Ankle Accelerometry for Assessing Physical Activity Among Adolescent Girls: Threshold Determination,Validity, Reliability,and Feasibility

Purpose: Ankle accelerometry allows for 24-hr data collection and improves data volume/integrity versus hip accelerometry. Using Actical ankle accelerometry, the purpose of this study was to (a) develop sensitive/specific thresholds, (b) examine validity/reliability, (c) compare new thresholds with those of the manufacturer, and (d) examine feasibility in a community sample (low-income, urban adolescent girls). Method: Two studies were conducted with 6th- through 7th-grade girls (aged 10–14 years old): First was a laboratory study (n = 24), in which 2 Actical accelerometers were placed on the ankle and worn while measuring energy expenditure (Cosmed K4b2, metabolic equivalents [METs]) during 10 prescribed activities. Analyses included device equivalence reliability (intraclass correlation coefficient [ICC], activity counts of 2 Acticals), criterion-related validity (correlation, activity counts and METs), and calculations of sensitivity, specificity, kappa, and receiver-operating characteristic curves for thresholds. The second was a free-living study (n = 459), in which an Actical was worn for more than 7 days on the ankle (full 24-hr days retained). Analyses included feasibility (frequencies, missing data) and paired t tests (new thresholds vs. those of the manufacturer). Results: In the laboratory study, the Actical demonstrated reliability (ICC = .92) and validity (r = .81). Thresholds demonstrated sensitivity (91%), specificity (84%), kappa = .73 (p = .043), area under curve range = .81–.97. In the free-living study, 99.6% of participants wore the accelerometer; 84.1% had complete/valid data (mean = 5.7 days). Primary reasons for missing/invalid data included: improper programming/documentation (5.2%), failure to return device (5.0%), and wear-time ≤ 2 days (2.8%). The moderate-to-vigorous physical activity threshold (> 3,200 counts/minute) yielded 37.2 min/day, 2 to 4.5 times lower than that of the manufacturer's software (effect size = 0.74–4.05). Conclusions: Validity, reliability, and feasibility evidences support Actical ankle accelerometry to assess physical activity in community studies of adolescent girls. When comparing manufacturers' software versus new thresholds, a major difference was observed.     

The validity of the commercially-available,low-cost,wrist-worn Movband accelerometer during treadmill exercise and free-living physical activity

Low-cost physical activity monitors may provide a more accurate measure of physical activity than subjective methods (e.g., self-report) while being less costly than research-grade accelerometers. The present study assessed the validity of a low-cost monitor (Movband 2) to estimate physical activity behavior/intensity. Participants (N = 23, n = 16 female, BMI = 22.9 ± 4.0 kg/m², age = 21.9 ± 1.6 years) completed four, five-minute treadmill stages (2.0, 3.0, 4.0, 5.0 MPH) while wearing both the Movband and the previously-validated Actigraph monitor. Oxygen consumption (VO₂) was recorded during each stage (Laboratory assessment). A subset (n = 15, n = 10 female, BMI = 22.2 ± 3.2 kg/m², age = 21.5 ± 0.8 years) of these participants then wore the two accelerometers for three days (Free-living assessment). During the Laboratory assessment there were strong, significant (r = 0.94, p ≤ 0.001) relationships between Movband and Actigraph counts and VO₂. During Free-living assessment there was also a strong, significant (r = 0.97, p < 0.001) correlation between Movband and Actigraph counts. The low-cost, Movband accelerometer appears to provide a valid assessment of physical activity behavior/intensity.     

