Associations between sociodemographic and clinical characteristics and adherence to self-management behaviours among community-dwelling older adults with mild cognitive impairment.

BACKGROUND

Self-management is vital for individuals with mild cognitive impairment (MCI) to enhance cognitive health and mitigate the risk of dementia. However, maintaining consistent engagement in self-management strategies can be challenging for older adults with MCI. This study aimed to explore the associations between sociodemographic and clinical characteristics and adherence to self-management behaviours among community-dwelling older adults with MCI.

METHODS

A cross-sectional study involving 467 community-dwelling older adults with MCI was conducted from July to November 2022 across Nanjing, Xuzhou, and Lianyungang in Jiangsu Province, China. Data on sociodemographic and clinical characteristics, as well as self-management behaviours for MCI, were collected. Multiple linear and binary logistic regressions were employed to identify factors influencing self-management behaviours.

RESULTS

Among the four recommended self-management behaviours for MCI, participants adopted an average of 1.37 ± 1.202 behaviours. Physical exercise (n = 239; 51.2%) was the most prevalent behaviour, followed by modifiable risk factors management (n = 156; 33.4%), cognitive training (n = 144; 30.8%), and cognitive function monitoring (n = 103; 22.1%). Higher education level (B = 0.286, P < 0.001), urban employee medical insurance (B = 0.621, P < 0.001), and non-smoking status (B = 0.389, P = 0.002) positively correlated with aggregate MCI self-management behaviours, while older age (B = -0.295, P < 0.001) had a negative association. Marital status, monthly income, employment status, gender, residence, and family history of cognitive disorders were also found to be associated with specific self-management behaviours for MCI.

CONCLUSIONS

Enhancing adherence to self-management among community-dwelling older adults with MCI is critical. Sociodemographic and clinical factors should be considered to pinpoint at-risk populations and accordingly tailor support strategies.