Energy expenditure prediction via a footwear-based physical activity monitor: accuracy and comparison to other devices

2011 Summer.Includes bibliographical references.Accurately estimating free-living energy expenditure (EE) is important for monitoring or altering energy balance and quantifying levels of physical activity. The use of accelerometers to monitor physical activity and estimate physical activity EE is common in both research and consumer settings. Recent advances in physical activity monitors include the ability to identify specific activities (e.g. stand vs. walk) which has resulted in improved EE estimation accuracy. Recently, a multi]sensor footwear-based physical activity monitor that is capable of achieving 98% activity identification accuracy has been developed. However, no study has compared the EE estimation accuracy for this monitor and compared this accuracy to other similar devices. PURPOSE: To determine the accuracy of physical activity EE estimation of a footwear-based physical activity monitor that uses an embedded accelerometer and insole pressure sensors and to compare this accuracy against a variety of research and consumer physical activity monitors. METHODS: Nineteen adults (10 male, 9 female), mass: 75.14 (17.1) kg, BMI: 25.07(4.6) kg/m2 (mean (SD)), completed a four hour stay in a room calorimeter. Participants wore a footwear-based physical activity monitor, as well as three physical activity monitoring devices used in research: hip]mounted Actical and Actigraph accelerometers and a multi-accelerometer IDEEA device with sensors secured to the limb and chest. In addition, participants wore two consumer devices: Philips DirectLife and Fitbit. Each individual performed a series of randomly assigned and ordered postures/activities including lying, sitting (quietly and using a computer), standing, walking, stepping, cycling, sweeping, as well as a period of self-selected activities. We developed branched (i.e. activity specific) linear regression models to estimate EE from the footwear-based device, and we used the manufacturer's software to estimate EE for all other devices. RESULTS: The shoe-based device was not significantly different than the mean measured EE (476(20) vs. 478(18) kcal) (Mean(SE)), respectively, and had the lowest root mean square error (RMSE) by two]fold (29.6 kcal (6.19%)). The IDEEA (445(23) kcal) and DirecLlife (449(13) kcal) estimates of EE were also not different than the measured EE. The Actigraph, Fitbit and Actical devices significantly underestimated EE (339 (19) kcal, 363(18) kcal and 383(17) kcal, respectively (p<.05)). Root mean square errors were 62.1 kcal (14%), 88.2 kcal(18%), 122.2 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for DirectLife, IDEEA, Actigraph, Actical and Fitbit respectively. CONCLUSIONS: The shoe based physical activity monitor was able to accurately estimate EE. The research and consumer physical activity monitors tested have a wide range of accuracy when estimating EE. Given the similar hardware of these devices, these results suggest that the algorithms used to estimate EE are primarily responsible for their accuracy, particularly the ability of the shoe-based device to estimate EE based on activity classifications

VALIDATION OF THE BODYMEDIA MINI ARMBAND TO ESTIMATE ENERGY EXPENDITURE OF FEMALE BASKETBALL PLAYERS DURING VARIABLE INTENSITY GAME-LIKE CONDITIONS

Monitoring an athlete’s energy intake and energy expenditure (EE) is an important consideration of nutritional planning for sport conditioning and peak performance. In order to provide appropriate recommendations regarding nutritional requirements and caloric needs, an accurate determination of energy requirements is necessary. By knowing an individual’s EE, a coach or trainer may be effectively able to determine training loads and volumes necessary for periodization, and seasonal planning for a particular sport. Purpose: To examine the accuracy of the BodyMedia mini armband, to assess EE in female basketball players during various-intensity game-like conditions. Methods: A cross-sectional correlation design with multiple observations was employed. This investigation required three testing sessions, an orientation session, and 2 experimental trial sections. Trials included a maximal multistage 20-m shuttle run (Trial I) and 30-minute basketball skills session (Trial II). The independent variable for this investigation was EE estimated by the Mini armband. The dependent variable was EE determined by the indirect calorimetry (IC) method. Results: EE assessed with the Mini and EE measured with the IC method was significantly correlated for both Trial I (r= 0.839) and Trial II (r= 0.833). EE calculated by the Mini was significantly underestimated in both Trial I (9.41 ± 26.1 total kcals) and Trial II (56.71 ± 14.1 total kcals). During Trial I the underestimation of EE increased with an increase in test level and intensity. Conclusion: Due to the underestimation of EE by the Mini, the development of exercise specific algorithms to improve the estimation of EE during intermittent exercise in basketball players is warranted

Accuracy of predicted energy expenditure from a Fitbit Inspire HR activity monitor during short- and long-duration exercise

