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Gianluca Vernillo, Aldo Savoldelli, Barbara Pellegrini and Federico Schena

Background:

Accurate assessments of physical activity and energy expenditure (EE) are needed to advance research on positive and negative graded walking.

Purpose:

To evaluate the validity of 2 SenseWear Armband monitors (Pro3 and the recently released Mini) during graded walking.

Methods:

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.

Results:

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.

Conclusions:

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.

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Akiko Sato, Yoshimitsu Shimoyama, Tomoji Ishikawa and Nobuko Murayama

The purpose of this study was to examine the effect of high-intensity physical activity during training on the biochemical status of thiamin and riboflavin in athletes. Thiamin and riboflavin concentrations in whole blood of a group of 19 athletes (6 men and 13 women) were measured during a low-intensity preparatory period and compared with measurements taken during a high-intensity training period. Additional variables measured included anthropometric characteristics, estimated energy expenditure during swim training, distance covered, resting energy expenditure obtained by indirect calorimetry, estimated energy requirement per day, and dietary intake of energy, thiamin, and riboflavin estimated from 3-day food records. For both male and female subjects, no major changes were observed in anthropometric characteristics or dietary intake, but energy expenditure during swim training per day significantly increased in the intensive-training period (496 ± 0 kcal in the preparation period compared with 995 ± 96 kcal in the intensive-training period for male subjects [p < .001] and 361 ± 27 kcal vs. 819 ± 48 kcal, respectively, for female subjects [p < .001]). Blood thiamin concentration decreased significantly during the intensive-training period compared with the preparation period (41 ± 6 ng/ml decreased to 36 ± 3 ng/ml for male subjects [p = .048], and 38 ± 10 ng/ml decreased to 31 ± 5 ng/ml for female subjects [p = .004]); however, the concentration of riboflavin was unchanged. These results suggest that intense training affects thiamin concentration, but not riboflavin concentration, in the whole blood of college swimmers.

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John S. Cuddy, Dustin R. Slivka, Walter S. Hailes, Charles L. Dumke and Brent C. Ruby

Purpose:

The purpose of this study was to determine the metabolic profile during the 2006 Ironman World Championship in Kailua-Kona, Hawaii.

Methods:

One recreational male triathlete completed the race in 10:40:16. Before the race, linear regression models were established from both laboratory and feld measures to estimate energy expenditure and substrate utilization. The subject was provided with an oral dose of 2H2 18O approximately 64 h before the race to calculate total energy expenditure (TEE) and water turnover with the doubly labeled water (DLW) technique. Body weight, blood sodium and hematocrit, and muscle glycogen (via muscle biopsy) were analyzed pre- and postrace.

Results:

The TEE from DLW and indirect calorimetry was similar: 37.3 MJ (8,926 kcal) and 37.8 MJ (9,029 kcal), respectively. Total body water turnover was 16.6 L, and body weight decreased 5.9 kg. Hematocrit increased from 46 to 51% PCV. Muscle glycogen decreased from 152 to 48 mmoL/kg wet weight pre- to postrace.

Conclusion:

These data demonstrate the unique physiological demands of the Ironman World Championship and should be considered by athletes and coaches to prepare sufficient nutritional and hydration plans.

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Meredith C. Peddie, Claire Cameron, Nancy Rehrer and Tracy Perry

Background:

Interrupting sedentary time induces improvements in glucose metabolism; however, it is unclear how much activity is required to reduce the negative effects of prolonged sitting.

Methods:

Sixty-six participants sat continuously for 9 hours except for required bathroom breaks. Participants were fed meal replacement beverages at 60, 240 and 420 min. Blood samples were obtained hourly for 9 hours, with additional samples collected 30 and 45 min after each feeding. Responses were calculated as incremental area under the curve (iAUC) for plasma glucose, insulin and triglyceride. Participants wore a triaxial accelerometer and a heart rate monitor. Energy expenditure was estimated using indirect calorimetry.

Results:

After controlling for age, sex and BMI, every 100 count increase in accelerometer derived total movement was associated with a 0.06 mmol·L-1·9 hours decrease in glucose iAUC (95% CI 0.004–0.1; P = .035), but not associated with changes in insulin or triglyceride iAUC. Every 1 bpm increase in mean heart rate was associated with a 0.76 mmol·L-1·9 hours increase in triglyceride iAUC (95% CI 0.13–1.38).

Conclusion:

Accelerometer measured movement during periods of prolonged sitting can result in minor improvements in postprandial glucose metabolism, but not lipid metabolism.

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Orjan Ekblom, Gisela Nyberg, Elin Ekblom Bak, Ulf Ekelund and Claude Marcus

Background:

Wrist-worn accelerometers may provide an alternative to hip-worn monitors for assessing physical activity as they are easier to wear and may thus facilitate long-term recordings. The current study aimed at a) assessing the validity of the Actiwatch (wrist-worn) for estimating energy expenditure, b) determining cut-off values for light, moderate, and vigorous activities, c) studying the comparability between the Actiwatch and the Actigraph (hip-worn), and d) assessing reliability.

Methods:

For validity, indirect calorimetry was used as criterion measure. ROC-analyses were applied to identify cut-off values. Comparability was tested by simultaneously wearing of the 2 accelerometers during free-living condition. Reliability was tested in a mechanical shaker.

