The Hydra-Alert System, manufactured by Acumen, can accurately predict fluid loss during physical activity utilizing a heart rate monitor. The purpose of this project was to create a pedometer that could give accurate estimations of fluid loss in a diverse population of users, across a varied range of walking and running speeds. The monitor could be worn during an exercise session only, or worn throughout the entire day to measure total fluid loss associated with normal daily activity.

Variables used to accurately measure fluid loss contain all of the inputs of a standard pedometer, with the addition of integrated temperature and humidity sensors. The ideal pedometer/hydration monitor will accomplish this task with a minimum number of user inputted variables, namely**: **

** Weight**

** Stride Length (Walking and Running) **

**Method:**

A diverse group of subjects will record stride lengths for both walking and running into the pedometer as per the instructions of the manufacturer. The subjects will then be weighed both before and after the exercise session and fitted with a heart rate monitor. Each subject will undergo a treadmill protocol utilizing indirect calorimetry while walking and running. Speeds will start at 1.5 mph and increasing .5 mph every five minutes until fatigue or a final speed of 7 mph is obtained. Subjects will be reweighed at the conclusion of the exercise, and results of the actual fluid loss will be compared with estimated fluid loss obtained from derived algorithms.

**Subjects:**

**Â Â Â Â Â **A total of 20 subjects volunteered for the study, and signed informed consent forms. All subjects were healthy individuals exercising at a local health club.Â Â The subjects were selected to obtain a wide range of physiological attributes that considered, age, weight, physical fitness level, and body dimension.Â Â The ability to predict accurate fluid loss in such a diverse sample population could help to further ensure a safe application for a national audience.Â Â

**Procedures:**

All subjects wore lightweight indoor clothing and running shoes, and were instructed to hydrate themselves normally the day of the test. After signing the informed consent forms, the subjects stride length was measured using a fiberglass measuring tape stretched out 40 feet. The subjects were instructed to use a â€œnormalâ€ walking gait from a moving start. A starting mark was used that gave the subject two full strides before hitting the tape at the zero mark. The subject was then instructed to walk 10 strides, and continue walking through to the end of the tape. An average stride of three similar trials was entered into the pedometer, (*Acumen Inc.*10K Stepper). The procedure was then repeated, this time with 8 â€“ 10 full jogging strides.

The subjects were then weighed nude to the nearest 0.1 pounds using a *Befour*PS-6600 electronic digital scale, (*Befour Inc*., Saukville, Wisconsin,). Each subject was requested to record the weight obtained from three identical trials. Respiratory gas analysis utilized a CPX Express System manufactured by *MedGraphics*, and was integrated to a*Quinton*Q55 Treadmill. Subjects were also fitted with a downloadable heart rate monitor, (Vantage XL, Manufactured by *Polar*), which recorded heart rates for each minute of exercise. A pedometer was placed as per the manufacturerâ€™s recommendation (*Acumen Inc*, Alexandria, Virginia), approximately three inches to the right of the naval on the waist band. Temperature and humidity was recorded each 5 minute stage of exercise with a wall mounted aneroid barometer, and thermometer located immediately to the rear of the subject.

Subjects were instructed to walk for five minutes at 1.5 mph. Heart rate, steps, temperature and humidity was recorded for each minute of exercise. Indirect calorimetry recorded oxygen consumption, and Respiratory Exchange Ratios (RERâ€™s) for each minute of exercise. Each subsequent 5 minutes the speed was increased by 0.5 mph. When the subject completed walking for five minutes at 4.0 mph, the pedometer was switched to the running mode, and the subject was instructed to jog at 4.5 mph. The subject continued running without hanging onto the treadmill rails, until voluntary fatigue or completion of the final 5 minute stage at 7.0 mph.

Exercise times for the subjects ranged from 39 minutes to 60 minutes.Â Â Temperatures were maintained between 64 – 72 degrees Fahrenheit, with humidity ranging from 58 – 77%.Â Â

**Oxygen Consumption Requirements for Walking and Running:**

Oxygen consumption values at each speed were averaged, and a resting Metabolic Equivalent of Task (MET) was subtracted to find the amount of oxygen consumption consumed due to the exercise only for each subject. The resting MET was computed using the Kleiber equation which scales for body dimension in the computation of a resting MET at a 3/4 exponential ratio to the subjects weight.