PURPOSE: To determine the accuracy of predicted Energy Expenditure (EE) reported by a wrist-worn activity monitor compared to measured EE during both a long- and short-duration exercise. METHODS: In addition to a VO2max treadmill test, a running speed at approximately 70 - 75% of that VO2max was found during the first visit. The second and third visit was comprised of either a 30-minute or 10-minute run at the speed previously determined. A wrist activity monitor was worn and VO2 and EE were recorded by a metabolic cart. Pearson correlation, paired samples t-test, and repeated measures ANOVAs compared predicted and measured EE. An independent samples t-test determined significant differences in characteristics between fitness groups (p \u3c 0.05). RESULTS: N = 25 (60% male). A significant correlation was found between predicted EE and measured EE for both short and long duration (p \u3c 0.001). The repeated measures ANOVA determined the interactive effect of measurement mode and fitness level was significant. CONCLUSIONS: Overall, there is a strong correlation between criterion and predictive measurement, however, consumers should exercise caution when using predicted measures

The Effects of ActiGraph Bandpass Filtering on Activity Counts During Continuous and Intermittent Lifestyle Activity

PURPOSE: The purpose of this study was to explore how increasing the upper limit of the bandpass filter frequency range affected accelerometer counts collected during treadmill walking and running, car driving and intermittent lifestyle activities METHODS: Part A included treadmill walking, running, and car driving (N=20) (mean ± [plus or minus] SD; age, 24.4±3.4 years; body mass index (BMI, 26.4±3.3 kg/m2 [kilograms per meter squared]). Part B included ten lifestyle activities ranging from sedentary behaviors to vigorous intensities (N=30) (mean±SD; age, 23.0±2.3 years; BMI, 25.1±3.8 kg/m2). Participants wore an ActiGraph accelerometer (GT3X+ in Part A and GT9X in Part B. on the hip. Participants completing Part B wore a Cosmed K4b2 [K4b squared]as a criterion measure of energy expenditure. Acceleration data were processed using a beta version of Actilife containing additional bandpass filter frequencies with upper limits of 5.0 Hz [Hertz] and 9.0 Hz, as well as, the 2.5 Hz default filter. Data were converted to 5-s epochs and the low frequency extension feature was employed. Cosmed data (VO2 [volume of oxygen] ml/min) were averaged over 30-s and then converted to relative VO2 (ml/kg/min) and metabolic equivalents (METs) for each activity. RESULTS: Part A: compared to the default bandpass filter, using a bandpass filter range of 0.25-9.0 Hz reduces the plateau effect seen during treadmill walking and running and significantly increases count values during car driving for all axes and vector magnitude. Part B: Increasing the bandpass filter frequency, significantly increased the count values on all axes during the lifestyle activities. Across all activities, the default filter had the strongest association between counts and METs, while the 5.0 Hz filter had the strongest association for lifestyle activities and the 9.0 Hz filter had the strongest association for locomotive activities. CONCLUSION: The plateau effect seen with the ActiGraph accelerometer can be reduced by increasing the bandpass filter frequency range. However, increasing the bandpass filter frequency range significantly increased the counts during car driving and lifestyle activities. Future work is needed to understand the impact that the increased count values will have on estimating energy expenditure

