Modifiable and Non-Modifiable Predictors of Exercise Capacity in Stroke Survivors: A Systematic Review

BACKGROUND: This systematic review aims to identify modifiable and non-modifiable predictors of exercise capacity (VO 2peak level or change) in stroke survivors. These insights may further optimize rehabilitation treatment and improve long-term health outcomes.

METHODS: PubMed (PubMed.gov), EMBASE (Elsevier), CINAHL (EBSCO), and Web of Science (Clarivate) were searched (last search on 7 October 2025). Inclusion criteria were: (1) adults (>18 years) who survived a stroke (ischemic and hemorrhagic), (2) outcome was a measurement of maximum exercise capacity (VO 2peak) measured with CPET (or equivalent), (3) predictors of exercise capacity were measured (e.g., personal factors, disease-related factors, components of rehabilitation), (4) predictors of exercise capacity were analyzed in multivariate regression models, (5) primary research, and (6) full-text available. During the data extraction phase, predictors were categorized into modifiable and non-modifiable predictors. Risk of bias was assessed with the McMaster Critical Review Form for Quantitative Studies.

RESULTS: Of 919 unique articles, seventeen were included. Modifiable factors such as BMI (4/8 articles) and fat mass (1/1), lower limb strength (3/3), cardiorespiratory fitness (e.g., baseline VO 2peak (2/4)), training intensity (2/2) and perceived fatigue (1/1) significantly predicted VO 2peak (level or change). Significant non-modifiable predictors were age (3/11), sex (1/8), diabetes (1/2), and stroke-specific (4/8) factors.

CONCLUSIONS: This systematic review highlights the significant role of modifiable and non-modifiable predictors in optimizing exercise capacity (VO 2peak) for stroke survivors. In addition, considering modifiable and non-modifiable predictors allows for more personalized treatment planning. The findings can guide healthcare professionals in tailoring rehabilitation programs, though further research is needed to develop a comprehensive prediction model.