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Comparative efficacy of non-pharmacological interventions on fatigue in people with multiple sclerosis: A systematic
Hui Chang1, Xuexue Wang2, Yanru Shi3
1Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China.
Background:
Fatigue is a common symptom of multiple sclerosis (MS) that adversely affects patients' health and quality of life and increases the burden on healthcare systems. Although non-pharmacological therapies are effective alternatives to pharmacological treatments, it remains unclear which non-pharmacological interventions are most effective in alleviating fatigue in people with MS.
Aims:
To compare the effectiveness of non-pharmacological interventions in reducing fatigue among people with MS.
Methods:
We searched seven databases from their inception to May 2024. After literature screening and data extraction, the Cochrane Bias Assessment Tool 2.0 version of randomized controlled trials (RCTs) was used to evaluate literature quality. A network meta-analysis was performed to evaluate the relative efficacy of non-pharmacological interventions on fatigue. Statistical analysis was performed using STATA 17.0 software.
Results:
A total of 73 RCTs involving nine non-pharmacological interventions were included. The results of the network meta-analysis showed that exercise therapy (SMD = -0.53, 95 % CI [-0.74, -0.33]), cognitive-behavioral therapy (SMD = -0.64, 95 % CI [-1.05, -0.22]), acupuncture or massage therapy (SMD = -0.67, 95 % CI [-1.28, -0.06]), eHealth interventions (SMD = -0.42, 95 % CI [-0.66, -0.18]), and combined interventions (SMD = -0.79, 95 % CI [-1.27, -0.31]) were more effective than the control group in improving MS fatigue. Among them, combined interventions may be the most effective non-pharmacological intervention to improve fatigue in people with MS.
Conclusion:
This network meta-analysis found that combined interventions, acupuncture or massage therapy, cognitive-behavioral therapy, exercise therapy, and eHealth interventions can improve fatigue in people with MS. These findings provide evidence-based data to support clinical decision-making.
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