The Impact of Psychological Insulin Resistance and Dyadic Coping on Insulin Medication Adherence in Elderly Diabetes Patients and Their Spouses: A Latent Profile Analysis

Yuanhong Wu,1 Huijing Xu,2 Yuxin Wang,1 Xiaohui Wang,3 Shanyu Wu1 1College of Nursing, Yanbian University, Yanji, People’s Republic of China; 2College of Nursing, Seoul National University, Seoul, South Korea; 3Department of Nursing, Binzhou People’s Hospital, Binzhou, People’s Republic of ChinaCorrespondence: Shanyu Wu, Email [email protected]: To investigate the characteristics of psychological insulin resistance and dyadic coping in elderly diabetic patients and their spouses, and their association with insulin medication adherence.Design: Observational, cross-sectional study.Methods: A convenience sampling method selected 300 elderly diabetic patient-spouse pairs from a community. Data were collected using general information questionnaires, the My Views on Insulin questionnaire, Dyadic Coping questionnaire, and ARMS refill and medication adherence questionnaire. Latent Profile Analysis (LPA) was used to categorize the “psychological-coping” patterns of psychological insulin resistance and dyadic coping among these pairs. Multiple linear regression analysis identified factors influencing insulin medication adherence.Results: Four latent classes were identified: patients and spouses with low resistance-high coping (30.3%), patients with low resistance-moderate coping and spouses with high resistance-low coping (16.3%), patients with high resistance-moderate coping and spouses with moderate resistance-high coping (33.7%), patients and spouses with high resistance-low coping (19.7%). Multiple linear regression analysis showed that the number of chronic diseases a patient has, the use of oral hypoglycemic agents, family per capita monthly income, and latent classes of psychological insulin resistance and dyadic coping significantly affected insulin medication adherence (P< 0.05).Conclusion: It is crucial to consider the psychological insulin resistance and dyadic coping of both patients and their spouses, include both in health plans, and develop comprehensive intervention strategies that address the couple as a unit.Implications for the Profession and/or Patient Care: This study informs healthcare professionals by raising awareness of the different “psychological-coping” characteristics between elderly diabetic patients and their spouses, with insulin medication adherence and reducing the likelihood of readmission.Keywords: diabetes, psychological insulin resistance, dyadic coping, medication compliance, latent profile analysis

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-12-01
Primary Topic
Diabetes Management and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Impact of Psychological Insulin Resistance and Dyadic Coping on Insulin Medication Adherence in Elderly Diabetes Patients and Their Spouses: A Latent Profile Analysis

SHILAP Revista de lepidopterología
Diabetes Management and Education
article

The Impact of Psychological Insulin Resistance and Dyadic Coping on Insulin Medication Adherence in Elderly Diabetes Patients and Their Spouses: A Latent Profile Analysis

article en

Abstract

Yuanhong Wu,1 Huijing Xu,2 Yuxin Wang,1 Xiaohui Wang,3 Shanyu Wu1 1College of Nursing, Yanbian University, Yanji, People’s Republic of China; 2College of Nursing, Seoul National University, Seoul, South Korea; 3Department of Nursing, Binzhou People’s Hospital, Binzhou, People’s Republic of ChinaCorrespondence: Shanyu Wu, Email [email protected]: To investigate the characteristics of psychological insulin resistance and dyadic coping in elderly diabetic patients and their spouses, and their association with insulin medication adherence.Design: Observational, cross-sectional study.Methods: A convenience sampling method selected 300 elderly diabetic patient-spouse pairs from a community. Data were collected using general information questionnaires, the My Views on Insulin questionnaire, Dyadic Coping questionnaire, and ARMS refill and medication adherence questionnaire. Latent Profile Analysis (LPA) was used to categorize the “psychological-coping” patterns of psychological insulin resistance and dyadic coping among these pairs. Multiple linear regression analysis identified factors influencing insulin medication adherence.Results: Four latent classes were identified: patients and spouses with low resistance-high coping (30.3%), patients with low resistance-moderate coping and spouses with high resistance-low coping (16.3%), patients with high resistance-moderate coping and spouses with moderate resistance-high coping (33.7%), patients and spouses with high resistance-low coping (19.7%). Multiple linear regression analysis showed that the number of chronic diseases a patient has, the use of oral hypoglycemic agents, family per capita monthly income, and latent classes of psychological insulin resistance and dyadic coping significantly affected insulin medication adherence (P< 0.05).Conclusion: It is crucial to consider the psychological insulin resistance and dyadic coping of both patients and their spouses, include both in health plans, and develop comprehensive intervention strategies that address the couple as a unit.Implications for the Profession and/or Patient Care: This study informs healthcare professionals by raising awareness of the different “psychological-coping” characteristics between elderly diabetic patients and their spouses, with insulin medication adherence and reducing the likelihood of readmission.Keywords: diabetes, psychological insulin resistance, dyadic coping, medication compliance, latent profile analysis

SHILAP Revista de lepidopterología
No poverty
Openalex Percentile: Top 44%
Diabetes Management and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.