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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Park, Jin-Hee Jung, Yong Sik Kim, Ji Young Bae, Sun Hyoung |
| Abstract | Background Although quality of life (QOL) improves over time for most breast cancer patients after their treatment, some patients may show different patterns of QOL. Beyond determining distinct QOL trajectories, identifying characteristics of patients who have different trajectories can help identify breast cancer patients who may benefit from intervention. We aimed to identify trajectories of QOL in breast cancer patients for one year after the end of primary treatment, to determine the factors influencing these changes. Methods This longitudinal study recruited 140 breast cancer patients. Patients' QOL, symptom experience, self-efficacy, and social support were assessed using the Functional Assessment of Cancer Therapy Scale-G, Memorial Symptom Assessment Scale-Short Form, Self-Efficacy Scale for Self-Management of Breast Cancer, and Interpersonal Support Evaluation List-12. Data were collected immediately after the end of primary treatment (T1) and at three (T2), six (T3), and 12 months (T4) after primary treatment. Group-based trajectory modeling was used to identify distinct subgroups of patients with similar patterns of QOL change after treatment. A one-way analysis of variance was used to determine which variables were associated with trajectory membership. A multinomial logistic regression was performed to identify factors associated with trajectory group membership. Results We analyzed 124 patients (mean age: 48.75 years). Latent class analysis of the QOL identified three trajectory groups: the low QOL group (n = 27; 21.1%), moderate QOL group (n = 57; 45.3%), and high QOL group (n = 40; 33.6%). The low QOL group showed consistently low QOL after the end of primary treatment, and the moderate QOL group showed a slight decrease in QOL from T1 to T3, which returned to the T1 level at T4. The high QOL group maintained a consistently high QOL. By multinomial logistic regression, psychological symptoms (odds ratio [OR] 0.46, 95% confidence interval [CI] 0.22–0.99) predicted a moderate QOL, and both psychological symptoms (OR 0.19, 95% CI 0.07–0.51) and belonging support (OR 1.60, 95% CI 1.06–2.39) predicted a high QOL. Conclusion Identifying high-risk groups for reduced QOL after the end of primary treatment is necessary. Moreover, psychosocial interventions should be provided to alleviate psychological symptoms and increase belonging support to enhance patients' QOL. Trial registration Not registered. |
| Related Links | https://bmcwomenshealth.biomedcentral.com/counter/pdf/10.1186/s12905-022-02153-7.pdf |
| Ending Page | 10 |
| Page Count | 10 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726874 |
| DOI | 10.1186/s12905-022-02153-7 |
| Journal | BMC Women's Health |
| Issue Number | 1 |
| Volume Number | 23 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2023-01-10 |
| Access Restriction | Open |
| Subject Keyword | Gynecology Maternal and Child Health Reproductive Medicine Breast neoplasms Cancer survivors Longitudinal studies Quality of life |
| Content Type | Text |
| Resource Type | Article |
| Subject | Obstetrics and Gynecology Reproductive Medicine |
| Journal Impact Factor | 2.4/2023 |
| 5-Year Journal Impact Factor | 2.9/2023 |
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