Measurement of Energy Expenditure During Laboratory and Field Activities

This dissertation was designed to examine the validity of heart rate (HR) and motion sensors for estimating energy expenditure (EE) during activities ranging from sedentary behaviors to vigorous exercise. A secondary purpose was to devise new ways to improve on current methods of estimating EE. Specific aims of the dissertation were: (1) to examine the use of pedometers to measure steps taken, distance traveled, and EE during treadmill walking at various speeds; (2) Examine the use of a Polar HR monitor to estimate EE during treadmill running, stationary cycling, and rowing; (3) compare the current Actigraph regression equations (relating counts·min-1 to EE) against three newer devices (Actiheart, Actical, and AMP-331) during sedentary, light, moderate, and vigorous intensity activities; and (4) development of a new 2-regression model to estimate EE using the Actigraph accelerometer. For the first aim, 10 participants performed treadmill walking for five minutes at five speeds while wearing two pedometers of different brands (10 pedometer brands were tested) on the right and left hip. Simultaneously oxygen consumption (VO2) was measured and actual steps were counted using a hand tally counter. Six of the 10 pedometers were within ± 3% of actual steps at 80 m·min-1 and faster. Most pedometers were within ± 10% of actual distance at 80 m·min-1, but they overestimate distance at slower speeds, and underestimate distance at faster speeds. Most pedometers gave estimates of gross EE within ± 30% of measured EE across all speeds. In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kcals. In the second aim, 10 males and 10 females performed a maximal treadmill test. On a separate day they performed treadmill, cycle, and rowing exercise for 10 minutes at three different intensities. During each trial EE was estimated using two Polar S410 HR monitors (one with predicted VO2max and HRmax (PHRM) and one with actual VO2max and HRmax (AHRM), input into the watch). Simultaneously, EE was measured by indirect calorimetry (IC). For males there were no differences among the mean values of EE for the AHRM, PHRM and IC for any exercise mode (P ≥ 0.05). In females, the AHRM significantly improved the estimate of EE compared to the PHRM (P \u3c 0.05), but it still overestimated mean EE on the treadmill and cycle (P \u3c 0.05). The Polar S410 HR monitor provides the best estimate of EE when the actual VO2max and HRmax are used. For the third aim, 48 participants performed various activities ranging from sedentary pursuits to vigorous exercise. The activities were split into three routines of six activities and each participant performed one routine. During each routine an Actigraph (right hip), Actical (left hip), Actiheart (chest), and AMP-331 (right ankle) were worn. Simultaneously, EE was measured by IC. The Actiheart HR algorithm was not significantly different from measured EE for any of the 18 activities (P ≥ 0.05). The Actiheart combined HR and activity algorithm was only significantly different from measured EE for vacuuming and ascending/descending stairs (P \u3c 0.05). All remaining prediction equations, for the devices examined, over- or underestimated EE for at least seven activities. The Actiheart HR algorithm provided the best estimate of EE over a wide range of activities. The Actical and Actigraph tended to overestimate walking and sedentary activities and underestimate most other activities. For the fourth aim, 48 participants performed various activities (sedentary, light, moderate, and vigorous intensities) that were split into three routines of six activities. Each participant performed one routine. During each test the participants wore an Actigraph accelerometer and EE was measured by IC. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare against existing equations. For each activity the coefficient of variation (CV) of the counts per 10 seconds was calculated to determine if the activity was walking/running, or some other activity. If the CV ≤ 10 then a walking/running regression equation (relating counts·min-1 to METs) was used, while if the CV \u3e 10 a lifestyle/leisure time physical activity (LTPA) regression was used. The new 2-regression model explained 73% of the variance in EE for walking/running, and 83.8% of the variance in EE for lifestyle/LTPA and it was within ± 0.84 METs of measured METs for each of the 17 activities performed (P ≥ 0.05). The new 2-regression model is a more accurate prediction of EE then the currently published regression equations using the Actigraph accelerometer

Measurement of physical activity and energy expenditure using heart rate, motion sensors and questionnaires

This dissertation was designed to examine new techniques to measure physical activity (PA) and energy expenditure (EE) during lifestyle activities. The specific aims were: 1) to evaluate heart rate (HR), using percent of HR reserve in relation to percent of oxygen uptake reserve, as a method for assessing moderate intensity PA in the field setting; 2) to validate the simultaneous heart rate-motion sensor (HR+M) technique to estimate EE of selected activities; 3) to validate the simultaneous HR +M technique to predict EE over an extended time period; and 4) to use the simultaneous HR+M technique to validate selected PA questionnaires over a 7-day period. For the first aim, sixty-one males performed physical tasks in both a laboratory and field setting. HR and oxygen uptake (V0 2) were continuously measured during 15- min tasks. HR data was used to predict EE using age-predicted maximum HR and estimated maximal V0 2. The correlation between HR and measured V0 2 was r=0.68. After adjusting for age and fitness level, HR provided an accurate estimate of EE, r=0.87. Using percent HR reserve to estimate percent V02 reserve significantly improved the estimation of EE

Resting energy expenditure using indirect calorimetry in individuals with moderate to low burns: A pilot study of associated factors, patient acceptability and comparison with predictive equations

Background: Energy expenditure increases following a burn injury. The extent of hypermetabolism is dependent on a range of factors including burn total body surface area. Moderate to low burn injuries (\u3c 15% TBSA) represent majority of hospital admissions for burn injuries however, their energy expenditure remains unpublished. While indirect calorimetry (IC) is the gold standard for determining energy requirements, less accurate predictive equations are often used in practice. Acceptability of IC from a burn patient perspective has not been published. Aim: To describe the resting energy expenditure (REE) of patients with a moderate to low burn injury using IC; compare measured REE to predictive equations; and determine the patient acceptability of IC. Methods: Demographic, anthropometric and dietary data were collected for five male and three female burn patients. REE was determined using indirect calorimetry (Ultima CPX) and five predictive methods (Schofield, Harris-Benedict, Toronto and the Ireton-Jones equations, and energy-per-kilogram formulae). A written questionnaire assessed patient acceptability. Results: Mean measured REE was 6494 ± 1625 kJ/day, lower than reported REE of major burn populations from the literature (p \u3c 0.05). At a group level, the Schofield and Toronto equation were accurate to within ± 10% of the measured REE with a mean difference of 5.21 ± 12.16% and 8.89 ± 12.64%, respectively. At an individual level, the Schofield equation was accurate for 67% of participants and overestimated REE for 33% of participants. The Toronto equation was accurate for 50% of participants and overestimated REE for 50% of participants. IC was acceptable from a patient perspective with all participants willing to repeat the measure. Conclusions: Results of this study support routine use of IC in moderate to low burn injuries, as it is acceptable to patients and avoids the inaccuracies of predictive equations. Where IC is not available, results suggest that the Schofield equation be used with caution to estimate REE for moderate to low burn injuries. Given the small sample size of this study, further research on the REE of moderate to low burn injuries is warranted