The American College of Sports Medicine (ACSM) recommends the use of their own Metabolic Equations for the estimation of oxygen consumption, at 0.1 milliliters of oxygen consumed per kilo of the subjectâ€™s body weight for each meter per minute of walking speed. This consumption is doubled to 0.2 milliliters with running paces. A resting MET of 3.5 ml for each kilo of the subjects body weight is added to the work component for speed as per the recommendations of the ACSM.

Â Â Â Â Â To test the accuracy of the ACSMâ€™s metabolic equations, nineteen subjectâ€™s oxygen consumption values were converted into a milliliter of oxygen consumed per kilo of body weight for each meter of minute for the speeds tested.Â Â Partial data was lost from one subject due to a technical malfunction, and this subjects remaining data was not used in the computations of walking and running oxygen requirements.Â Â At speeds of 1.5 mph â€“ 4.0 mph, an average oxygen consumption value of 1.07, (Std. Dev: .0228) for walking and a value of 1.74, (Std Dev: .0219), for running, was attained in milliliters of oxygen consumed per kilo of the subjectâ€™s body weight for each meter per minute of speed.Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â

**Figure 1: **Oxygen Requirements, (ml) per Kilo and Various Speeds*

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â * mph speeds converted to meters/min for computation.

**Calorie Requirements for Walking and Running:**

Oxygen consumption values of .107 milliliters of oxygen consumed per kilo, times the meters per minute for walking speeds and .174 for running speeds were derived from the pedometer data. A resting MET, (Kleiber) was added to compensate for the oxygen requirements at the individuals basal state. Oxygen consumption values were recorded at each speed in liters per minute. Average RER values obtained from the indirect calorimetery data of the 19 subjects tested for both walking (RER=.85), and running ( RER=.96) were determined. Utilizing a chart of Non-Protein R values, (Lusk, 1928), calorie production of 4.87 and 5.00 Kcal per liter of oxygen consumed were used in the calorie estimates for walking and running speeds respectively. Pedometer formulas for the predicting calories for walking and running are:

**Walking: ( m * t) * ((((((s * l) / 39.37) /t) *.107) *w) * t) *4.87 **

**Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Running:Â Â Â (Â m * t) * ((((((s * lr) / 39.37) /t) *.174) *w) *Â t) *5.00**

Where:Â Â Â Â Â Â Â Â Â *m*Â Â Â Â Â Â Â Â Â Â Â Â = Resting MET (Kleiber)

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â *Â t*Â Â Â Â Â Â Â Â Â Â Â = Time in minutes

*s* = Strides taken

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â *l*Â Â Â Â Â Â Â Â Â Â Â Â = Stride length in inches walking

* lr* = Stride length in inches running

*w* = Weight in kilos

A Paired Sampled T-Test was run using the SPSS version 10 software, and correlations were determined at all speeds comparing actual calories determined by indirect calorimetry to derived calories from the recorded pedometer data. All speeds showed a significant correlation ( p <= .001), ranging from .710 to .932. It was noted that the mean total calories were predicted to within one calorie between the actual and predictive formula with a correlation of .959.

Â Â Â Â Â A comparison of the ACSM and the Pedometer equations noted an approximate 28.6% increase in accuracy in the standard error of estimate for the Pedometer calorie equations, versus the use of the American College of Sports Medicine equations, (9.58 vs 13.42 kcal respectively).Â Â By incorporating the compensated MET formula (Surina 2006), into the Pedometer equation to estimate calories, an 11% in accuracy was noted to a standard error of 8.22 kcal.Â

**Estimating Hydration:**

To estimate hydration, the predicted calories, and the average temperature and humidity levels were recorded at each speed change and incorporated into the following hydration equation:

(Humidity^2)/1270)*((Total Exercise Time)/60)+((Temp*Total Calories)/1630)

The total fluid loss was determined as the accumulated fluid loss from all the tested walking and running velocities.Â Â Estimated fluid loss was compared using a Paired Samples T-test with actual fluid loss from the total exercise session.Â Â The statistical analysis showed a significant correlation, .928 (p<.001) between the use of a pedometer to estimate fluid loss versus actual fluid loss in the exercise protocol used in the study.

