Latent profile types and influencing factors of medication adherence mechanisms among rural older adults with multiple chronic conditions.
10.11817/j.issn.1672-7347.2025.250141
- Author:
Zhige YAN
1
,
2
;
Jun ZHOU
3
;
Xing CHEN
3
;
Yao WANG
2
,
4
Author Information
1. Xiangya Nursing School, Central South University, Changsha 410013, China. yanzhige0917@
2. com.
3. Xiangya Nursing School, Central South University, Changsha 410013, China.
4. Xiangya Nursing School, Central South University, Changsha 410013, China. yaowang0428@
- Publication Type:Journal Article
- Keywords:
COM-B model;
latent profile analysis;
medication adherence;
multiple chronic conditions;
older adults
- MeSH:
Humans;
Aged;
Rural Population;
Male;
Female;
China;
Medication Adherence/psychology*;
Surveys and Questionnaires;
Chronic Disease/drug therapy*;
Multiple Chronic Conditions/drug therapy*;
Social Support;
Motivation;
Middle Aged;
Health Literacy;
Aged, 80 and over
- From:
Journal of Central South University(Medical Sciences)
2025;50(8):1443-1454
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:Older adults in rural areas with multiple chronic conditions (MCC) generally exhibit poorer medication adherence than the general elderly population. Considering individual heterogeneity helps to design precise subgroup-based interventions. This study aims to identify latent profile types of medication adherence mechanisms among rural older adults with MCC based on the capability-opportunity-motivation-behavior (COM-B) model, and to explore factors influencing medication adherence.
METHODS:A multistage sampling method was used to recruit 349 rural older adults with MCC from 10 administrative villages in Jianghua County, Yongzhou City, Hunan Province, between July and September, 2024. Participants were surveyed using a general information questionnaire, the Health Literacy Scale for Chronic Patients, the Beliefs about Medicines Questionnaire-Specific, the Multidimensional Scale of Perceived Social Support, and the Morisky Medication Adherence Scale. Latent profile analysis based on the COM-B model was conducted to identify subgroups of medication adherence mechanisms. Univariate and Logistic regression analyses were used to identify influencing factors associated with different latent profiles and adherence levels.
RESULTS:Among the participants, 33.5% demonstrated good medication adherence. The 5 most prevalent chronic diseases were hypertension (86.5%), diabetes (36.7%), arthritis or rheumatism (34.4%), stroke (21.8%), and heart disease (17.5%). Overall, rural older adults with MCC exhibited relatively good medication capability, opportunity, and motivation. Their medication adherence mechanisms were classified into 3 latent profiles: "family-support restrained type" (5.2%), "family-support driven type" (52.1%), and "comprehensive advantage type" (42.7%). Significant differences were observed among the three profiles in terms of education level, marital status, living arrangement, and per capita monthly household income (all P<0.05). Multivariate Logistic regression revealed that higher education level was a protective factor for belonging to the "comprehensive advantage type" rather than the "family-support driven type" [OR=0.277, 95% CI (PL) 0.126 to 0.614, P=0.002]. Furthermore, significant differences in education level, self-rated health status, and latent profile type were found between participants with good and poor adherence (P<0.05). Binary Logistic regression indicated that with each one-level increase in self-rated health status, the risk of poor adherence increased by 293.9% [OR=3.939, 95% CI (PL) 1.610 to 9.636, P=0.003]. Compared with the "family-support restrained type", individuals classified as the "comprehensive advantage type" had a 96.8% [OR=0.032, 95% CI (PL) 0.008 to 0.123, P<0.001] lower risk of poor medication adherence.
CONCLUSIONS:The mechanisms underlying medication adherence among rural older adults with MCC show clear heterogeneity. Primary healthcare providers should focus on the "family-support restrained type" subgroup, strengthen social support networks, and implement targeted interventions to improve medication adherence.
