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Record W4407181573 · doi:10.3389/fnagi.2025.1505815

Role of visual function and performance of activities of daily living in cognitive function in patients with mild cognitive impairment: a cross-sectional study

2025· article· en· W4407181573 on OpenAlexaboutno aff
Chunhui Zhou, Ganfeng Yang, Marc Theeboom, Hua Yang, Ruiting Zhu, Zijian Zhou, Dong Zhu

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Social Science Fund of ChinaScience and Technology Commission of Shanghai Municipality
KeywordsActivities of daily livingCognitionDementiaPsychologyMontreal Cognitive AssessmentVisual impairmentCognitive declineGerontologyCross-sectional studyPsychological interventionClinical psychologyMedicineAudiologyCognitive impairmentPsychiatryDisease

Abstract

fetched live from OpenAlex

Background Mild cognitive impairment (MCI) is a precursor to dementia, with many patients showing early decline in activities of daily living (ADLs). However, the role of visual impairment in this process is underexplored despite evidence suggesting it may accelerate cognitive decline. Current research does not understand how visual dysfunction affects cognitive abilities and how ADLs might moderate this relationship. This gap is crucial because early interventions targeting visual impairments could potentially delay progression to dementia, offering new avenues for supporting MCI patients. Objective This study investigates the relationship between visual function and cognitive abilities in patients with MCI. Specifically, it seeks to determine how different aspects of visual function, such as visual field indices and visual acuity, correlate with various cognitive domains measured by standardized assessments. Additionally, the study aims to examine the role of ADLs as a potential moderating factor in this relationship. By analyzing how ADL performance influences the strength and direction of the association between visual impairments and cognitive function, this research intends to identify key areas where visual deficits may contribute to cognitive decline in MCI. Methods This is a cross-sectional study. Two hundred and seventy four elderly patients were diagnosed with MCI from various communities in Taicang City, Jiangsu Province, China. Most participants were women (68.6%), and the average age was 69 years. Notable comorbidities included hypertension (41.6%) and diabetes (33.2%), indicating a higher burden of health conditions than typical older adult populations. Visual function, Montreal Cognitive Assessment (MoCA), and the ADLs were measured. Pearson's correlation coefficients were used to examine the unadjusted associations between visual function measures and cognitive performance. Multivariable linear regression models were employed to further explore these relationships and to assess the moderating role of ADLs. Results Significant positive correlations were found between visual function and cognitive performance, particularly with Visual Function Index (VFI) showing a strong initial correlation with the total MoCA score (r = 0.61, p < 0.001), which was attenuated after adjusting for ADL (β = 0.06, p = 0.23). Maximum Visual Field (Max VF) was positively correlated with language skills (r = 0.13, p < 0.05). The negative correlation of Minimum Visual Field (Min VF) with cognitive performance became positive after ADL adjustment (β = 0.12, p = 0.04). Conclusions These findings suggest that visual function plays a crucial role in the cognitive and functional abilities of patients with MCI. Early interventions targeting visual impairments may help mitigate cognitive decline and improve the quality of life for these patients.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.292
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2025
Admission routes1
Has abstractyes

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