Lifesource XL-18 pedometer for measuring steps under controlled and free-living conditions

The primary aim was to examine the criterion and construct validity and test–retest reliability of the Lifesource XL-18 pedometer (A&D Medical, Toronto, ON, Canada) for measuring steps under controlled and free-living activities. The influence of body mass index, waist size and walking speed on the criterion validity of XL-18 was also explored. Forty adults (35–74 years) performed a 6-min walk test in the controlled condition, and the criterion validity of XL-18 was assessed by comparing it to steps counted manually. Thirty-five adults participated in the free-living condition and the construct validity of XL-18 was assessed by comparing it to Yamax SW-200 (YAMAX Health & Sports, Inc., San Antonio, TX, USA). During the controlled condition, XL-18 did not significantly differ from criterion (P > 0.05) and no systematic error was found using Bland–Altman analysis. The accuracy of XL-18 decreased with slower walking speed (P = 0.001). During the free-living condition, Bland–Altman analysis revealed that XL-18 overestimated daily steps by 327 ± 118 than Yamax (P = 0.004). However, the absolute percent error (APE) (6.5 ± 0.58%) was still within an acceptable range. XL-18 did not differ statistically between pant pockets. XL-18 is suitable for measuring steps in controlled and free-living conditions. However, caution may be required when interpreting the steps recorded under slower speeds and free-living conditions.     

Validity of Activity Trackers in Estimating Energy Expenditure During High-Intensity Functional Training

ABSTRACT

Purpose: The purpose of this study was to evaluate the agreement of five commercially available accelerometers in estimating energy expenditure while performing an acute bout of high-intensity functional training (HIFT). Methods: Participants (n = 47; average age: 28.5 ± 11.6 years) consisted of recreationally active, healthy adults. Each participant completed a session of HIFT: a 15-minute workout consisting of 12 repetitions each of air-squats, sit-ups, push-ups, lunges, pull-ups, steps-ups, and high-knees; performed circuit-style by completing as many rounds as possible. During this session, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move). Results: Four of the five activity trackers reported lower (p < .05) total EE values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcal) was not significantly different from, and most closely estimated caloric expenditure compared to the PMA (144.99 ± 37.13 kcal) (p = .056). A repeated-measures ANOVA showed that all activity trackers were significantly different from the reference measure (PMA) (p < .05). Systematic relative agreement between the activity trackers was calculated, exhibiting a significant ICC = 0.426 (F [46,230] = 5.446 [p < .05]). Conclusion: The wrist- and hip-mounted activity trackers did not accurately assess energy expenditure during HIFT exercise. With the exception of the ActiGraph GT3X, the remaining four activity trackers showed inaccurate estimates of the amount of kilocalories expended during the HIFT exercise bout compared to the PMA.     

Comparison of Indirect Calorimetry- and Accelerometry-Based Energy Expenditure During Children‘s Discrete Skill Performance

ABSTRACT

Purpose: To compare children’s energy expenditure (EE) levels during object projection skill performance (OPSP; e.g., kicking, throwing, striking) as assessed by hip- and wrist-worn accelerometers. Method: Forty-two children (female n = 20, Mage = 8.1 ± 0.8 years) performed three, nine-minute sessions of kicking, over-arm throwing, and striking at performance intervals of 6, 12, and 30 seconds. EE was estimated using indirect calorimetry (COSMED k4b2) and accelerometers (ActiGraph GT3X+) worn on three different locations (hip, dominant-wrist, and non-dominant-wrist) using four commonly used cut-points. Bland-Altman plots were used to analyze the agreement in EE estimations between accelerometry and indirect calorimetry (METS). Chi-square goodness of fit tests were used to examine the agreement between accelerometry and indirect calorimetry. Results: Hip- and wrist-worn accelerometers underestimated EE, compared to indirect calorimetry, during all performance conditions. Skill practice at a rate of two trials per minute resulted in the equivalent of moderate PA and five trials per minute resulted in vigorous PA (as measured by indirect calorimetry), yet was only categorized as light and/or moderate activity by all measured forms of accelerometry. Conclusion: This is one of the first studies to evaluate the ability of hip- and wrist-worn accelerometers to predict PA intensity levels during OPSP in children. These data may significantly impact PA intervention measurement strategies by revealing the lack of validity in accelerometers to accurately predict PA levels during OPSP in children.     

