1. Assessing Dietary Water Intake: A Validation Study
Andy Mauromoustakos, Stavros Kavouras, Evan Johnson, and J.D. Adams
Exercise Science Program : Health, Human Performance and Recreation, U of Arkansas
JMP Pro Imputation Add-Inn
(click on Fig 6.)
JMP Method Comparison Add-Inn
(click on Fig 7.)
Abstract
Methods
A “large” Hydration Study of 102 US adults was conducted at UofA by Dr Kavouras and his team of researches to investigate and validate ways of measuring the subject’s hydration.
This poster is a first attempt to report some of the data completed by the researches that said light to some of their objectives
The data was based on 102 that represent a stratified sample of the adults US population.
The subjects were followed for a month that comprised using a standard protocol and urine spot samples collected on each visit to help assess the individual’s hydration status
We will explore some of JMP PRO 12 new features to help with the data manipulation, exploration, analysis and modeling.
Answers to some of the objectives of the data will be explored using various graphs and analysis in JMP
Use JMP to merge and update several different Excel files to help create a master database with all information. (Still in progress)
Visualize the data using Graph Builder
Conduct analysis using platforms and JMP Add-Ins (Imputation and Method Comparison) to help answer objectives
Model Data to predict hydration status based on fluids and food in addition to various environmental and exercise personal logs (ongoing since the database it is not completed)
( Click on the Figures to see related output )
Conclusions
Objectives
JMP 12 Excel Importing facility and new Column RECODE functionality provides and excellent tool for managing cleaning and organizing the data. Add-Ins provide increase functionality
JMP 12 improved Graph Builder enhances visualizations and brainstorming related to the objectives (Click on Fig 4 and Fig 5 )
The improvements in communicating to SAS to extend modeling utilizing PROC SURVEYLOGISTIC, ADAPTIVEREG and QUANTILEREG will help further modeling of the data
The models to analyze the data will be examine in the futute when the most critical input variable how much liquids each individuals consumes is completed.
Results
Document the actual % of subjects that are dehydrated during the visit and overall
Visualize how these vary by gender, age, categories (young middle age and older adults), body mass etc.
Compare some “quick and dirty measurements” from “spot samples” to more tedious and reliable 24 h urine collection samples
Model the data trying to predict individual’s hydration status
The quick and dirty measurements are less discriminatory (Click to Fig 2)
About 1 in 5 people measured where never dehydrated during their 12 visits and about half are dehydrated on average (click to view Fig 3 )
Dehydration tends to be higher for Males, decrease with AGE and increase with Body mass.(click Fig 2)
References
Kavouras, S.A. Assessing hydration status. Curr. Opin. Clin. Nutr. Metab. Care. 5: , 2002
Cheuvront SN, Carter R, Montain SJ, Sawka MN. Daily body mass variability and stability in active men undergoing exercise-heat stress. Int J Sport Nutr Exerc Metab 14: 532–540, 2004.

2. Fig. 1 Individual Subject Variation (based on 12 visits) for the three main Hydration responses from the most expensive and tedious to the cheaper and easiest
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3. Fig. 2 The three main Hydration responses based on subject means
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4. Fig. 3 About 1 in 5 Subjects where never hypo hydrated (UOSMO<700) and their profiles are highlighted below
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5. Fig. 4 Summary Dataset by Subject with the NEW SubProf Expression of JMP12 that has a the ability to store a “profile picture for that subject”
These jpg were not added here via JSL scripting using the New JMP 12 Expression Data Type.
Instead Distribution output was created and saved using the new PPT option for Journal that was then saved as JPG creating a single JPG slide for each subject …
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6. New Overlay Histogram feature
Fig. 5 Summary Trends with Overlay Features of Graph Builder for JMP 12 like the overlay of histograms and Curve lines for connecting means
New Overlay Histogram feature
Curve lines
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7. Fig. 6 JMP Pro Imputation Add-In https://community. jmp
Only 2 Individuals missed each one of the 12 scheduled visits so the decision is made to impute those visits urine duration three main responses with the mean of rest 11 visits so that the imputation will not effect the mean and the SD for that individual
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8. Fig. 7 Method Comparison Add-In Related output https://community. jmp
After calculating several summary statistics the responses were compared based the average of the two spot samples on successive visits to the 24 hour collected between those two samples and measured based on the cumulative urine sample for that day.
Method Comparison Add-In performs four primary routines:  Accuracy (shown on top), Precision (shown on bottom), Linearity, and Performance. It contains reports similar to a matched pairs platform shown here with Blant Altman plot.
The results shown verified a well know fact the spot morning samples are usually more concentrated and display higher readings of osmolality with bigger variation as compared to the inconvenient 24hr day samples that are probably the “gold standard”
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