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Published on: December 20, 2024
A latent profile analysis of patient activation in postoperative breast cancer patients: a cross-sectional study
Yongfeng Tang1, Dinghua Yang1, Shijiao Zhang2
1Department of Anesthesiology, West China Hospital, Sichuan University, West China School of Nursing, Sichuan University, Chengdu, Sichuan, China.
Purpose:
To explore the level of patient activation (PA) and its subgroups among postoperative breast cancer patients, and to analyze the differences and influencing factors across these subgroups.
Methods:
A cross-sectional study was conducted from May to December 2024 using convenience sampling. A total of 230 postoperative breast cancer patients from a tertiary hospital in China completed questionnaires including general information, the Patient Activation Measure, the Posttraumatic Growth Inventory, the Social Impact Scale (for stigma), and the Perceived Social Support Scale. Latent profile analysis was used to identify PA subgroups. Differences among subgroups were analyzed using ANOVA, Kruskal-Wallis, or chi-square tests, followed by multinomial logistic regression to determine influencing factors.
Results:
The average PA score was 51.0 ± 11.5, indicating that patients recognize their important role in disease management but lack the confidence and knowledge to take action. Three PA subgroups were identified: high PA-relatively proactive type (30.4%), moderate PA-knowledge deficient type (46.1%), and low PA-passive dependent type (23.5%). Protective factors for higher PA included urban residence, being employed, higher posttraumatic growth, and monthly family income ≥ 3000 yuan (all P < 0.05). Obstructive factors included not undergoing breast-conserving surgery and higher perceived stigma (both P < 0.05).
Conclusion:
The PA score of postoperative breast cancer patients is classified at the second level, revealing three distinct categories with clear classification characteristics. Clinicians can identify patients exhibiting varying PA traits based on readily available demographic and disease-related data in clinical practice. This enables them to implement targeted interventions tailored to the specific characteristics and influencing factors of each group, ultimately enhancing PA levels.
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