Parental Support and Objectively Measured Physical Activity in Children: A Yearlong Cluster-Randomized Controlled Efficacy Trial

Purpose: We studied whether physical activity (PA) counseling for parents influenced the level of parental support of children’s PA and leisure-time PA in children of different levels of initial parental support. We hypothesized that the initial level of parental support would moderate the intervention efficacy. Method: Children (n = 44, M_age = 6.09 ± 1.17 years) and their parents (n = 61) randomly assigned to an intervention group received counseling for 6 months. Children in the control group (n = 47, M_age = 6.12 ± 1.11 years) and their parents (n = 63) did not receive any counseling. Parental support was assessed using the Family Physical Activity Environment Questionnaire, and children’s leisure-time PA was recorded using triaxial accelerometers at baseline, at 6 months, and at 12 months. The efficacy of the intervention was tested by linear mixed-effects modeling adjusted for confounding variables (Model 1) and additionally for children’s participation in organized PA or sports (Model 2). Results: Parents within the lowest initial parental support intervention tertile significantly increased their support, and their children’s mean level of leisure-time PA significantly improved compared with the corresponding controls during the counseling period. On the other hand, intervention was found to have an unfavorable influence especially in the PA of children of initially highly supportive parents. Conclusion: Targeting PA counseling for parents with low support of their children’s PA could contribute to better family-based PA counseling efficacy.     

In-school and out-of-school physical activity in primary and secondary school children

Abstract

The aim of this study was to compare in-school and out-of-school physical activity within a representative sample. Socio-demographic, physical activity, and anthropometric data were collected from a random sample of children (250 boys, 253 girls) aged 3–16 years attending nine primary and two secondary schools. Actigraph GT1M accelerometers, worn for seven days, were used to estimate physical activity levels for in-school (typically 09.00–15.00 h), out-of-school (weekday), and weekend periods. Physical activity as accelerometer counts per minute were lower in school versus out of school overall (in school: 437.2 ± 172.9; out of school: 575.5 ± 202.8; P < 0.001), especially in secondary school pupils (secondary: 321.6 ± 127.5; primary: 579.2 ± 216.3; P < 0.001). Minutes of moderate-to-vigorous physical activity accumulated in school accounted for 29.4 ± 9.8% of total weekly moderate-to-vigorous physical activity overall but varied by sector (preschool: 37.4 ± 6.2%; primary: 33.6 ± 8.1%; secondary: 23.0 ± 9.3%; F = 114.3, P < 0.001). Approximately half of the children with the lowest in-school activity compensated out of school during the week (47.4%) and about one-third at the weekend (30.0%). Overall, physical activity during the school day appears to be lower than that out of school, especially in secondary school children, who accumulate a lower proportion of their total weekly moderate-to-vigorous physical activity at school than younger children. As low in-school activity was compensated for beyond the school setting by less than half of children, promoting physical activity within the school day is important, especially in secondary schools.     

California Districts and Schools Underutilize Websites to Demonstrate Compliance to a Physical Education Lawsuit

ABSTRACT

Purpose: Quality physical education (PE) reaches many objectives (e.g., knowledge, physical fitness, and physical skills) and could provide at least half the dose of recommended daily physical activity for youths if their opportunity to learn is provided according to national professional recommendations (min/week) and related state mandates. A 2015 California class-action lawsuit required affected schools to post data indicating they scheduled PE time meeting the state mandate of 200 min per 10-day period. The extent to which schools posted PE schedules on their websites and demographic factors related to their compliance was investigated in this study. Method: We performed a quantitative, cross-sectional content analysis of the websites of 37 school districts plus a random sample of 860 elementary schools in them. Z tests were used to analyze frequencies/proportions and associations among demographic (e.g., Hispanic enrollment, PE specialist) and PE schedule variables (e.g., schools meeting state-mandated PE time). Results: Twenty-two districts (59.4%) had websites with ≥1 page/document related to PE opportunities. Only 11% of schools posted PE schedules, an event that was associated with employing a PE specialist (p = .01). Of schools posting schedules, 68% specified a PE volume that met the state mandate. Meeting the mandate was independently associated with enrolling a minority of Hispanic students (p = .02). Conclusion: Websites can provide information about the importance/occurrence of PE; however, schools in the lawsuit did not use the potential of their websites to inform constituents either about the lawsuit or their PE programs. Non-compliant schools should adjust PE schedules to meet statutory requirements.     

