{"title":"Lactate threshold evaluation in swimming using a sweat lactate sensor: A prospective study","authors":"Hiroki Okawara, Tomonori Sawada, Daisuke Nakashima, Haruki Fujitsuka, Yuki Muramoto, Daigo Hinokuma, Yuta Oshikiri, Keisuke Ishizaki, Jiro Miki, Reira Hara, Motoaki Sano, Kazuki Sato, Masaya Nakamura, Takeo Nagura, Yoshinori Katsumata","doi":"10.1002/ejsc.12179","DOIUrl":null,"url":null,"abstract":"<p>Since assessing aerobic capacity is key to enhancing swimming performance, a simple and widely applicable technology should be developed. Therefore, we aimed to noninvasively visualize real-time changes in sweat lactate (sLA) levels during swimming and investigate the relationship between lactate thresholds in sweat (sLT) and blood (bLT). This prospective study included 24 university swimmers (age: 20.7 s ± 1.8 years, 58% male) who underwent exercise tests at incremental speeds with or without breaks in a swimming flume to measure heart rate (HR), bLT, and sLT based on sLA levels using a waterproof wearable lactate sensor attached to the dorsal upper arm on two different days. The correlation coefficient and Bland–Altman methods were used to verify the similarities of the sLT with bLT and personal performance. In all tests, dynamic changes in sLA levels were continuously measured and projected onto the wearable device without delay, artifacts, or contamination. Following an initial minimal current response, with increasing speed the sLA levels increased substantially, coinciding with a continuous rise in HR. The speed at sLT strongly correlated with that at bLT (<i>p</i> < 0.01 and <i>r</i> = 0.824). The Bland–Altman plot showed a strong agreement (mean difference: 0.08 ± 0.1 m/s). This prospective study achieved real-time sLA monitoring during swimming, even with vigorous movement. The sLT closely approximated bLT; both were subsequently validated for their relevance to performance.</p>","PeriodicalId":93999,"journal":{"name":"European journal of sport science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ejsc.12179","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European journal of sport science","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ejsc.12179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Since assessing aerobic capacity is key to enhancing swimming performance, a simple and widely applicable technology should be developed. Therefore, we aimed to noninvasively visualize real-time changes in sweat lactate (sLA) levels during swimming and investigate the relationship between lactate thresholds in sweat (sLT) and blood (bLT). This prospective study included 24 university swimmers (age: 20.7 s ± 1.8 years, 58% male) who underwent exercise tests at incremental speeds with or without breaks in a swimming flume to measure heart rate (HR), bLT, and sLT based on sLA levels using a waterproof wearable lactate sensor attached to the dorsal upper arm on two different days. The correlation coefficient and Bland–Altman methods were used to verify the similarities of the sLT with bLT and personal performance. In all tests, dynamic changes in sLA levels were continuously measured and projected onto the wearable device without delay, artifacts, or contamination. Following an initial minimal current response, with increasing speed the sLA levels increased substantially, coinciding with a continuous rise in HR. The speed at sLT strongly correlated with that at bLT (p < 0.01 and r = 0.824). The Bland–Altman plot showed a strong agreement (mean difference: 0.08 ± 0.1 m/s). This prospective study achieved real-time sLA monitoring during swimming, even with vigorous movement. The sLT closely approximated bLT; both were subsequently validated for their relevance to performance.