Exploring the minds of rural seniors: A journey into cognitive health in aging communities
Authors: Naganandini Raju 
Published: December 2024

ABSTRACT

Background:
Cognitive function in older adults is a crucial aspect of overall health and well-being, particularly as the global population continues to age. Rural areas often face unique challenges that can impact cognitive health, including limited access to health-care services, lower educational opportunities, and lifestyle factors that may differ significantly from urban counterparts. By identifying the predictors of cognitive function and understanding the geographical disparities, the study seeks to inform targeted public health strategies and interventions to support cognitive health in rural populations.

Aim:
This study aims to investigate the level of cognitive function among older adults in four different rural areas, examining how demographic, socioeconomic, and lifestyle factors contribute to cognitive health.

Methodology:
A cross-sectional study was conducted with 800 participants (200 from each rural area). Cognitive function was assessed using the mini–mental state examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Demographic, socioeconomic, and lifestyle variables were recorded. Correlation analyses, analysis of variance, analysis of covariance, and multivariable regression analyses were performed to identify significant relationships and differences.

Results:
The study’s participants had a mean age of 72.4 years, with females comprising 55% of the sample. A quarter of participants reported education beyond primary school, and 42.5% had low socioeconomic status. Smoking was reported by 28.75% of participants, while 46.25% engaged in regular physical activity. Significant differences were observed in MMSE and MoCA scores between rural areas (P < 0.001), with rural area D scoring the highest (MMSE: 27.5, MoCA: 24.2) and rural area C scoring the lowest (MMSE: 24.1, MoCA: 20.7). Positive correlations were found between cognitive scores and education level (MMSE: r = 0.35, MoCA: r = 0.40) and physical activity (MMSE: r = 0.21, MoCA: r = 0.22), while negative correlations were observed with age (MMSE: r = −0.15, MoCA: r = −0.12), smoking status (MMSE: r = −0.28, MoCA: r = −0.27), and alcohol use (MMSE: r = −0.25, MoCA: r = −0.23). ANOVA indicated significant differences in MMSE scores between areas (F[3, 796] =12.34, P < 0.001). ANCOVA, adjusting for confounders, confirmed these differences (F[3, 792] =10.47, P < 0.001). Post hoc Tukey tests revealed that rural area D had significantly higher MMSE scores than Areas B and C (P < 0.01), and Area A had higher scores than Area C (P < 0.05). Significant factors associated with MMSE scores included age (β = −0.12, P = 0.01), education level (β =0.35, P < 0.001), physical activity (β =0.21, P < 0.05), smoking status (β = −0.28, P < 0.01), and alcohol use (β = −0.25, P < 0.01).

Conclusion:
Cognitive function among older adults varies significantly across different rural areas. Higher education levels and regular physical activity are associated with better cognitive performance, while older age, smoking, and alcohol use are negatively associated. These findings underscore the importance of targeted interventions to improve cognitive health in rural aging populations.