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. 2020 Oct 28;15(1):324.
doi: 10.1186/s13019-020-01338-1.

How we assess the perioperative anxiety of surgical patients with pulmonary nodules: the revision of state-trait anxiety inventory

Affiliations

How we assess the perioperative anxiety of surgical patients with pulmonary nodules: the revision of state-trait anxiety inventory

Zhenyu Zhou et al. J Cardiothorac Surg. .

Abstract

Purpose: The aim of the study was to develop a short form of State-Trait Anxiety Inventory (STAI) and calculate the norms for the assessment of anxiety in surgical patients in mainland China.

Methods: Patients who were scheduled to carry out pulmonary surgery in our department were included. The sinicized 40-item STAI Form-Y was used to assess the anxiety on the surgery eve. Then the coefficient of variation, coefficient of correlation, stepwise regression analysis, principal component analysis, and structural equation model were successively to filter the items. The reliability and validity of the revised STAI was estimated and the norms were computed.

Results: 445 intact replies were collected. A 13-item STAI with 6 items in state subscale and 7 items in trait subscale produced similar scores with the full version of STAI. The Cronbach alpha coefficients for the state and trait subscales were 0.924 and 0.936, respectively. The determinant coefficients were 0.781 and 0.822, respectively. Moreover, the norms of both state subscale and trait subscale are provided according to the age and gender.

Conclusions: The revised short form of STAI has good reliability and validity. It is likely to be more acceptable by reducing the fatigue effects, and is suitable for follow-up study on the assessment and intervention of perioperative anxiety of surgical patients with pulmonary nodules.

Keywords: Perioperative anxiety; State-trait anxiety inventory; Structural equation model; Surgical patients.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The Flow Chart of Patients Selection
Fig. 2
Fig. 2
The Structural Equation Model

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