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Record W7117883087 · doi:10.5195/ijms.2025.4074

Cognitive Impairment and Its Influencing Factors Among Elderly at Residential Homes in Western Tamil Nadu -A Cross-sectional Study

2025· article· W7117883087 on OpenAlexaboutno aff
S. Iruthaya K

Bibliographic record

VenueInternational Journal of Medical Students · 2025
Typearticle
Language
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusCognitive impairmentAnthropometrySafeguardingAutonomyPsychological interventionCognitionPopulation

Abstract

fetched live from OpenAlex

Background: The global population is experiencing rapid aging, with projections indicating that the number of older adults will double by 2050. In India, it is anticipated that one in every five individuals will be aged 60 years or above in the near future. This demographic transition brings with it an increasing prevalence of cognitive impairment (CI), characterized by declines in memory, attention, or executive functioning that are more severe than typical age-related changes but do not meet criteria for dementia. Elderly individuals living in old age homes face additional vulnerabilities due to social isolation, weakened family support, and adjustments to institutional care. Despite these challenges, cognitive health within residential care settings has received limited attention in the Indian context. Identifying the prevalence and contributing factors of CI is essential for promoting appropriate healthcare interventions and safeguarding the autonomy and well-being of older adults. Aim: The present study was designed to evaluate the prevalence of cognitive impairment and to examine the factors associated with it among elderly residents of old age homes in Coimbatore. Methods: A cross-sectional study was carried out between November 2024 and February 2025 across selected old age homes in Coimbatore. Using multistage random sampling, 200 individuals aged ≥60 years were recruited. Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). A pretested semi-structured questionnaire collected data on demographic and socioeconomic characteristics, financial dependence, and past occupations, as well as health conditions and lifestyle factors. Anthropometric measurements and clinical parameters such as blood pressure and pulse were recorded. Data were analysed using SPSS v25.0, and associations were tested at a significance level of p<0.05. Results: Among 200 elderly participants (mean (± SD) age 69.8 ± 9.8 years; 57% men, 43% women), one-third were illiterate and only 10.5% received a pension. The mean MoCA score was 15.2 ± 5.6, with 30.5% showing mild, 49% moderate, and 18% severe cognitive impairment, and it was notably higher among women (figure 1). Cognitive impairment was significantly associated with sex (p = 0.03), education (p < 0.001), past occupation (p = 0.020), pension status (p < 0.001), and financial dependence (p < 0.001). Hypertension (34%), diabetes (16.5%), and sleep disturbances (28.5%) were the common comorbidities. These findings indicate a high burden of cognitive impairment among institutionalized older adults, with socioeconomic and health factors playing a critical role. Conclusion: Cognitive impairment was highly prevalent among elderly residents of old age homes and was associated with socioeconomic factors. These findings highlight the urgent need for the annual cognitive screening in the institutional settings and its integration into the existing national geriatric health program in the country. Addressing modifiable risk factors including financial dependence, limited social interaction, and sleep problems can improve the overall quality of life, reduce the caregiver burden, and promote cognitive health as a cornerstone of dignity and healthy aging.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.419
Teacher spread0.398 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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