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

## TL;DR

This review identifies factors that influence exercise capacity in stroke survivors, distinguishing between those that can and cannot be changed to improve rehabilitation outcomes.

## Contribution

The study systematically categorizes modifiable and non-modifiable predictors of exercise capacity in stroke survivors using multivariate regression models.

## Key findings

- Modifiable factors like BMI, lower limb strength, and cardiorespiratory fitness significantly predict exercise capacity.
- Non-modifiable factors such as age, diabetes, and stroke severity also significantly predict exercise capacity.
- Incorporating both types of factors can lead to more personalized rehabilitation strategies.

## Abstract

What are the main findings?
Modifiable factors—such as body composition (e.g., BMI), lower limb strength, cardiorespiratory fitness (e.g., baseline VO2peak), training intensity, and fatigue—significantly predict exercise capacity in stroke survivors in separate prediction models.Non-modifiable factors, including age, diabetes, and stroke severity, also significantly predict exercise capacity in separate prediction models.

Modifiable factors—such as body composition (e.g., BMI), lower limb strength, cardiorespiratory fitness (e.g., baseline VO2peak), training intensity, and fatigue—significantly predict exercise capacity in stroke survivors in separate prediction models.

Non-modifiable factors, including age, diabetes, and stroke severity, also significantly predict exercise capacity in separate prediction models.

What are the implications of the main findings?
Rehabilitation strategies should focus on improving modifiable factors to enhance exercise capacity.Incorporating non-modifiable factors into baseline assessments can support more personalized and effective rehabilitation planning.

Rehabilitation strategies should focus on improving modifiable factors to enhance exercise capacity.

Incorporating non-modifiable factors into baseline assessments can support more personalized and effective rehabilitation planning.

Background: This systematic review aims to identify modifiable and non-modifiable predictors of exercise capacity (VO2peak 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 (VO2peak) 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 VO2peak (2/4)), training intensity (2/2) and perceived fatigue (1/1) significantly predicted VO2peak (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 (VO2peak) 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.