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Table 3 Predictors of body image, PCS, MCS, and FACT-Bv4.0 scores: multiple linear stepwise regression analysis (N = 406)

From: Body image mediates the relationship between post-surgery needs and health-related quality of life among women with breast cancer: a cross-sectional study

Dependent variable

Independent variables

B (95%CI)

P

VIF

Body imagea

Needs satisfaction

− 0.34 (− 0.36, − 0.18)

< 0.001

1.02

Needs importance

0.30 (0.17, 0.36)

< 0.001

1.01

Lumpectomy and axillary dissection (ref. modified radical mastectomy)

−0.11 (− 7.80, − 0.54)

0.025

1.06

PCSb

Body image

−0.12 (− 0.13, − 0.01)

0.019

1.05

Needs importance

−0.11 (− 0.10, − 0.007)

0.026

1.03

Chronic disease (ref. yes)

0.10 (0.03, 3.21)

0.045

1.03

MCSc

Body image

−0.40 (− 0.42, − 0.27)

< 0.001

1.02

Residence (ref. rural)

0.14 (1.03, 4.49)

0.002

1.02

Lumpectomy and axillary dissection (ref. modified radical mastectomy)

−0.10 (−6.49, − 0.50)

0.022

1.05

FACT-Bv4.0d

Body image

−0.42 (− 0.80, − 0.44)

< 0.001

1.03

Unemployed (ref. employed)

−0.21 (−11.68, −3.45)

< 0.001

1.05

Needs satisfaction

0.16 (0.06, 0.37)

0.008

1.02

Radiotherapy (ref. yes)

0.13 (0.69, 12.06)

0.028

1.05

Marital status (ref. married)

−0.13 (−22.80, −1.02)

0.032

1.05

  1. Multiple linear stepwise regression analysis was performed after controlling for the following dummy variables: education level (ref. primary and below), marital status (ref. married), employment status (ref. employed), average monthly income over the past year (Chinese yuan, ref. < 1000), residence (ref. rural), chronic disease (ref. yes), illness stage (ref. 0-I), surgery type (ref. modified radical mastectomy), chemotherapy (ref. yes), radiotherapy (ref. yes), and endocrinotherapy (ref. yes), as well as continuous characteristics (age, body image, psychosocial needs importance, and psychosocial needs satisfaction)
  2. aBody image predictor model: R2 = 0.11, F = 15.72, P < 0.001
  3. bPCS predictor model: R2 = 0.04, F = 5.23, P = 0.001
  4. cMCS predictor model: R2 = 0.19, F = 30.97, P < 0.001
  5. dFACT-Bv4.0 predictor model: R2 = 0.35, F = 20.37, P < 0.001
  6. VIF < 10 indicates no significant multicollinearity
  7. 95%CI: 95% confidence interval. VIF: variance inflation factor