Nonpharmacological Interventions for Management of the Pain-Fatigue-Sleep Disturbance Symptom Cluster in Breast Cancer Patients: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

Abstract

Background

The pain-fatigue-sleep disturbance symptom cluster is commonly experienced by breast cancer patients, and a variety of nonpharmacological interventions are used to treat this symptom cluster.

Objective

To compare the efficacy of nonpharmacological interventions in improving the symptoms of the pain-fatigue-sleep disturbance symptom cluster in breast cancer patients.

Methods

A comprehensive literature search was conducted in the PubMed, EMBASE, Cochrane Library, CINAHL, CNKI, and Wanfang databases to identify randomized controlled studies from database inception to May 2022. Two reviewers independently performed data retrieval and risk of bias assessments. The consistency model was used to conduct network meta-analyses (NMA) based on the frequentist framework to assess the interventions, which were ranked by the surface under the cumulative ranking curve (SUCRA). Finally, the CINeMA application was used to evaluate the results of the NMA and the evidence of quality. The results Twenty-three eligible studies assessing 14 interventions were included. According to SUCRA values, among the management effects of the three symptoms, the effect of progressive muscle relaxation (PMR) ranked first, followed by mindfulness-based stress reduction (MBSR). The overall evidence quality of our study ranges from very low to moderate.

Conclusion

PMR and MBSR were effective interventions for the pain-fatigue-sleep disturbance symptom cluster in breast cancer patients. Clinical recommendations prioritize PMR for symptom management, followed by MBSR. However, this should be interpreted cautiously, as the confidence in the evidence was not high.

Keywords:

• breast cancer
• pain-fatigue-sleep disturbance symptom cluster
• nonpharmacological intervention
• systematic review
• network meta-analysis

Acknowledgments

Thank Professor Jinhui Tian for his help in the statistical analysis of this study, thanks for AJE language editing company’s professional polishing.

Disclosure

The authors have no conflicts of interest to disclose for this work.