Commentaries on Viewpoint: V̇o2peak is an acceptable estimate of cardiorespiratory fitness but not V̇o2max

Articles with impact: insights into 10 years of research with machine learning

Worldwide scientific output is growing faster and faster. Academics should not only publish much and fast, but also publish research with impact. The aim of this study is to use machine learning to investigate characteristics of articles that were published in the Journal of Applied Physiology between 2009 and 2018, and characterize high-impact articles. Article impact was assessed for 4,531 publications by three common impact metrics: the Altmetric Attention Scores, downloads, and citations. Additionally, a broad collection of (more than 200) characteristics was collected from the article's title, abstract, authors, keywords, publication, and article engagement. We constructed random forest (RF) regression models to predict article impact and articles with the highest impact (top-25% and top-10% for each impact metric), which were compared with a naive baseline method. RF models outperformed the baseline models when predicting the impact of unseen articles (P < 0.001 for each impact metric). Also, RF models predicted top-25% and top-10% high-impact articles with a high accuracy. Moreover, RF models revealed important article characteristics. Higher impact was observed for articles about exercise, training, performance and V̇o_2max, reviews, human studies, articles from large collaborations, longer articles with many references and high engagement by scientists, practitioners and public or via news outlets and videos. Lower impact was shown for articles about respiratory physiology or sleep apnea, editorials, animal studies, and titles with a question mark or a reference to places or individuals. In summary, research impact can be predicted and better understood using a combination of article characteristics and machine learning.NEW & NOTEWORTHY Common measures of article impact are the Altmetric Attention Scores, number of downloads, and number of citations. To our knowledge, this is the first study that applies machine learning on a comprehensive collection of article characteristics to predict article attention scores, downloads, and citations. Using 10 years of research articles, we obtained accurate predictions of high-impact articles and discovered important article characteristics related to article impact.

Is V̇O2peak a Valid Estimation of V̇O2max in Swimmers with Physical Impairments?

Purpose: Peak and maximal oxygen uptake ([Formula: see text] and [Formula: see text], respectively) are used in assessing aerobic power. For swimmers with physical impairments, it is unclear whether the physiological variables obtained in 200-m and Nx200-m tests are similar. The objective of this study is to assess the validity of [Formula: see text] as an estimator of [Formula: see text] and complementary physiological variables, in particular, carbon dioxide production ([Formula: see text]), respiratory exchange ratio (RER), minute-ventilation ([Formula: see text] and absolute (HR) and relative (%HRmax) heart rates-which were obtained in a time trial test (200-m) and an incremental intermittent test (Nx200-m) performed by swimmers with physical impairments. Methods: Eleven well-trained swimmers with physical impairments performed 200-m all-out and Nx200-m from low to all-out (controlled by a visual pacer), both with a respiratory valve system and a portable gas analyzer. Results: A paired Student's t-test showed no statistical difference (p > .05) for all comparisons. The intraclass correlation coefficient (ICC) was 0.97 and 0.98 for [Formula: see text] in l/min and ml/kg/min, respectively; ICC = 0.75 to 0.9 for [Formula: see text] (l/min and ml/kg/min),[Formula: see text] (in l/min) and HR (beats/min); ICC = 0.5 and 0.75 for %HRmax; and ICC < 0.5 for RER. Passing-Bablok regression showed that the dispersions were acceptable, considering the proportionality, except for HR and %HRmax. Bland-Altman method showed a high level of agreement for all variables. Conclusions: The [Formula: see text] and [Formula: see text], as well as the physiological variables [Formula: see text] and HR obtained, respectively, by 200-m and Nx200-m tests in swimmers with physical impairment were not different.