Does Exercise Training Improve Cardiac-Parasympathetic Nervous System Activity in Sedentary People? A Systematic Review with Meta-Analysis.
Antonio Casanova-LizónAgustín Manresa-RocamoraAndrew A FlattJose Manuel SarabiaManuel Moya-RamónPublished in: International journal of environmental research and public health (2022)
The aim of this study was to investigate the training-induced effect on cardiac parasympathetic nervous system (PNS) activity, assessed by resting heart rate variability (HRV) and post-exercise heart rate recovery (HRR), in sedentary healthy people. Electronic searches were carried out in PubMed, Embase, and Web of Science. Random-effects models of between-group standardised mean difference (SMD) were estimated. Heterogeneity analyses were performed by means of the chi-square test and I 2 index. Subgroup analyses and meta-regressions were performed to investigate the influence of potential moderator variables on the training-induced effect. The results showed a small increase in RMSSD (SMD + = 0.57 [95% confidence interval (CI) = 0.23, 0.91]) and high frequency (HF) (SMD + = 0.21 [95% CI = 0.01, 0.42]) in favour of the intervention group. Heterogeneity tests reached statistical significance for RMSSD and HF ( p ≤ 0.001), and the inconsistency was moderate ( I 2 = 68% and 60%, respectively). We found higher training-induced effects on HF in studies that performed a shorter intervention or lower number of exercise sessions ( p ≤ 0.001). Data were insufficient to investigate the effect of exercise training on HRR. Exercise training increases cardiac PNS modulation in sedentary people, while its effect on PNS tone requires future study.
Keyphrases
- heart rate variability
- heart rate
- physical activity
- high frequency
- high glucose
- diabetic rats
- blood pressure
- high intensity
- randomized controlled trial
- skeletal muscle
- left ventricular
- transcranial magnetic stimulation
- oxidative stress
- heart failure
- endothelial cells
- virtual reality
- resistance training
- artificial intelligence
- climate change
- neural network
- case control