Segmental multi-frequency bioelectrical impedance analysis indicates dehydration status after exercise
Aim: Hydration status monitoring is critical to help those in exercise training to avoid serious dehydration. Bioelectrical impedance analysis (BIA) is a convenient and timely method to monitor body composition in resting conditions. The study obtained bioelectrical impedance (BI) from different seg...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-09-01
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| Series: | Advanced Exercise and Health Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950273X24000572 |
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| Summary: | Aim: Hydration status monitoring is critical to help those in exercise training to avoid serious dehydration. Bioelectrical impedance analysis (BIA) is a convenient and timely method to monitor body composition in resting conditions. The study obtained bioelectrical impedance (BI) from different segments at multi-frequency before and after exercise aiming to evaluate whether segmental multi-frequency (SMF)-BIA can indicate exercise dehydration in humans. Methods: A total of 43 young adults were recruited. Body weight (BW) and segmental BI at frequencies of 1, 5, 50, 250, 500, and 1000 kHz from each subject were measured before and after each bout of exercise. Hydration status was assessed by body weight loss (BWL), indicated by changes in body weight (ΔBW) and the percentage of body weight change (ΔBW%). Each subject participated in 3 to 4 exercise sessions on different days. Results: A total of 143 pieces of data were collected with various degrees of dehydration: BWL ranging from 0.2–4.2 kg (0.4 %–5.8 %) in participants (aged 24.40 ± 6.47). Correlation analysis revealed that the changes of BI (ΔZ) in arms and legs during exercise were correlated with the BWL. Multivariate regression and machine learning analysis showed that predicted values of dehydration were correlated with true values of dehydration (ΔBW: R2 = 0.4471, R2 = 0.5502; ΔBW%: R2 = 0.4469, R2 = 0.5073). When the diagnosis of dehydration is based on BWL > 1 %: the AUC of the SMF-BIA regression model was 0.7262 (p < 0.001) and the specificity and sensitivity were 85.19 %, 60 %. When the diagnosis of dehydration is based on BWL > 2 %: the AUC of the SMF-BIA regression model was 0.8757 (p < 0.0001) and the specificity and sensitivity were 77.22 %, 81 %. Conclusion: These results suggested that SMF-BIA can indicate exercise dehydration in humans. |
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| ISSN: | 2950-273X |