Validity of the ActiGraph GT1M during walking and cycling

Abstract

The ActiGraph activity monitors have developed and newer versions of the ActiGraph accelerometers (GT1M, GT3X and GT3X +) are now available, including changes in hardware and software compared to the old version (AM7164). This is problematic as most of the validation and calibration work includes the AM7164. The aims of the study were to validate the ActiGraph GT1M during level and graded walking and to assess the potential underestimation of physical activity during cycling. Data were obtained from 20 participants during treadmill walking and ergometer cycling. Energy expenditure was measured via indirect calorimetry and used as the criterion method. Activity counts were highly correlated with energy expenditure during level walking (R² = 0.82) and graded walking at 5% and 8% (R² = 0.82 and R² = 0.67, respectively). There was no linear relationship between activity counts and energy expenditure during cycling. The average activity counts for all data points during cycling was 1,157 counts per minute (CPM) (SD = 974), and mean energy expenditure was 5.0 metabolic equivalents. The GT1M is a valid tool for assessing walking across a wide range of speeds and gradients. However, there is no relationship between activity counts and energy expenditure during cycling and physical activity is underestimated by ≈73% during cycling compared to walking.     

The Influence of Exercise Intensity on Postexercise Baroreflex Sensitivity

Purpose: The purpose of this study was to investigate the influence of exercise intensity on postexercise supine and tilt baroreflex sensitivity (BRS). Method: Nine healthy, active men performed 2 conditions of interval cycling of 40% maximal work rate (WR_max) and 75% WR_max of matched work done and a control condition of no exercise in a counterbalanced order. BRS outcome measures were determined at preexercise and postexercise up to +24 hr in supine and tilt positions. R-R interval and blood pressure data were collected over consecutive 10-min periods and were analyzed by Fast Fourier transformation analysis. Results: A fully repeated analysis of variance revealed a significant interaction (p < .05) between time and condition in supine for BRS_αLF, F(3, 134) = 5.19, p < .05, ES = .39, and BRS_TFTG, F(3, 134) = 5.65, p < .05, ES = .41, and in tilt for BRS_UpUp, F(3, 134) = 3.54, p < .05, ES = .31, BRS_DownDown, F(3, 134) = 5.94, p < .05, ES = .43, BRS_αLF, F(4, 134) = 6.23, p < .05, ES = .44, and BRS_TFTG, F(4, 134) = 9.22, p < .05, ES = .54. There were significant differences (p < .05) between condition comparisons at +15 min and between control and 75% WR_max and between the 40% WR_max and 75% WR_max conditions at +60 min. At +15 min, BRS was lower in the 75% WR_max condition compared with the 40% WR_max condition and the control condition, and it was lower in the 40% WR_max condition than in the control condition. Conclusion: The findings demonstrate an intensity-dependent relationship in the BRS response following exercise.     