Detecting Periods of Eating in Everyday Life by Tracking Wrist Motion — What is a Meal?

Eating is one of the most basic activities observed in sentient animals, a behavior so natural that humans often eating without giving the activity a second thought. Unfortunately, this often leads to consuming more calories than expended, which can cause weight gain - a leading cause of diseases and death. This proposal describes research in methods to automatically detect periods of eating by tracking wrist motion so that calorie consumption can be tracked. We first briefly discuss how obesity is caused due to an imbalance in calorie intake and expenditure. Calorie consumption and expenditure can be tracked manually using tools like paper diaries, however it is well known that human bias can affect the accuracy of such tracking. Researchers in the upcoming field of automated dietary monitoring (ADM) are attempting to track diet using electronic methods in an effort to mitigate this bias. We attempt to replicate a previous algorithm that detects eating by tracking wrist motion electronically. The previous algorithm was evaluated on data collected from 43 subjects using an iPhone as the sensor. Periods of time are segmented first, and then classified using a naive Bayesian classifier. For replication, we describe the collection of the Clemson all-day data set (CAD), a free-living eating activity dataset containing 4,680 hours of wrist motion collected from 351 participants - the largest of its kind known to us. We learn that while different sensors are available to log wrist acceleration data, no unified convention exists, and this data must thus be transformed between conventions. We learn that the performance of the eating detection algorithm is affected due to changes in the sensors used to track wrist motion, increased variability in behavior due to a larger participant pool, and the ratio of eating to non-eating in the dataset. We learn that commercially available acceleration sensors contain noise in their reported readings which affects wrist tracking specifically due to the low magnitude of wrist acceleration. Commercial accelerometers can have noise up to 0.06g which is acceptable in applications like automobile crash testing or pedestrian indoor navigation, but not in ones using wrist motion. We quantify linear acceleration noise in our free-living dataset. We explain sources of noise, a method to mitigate it, and also evaluate the effect of this noise on the eating detection algorithm. By visualizing periods of eating in the collected dataset we learn that that people often conduct secondary activities while eating, such as walking, watching television, working, and doing household chores. These secondary activities cause wrist motions that obfuscate wrist motions associated with eating, which increases the difficulty of detecting periods of eating (meals). Subjects reported conducting secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals, compared to only 6.8% of the time in a cafeteria dataset. Walking motion was found during 5.5% of the time during meals in free-living, compared to 0% in the cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved the average per person accuracy from 74% to 77% on our free-living dataset (t[353]=7.86, p\u3c0.001). This suggests that future data collections for eating activity detection should also collect detailed ground truth on secondary activities being conducted during eating. Finally, learning from this data collection, we describe a convolutional neural network (CNN) to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts appx 1-5 sec. The novelty of our new approach is that we analyze a much longer window (0.5-15 min) that can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We found that accuracy at detecting eating increased by 15% in longer windows compared to shorter windows. Overall results on CAD were 89% detection of meals with 1.7 false positives for every true positive (FP/TP), and a time weighted accuracy of 80%

Top Quark Physics at the Tevatron

The discovery of the top quark in 1995, by the CDF and D0 collaborations at the Fermilab Tevatron, marked the dawn of a new era in particle physics. Since then, enormous efforts have been made to study the properties of this remarkable particle, especially its mass and production cross section. In this article, we review the status of top quark physics as studied by the two collaborations using the p-pbar collider data at sqrt(s) = 1.8 TeV. The combined measurement of the top quark mass, m_t = 173.8 +- 5.0 GeV/c^2, makes it known to a fractional precision better than any other quark mass. The production cross sections are measured as sigma (t-tbar) = 7.6 -1.5 +1.8 pb by CDF and sigma (t-tbar) = 5.5 +- 1.8 pb by D0. Further investigations of t-tbar decays and future prospects are briefly discussed.Comment: 119 pages, 59 figures, 17 tables Submitted to Int. J. Mod. Phys. A Fixed some minor error