This study examined the intensity of activity contributing to physical activity energy expenditure in older adults. In 57 men and women aged ≥ 65, total energy expenditure (TEE) was measured using doubly labeled water and resting metabolic rate was measured using indirect calorimetry to calculate a physical activity index (PAI). Sedentary time and physical activity of light and moderate to vigorous (mod/vig) intensity was measured using an accelerometer. The subjects were 75 ± 7 yrs (mean ± SD) of age and 79% female. Subjects spent 66 ± 8, 25 ± 5, and 9 ± 4% of monitor wear time in sedentary, light, and mod/vig activity per day, respectively. In a mixture regression model, both light (β = 29.6 [15.6–43.6, 95% CI]), p < .001) and mod/vig intensity activity (β = 28.7 [7.4−50.0, 95% CI]), p = .01) were strongly associated with PAI, suggesting that both light and mod/vig intensity activities are major determinants of their physical activity energy expenditure.
Lisa H. Colbert, Charles E. Matthews, Dale A. Schoeller, Thomas C. Havighurst and KyungMann Kim
Scott A. Conger and David R. Bassett Jr.
The purpose of this study was to develop a compendium of wheelchair-related physical activities. To accomplish this, we conducted a systematic review of the published energy costs of activities performed by individuals who use wheelchairs. A total of 266 studies were identified by a literature search using relevant keywords. Inclusion criteria were studies utilizing individuals who routinely use a manual wheelchair, indirect calorimetry as the criterion measurement, energy expenditure expressed as METs or VO2, and physical activities typical of wheelchair users. Eleven studies met the inclusion criteria. A total of 63 different wheelchair activities were identified with energy expenditure values ranging from 0.8 to 12.5 kcal·kg-1·hr-1. The energy requirements for some activities differed between individuals who use wheelchairs and those who do not. The compendium of wheelchair-related activities can be used to enhance scoring of physical activity surveys and to promote the benefits of activity in this population.
Lesley J. White, Rudolph H. Dressendorfer, Eric Holland, Sean C. McCoy and Michael A. Ferguson
We examined the acute effect of cold-water temperature on post-exercise energy intake (EI) for 1 h. In a randomized, crossover design, 11 men (25.6 ± 5 y) exercised for 45 min on a submersed cycle ergometer at 60 ± 2% VO2max in 33°C (neutral) and 20° (cold) water temperatures, and also rested for 45 min (control). Energy expenditure (EE) was determined using indirect calorimetry before, during, and after each condition. Following exercise or rest, subjects had free access to a standard assortment of food items of known caloric value. EE was similar for the cold and neutral water conditions, averaging 505 ± 22 (± standard deviation) and 517 ± 42 kcal, respectively (P = NS). EI after the cold condition averaged 877 ± 457 kcal, 44% and 41% higher (P < 0.05) than for the neutral and resting conditions, respectively. Cold-water temperature thus stimulated post-exercise EI. Water temperature warrants consideration in aquatic programs designed for weight loss.
Gianluca Vernillo, Aldo Savoldelli, Barbara Pellegrini and Federico Schena
The current study aimed to show the validity of a portable motion sensor, the SenseWear Armband (SWA), for the estimation of energy expenditure during pole walking. Twenty healthy adults (mean ± SD: age 30.1 ± 7.2 year, body mass 66.1 ± 10.6 kg, height 172.4 ± 8.0 cm, BMI 22.1 ± 2.4 kg·m−2) wore the armband during randomized pole walking activities at a constant speed (1.25 m·s−1) and at seven grades (0%, ±5%, ±15% and ±25%). Estimates of total energy expenditure from the armband were compared with values derived from indirect calorimetry methodology (IC) using a 2–way mixed model ANOVA (Device × Slope), correlation analyses and Bland-Altman plots. Results revealed significant main effects for device, and slope (p < .025) as well as a significant interaction (p < .001). Significant differences between IC and SWA were observed for all conditions (p < .05). SWA generally underestimate the EE values during uphill PW by 0.04 kcal·kg−1·min−1 (p < .05). Whereas, a significant overestimation has been detected during flat and downhill PW by 0.01 and 0.03 kcal·kg−1·min−1 (p < .05), respectively. The Bland-Altman plots revealed bias of the armband compared with the indirect calorimetry at any condition examined. The present data suggest that the armband is not accurate to correctly detect and estimate the energy expenditure during pole walking activities. Therefore, the observed over- and under-estimations warrants more work to improve the ability of SWA to accurately measure EE for these activities.
Jennie A. Gilbert and James E. Misner
This study examined the metabolic response to a 763-kcal mixed meal at rest and during 30 min of exercise at 50% maximal oxygen consumption (
Benjamin J. Darter, Kathleen F. Janz, Michael L. Puthoff, Barbara Broffitt and David H. Nielsen
A new triaxial accelerometer (AMP 331) provides a novel approach to understanding free-living activity through its ability to measure real time speed, cadence, and step length. This study examined the reliability and accuracy of the AMP 331, along with construction of prediction equations for oxygen consumption and energy cost.
Young adult volunteers (n = 41) wearing two AMP units walked and ran on a treadmill with energy cost data simultaneously collected through indirect calorimetry.
