frontiers in psychology · 2025Review
- Editorial: Methodological and technical issues of tele-neuropsychology: remote cognitive assessment and intervention across the life span.Frontiers in psychology · 2025Article
- From cognitive screening to digital phenotyping: rethinking early detection of cognitive impairment in primary care.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mild cognitive impairment (MCI) represents an intermediate stage between typical aging and early cognitive decline. As such, an early and accurate diagnosis is essential in making timely interventions. Digital tools, including mobile applications, web platforms, wearable devices, and artificial intelligence-driven systems, have been developed and validated to capture multidimensional data, offering innovative screening solutions. This meta-analysis aims to evaluate the diagnostic accuracy of digital tools for MCI detection in different populations and settings, with a particular focus on three key issues: (i) the overall diagnostic performance of digital tools, (ii) the influence of methodological quality of studies, and (iii) the impact of demographic factors and familiarity with technologies on diagnostic accuracy. This meta-analysis assessed diagnostic accuracy across 32 studies, reporting pooled sensitivity (0.808, 95% CI: 0.775-0.838) and specificity (0.795, 95% CI: 0.757-0.828), but with considerable heterogeneity (I
Indexed as
Identifiers
41126003What OpenQuestion 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 OpenQuestion graph.