Effect of the Great Activity Programme on healthy lifestyle behaviours in 7–11 year olds

Abstract

The study investigated the effect of a school-based healthy lifestyles intervention on physical activity and dietary variables. In total 378 children (177 intervention, 201 control; age 9.75 ± 0.82 years (mean ± s)) took part in the 7-month intervention comprising: preparation for and participation in 3 highlight events (a dance festival, a walking event and a running event); an interactive website for pupils, teachers and parents; and vacation activity planners. Primary outcome measures were objectively measured physical activity (pedometers and accelerometers), endurance fitness and dietary variables. Multi-level modelling was employed for data analysis. The increase in physical activity was greater in the intervention group than the control group (steps: 1049 vs 632 daily steps each month; moderate to vigorous physical activity (MVPA) total: 4.6 min · day^?1 · month^?1 vs 1.3 min · day^?1 · month^?1; MVPA bouts: 5.4 min · day^?1 · month^?1 vs 2.6 min · day^?1 · month^?1; all P < 0.05). The increase in multi-stage fitness test distance was greater for intervention participants (46 vs 29 m · month^?1 of intervention, group × month interaction, P < 0.05). There were no differences between groups in dietary variables, body composition, knowledge of healthy lifestyles or psychological variables. Thus an intervention centred around highlight events and including relatively few additional resources can impact positively on the objectively measured physical activity of children.     

Validity of proximity sensor-based wear-time detection using the ActiGraph GT9X

Our study investigated the performance of proximity sensor-based wear-time detection using the GT9X under laboratory and free-living settings. Fifty-two volunteers (23.2 ± 3.8 y; 23.2 ± 3.7 kg/m²) participated in either a laboratory or free-living protocol. Lab participants wore and removed a wrist-worn GT9X on 3–5 occasions during a 3-hour directly observed activity protocol. The 2-day free-living protocol used an independent temperature sensor and self-report as the reference to determine if wrist and hip-worn GT9X accurately determined wear (i.e., sensitivity) and non-wear (i.e., specificity). Free-living estimates of wear/non-wear were also compared to Troiano 2007 and Choi 2012 wear/non-wear algorithms. In lab, sensitivity and specificity of the wrist-worn GT9X in detecting total minutes of wear-on and off was 93% and 49%, respectively. The GT9X detected wear-off more often than wear-on, but with a greater margin of error (4.8 ± 11.6 vs. 1.4 ± 1.4 min). In the free-living protocol, wrist and hip-worn GT9X’s yielded sensitivity and specificity of 72 and 90% and 84 and 92%, respectively. GT9X estimations had inferior sensitivity but superior specificity to Troiano 2007 and Choi 2012 algorithms. Due to inaccuracies, it may not be advisable to singularly use the proximity-sensor-based wear-time detection method to detect wear-time.     

Accelerometer Bluetooth proximity validation in parents and early years children

The purpose of this study was to examine the validity of a novel technique determining parent–child proximity using Bluetooth-enabled accelerometers. Nineteen parents and children (18–60 months; 38.4 ± 13.2 months) wore an ActiGraph wGT3X-BT accelerometer for 7 days. Parental accelerometers continuously emitted a Bluetooth signal, while children’s accelerometers recorded signal detection once per minute. Parents reported whether they were “AWAY”/“WITH” their child using time-use diaries in 5-minute intervals for 2 days. Presence (1) or absence (0) of a Bluetooth signal was averaged over the 5-minute time frame. Accelerometer and time-use diary variables produced 8,331 comparative observations. Area under the curve (AUC) via receiver operator characteristic (ROC) curve was 0.84 (95% confidence intervals: 0.84, 0.85), and the optimal cut-point was 0.0. Specifically, sensitivity (0.82) and specificity (0.81) were maximized when classifying all positive proximity tags (0.20–1.00) as “WITH” and all negative proximity tags (0.0) as “AWAY”. Accelerometer-derived parent–child proximity demonstrated good concurrent validity.     

