JMIR Publications

ABSTRACT

Background:

Background:

Many lifestyle behaviors—including smoking, alcohol consumption, engagement in physical activity and social activity—have been identified as potential determinants of cognitive impairment risk in older adults. Gaining insight into how these lifestyle behavior patterns influence cognitive function is crucial for the development of targeted evidence-based interventions.

Objective:

Objective:

This study aimed to identify lifestyle behavior patterns among community-dwelling older adults in China and to examine their associations with cognitive impairment.

Methods:

Methods:

A cross-sectional study was conducted between January and June 2023 in communities in Beijing, China. Self-reported data were collected using standardized questionnaires to assess unhealthy behaviors (smoking and alcohol consumption) and health-promoting behaviors (physical and social activity). Each behavior was dichotomized: healthy behavior scored as 1, and unhealthy behavior as 0. Cognitive function was evaluated using the Mini-Mental State Examination. Latent Class Analysis (LCA) was employed to identify lifestyle behavior classes characterized by similar characteristics of lifestyle behaviors. The resultant classes, were then used to further explore associations with cognitive function using multiple logistic regression.

Results:

Results:

A total of 2060 eligible older adults were included, with a cognitive impairment prevalence of 11.0% (228/2,060) (228/2060). Three distinct lifestyle behavior patterns emerged: (1) High control-High engagement (n=685, 33.3%), (2) High control-Low engagement (n=1210, 58.7%), and (3) Low control-Low engagement (n=165, 8.0%). Older adults in the High control-High engagement group, characterized by non-smoking, low-to-moderate alcohol consumption, and frequent engagement in physical and social activities, exhibited the lowest risk of cognitive impairment. Using the High control-High engagement group as the reference, participants in both the High control-Low engagement group and the Low control-Low engagement group exhibited significantly higher risks across the analyses. Specifically, in the crude model, the High control-Low engagement group had an odds ratio (OR) of 2.026 (95% CI: 1.448-2.885; P < 0.001), while the Low control-Low engagement group had an OR of 2.839 (95% CI: 1.698-4.677; P < 0.001). Similarly, in the fully adjusted model, the corresponding ORs were 1.852 (95% CI: 1.314-2.655; P < 0.001) for the High control-Low engagement group and 2.905 (95% CI: 1.670-5.001; P < 0.001) for the Low control-Low engagement group. Subgroup analyses revealed that males (P for interaction=0.018) and hypertensive (P for interaction=0.021) individuals within the High control-Low engagement group were at an even greater risk.

Conclusions:

Conclusions:

Our findings revealed that both avoiding harmful behaviors and actively engaging in health-promoting activities are important for cognitive health in older adults. Based on these results, we recommend a dual-pathway intervention model in policymaking, simultaneously optimizing risk behaviors management and healthy behaviors promotion mechanisms.