# Latent profile analysis of self-management and its association with quality of life differences in patients with cancer treated with immune checkpoint inhibitors

## TL;DR

This study identifies three self-management profiles in cancer patients using immune checkpoint inhibitors and links them to quality of life differences.

## Contribution

The study introduces a novel latent profile analysis of self-management in cancer patients receiving immune checkpoint inhibitors.

## Key findings

- Three self-management profiles were identified: low, average with information avoidance, and high self-management.
- Coping modes, education, insurance, age, income, and communication styles influence self-management profiles.
- Quality of life scores varied significantly across the three self-management groups, except for emotional well-being.

## Abstract

This study aimed to explore latent profiles of self-management ability in patients with cancer treated with immune checkpoint inhibitors, analyze each subgroup's characteristics, and determine the relationship between self-management and quality of life.

This cross-sectional study included 393 patients treated with immune checkpoint inhibitors. The participants completed questionnaires containing sociodemographic information, the Functional Assessment of Cancer Therapy-Immune Checkpoint Modulator (FACT-ICM), the Cancer Patient Self-management Evaluation Scale, and the Medical Coping Modes Questionnaire. Latent profile analysis was used to examine potential latent groups of self-management. Multivariate logistic regression was used to analyze the sociodemographic variables in each profile. Kruskal-Wallis H-rank sum test was used to explore the relationships between self-management profiles and quality of life.

The self-management abilities of the patients treated with immune checkpoint inhibitors were grouped into three latent profiles: “low self-management” (16.8%), “average self-management-avoidance of information” (44.3%), and “high self-management” (38.9%). The coping modes, educational levels, medical insurances, age, monthly family income per capita, and communication styles with health care professionals post-discharge significantly influenced the distribution of self-management. There were significant differences in the FACT-ICM scores across all three groups, except for the emotional well-being dimension.

The patients with cancer treated with immune checkpoint inhibitors exhibit three distinct self-management profiles. To enhance patients' quality of life, healthcare professionals should develop targeted self-management strategies focusing on information management and communication between patients and healthcare providers.