Search Results

You are looking at 11 - 20 of 50 items for :

  • User-accessible content x
Clear All
Open access

Maurice R. Puyau, Anne L. Adolph, Yan Liu, Theresa A. Wilson, Issa F. Zakeri and Nancy F. Butte

Background:

The absolute energy cost of activities in children increases with age due to greater muscle mass and physical capability associated with growth and developmental maturation; however, there is a paucity of data in preschool-aged children. Study aims were 1) to describe absolute and relative energy cost of common activities of preschool-aged children in terms of VO2, energy expenditure (kilocalories per minute) and child-specific metabolic equivalents (METs) measured by room calorimetry for use in the Youth Compendium of Physical Activity, and 2) to predict METs from age, sex and heart rate (HR).

Methods:

Energy expenditure (EE), oxygen consumption (VO2), HR, and child-METs of 13 structured activities were measured by room respiration calorimetry in 119 healthy children, ages 3 to 5 years.

Results:

EE, VO2, HR, and child-METs are presented for 13 structured activities ranging from sleeping, sedentary, low-, moderate- to high-active. A significant curvilinear relationship was observed between child-METs and HR (r 2 = .85; P = .001).

Conclusion:

Age-specific child METs for 13 structured activities in preschool-aged children will be useful to extend the Youth Compendium of Physical Activity for research purposes and practical applications. HR may serve as an objective measure of MET intensity in preschool-aged children.

Open access

Wonwoo Byun, Allison Barry and Jung-Min Lee

Background:

There has been a call for updating the Youth Compendium of Energy Expenditure (YCEE) by including energy expenditure (EE) data of young children (ie, < 6-year-old children). Therefore, this study examined the activity EE in 3 to 6 year old children using indirect calorimetry.

Methods:

Using Oxycon Mobile portable indirect calorimetry, both the oxygen consumption (VO2) and the EE of 28 children (Girls: 46%, Age: 4.8 ± 1.0, BMI: 16.4 ± 1.6) were measured while they performed various daily living activities (eg, watching TV, playing with toys, shooting baskets, soccer).

Results:

Across physical activities, averages of VO2 (ml·kg·min-1), VO2 (L·min-1), and EE ranged from 8.9 ± 1.5 to 33.3 ± 4.8 ml·kg·min-1, from 0.17 ± 0.04 to 0.64 ± 0.16 L·min-1, and from 0.8 ± 0.2 to 3.2 ± 0.7 kcal·min-1, respectively.

Conclusions:

These findings will contribute to the upcoming YCEE update.

Open access

Jung-Min Lee, Pedro F. Saint-Maurice, Youngwon Kim, Glenn A. Gaesser and Gregory Welk

Background:

The assessment of physical activity (PA) and energy expenditure (EE) in youth is complicated by inherent variability in growth and maturation during childhood and adolescence. This study provides descriptive summaries of the EE of a diverse range of activities in children ages 7 to 13.

Methods:

A sample of 105 7- to 13-year-old children (boys: 57%, girls: 43%, and Age: 9.9 ± 1.9) performed a series of 12 activities from a pool of 24 activities while being monitored with an indirect calorimetry system.

Results:

Across physical activities, averages of VO2 ml·kg·min-1, VO2 L·min-1, EE, and METs ranged from 3.3 to 53.7 ml·kg·min-1, from 0.15 to 3.2 L·min-1, from 0.7 to 15.9 kcal·min-1, 1.5 MET to 7.8 MET, respectively.

Conclusions:

The energy costs of the activities varied by age, sex, and BMI status reinforcing the need to consider adjustments when examining the relative intensity of PA in youth.

Open access

Avish P. Sharma, Philo U. Saunders, Laura A. Garvican-Lewis, Brad Clark, Jamie Stanley, Eileen Y. Robertson and Kevin G. Thompson

Purpose:

To determine the effect of training at 2100-m natural altitude on running speed (RS) during training sessions over a range of intensities relevant to middle-distance running performance.

Methods:

In an observational study, 19 elite middle-distance runners (mean ± SD age 25 ± 5 y, VO2max, 71 ± 5 mL · kg–1 · min–1) completed either 4–6 wk of sea-level training (CON, n = 7) or a 4- to 5-wk natural altitude-training camp living at 2100 m and training at 1400–2700 m (ALT, n = 12) after a period of sea-level training. Each training session was recorded on a GPS watch, and athletes also provided a score for session rating of perceived exertion (sRPE). Training sessions were grouped according to duration and intensity. RS (km/h) and sRPE from matched training sessions completed at sea level and 2100 m were compared within ALT, with sessions completed at sea level in CON describing normal variation.

Results:

In ALT, RS was reduced at altitude compared with sea level, with the greatest decrements observed during threshold- and VO2max-intensity sessions (5.8% and 3.6%, respectively). Velocity of low-intensity and race-pace sessions completed at a lower altitude (1400 m) and/or with additional recovery was maintained in ALT, though at a significantly greater sRPE (P = .04 and .05, respectively). There was no change in velocity or sRPE at any intensity in CON.

Conclusion:

RS in elite middle-distance athletes is adversely affected at 2100-m natural altitude, with levels of impairment dependent on the intensity of training. Maintenance of RS at certain intensities while training at altitude can result in a higher perceived exertion.

