Validity of traditional physical activity intensity calibration methods and the feasibility of self-paced walking and running on individualised calibration of physical activity intensity in children

Examining and Comparing the Energy Expenditure of Two Modes of a Virtual Reality Fitness Game (Supernatural): Indirect Calorimetry Study

BACKGROUND

The effectiveness of virtual reality (VR) fitness games as a form of moderate to vigorous physical activity has yet to be thoroughly quantified through gold standard energy expenditure measures.

OBJECTIVE

The purpose of this study was to examine the energy expenditure of 2 medium-intensity modes ("Flow and "Boxing") of a VR fitness game, Supernatural, using indirect calorimetry.

METHODS

Indirect calorimetry was used to examine relative and objective maximal oxygen consumption (VO2 max), metabolic equivalents of task (METs), and calories burned during medium-intensity bouts of both Flow and Boxing gameplay modes in young (mean age 25.42, SD 3.25 years), active individuals (n=12 female and n=11 male). METs and calories were also compared using a triaxial waist-worn accelerometer, an Apple smartwatch, and a VR headset. Mood states were assessed pre- and postbout using the shortened Profile of Mood States Questionnaire. Paired 2-tailed t tests were used to examine differences in game modes, between sexes, and pre-post exercise sessions.

RESULTS

Objective and relative VO2 max averaged 1.93 (SD 0.44) L/min and 27.61 (SD 5.60) mL/kg/min, respectively, between modes. Flow (mean 8.2, SD 1.54 METs) and Boxing (mean 7.6, SD 1.66 METs) are both classified as high energy expenditure, vigorous activities. Calorie expenditure data of the accelerometer and VR headset differed significantly from the metabolic cart. Mood changes pre- to post exercise were consistent with expected values for moderate- to vigorous-intensity physical activity, with participants reporting that they felt more "active," "full of pep," "vigorous," and "lively" (P<.05) following bouts. Male individuals reported higher objective oxygen consumption (VO2) for both Flow and Boxing modes; no other sex-specific differences were observed.

CONCLUSIONS

Both Flow and Boxing gameplay modes of Supernatural classify as vigorous physical activity and demonstrate the potential to promote mental and physical health benefits. Supernatural may be an effective exercise modality in a VO2 training program.

Accelerometer-based osteogenic indices, moderate-to-vigorous and vigorous physical activity, and bone traits in adolescents

OBJECTIVES

We investigated the associations of accelerometry-derived osteogenic indices (OIs), moderate-to-vigorous (MVPA), and vigorous intensity physical activity (VPA) with peripheral quantitative computed tomography (pCQT) parameters in 99 adolescents aged 10-13 years.

METHODS

Bone parameters were assessed at the distal (4%) and shaft (66%) of the tibia using pQCT. Accelerometers were worn on the right hip for 7 consecutive days. OIs were calculated based on acceleration peak histograms either using all of the peaks (OI) or peaks with acceleration ≥5.2 g (HOI). MVPA and VPA were defined using previously published cut-points.

RESULTS

HOI was positively associated with total area (Partial correlation= 0.22, 95% CI=0.01 to 0.41), cortical area (CoA) (0.33, 95% CI=0.13 to 0.50), and stress strain index (SSI) (0.29, 95% CI=0.09 to 0.47) of tibial shaft and with total density at the distal tibia (0.23, 95% CI=0.02 to 0.42). OI was positively associated with CoA (0.31, 95% CI=0.11 to 0.49) and SSI (0.26, 95% CI=0.05 to 0.44) of tibial shaft. MVPA was positively associated with CoA (0.28, 95% CI=0.07 to 0.46) of the tibial shaft.

CONCLUSIONS

OI and HOI were positively associated with pQCT parameters while MVPA and VPA demonstrated less consistent associations with them.

Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers

Modeling approaches for translating accelerometer data into physical activity metrics are often developed using a group calibration approach. However, it is unknown if models developed for specific individuals will improve measurement accuracy.

PURPOSE

We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment.

