Evidence map›Paper›PMID 42400667›Full record

SynthesisSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026

Efficacy of different non-pharmacological therapies on cancer-related fatigue in breast cancer patients: a network meta-analysis.

Shuqin Li, Lingyu Li, Lan Fu

Abstract readNetwork Meta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Shuqin LiBreast Center, West China Hospital, Sichuan University, Chengdu, China. 529120827@qq.com.
Lingyu LiBreast Center, West China Hospital, Sichuan University, Chengdu, China.
Lan FuBreast Center, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer-related fatigue (CRF) is a highly prevalent and debilitating symptom among breast cancer patients, yet optimal non-pharmacological management strategies remain uncertain. We aimed to compare the relative efficacy of various non-pharmacological interventions for alleviating CRF in this population through a systematic review and network meta-analysis (NMA).

methodsWe systematically searched PubMed, EMBASE, Web of Science, CINAHL, and CENTRAL from inception to June 2025 for randomized controlled trials (RCTs). Studies were included if they evaluated any non-pharmacological intervention for CRF in adult women with breast cancer. A frequentist random-effects NMA was conducted, and interventions were ranked using the Surface Under the Cumulative Ranking Curve (SUCRA). The primary outcome was CRF severity measured by validated scales.

resultsTwenty-six RCTs involving 2,811 patients were included, evaluating 21 distinct interventions. The network demonstrated good connectivity and no significant inconsistency (P > 0.05 for both global and local tests). Tai Chi Chih (TCC) was ranked as the most effective intervention (SUCRA = 99.1%), followed by cognitive behavioral therapy (CBT; SUCRA = 92.0%) and massage therapy (MT; SUCRA = 90.6%). Compared with usual care, all three showed large and statistically significant reductions in fatigue: TCC (SMD = -4.61, 95% CI: -6.20 to -3.02), CBT (SMD = -3.63, 95% CI: -4.87 to -2.40), and MT (SMD = -3.43, 95% CI: -4.66 to -2.20). Health education ranked lowest among active interventions (SUCRA = 19.4%). The funnel plot indicated no significant publication bias.

conclusionThis comprehensive NMA identifies Tai Chi Chih, cognitive behavioral therapy, and massage therapy as the most effective non-pharmacological strategies for reducing cancer-related fatigue in breast cancer patients. These findings provide robust evidence to prioritize these mind-body, psychological, and somatic approaches in clinical practice and supportive care guidelines.

trial registrationThe study was registered at PROSPERO on 5 February 2025 (CRD42025642351).

Indexed as

Breast NeoplasmsFatigueFemaleHumansMassageRandomized Controlled Trials as TopicTai JiBreast cancerCancer-related fatigueNetwork meta-analysisNon-pharmacological therapy

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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.