Evidence map›Paper›PMID 42205431›Full record

ArticleFrontiers in public health2026

Chain mediating effects of self-efficacy and social support on the relationship between engagement in an internet-based traditional Chinese medicine nursing program and quality of life among lung cancer patients: a cross-sectional questionnaire study.

Xiangni Zou, Rui Guo, Yuyang Tang, Xiaofang Guan, Xinxin Zhang, Wenxia Guo

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Xiangni Zou *Department of Nursing, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
Rui Guo *Department of Nursing, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
Yuyang TangDepartment of Nursing, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
Xiaofang GuanDepartment of Nursing, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
Xinxin ZhangDepartment of Nursing, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
Wenxia GuoHeilongjiang Provincial Hospital, Harbin, Heilongjiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Internet-based Traditional Chinese Medicine (TCM) nursing programs may support continuous symptom management and psychosocial adaptation in lung cancer patients; however, potential pathways linking program engagement to quality of life (QOL) remain unclear. Objective: This study aimed to examine the association between engagement in an Internet-based TCM nursing program and QOL in lung cancer patients, and to test whether social support and self-efficacy sequentially mediate this relationship. Methods: We conducted a cross-sectional questionnaire study among lung cancer patients treated at the First Affiliated Hospital of Heilongjiang University of Chinese Medicine (March 2022-March 2023). QOL was assessed using the EORTC QLQ-C30, social support using the Social Support Rating Scale (SSRS), and self-efficacy using the Strategies Used by People to Promote Health (SUPPH) questionnaire. Program engagement was quantified using a prespecified implementation and evaluation score derived from platform logs and caregiver feedback. Multivariable linear regression examined associations between program engagement and QOL. Chain mediation was tested using structural equation modeling (lavaan) with bias-corrected bootstrap confidence intervals. Results: A total of 120 patients were included. In multivariable regression, higher program engagement was associated with better QOL (Model A: Conclusion: Greater engagement in an Internet-based TCM nursing program was associated with better QOL among lung cancer patients, and the association was consistent with a chain pathway involving social support and self-efficacy. Prospective and randomized studies are warranted to confirm temporal ordering and causal mechanisms.

Indexed as

InternetLung NeoplasmsMedicine, Chinese TraditionalQuality of LifeSelf EfficacySocial SupportAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and Questionnaireschain mediating effectinternet-based traditional Chinese medicine nursinglung cancerquality of lifeself-efficacysocial support

Identifiers

PMID42205431
PMCPMC13201503

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.