Results:

All-over correlation between accelerometer output and energy expenditure were found to be 0.80 (P < .001).Based on ROC-analysis, cut-off values for 1.5, 3, and 6 METs were found to be 80, 262, and 406 counts per 15 s, respectively. Energy expenditure estimates differed between the Actiwatch and the Actigraph (P < .05). The intra- and interinstrument coefficient of variation of the Actiwatch ranged between 0.72% and 8.4%.

Conclusion:

The wrist-worn Actiwatch appears to be valid and reliable for estimating energy expenditure and physical activity intensity in children aged 8 to 10 years.

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Daniel Arvidsson, Mark Fitch, Mark L. Hudes and Sharon E. Fleming

Background:

Overweight children show different movement patterns during walking than normal-weight children, suggesting the accuracy of multisensory activity monitors may differ in these groups.

Methods:

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.

Results:

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.

Conclusions:

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.

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Steven K. Malin, Brooke R. Stephens, Carrie G. Sharoff, Todd A. Hagobian, Stuart R. Chipkin and Barry Braun

Exercise and metformin may prevent or delay Type 2 diabetes by, in part, raising the capacity for fat oxidation. Whether the addition of metformin has additive effects on fat oxidation during and after exercise is unknown. Therefore, the purpose of this study was to evaluate the effect of metformin on substrate oxidation during and after exercise. Using a double-blind, counter-balanced crossover design, substrate oxidation was assessed by indirect calorimetry in 15 individuals taking metformin (2,000 mg/d) and placebo for 8–10 d. Measurements were made during cycle exercise at 5 submaximal cycle workloads, starting at 30% peak work (Wpeak) and increasing by 10% every 8 min to 70% Wpeak. Substrate oxidation was also measured for 50 min postexercise. Differences between conditions were assessed using analysis of variance with repeated measures, and values are reported as M ± SE. During exercise, fat oxidation (0.19 ± 0.03 vs. 0.15 ± 0.01 g/min, p < .01) and percentage of energy from fat (32% ± 3% vs. 28% ± 3%, p < .01) were higher with metformin than with placebo. Postexercise, metformin slightly lowered fat oxidation (0.12 ± 0.02 to 0.10 ± 0.02 g/min, p < .01) compared with placebo. There was an inverse relationship between postexercise fat oxidation and the rate of fat oxidation during exercise (r = –.68, p < .05). In healthy individuals, metformin has opposing actions on fat oxidation during and after exercise. Whether the same effects are evident in insulin-resistant individuals remains to be determined.

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John M. Schuna Jr., Daniel S. Hsia, Catrine Tudor-Locke and Neil M. Johannsen

Background: Active workstations offer the potential for augmenting energy expenditure (EE) in sedentary occupations. However, comparisons of EE during pedal and treadmill desk usage at self-selected intensities are lacking. Methods: A sample of 16 adult participants (8 men and 8 women; 33.9 [7.1] y, 22.5 [2.7] kg/m2) employed in sedentary occupations had their EE measured using indirect calorimetry during 4 conditions: (1) seated rest, (2) seated typing in a traditional office chair, (3) self-paced pedaling on a pedal desk while typing, and (4) self-paced walking on a treadmill desk while typing. Results: For men and women, self-paced pedal and treadmill desk typing significantly increased EE above seated typing (pedal desk: +1.20 to 1.28 kcal/min and treadmill desk: +1.43 to 1.93 kcal/min, P < .001). In men, treadmill desk typing (3.46 [0.19] kcal/min) elicited a significantly higher mean EE than pedal desk typing (2.73 [0.21] kcal/min, P < .001). No significant difference in EE was observed between treadmill desk typing (2.68 [0.19] kcal/min) and pedal desk typing among women (2.52 [0.21] kcal/min). Conclusions: Self-paced treadmill desk usage elicited significantly higher EE than self-paced pedal desk usage in men but not in women. Both pedal and treadmill desk usage at self-selected intensities elicited approximate 2-fold increases in EE above what would typically be expected during traditional seated office work.

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Sofiya Alhassan, Kate Lyden, Cheryl Howe, Sarah Kozey Keadle, Ogechi Nwaokelemeh and Patty S. Freedson

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8–16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (±95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

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Samantha Stephens, Tim Takken, Dale W. Esliger, Eleanor Pullenayegum, Joseph Beyene, Mark Tremblay, Jane Schneiderman, Doug Biggar, Pat Longmuir, Brian McCrindle, Audrey Abad, Dan Ignas, Janjaap Van Der Net and Brian Feldman

The purpose of this study was to assess the criterion validity of existing accelerometer-based energy expenditure (EE) prediction equations among children with chronic conditions, and to develop new prediction equations. Children with congenital heart disease (CHD), cystic fibrosis (CF), dermatomyositis (JDM), juvenile arthritis (JA), inherited muscle disease (IMD), and hemophilia (HE) completed 7 tasks while EE was measured using indirect calorimetry with counts determined by accelerometer. Agreement between predicted EE and measured EE was assessed. Disease-specific equations and cut points were developed and cross-validated. In total, 196 subjects participated. One participant dropped out before testing due to time constraints, while 15 CHD, 32 CF, 31 JDM, 31 JA, 30 IMD, 28 HE, and 29 healthy controls completed the study. Agreement between predicted and measured EE varied across disease group and ranged from (ICC) .13–.46. Disease-specific prediction equations exhibited a range of results (ICC .62–.88) (SE 0.45–0.78). In conclusion, poor agreement was demonstrated using current prediction equations in children with chronic conditions. Disease-specific equations and cut points were developed.