Fear of progression profiles and their association with quality of life and depression in ischemic stroke survivors: a latent profile analysis.

BACKGROUND

Fear of progression (FoP) has been well-documented in patients with cancer and chronic disease. However, there is little information in stroke patients. The purpose of this study was to explore the profiles and associated factors of FoP among ischemic stroke survivors, and to examine the association of FoP profiles with quality of life (QoL) and depression.

METHODS

A cross-sectional study was conducted from July 2023 to June 2024. Two hundred ischemic stroke survivors were recruited. Data collection included a self-designed sociodemographic and clinical information questionnaire, the Fear of Progression Questionnaire-Short Form, the Stroke Specific Quality of Life Scale, and the Self-Rating Depression Scale. Latent profile analysis (LPA), univariate analysis, multinomial logistic regression analysis, and multiple hierarchical regression analysis were adopted in this study.

RESULTS

LPA identified three subgroups of FoP: Group 1-the low FoP (23%), Group 2-the moderate FoP with family-related concerns (45%), and Group 3-the high FoP with work-related concerns (32%). Older age (OR = 0.245, p = 0.003) and lower functional independence (Barthel index, BI) (OR = 0.976, p = 0.034) were associated with Group 2, while younger age (OR = 8.771, p = 0.018) and lower monthly family income (OR = 9.960, p = 0.004) were associated with Group 3. FoP profiles explained 3.0% variance in QoL and 3.3% variance in depression (p < 0.05). Group 2 and 3 showed significantly lower QoL than Group 1 (p < 0.05), while Group 2 and Group 3 had higher depression than Group 1 (p < 0.05).

CONCLUSION

Three heterogeneous subgroups of FoP were identified in ischemic stroke survivors. Age, household monthly income, and functional independence were associated with different latent profiles of FoP. Healthcare providers should categorize patients with FoP into subgroups and provide targeted interventions based on each profile's characteristics and associated factors. This could contribute to promoting their QoL and lowering their depression.