Statistically significant differences exist in inter-AMP unit reliability for speed and step length and in accuracy between the AMP units and criterion measures for speed, oxygen consumption, and energy cost. However, the differences in accuracy for speed were very small during walking (≤ 0.16 km/h) and not clinically relevant. Prediction equations constructed for walking oxygen uptake and energy expenditure demonstrated R 2 between 0.76 to 0.90 and between subject deviations were 1.53 mL O2 · kg-1 · min−1 and 0.43 kcal/min.
In young adults, the AMP 331 is acceptable for monitoring walking speeds and the output can be used in predicting energy cost during walking but not running.
Daniel Arvidsson, Mark Fitch, Mark L. Hudes and Sharon E. Fleming
Overweight children show different movement patterns during walking than normal-weight children, suggesting the accuracy of multisensory activity monitors may differ in these groups.
Eleven normal and 15 high BMI African American children walked at 2, 4, 5, and 6 km/h on a treadmill wearing the Intelligent Device for Energy Expenditure and Activity (IDEEA) and SenseWear (SW). Accuracy was determined using indirect calorimetry and manually counted steps as references.
For IDEEA, no significant differences in accuracy were observed between BMI groups for energy expenditure (EE), but differences were significant by speed (+15% at 2 km/h to −10% at 6 km/h). For SW, EE accuracy was significantly different for high (+21%) versus normal BMI girls (−13%) at 2 km/h. For high BMI girls, EE was overestimated at low speed and underestimated at higher speeds. Underestimations in steps did not differ by BMI group at 4 to 6 km/h, but were significantly larger at 2 km/h than at the other speeds for all groups with IDEEA, and for normal BMI children with SW.
Similar accuracies during walking may be expected in normal and overweight children using IDEEA and SW. Both monitors showed small errors for steps provided speed exceeded 2 km/h.
Gianluca Vernillo, Aldo Savoldelli, Barbara Pellegrini and Federico Schena
Accurate assessments of physical activity and energy expenditure (EE) are needed to advance research on positive and negative graded walking.
To evaluate the validity of 2 SenseWear Armband monitors (Pro3 and the recently released Mini) during graded walking.
Twenty healthy adults wore both monitors during randomized walking activities on a motorized treadmill at 7 grades (0%, ±5%, ±15%, and ±25%). Estimates of total EE from the monitors were computed using different algorithms and compared with values derived from indirect calorimetry methodology using a 2-way mixed model ANOVA (Device × Condition), correlation analyses and Bland-Altman plots.
There was no significant difference in EE between the 2 armbands in any of the conditions examined. Significant main effects for device and condition, as well as a consistent bias, were observed during positive and negative graded walking with a greater over- and under-estimation at higher slope.
Both the armbands produced similar EE values and seem to be not accurate in estimation of EE during activities involving uphill and downhill walking. Additional work is needed to understand factors contributing to this discrepancy and to improve the ability of these monitors to accurately measure EE during graded walking.
Chinmay Manohar, Shelly McCrady, Ioannis T. Pavlidis and James A. Levine
Physical activity is important in ill-health. Inexpensive, accurate and precise devices could help assess daily activity. We integrated novel activity-sensing technology into an earpiece used with portable music-players and phones; the physical-activity-sensing earpiece (PASE). Here we examined whether the PASE could accurately and precisely detect physical activity and measure its intensity and thence predict energy expenditure.
Experiment 1: 18 subjects wore PASE with different body postures and during graded walking. Energy expenditure was measured using indirect calorimetry. Experiment 2: 8 subjects wore the earpiece and walked a known distance. Experiment 3: 8 subjects wore the earpiece and ‘jogged’ at 3.5mph.
The earpiece correctly distinguished lying from sitting/standing and distinguished standing still from walking (76/76 cases). PASE output showed excellent sequential increases with increased in walking velocity and energy expenditure (r 2 > .9). The PASE prediction of free-living walking velocity was, 2.5 ± (SD) 0.18 mph c.f. actual velocity, 2.5 ± 0.16 mph. The earpiece successfully distinguished walking at 3.5 mph from ‘jogging’ at the same velocity (P < .001).
The subjects tolerated the earpiece well and were comfortable wearing it. The PASE can therefore be used to reliably monitor free-living physical activity and its associated energy expenditure.
Scott A. Conger, Stacy N. Scott, Eugene C. Fitzhugh, Dixie L. Thompson and David R. Bassett
It is unknown if activity monitors can detect the increased energy expenditure (EE) of wheelchair propulsion at different speeds or on different surfaces.
Individuals who used manual wheelchairs (n = 14) performed 5 wheeling activities: on a level surface at 3 speeds, on a rubberized track at 1 fixed speed and on a sidewalk course at a self-selected speed. EE was measured using a portable indirect calorimetry system and estimated by an Actical (AC) worn on the wrist and a SenseWear (SW) activity monitor worn on the upper arm. Repeated-measures ANOVA was used to compare measured EE to the estimates from the standard AC prediction equation and SW using 2 different equations.
Repeated-measures ANOVA demonstrated a significant main effect between measured EE and estimated EE. There were no differences between the criterion method and the AC across the 5 activities. The SW overestimated EE when wheeling at 3 speeds on a level surface, and during sidewalk wheeling. The wheelchair-specific SW equation improved the EE prediction during low intensity activities, but error progressively increased during higher intensity activities.
During manual wheelchair propulsion, the wrist-mounted AC provided valid estimates of EE, whereas the SW tended to overestimate EE.