Closed-loop control of air supply to whole-room indirect calorimeters to improve accuracy and standardize measurements during 24-hour dynamic metabolic studies.

OBJECTIVE: The aim of this study was to test proportional-integral-derivative (PID) control of air inflow rate in a whole-room indirect calorimeter to improve accuracy in measuring oxygen (O2 ) consumption ( V ̇ O 2 ) and carbon dioxide (CO2 ) production ( V ̇ CO 2 ). METHODS: A precision gas blender infused nitrogen (N2 ) and CO2 into the calorimeter over 24 hours based on static and dynamic infusion profiles mimicking V ̇ O 2 and V ̇ CO 2 patterns during resting and non-resting conditions. Constant (60 L/min) versus time-variant flow set by a PID controller based on the CO2 concentration was compared based on errors between measured versus expected values for V ̇ O 2 , V ̇ CO 2 , respiratory exchange ratio, and metabolic rate. RESULTS: Compared with constant inflow, the PID controller allowed both a faster rise time and long-term maintenance of a stable CO2 concentration inside the calorimeter, resulting in more accurate V ̇ CO 2 estimates (mean hourly error, PID: -0.9%, 60 L/min = -2.3%, p < 0.05) during static infusions. During dynamic infusions mimicking exercise sessions, the PID controller achieved smaller errors for V ̇ CO 2 (mean: -0.6% vs. -2.7%, p = 0.02) and respiratory exchange ratio (mean: 0.5% vs. -3.1%, p = 0.02) compared with constant inflow conditions, with similar V ̇ O 2 (p = 0.97) and metabolic rate (p = 0.76) errors. CONCLUSIONS: PID control in a whole-room indirect calorimeter system leads to more accurate measurements of substrate oxidation during dynamic metabolic studies.

Improving temporal accuracy of human metabolic chambers for dynamic metabolic studies.

Metabolic chambers are powerful tools for assessing human energy expenditure, providing flexibility and comfort for the subjects in a near free-living environment. However, the flexibility offered by the large living room size creates challenges in the assessment of dynamic human metabolic signals-such as those generated during high-intensity interval training and short-term involuntary physical activities-with sufficient temporal accuracy. Therefore, this paper presents methods to improve the temporal accuracy of metabolic chambers. The proposed methods include 1) adopting a shortest possible step size, here one minute, to compute the finite derivative terms for the metabolic rate calculation, and 2) applying a robust noise reduction method-total variation denoising-to minimize the large noise generated by the short derivative term whilst preserving the transient edges of the dynamic metabolic signals. Validated against 24-hour gas infusion tests, the proposed method reconstructs dynamic metabolic signals with the best temporal accuracy among state-of-the-art approaches, achieving a root mean square error of 0.27 kcal/min (18.8 J/s), while maintaining a low cumulative error in 24-hour total energy expenditure of less than 45 kcal/day (188280 J/day). When applied to a human exercise session, the proposed methods also show the best performance in terms of recovering the dynamics of exercise energy expenditure. Overall, the proposed methods improve the temporal resolution of the chamber system, enabling metabolic studies involving dynamic signals such as short interval exercises to carry out the metabolic chambers.

Energy expenditure: measurement of human metabolism.

Obesity is one of the most prevalent chronic diseases globally, especially in the United States. While the United States gained an early lead in unnecessary weight gain, most other countries are quickly closing the gap. The latest U.S. National Health and Nutrition Examination Survey (NHANES, http://www.cdc.gov/nchs/nhanes.htm) documents that about one third of adults in the United States are now overweight [a body-mass index (BMI) between 25 and 30 kg/m2] and another one third (61 million) are considered obese (BMI >30 kg/m2). Being obese is a strong risk factor for cardiovascular diseases, type 2 diabetes, osteoporosis, some cancers, and depression. The economic impact of this condition is staggering: in 2008, more than 147 billion dollars were spent just in the United States for medical costs related to obesity. Time lost from work and spending on weight loss costs even more.

Data upload capability of 3G mobile phones.

Mobile phones are becoming an important platform to measure free-living energy balance and to support weight management therapies. Sensor data, camera images and user input are needed by clinicians and researchers in close to real time. We assessed upload (reverse link) data transport rates for 2007-2008 model mobile phones on two major US wireless systems. Even the slowest phone (EVDO Rev 0) reliably uploaded 40 MB of data in less than 1 h. More than 95% of file uploads were successful in tests that simulated normal phone use over 3 d. Practical bandwidth and data currency from typical smart phones will likely keep pace with the data needs of energy balance studies and weight management therapy.

Smart phones are useful for food intake and physical activity surveys.

Current self-report methods of recording food intake and Physical Activity (PA) are cumbersome and inaccurate. Food and activity surveys implemented on a smart phone will allow for immediate entry, data transfer to a researcher, and feedback to the user. Ten subjects followed a script, representative of one day, to enter food intake and PA on a smart phone. In the follow-up report, all subjects were interested in using the tested program to compare food intake with PA to predict weight gain and loss.