Physical Activity Indicators,Metabolic Biomarkers,and Comorbidity in Type 2 Diabetes

ABSTRACT

Purpose: This study aimed (1) to compare physical activity (PA) indicators, metabolic biomarkers, and comorbidity, (2) to investigate the relationship between PA indicators and metabolic biomarkers, comorbidity and (3) to identify barriers to PA in patients with type 2 diabetes (T2DM) who are using oral hypoglycaemic agent (OHA) or combined OHA and insulin (OHAiN). Methods: Sixty-one patients were classified as patients using only OHA or combined OHAiN. Metabolic biomarkers (waist-to-hip ratio, body mass index (BMI), lipid profile, glycosylated haemoglobin (HbA1c), fasting blood glucose, comorbidity and PA indicators (self-reported PA, number of steps (NOS), 6-minute walking distance (6MWD)) were assessed. PA perceptions and reasons for inactivity were questioned. Results: The comorbidity (p = .013), low-density lipoprotein-cholesterol (p = .026), total cholesterol (p = .008) and HbA1c (p = .020) were higher and PA level was lower (p = .007) in the OHAiN group. NOS was positively correlated with high-density lipoprotein-cholesterol (p = .037) and negatively correlated with BMI (p = .007). 6MWD was negatively correlated with BMI (p = .014) and comorbidity (p = .004) in the OHA group. BMI was a significant predictor of NOS (adjusted R² = 0.242) and comorbidity for 6MWD (adjusted R² = 0.250) in the OHA group. The majority of the patients (OHA = 34.3%, OHAiN = 42.3%) reported “lack of time” as the most common barrier to PA. Conclusions: This study showed that patients on OHAiN have lower PA levels, poorer metabolic profiles, and higher comorbidity rates than OHA users. PA indicators were related with some metabolic biomarkers and comorbidity in only OHA users. The most common reason for inactivity was “the lack of time” in both groups.     

Comparison of two Actigraph models for assessing free-living physical activity in Indian adolescents

Abstract

This aim of this study was to compare the new Actigraph (GT1M) with the widely used Model 7164. Seven days of free-living physical activity were measured simultaneously using both the Model 7164 and GT1M in 30 Indian adolescents (mean age 15.8 years, s = 0.6). The GT1M was on average 9% lower per epoch than model 7164, thus a correction factor of 0.91 is suggested for comparison between the two monitors. The differences between monitors increased in magnitude with intensity of activity (P < 0.001) but remained randomly distributed (r = 0.01, P = 0.96). No significant difference was observed between monitors for time spent in moderate (P = 0.31) and vigorous (P = 0.34) physical activity when using the same epoch length. The Model 7164 classified less time as sedentary (P < 0.001) and more time as light-intensity activity (P < 0.001) than the GT1M. In conclusion, data from the GT1M can be compared with historical data using average counts per minute with a correction factor, and the two models might be comparable for assessing time spent in moderate to vigorous physical activity in children when using the same epoch length.     

Technical variability of the Vivago® wrist-worn accelerometer

Abstract

The aim was to assess the technical variability of a new wrist-worn accelerometer under controlled conditions with a shaker device and during normal daily physical activities (PAs). In the first experiment, 10 wrist-worn accelerometers (Vivago^® Wellness, Paris, France) were attached to the shaker device. Variability was tested at five shaking frequencies (1.1, 2.1, 3.1, 4.1, and 10 Hz) for 10 min at each frequency. In the second experiment, 21 participants wore wrist-worn accelerometers and performed six consecutive 10-min periods of activity at increasing levels of intensity from sedentary to vigorous. Results from the first experiment show a modest inter- and intra-instrument reliability at low frequencies and that reliability improved as frequency increased. The inter-instrument coefficient of variation (CV) was 2.6–18.3%. The intra-instrument CV was 4.1–23.2%. Variability was similar in the second experiment with a CV inversely related to PA intensity. The inter- and intra-instrument CV varied from 24.2% and 19.9% for sedentary activities to 3.7% and 4.3% for vigorous PA, respectively. Results suggest that reliability was higher at high intensities, corresponding to moderate and vigorous PA, intensities generally recommended for public health purposes.     