Open access

Jeffery J. Honas, Erik A. Willis, Stephen D. Herrmann, Jerry L. Greene, Richard A. Washburn and Joseph E. Donnelly

Background:

There is limited data regarding objectively measured energy cost and intensity of classroom instruction. Therefore, the purpose of current study was to objectively measure energy cost and subsequently calculate MET values using a portable indirect calorimeter (IC) for both normal classroom instruction (NCI) and active classroom instruction (ACI).

Methods:

We assessed energy expenditure (EE) and intensity levels (METs) in elementary school children (17 boys and 15 girls) using an IC (COSMED K4b2). Independent t-tests were used to evaluate potential sex and grade level differences for age, BMI, VO2, EE, and METs.

Results:

The average EE for NCI and ACI were 1.8 ± 0.4 and 3.9 ± 1.0, respectively. The average intensity level for NCI and ACI were 1.9 ± 0.4 and 4.2 ± 0.9 METs, respectively.

Conclusions:

PA delivered through ACI can elicit EE at a moderate intensity level. These results provide evidence for ACI as a convenient/feasible avenue for increasing PA in youth without decreasing instruction time.

Open access

Stephen Seiler and Øystein Sylta

The purpose of this study was to compare physiological responses and perceived exertion among well-trained cyclists (n = 63) performing 3 different high-intensity interval-training (HIIT) prescriptions differing in work-bout duration and accumulated duration but all prescribed with maximal session effort. Subjects (male, mean ± SD 38 ± 8 y, VO2peak 62 ± 6 mL · kg–1 · min–1) completed up to 24 HIIT sessions over 12 wk as part of a training-intervention study. Sessions were prescribed as 4 × 16, 4 × 8, or 4 × 4 min with 2-min recovery periods (8 sessions of each prescription, balanced over time). Power output, HR, and RPE were collected during and after each work bout. Session RPE was reported after each session. Blood lactate samples were collected throughout the 12 wk. Physiological and perceptual responses during >1400 training sessions were analyzed. HIIT sessions were performed at 95% ± 5%, 106% ± 5%, and 117% ± 6% of 40-min time-trial power during 4 × 16-, 4 × 8-, and 4 × 4-min sessions, respectively, with peak HR in each work bout averaging 89% ± 2%, 91% ± 2%, and 94% ± 2% HRpeak. Blood lactate concentrations were 4.7 ± 1.6, 9.2 ± 2.4, and 12.7 ± 2.7 mmol/L. Despite the common prescription of maximal session effort, RPE and sRPE increased with decreasing accumulated work duration (AWD), tracking relative HR. Only 8% of 4 × 16-min sessions reached RPE 19–20, vs 61% of 4 × 4-min sessions. The authors conclude that within the HIIT duration range, performing at “maximal session effort” over a reduced AWD is associated with higher perceived exertion both acutely and postexercise. This may have important implications for HIIT prescription choices.

Open access

Twan ten Haaf, Selma van Staveren, Erik Oudenhoven, Maria F. Piacentini, Romain Meeusen, Bart Roelands, Leo Koenderman, Hein A.M. Daanen, Carl Foster and Jos J. de Koning

Purpose:

To investigate whether monitoring of easily measurable stressors and symptoms can be used to distinguish early between acute fatigue (AF) and functional overreaching (FOR).

Methods:

The study included 30 subjects (11 female, 19 male; age 40.8 ± 10.8 y, VO2max 51.8 ± 6.3 mL · kg–1 · min–1) who participated in an 8-d cycling event over 1300 km with 18,500 climbing meters. Performance was measured before and after the event using a maximal incremental test. Subjects with decreased performance after the event were classified as FOR, others as AF. Mental and physical well-being, internal training load, resting heart rate, temperature, and mood were measured daily during the event. Differences between AF and FOR were analyzed using mixed-model ANOVAs. Logistic regression was used to determine the best predictors of FOR after 3 and 6 d of cycling.

Results:

Fifteen subjects were classified as FOR and 14 as AF (1 excluded). Although total group changes were observed during the event, no differences between AF and FOR were found for individual monitoring parameters. The combination of questionnaire-based changes in fatigue and readiness to train after 3 d cycling correctly predicted 78% of the subjects as AF or FOR (sensitivity = 79%, specificity = 77%).

Conclusions:

Monitoring changes in fatigue and readiness to train, using simple visual analog scales, can be used to identify subjects likely to become FOR after only 3 d of cycling. Hence, we encourage athlete support staff to monitor not only fatigue but also the subjective integrated mental and physical readiness to perform.

Open access

Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning

Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.

Full access

Laura K. Fewell, Riley Nickols, Amanda Schlitzer Tierney and Cheri A. Levinson

) was assessed using a medical grade Detecto precision scale and height tool at both treatment admission and discharge by an approved staff. Patients were weighed in light clothing and were not informed of their weight. Maximal Oxygen Consumption (VO 2 max) measures the amount of oxygen used during

Open access

Carl Foster

anchors on which we base our understanding will change. At a certain point in my career, the maximal oxygen uptake (VO 2 max) was everything. 2 Athletes with big values for VO 2 max were destined for success; athletes with lower values were supposedly doomed to be second-tier performers. 3 Someone