Performance nutrition最新文献

{"title":"The accuracy of a 4-item hydration self-assessment model to classify urine concentration using different cut-offs.","authors":"Floris C Wardenaar, Kinta D Schott, Ryan G N Seltzer, Brooke Butterick, Emily Dow, Parker Kooima, Raul Freire, Kyle D Reid, Zack Stow, Sai Tejaswari Gopalakrishnan, Richard Remington, David Sklar, Gwyneth Gordon, Brent Ruby, Jefferey L Burgess, Stavros A Kavouras","doi":"10.1186/s44410-026-00024-y","DOIUrl":"10.1186/s44410-026-00024-y","url":null,"abstract":"<p><strong>Background: </strong>Hydration is important for health and performance, but accurate self-assessment methods for active populations are limited. The study objective was to determine a model to classify by self-assessment a low vs. high 24-hour urine concentration, and to determine the accuracy of morning vs. afternoon assessments. The study describes the development and validation of a self-assessment model through exploratory modeling, using stepwise logistic regression. The aim was to determine which hydration markers predict urine specific gravity (using two different USG cut-offs: low ≤ 1.012, and high ≥ 1.020), in two intentionally overlapping 24-hour urine collections leading up to a morning and afternoon hydration self-assessment, and to assess its diagnostic accuracy based on the area under the curve (AUC).</p><p><strong>Results: </strong>A total of of <i>n</i> = 62 wildland firefighters (WLFFs), and <i>n</i> = 23 recreational athletes (12% female, median age 25 years, with interquartile range of 24-32) were included. The median USG values of the 24-hour urine collections leading up to the morning and afternoon assessments, were 1.011 and 1.012, respectively. The AUC for the final model, which included self-reported fluid intake; urine frequency, volume, and color, to classify low vs. high USG, for low cut-off (≤ 1.012) was fair in the morning (0.72), and good in the afternoon (0.85), and for high cut-off (≥ 1.020) good for morning (0.81) and afternoon (0.86), respectively.</p><p><strong>Conclusion: </strong>This 4-item hydration self-assessment model classifies a low vs. high USG acceptable, with slightly higher accuracy in the afternoon. This insight may inform targeted hydration monitoring strategies in occupational settings, for example among WLFFs.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44410-026-00024-y.</p>","PeriodicalId":521058,"journal":{"name":"Performance nutrition","volume":"2 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13056763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3D color model reports urine color similarly to a printed color chart with similar accuracy to determine a low vs. high urine concentration.","authors":"Raul Freire, Kinta D Schott, Brooke Butterick, Zack Stow, Parker Kooima, Sai Tejaswari Gopalakrishnan, Emily Dow, Jason C Siegler, Jefferey L Burgess, Floris C Wardenaar","doi":"10.1186/s44410-025-00009-3","DOIUrl":"10.1186/s44410-025-00009-3","url":null,"abstract":"<p><strong>Background: </strong>Urine color (Uc) assessment is impacted by container, volume, and light conditions, potentially influencing Uc scoring. This study aimed to evaluate a 3D Uc model vs. Printed Uc chart's accuracy for identifying a low vs. high urine concentration in the morning and afternoon.</p><p><strong>Results: </strong>Participants (<i>n</i> = 73, 12% female, age: ~ 27 years) collected all urine voids for two consecutive days (32 h total), resulting in two partially concurrent 24-h urine collections that were compared with a morning and afternoon assessment. Median and [interquartile range] Uc differed between the Printed chart and 3D Uc model in the morning (2.0 [1.5-3.0] vs. 2.0 [2.0-4.0], <i>p</i> < 0.001), but not in the afternoon (2.0 [1.0 - 3.0] vs. 2.0 [1.0 - 3.0], <i>p</i> = 0.07). The models had a moderate correlation in the morning (<i>r</i> = 0.66, <i>p</i> < 0.001) and a strong correlation in the afternoon (<i>r</i> = 0.83, <i>p</i> < 0.001). Bland-Altman plots revealed a slight bias (-0.60 and -0.27 for morning and afternoon, respectively), with significant reporting bias only for the morning, <i>p</i> = 0.04. The Area Under the Curve (AUC), to correctly classify urine concentration, was poor for morning Printed (0.62) and 3D (0.59) vs. fair for afternoon Printed (0.78) and 3D (0.74) for USG 24 h. Interestingly, the AUC for spot urine sample color was high (i.e., morning: 0.84 and 0.79), and afternoon: 0.91 and 0.92) for the Printed chart and 3D Uc model, respectively.</p><p><strong>Conclusions: </strong>The printed chart and 3D Uc model presented similar scores but with slightly higher accuracy in the afternoon.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44410-025-00009-3.</p>","PeriodicalId":521058,"journal":{"name":"Performance nutrition","volume":"1 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}