Validation of two accelerometers to determine mechanical loading of physical activities in children

The purpose of this study was to assess the validity of accelerometers using force plates (i.e., ground reaction force (GRF)) during the performance of different tasks of daily physical activity in children. Thirteen children (10.1 (range 5.4–15.7) years, 3 girls) wore two accelerometers (ActiGraph GT3X+ (ACT), GENEA (GEN)) at the hip that provide raw acceleration signals at 100 Hz. Participants completed different tasks (walking, jogging, running, landings from boxes of different height, rope skipping, dancing) on a force plate. GRF was collected for one step per trial (10 trials) for ambulatory movements and for all landings (10 trials), rope skips and dance procedures. Accelerometer outputs as peak loading (g) per activity were averaged. ANOVA, correlation analyses and Bland–Altman plots were computed to determine validity of accelerometers using GRF. There was a main effect of task with increasing acceleration values in tasks with increasing locomotion speed and landing height (P < 0.001). Data from ACT and GEN correlated with GRF (r = 0.90 and 0.89, respectively) and between each other (r = 0.98), but both accelerometers consistently overestimated GRF. The new generation of accelerometer models that allow raw signal detection are reasonably accurate to measure impact loading of bone in children, although they systematically overestimate GRF.     

Children’s metabolic expenditure during object projection skill performance: New insight for activity intensity relativity

Objective: To examine children’s energy expenditure (EE) during object projection skill performance at three intensity intervals. Methods: Children’s (42, Mage = 8.1) average metabolic equivalents of task (METs) were calculated using a COSMED K4b2 while they repeatedly performed blocks of kicking, throwing (overhand), and striking (two-handed) during 6, 12, and 30-s interval conditions. A repeated-measures analysis of covariance examined differences in METs while controlling for skill level. Results: Data indicated a main effect for interval condition (df = 2, 123, F = 94.36, p <.001, η2 = .605). Post hoc t-tests demonstrated decreasing performance interval times yielded progressively higher METs (p <.001) across the three conditions (30s = 4.5±0.8 METs, 12s = 6.3±1.3, 6s = 8.3±1.6). There also was a main effect for sex (df = 1,120, F = 52.28, p <.001 η2 = .305). Boys demonstrated higher METs at each performance interval (p <.001). Conclusion: Skill practice with a maximum of one trial every 30s resulted in the equivalent of at least moderate physical activity (>4.0 METs) and intervals of 6s demonstrated vigorous physical activity (>7.0 METs). Practicing/performing object projection skills, even at intervals that allow for adequate instruction and feedback (i.e., 1 trial/30s), promotes MVPA in children.     

Investigating elementary school children’s daily physical activity and sedentary behaviours during weekdays

The purpose of the study was to quantify the contributions of physical education, exergaming (active video games that also are a type of exercise), recess, lunch break and after-school time segments to children’s daily physical activity and sedentary behaviours. Participants were 138 second and third graders (71 girls) who attended 20-min recess and 75-min lunch time daily, 25-min regular physical education or exergaming-based classes being alternated daily. The after-school period was defined as 3:20–10:00pm. Physical activity was assessed via accelerometry and the dependent variables were children’s time spent in moderate-to-vigorous physical activity (MVPA), light physical activity and sedentary behaviour. Children’s percentages of time spent in MVPA (P < .001; except for the difference between exergaming and lunch break: P = .63), light physical activity (P < .001) and sedentary behaviour (P < .001) differed significantly across the time segments (i.e., physical education/exergaming, recess, lunch break and after-school). Additionally, children accumulated significantly more MVPA (t = 10.22, P < .001) but less light physical activity (t = ?3.17, P = .002) and sedentary behaviour (t = ?3.91, P < .001) in physical education than in exergaming. Overall, physical education was more effective in generating MVPA than other segments over the school day. The after-school segment holds potential as an avenue for promoting children’s MVPA, as this long period could be better utilised to organise structured physical activity.