Faculty of Engineering and Built Environment
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Item Using computational intelligence(FIMS, 2014-12) Singh, Navin Runjit; Peters-Futre, Edith M.The aim of this study was to assess the efficacy of using artificial neural networks (ANNs) to classify hydration status and predict the fluid requirements of endurance athletes. Hydration classification models were built using a total of 237 data sets obtained from 148 participants (106 males,42 females) in field-and laboratory studies involving running or cycling. 116 data sets obtained from athletes who completed endurance events euhydrated (plasma osmolality: 275-295 mmol.kg-1) following ad libitum replenishment of fluid intake was used to design prediction models. A filtering algorithm was used to determine the optimal inputs to the models from a selection of 13 anthropometric, exercise performance, fluid intake and environmental factors. The combination of gender, body mass, exercise intensity and environmental stress index in the prediction model generated a root mean square error of 0.24 L.h-1 and a correlation of 0.90 between predicted and actual drinking rates of the euhydrated participants. Additional inclusion of actual fluid intake resulted in the design of a model that was 89% accurate in classifying the post-exercise hydration status of athletes. These findings suggest that the ANN modelling technique has merit in the prediction of fluid requirements and as a supplement to ad libitum fluid intake practices.Item Markers of hydration status in a 3-day trail running event(Lippincott, Williams & Wilkins, 2013) Singh, Navin Runjit; Peters, Edith M.Objective: To examine the relationships between changes in static prestage and poststage measures of commonly used hematological and urinary markers of hydration status and body mass (BM) in participants in a 3-day trail run. Design: Descriptive field study. Setting: Three Cranes Challenge trail run, South Africa. Participants: Twenty (6 men and 14 women) amateur runners. Interventions: In stage 1 (S1), 29.3 km and 37.9 km in stage 2 (S2), and 27.8 km in stage 3 (S3). Main Outcome Measures: Prestage and poststage individual changes in serum osmolality (Sosm), serum sodium (s[Na+]), plasma volume (PV), urine osmolality (Uosm), urine specific gravity (Usg), and BM. Results: Consistently, mild environmental conditions were experienced on the 3 days of the race (ambient temperature range, 11.5-22.8°C). Mean Sosm increased by 5 ± 6, 7 ± 9, and 3 ± 4 mOsm/kg during S1, S2, and S3, respectively, and returned to baseline pre-S2 and pre-S3. The correlation between individual prestage and poststage changes in Sosm, Uosm, and Usg (n = 60) were nonsignificant (P > 0.05; r = 0.0047, r = 0.0074). There was a significant, but relatively low correlation between changes in Sosm and percentage reduction in BM (r = 0.35; P < 0.01) and prechange and postchange in s[Na+] (r = 0.45; P < 0.001). Conclusions: Serum osmality values confirm appropriate interstage rehydration. Changes in Uosm, Usg, BM, s[Na+], and PV are not closely related to changes in Sosm as markers of hydration assessment in multiday events in which single static measures of hydration status are required. These measures of hydration station status are therefore not recommended in this field setting.