Energy Metabolism

With the development of several different commercial systems, measures of energy expenditure in mice are now much more common than 10–15 years ago, but results are sometimes misinterpreted^{2 –4 (link)}. Using direct calorimetry, energy expenditure is assessed by the direct measurement of the body’s heat production in a calorimeter^{21 (link)–23 (link)}. Despite high reproducibility and measurement errors of only 1–3%, these calorimeters are expensive, have slow response time^{22 (link)} and do not provide information about the nature of the oxidized substrates. In indirect calorimetry, energy expenditure is calculated based on the amount of oxygen consumed and carbon dioxide produced (Supplementary Note 1). The most common indirect calorimeter types are ventilated, open-circuit systems, in which the animals are placed in gas-tight metabolic cages through which a flow of fresh air is passed. The system collects and mixes the expired air, measures the flow rate and analyzes the gas concentration of the incoming and outgoing air for both O₂ and CO₂ (ref. 22 (link)). Another indirect method of calorimetry is the doubly labeled water method, an isotope-elimination technique developed in the 1950s (refs. 24 (link)–26 ). This method has been traditionally used to measure the metabolic rate of small free-living animals, which are released in the field between two time points: it is often referred to as field metabolic rate^{27 (link)}. In the laboratory, the main advantage of the method is that it allows the measurement of energy demands of an animal embedded in a social environment^{28 (link)}. However, the time intervals between blood sampling are often too long to permit measurements of short-term or diurnal changes of the metabolic rate.

The potential to model physical activity energy expenditure (PAEE) from counts was recognized at the beginning of the development of modern accelerometers, [1 (link)] and the simplicity of linear regression approaches for both developing and applying counts made this approach exceedingly popular with many researchers. Although most of the calibration regression equations estimate average PAEE relatively well for groups (of generally healthy adults and children), the challenges of predicting PAEE accurately for individuals and over a wide range of activities are also well-known. [5 (link)] The large errors associated with EE estimates for individuals preclude use of accelerometers to calibrate dietary intake for energy balance or estimate changes in PAEE in response to an intervention, two applications for which there is high demand. [6 (link)] Moreover, multiple calibration studies have generated widely divergent regression models for converting counts to PAEE, yielding different cut-points for physical activity categories. [7 (link)] These diverse equations and cut-points created considerable confusion and frustration for PA and other health researchers who wished to select the appropriate way to analyze their accelerometer data. [7 (link)–9 (link)]
A noteworthy shift in the past decade was the demonstration of significantly improved PAEE estimation compared to regression calibrations by using signal features and patterns extracted from raw acceleration data with machine-learning techniques to derive more sophisticated models. [10 (link)] Through the model development processes, researchers also recognized that PAEE was not the only outcome variable that could be extracted from acceleration signals. With the implementation of piezo-resistive and capacitive accelerometer transducers, static acceleration (the direct current or DC component) from the raw signals can be used to estimate limb angles and thus infer postures. [11 (link)] Combining the positional information with the movement acceleration data (the alternating current or AC component) in orthogonal directions provides rich feature sets that allow modeling experts and statisticians to utilize the power of pattern recognition, machine learning, and fusion of different techniques to respond to an ever-expanding application field. [12 (link)] The ability to differentiate PA types is providing new insights and promises to expand the scope of PA research in behavioral and clinical sciences.
Accompanying the enthusiasm regarding high resolution raw acceleration signal capture are concerns related to storage and transmission of the high data volumes as well as appropriate data modeling methods. With rapidly expanding computer memory sizes at comparable or lower cost, storage is no longer a significant limitation. Data transfer from the onboard memory of raw-data accelerometers (about 0.5 Gigabytes for each 7-day collection) can now be performed within minutes. However, it is currently challenging to translate the raw data to the desirable results of PA types and PAEE. The raw-data based analytic models, particularly multidimensional algorithms, are still being developed, validated, and optimized by researchers and device manufacturers. However, the widespread interest in “big data” provides analytic approaches that are being applied to accelerometer signal data.
To reduce barriers to adoption and support replication and cross-validation of new models, the models need to be built into easy-to-use software or in open-source shareware forms so that they are useful for applied researchers and clinicians. A number of efforts are currently under way within the academic, small business, and government sectors to address the specific computational requirements to implement signal processing methods for large volumes (e.g., Terabytes) of acceleration and related sensor (e.g., gyroscope or heart rate) data. For example, the U.S. National Cancer Institute has supported development of scalable systems for collection, storage, analysis, and reporting of data from diverse sensor platforms via Small Business Innovation Research (SBIR) contracts. A specific requirement of these systems was the implementation of fully transparent (and customizable) analytic tools to process data from raw sensor signals into outcome measures. Device manufacturers and application developers have also continued to invest in software solutions or support for open-source tools (e.g., such as R-code and libraries) in order to support their users’ analytic needs. The availability of efficient raw signal data analytic approaches will ultimately encourage researchers toward new models of accelerometer data analysis. These new models may decrease reliance on batch processing on desktop computers and increase implementation of rolling data analysis, perhaps on cloud-based computing platforms.
Another concern within the PA research field is the comparability and accuracy of information extracted from acceleration signals recorded from different body locations. For example, the correlation between activity counts and PAEE from uniaxial accelerometers was found to be much lower when positioned on the wrist rather than at the hip. [13 (link)] However, several recent studies that used features from triaxial raw accelerometer signals have narrowed the gap between PAEE estimates from wrist- and hip worn-accelerometers [14 (link), 15 (link)] and for classifying PA into sedentary, household, walking and running types. [16 (link)] Such efforts will certainly grow and mature over the next few years.
Current accelerometer-based devices have moved beyond small-capacity (< 1 Megabyte) onboard memory chips and piezo-electric sensors, which are now expensive and difficult for device manufacturers to find. In the near future, the PA field may also move beyond reliance on count-based linear regressions and cut-points for data extraction from accelerometers.