JOURNAL ARTICLE

Preoperative telenursing for hernioplasty and cholecystectomy patients.

  • Published In: Japan Journal of Nursing Science, 2025, v. 22, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bandeira, Tatiany Marques; Santana, Rosimere Ferreira; da Silva Rocha, Greiciane; do Carmo, Thalita Gomes 3 of 3

Abstract

Aim: To examine the efficacy of telenursing interventions on the preoperative phase for patients undergoing hernioplasty or cholecystectomy procedures at a healthcare facility in Brazil. Methods: A quasi‐experimental study was conducted (July to December 2021) with 151 patients in the control group and 99 in the intervention group. The comparative analysis focused on anxiety, knowledge of preoperative care, postoperative complications, and institutional indicators (cancellation rate, delays, inadequate preparation, fasting, and examinations). The Zung Self‐Rating Anxiety Scale measured anxiety, while structured forms collected demographic and knowledge‐related data. Results: The intervention group demonstrated superior outcomes compared to controls: knowledge (1.0% vs. 57.6% poor knowledge), anxiety (88.9% vs. 74.8% normal), postoperative complications (20.8% vs. 33.6%), incomplete exams (13.6% vs. 34.8%), and inadequate fasting (8.3% vs. 13.8%). Conclusions: Telenursing had a positive impact on anxiety, knowledge, postoperative complications, and institutional indicators. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Japan Journal of Nursing Science. 2025/04, Vol. 22, Issue 2, p1
  • Document Type:Article
  • Subject Area:Nursing and Allied Health
  • Publication Date:2025
  • ISSN:1742-7932
  • DOI:10.1111/jjns.70011
  • Accession Number:184675066
  • Copyright Statement:Copyright of Japan Journal of Nursing Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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