Portable metabolic units (PMUs) are used to assess energy expenditure, with the assumption that physical activity level and enjoyment are unaffected due to the light weight and small size. Purpose: To assess differences in physical activity level and enjoyment while wearing and not wearing a PMU. Method: Youth (8–17 y; N = 73) played children’s games or active video games while wearing and not wearing a PMU (crossover design). Participants wore an accelerometer and heart rate monitor and responded to questions about enjoyment on a facial affective scale. A repeated-measures analysis of variance was used to determine if accelerometer measures, heart rate, or enjoyment differed between conditions overall and by sex and weight status. Results: Steps per minute were lower while wearing the PMU than not wearing the PMU (40 vs 44, P = .03). There was an interaction between PMU condition and weight status for enjoyment (P = .01), with overweight participants reporting less enjoyment when wearing the PMU compared with not wearing the PMU (72 vs 75 out of 100). Heart rate, vector magnitude, and counts per minute were not different. Conclusion: There may be psychosocial effects of wearing the PMU, specifically in overweight participants. Activity level was minimally affected, but the practical significance for research is still unknown.
Kimberly A. Clevenger, Karin A. Pfeiffer, and Cheryl A. Howe
Glen E. Duncan, Anthony D. Mahon, Cheryl A. Howe, and Pedro Del Corral
This study examined the influence of test duration and anaerobic capacity on VO2max and the occurrence of a VO2 plateau during treadmill exercise in 25 boys (10.4 ± 0.8 years). Protocols with 1-min (P1) and 2-min (P2) stages, but identical speed and grade changes, were used to manipulate test duration. On separate days, VO2max was measured on P1 and P2, and 200-m run time was assessed. At maximal exercise, VO2, heart rate (HR), and pulmonary ventilation (VE) were similar between protocols, however, respiratory exchange ratio (RER) and treadmill elevation were higher (p < .05) on P1 than on P2. Plateau achievement was not significantly different. On P1, there were no differences between plateau achievers and nonachievers. On P2, test duration and 200-m run time were superior (p < .05), and relative VO2max tended to be higher (p < .10) in plateau achievers. Indices of aerobic and anaerobic capacity may influence plateau achievement on long, but not short duration tests.
Kimberly A. Clevenger, Michael J. Wierenga, Cheryl A. Howe, and Karin A. Pfeiffer
The authors conducted a systematic review of children’s and adolescent’s physical activity by schoolyard location. PubMed and Web of Science were searched and articles were selected that included 3- to 17-year-olds and specifically examined and reported physical activity by schoolyard location. The primary outcomes of interest were the percentage of total time or observation intervals spent in each location and percentage of time or observation intervals in each location being sedentary or participating in moderate to vigorous physical activity. Included studies (N = 24) focused on preschoolers (n = 6), children (n = 11), adolescents (n = 2), or children and adolescents (n = 5) and primarily used direct observation (n = 17). Fields, fixed equipment, and blacktop were all important locations for physical activity participation, but there were differences by age group and sex. More research is needed that uses consistent methodology and accounts for other factors such as time of year, provided equipment, and differences in schoolyard designs.
Cheryl A. Howe, Kimberly A. Clevenger, Danielle McElhiney, Camille Mihalic, and Moira A. Ragan
Background: This study validated the How(e) Happy Scale (HHS) for measuring children’s real-time physical activity (PA) enjoyment across PA type, intensity, sex, and weight status and compared state versus trait enjoyment. Methods: Children’s (N = 31; 9.7 [1.7] y) PA intensity was measured during sport, play, and locomotive PA. Following each activity, children rated their perceived state (HHS) of enjoyment across 4 constructs (social engagement). Questionnaires measured trait PA enjoyment prior to play. Rasch Rating Scale analysis assessed model-data fit and probability distribution of HHS responses. Analyses of variance compared state versus trait PA enjoyment across main effects, and correlations assessed relationships between measured PA intensity versus state and trait PA enjoyment. Results: Trait PA enjoyment was neither different across sex and weight status nor correlated with PA intensity (r = −.16 to .22). By contrast, HHS responses differed across sex, weight status, and PA type and intensity and correlated with PA type (r = −.56 to −.28) and intensity (r = −.29 to −.32). HHS responses were ordered along the probability curve and showed good infit (0.76–1.22) and outfit (0.71–1.28) statistics and good person (r = .62) and item (r = .88) reliability. Conclusion: HHS is valid for detecting differences in real-time enjoyment across PA type and intensity in all children.
Cheryl A. Howe, Marcus W. Barr, Brett C. Winner, Jenelynn R. Kimble, and Jason B. White
Although promoted for weight loss, especially in young adults, it has yet to be determined if the physical activity energy expenditure (PAEE) and intensity of the newest active video games (AVGs) qualifies as moderate-to-vigorous physical activity (MVPA; > 3.0 METs). This study compared the PAEE and intensity of AVGs to traditional seated video games (SVGs).
Fifty-three young adults (18−35 y; 27 females) volunteered to play 6 video games (4 AVGs, 2 SVGs). Anthropometrics and resting metabolism were measured before testing. While playing the games (6−10 min) in random order against a playmate, the participants wore a portable metabolic analyzer for measuring PAEE (kcal/min) and intensity (METs). A repeated-measures ANOVA compared the PAEE and intensity across games with sex, BMI, and PA status as main effects.
The intensity of AVGs (6.1 ± 0.2 METs) was significantly greater than SVGs (1.8 ± 0.1 METs). AVGs elicited greater PAEE than SVGs in all participants (5.3 ± 0.2 vs 0.8 ± 0.0 kcal/min); PAEE during the AVGs was greater in males and overweight participants compared with females and healthy weight participants (p’s < .05).
The newest AVGs do qualify as MVPA and can contribute to the recommended dose of MVPA for weight management in young adults.
Cheryl A. Howe, Kimberly A. Clevenger, Brian Plow, Steve Porter, and Gaurav Sinha
Purpose : Traditional direct observation cannot provide continuous, individual-level physical activity (PA) data throughout recess. This study piloted video direct observation to characterize children’s recess PA overall and by sex and weight status. Methods: Children (N = 23; 11 boys; 6 overweight; third to fifth grade) were recorded during 2 recess periods, coding for PA duration, intensity, location, and type. Duration of PA type and intensity across sex and weight status overall and between/within locations were assessed using 1- and 2-way analysis of variances. Results: The field elicited more sedentary behavior (39% of time) and light PA (17%) and less moderate to vigorous PA (41%) compared with the fixed equipment (13%, 7%, and 71%, respectively) or the court (21%, 7%, and 68%, respectively). Boys engaged in significantly more vigorous-intensity activity on the court (35%) than girls (14%), whereas girls engaged in more moderate to vigorous PA on the fixed equipment (77% vs 61%) and field (46% vs 35%) than boys (all Ps > .05). PA type also differed by sex and weight status. Conclusion: Video direct observation was capable of detecting and characterizing children’s entire recess PA while providing valuable context to the behavior. The authors confirmed previous findings that PA intensity was not uniform by schoolyard location and further differences exist by sex and weight status.
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.
Jeffer Eidi Sasaki, Cheryl A. Howe, Dinesh John, Amanda Hickey, Jeremy Steeves, Scott Conger, Kate Lyden, Sarah Kozey-Keadle, Sarah Burkart, Sofiya Alhassan, David Bassett Jr, and Patty S. Freedson
Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.
Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.
Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.
This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.