Skeletal Muscle Changes in the First Three Months of Stroke Recovery: A Systematic Review.

ABSTRACT

BACKGROUND: Rehabilitation is important in the first months after a stroke for recovery of functional ability, but it is also challenging, since distinct recovery trajectories are seen. Therefore, studying the early changes in muscle characteristics over time (e.g. muscle strength, muscle mass and muscle volume), which are known to be associated with functional abilities, may deepen our understanding of underlying recovery mechanisms of stroke survivors. OBJECTIVE: This systematic review aims to describe the longitudinal changes in skeletal muscles, including muscle strength, muscle mass and muscle volume, during the first 3 months post-stroke. METHODS: Electronic searches were conducted in Medline, Scopus and CENTRAL. Longitudinal cohort studies or controlled interventional trials that report data about patients in the first 3 months after stroke were identified. Skeletal muscle characteristics should be measured at least twice within 3 months post-stroke by objective, quantitative assessment methods (e.g. dynamometry, ultrasound, computed tomography). Effect sizes were calculated as Hedges' g using standardized mean differences. RESULTS: A total of 38 studies (1,097 subjects) were found eligible. Results revealed an mean increase on the paretic side for upper and lower limb muscle strength (small to moderate effect sizes), whereas muscle thickness decreased (moderate to large effect sizes). Similar, but smaller, effects were found on the non-paretic side. There were insufficient data available to draw conclusions about lean muscle mass and muscle cross-sectional area. No studies aimed at investigating distinct trajectories of the muscle changes. CONCLUSION: Muscle strength and thickness changes during the first 3 months after stroke in both the paretic and non-paretic side. Future studies should aim to understand "how" the stroke-induced muscle strength changes are achieved. Exploring existing data from longitudinal studies, by using cluster analyses, such as pattern recognition, could add to the current knowledge-base.