Nasal dilator and physiological parameters associated to running performance: A systematic review and meta-analysis.
Suellen Karla Silva Pereira GomesCarla MalagutiJosé Elias FilhoRaphael Oliveira CaetanoChristiano Vieira da SilvaTúlio Medina Dutra de OliveiraLuiz HespanholDiogo Carvalho FelícioPublished in: Journal of sports sciences (2022)
Nasal dilators were created to expand the nasal valve area. The aim of this systematic review was to verify physiological parameters associated to running performance with the use of nasal dilators. This study was registered in PROSPERO (CRD42021225795). According to the PICOS framework studies were included: Population: healthy subjects; Intervention: nasal dilators; Comparison: control group, placebo, minimal intervention, health education or other intervention; Outcomes: cardiorespiratory parameters and subjective perceptions; Study: randomized controlled trials, repeated measures or within-subjects design. The databases searched were MEDLINE, EMBASE, CENTRAL The Cochrane Library, CINAHL, SPORTDiscus, Web of Science, PEDro and Scopus. The descriptors "Running", "Nasal Dilator", "Randomized Controlled Trial", and synonyms were used. The risk of bias was assessed using the PEDro scale. Random effects Der Simonian and Laird model were used. The assessment of the certainty of the evidence was carried out using the GRADE approach. Eleven articles were included. There was a difference in favour of the nasal dilator when compared to placebo for maximal oxygen uptake and rating of perceived exertion. The certainty of the evidence was very low. Future studies will probably have an impact on estimation of the effect.
Keyphrases
- randomized controlled trial
- chronic rhinosinusitis
- systematic review
- healthcare
- public health
- high intensity
- meta analyses
- study protocol
- primary care
- mental health
- clinical trial
- aortic valve
- adipose tissue
- coronary artery disease
- skeletal muscle
- resistance training
- quality improvement
- social support
- left ventricular
- blood pressure
- machine learning
- social media
- body composition
- neural network
- placebo controlled