**Discussion: **

The prediction of hydration using a pedometer can be done with significant results. It seemed very clear that the programming of stride length would be the most crucial component in the programming of the device. In the programming of the pedometer for this study, it should be noted that the subjects were given minimal instruction, and the investigator expressly followed the manufacturerâ€™s recommendations. There appeared to be optimal walking and running speed, where the stride length and stride count matched the distance. For walking gaits this speed was at 3.8 MPH, and a running speed of 6.1 MPH. Attempts were made to adjust for the stride length based on speed, using linear regression equations. Although there was an increase in the ability to predict calories, there was no increase in the accuracy of predicting fluid loss.

The ability to correct for body dimension by using the scaling methods of West & Gilooley (1999) or that of White and Seymour (2003) can greatly increase the accuracy of calorie and fluid loss estimations. Using the respiratory data obtained in the study, and comparing scaling computations to the ACSM equations, it was discovered that the weight of the subjects used in the ACSM walking equation would need to approximate 214 to 231 lbs to utilize a .1 ml/kg of oxygen for every meter per minute of walking speed. For the running equation of .2 ml/kg of oxygen for every meter per minute of walking speed, a subject weight of 121 â€“ 131 lbs would need to be used in the ACSM equation to determine this level of energy need. Since our study population was diverse, a body dimension correction of .107 ml/kg for walking and .174 ml/kg was made to mimic a more typical adult population.

The use of the Kleiber equation, scaling to a power of Â¾, to correct for body dimension for the subjects basal state was incorporated into the pedometer algorithm. This would further ensure that for larger and smaller subjects, wearing the monitor for extended periods of the day, their will be a certain degree of assurance that these subjects will not over or under hydrate themselves. Further correction for body dimension was not required to elicit more accurate fluid loss predictions, but did aid in further improving the accuracy of estimating calorie by an additional 11%. The current prediction algorithms resulted in a high degree of predictability of fluid loss in subjects from 107 to 232 lbs, and maintain the ability to compensate for very large and very small subjects using the device for long term fluid loss estimates, (i.e. >24 hours.).

Â Â Â Â Â Lastly, increased fluid loss correlations and mean standard error of measurements were further possible, but a slight over prediction of hydration was built into the formula to ensure safety margin against dehydration for the application to large user populations.Â Â Â This was noted in the mean difference in fluid loss of -2.525 between the actual losses of 18.56 oz, vs the predicted losses of 21.09 oz, (Table 3).Â Â This slight over prediction of fluid loss is further illustrated in Figure 2, where the individual data is plotted graphically.

**Figure 2: **Individual Data of Actual to Predicted Fluid Loss Using a Pedometer

Further research is needed regarding the effects of varied levels of body fat, clothing worn, sensor placement and movement efficiency demonstrated by individual subjects as it relates to fluid loss. The results of this study provide strong evidence that significantly accurate predictions of fluid loss are possible with a very inexpensive and uncomplicated pedometer device tailored to the individual user.

**References:**

Agutter, Paul S., Wheatley, Denys. Metabolic scaling: consensus or controversy? Theor Biolo Med Model. 2004;1: 1-13.

Kleiber, M. Body size and metabolism. Hilgardia,6,1932; 6:315-353.

White, Craig R., Seymour, RS. Mammalian basal metabolic rate is proportional to body mass ^{2/3}. Proc Natl Acad Sci U S A. 2003 April; 100(7): 4046-4049.

Surina, BJ, Method for adjusting metabolic parameters according to a subjects body weight. US patent application #20060090765, May 4, 2006.

Lusk, G. (1923) Science of Nutrition 4^{th}Ed.Philadelphia: W.B. Saunders, p.65.

American College of Sports Medicine, (1991) Guidelines for Exercise Testing and Prescription, 4^{th}Ed. Philadelphia: Lea & Febiger.

*The Exercise Science Center would like to thank The Beverage Institute for Health & Wellness, L.L.C., for the opportunity to conduct the research for this study, and to be an integral part in the development of a final product for this potentially important endeavor. *