METHODS

Participants (n = 48) wore accelerometers on the right hip and non-dominant wrist while performing activities of daily living in a semi-structured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-second epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs. group), protocol (laboratory vs. free-living), and placement (hip vs. wrist). A 2x2x2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [k]) of the models when used to determine activity intensity in an independent sample of free-living participants.

RESULTS

Main effects were significant for the type of training data (group: accuracy = 80%, k = 0.59; individual: accuracy = 74% [p = 0.02], k = 0.50 [p = 0.01]) and protocol (free-living: accuracy = 81%, k = 0.63; laboratory: accuracy = 74% [p = 0.04], k = 0.47 [p < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, k = 0.58; wrist: accuracy = 75% [p = 0.18]; k = 0.52 [p = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement.

CONCLUSION

Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. Additionally, models should be developed in free-living settings when possible to optimize predictive accuracy.

Associations of fitness, motor competence, and adiposity with the indicators of physical activity intensity during different physical activities in children

We investigated the associations of peak oxygen uptake (V̇O2peak), ventilatory threshold (VT), muscle strength, motor competence (MC), and adiposity with the indicators of PA intensity during different physical activities used to create absolute PA intensity cut-offs among 35 children 7-11-years-of-age. V̇O2peak was defined as the highest V̇O2 achieved in the maximal cardiopulmonary exercise test (CPET) on a cycle ergometer, self-paced running, or running on a treadmill at 8 km/h. VT was defined from the CPET data. Peak isometric knee extensor and flexor strength was assessed by a dynamometer, MC by the Körperkoordination test für Kinder tests, and body composition by the bioelectrical impedance analysis. PA intensity was assessed using V̇O2 as a % of V̇O2reserve or V̇O2 at VT, mean amplitude deviation (MAD) measured by accelerometry, metabolic equivalent of task (MET), and muscle activity measured by textile electromyography during walking or running on a treadmill at 4, 6, and 8 km/h, playing hopscotch, walking up and down the stairs, self-paced walking, and self-paced running. Children with lower V̇O2peak, V̇O2 at VT, and MC operated at higher intensity level during given physical task than their peers with higher fitness and MC when PA intensity was expressed using relative PA intensity using V̇O2 as a % of V̇O2reserve or V̇O2 at VT (p < 0.05). MAD and METs during different tasks were not able to discriminate PA intensity between children with varying levels of physical fitness or MC. Traditionally used absolute measures of PA intensity based on accelerometry or MET underestimated PA intensity in children with lower V̇O2peak, V̇O2 at VT, and MC.

Physical activity accumulation along the intensity spectrum differs between children and adults

PURPOSE

Detailed exploration of physical activity accumulation with fine grading along the intensity spectrum has indicated the potential pragmatic utility of such an approach. However, it is currently unclear what sorts of accumulation patterns along particular intensity bands are found in the children and adult populations. Therefore, we conducted a comparison of activity accumulation in specific intensity bands between four distinct populations: children, adults with sedentary lifestyles, habitual joggers, habitual marathon runners.

METHODS

Free-living waist-worn accelerometry records from 28 children aged 7 to 11, and 61 adults aged 25 to 35 were analysed. Activity intensity was evaluated in 5 s non-overlapping epochs as mean amplitude deviation (MAD) and normalised to acceleration intensities corresponding to walking at 3 metabolic equivalents of a task (METs). Adult data were normalised to 0.091 g MAD based on literature, and data from children to 0.170 g MAD based on laboratory experimentation. The normalised epoch values were divided into 100 intensity gradations.

RESULTS

Children accumulated more activity in 0.74 to 1.58 normalised acceleration intensities (all p < 0.005) compared to adults. Adult joggers/runners accumulated more activity in normalised acceleration intensities from 7.1 to 11.1 compared to the other groups (p < 0.008).

CONCLUSION

The primary bulk of children's free-living activities are of relatively low intensity not likely to provoke cardiometabolic improvement. These sorts of explorations could be used in informing intervention development aiming at optimising healthy development. Evidence is mounting to justify randomised controlled trials based on intervention targets identified based on exploring the intensity spectrum.

Associations of age, body size, and maturation with physical activity intensity in different laboratory tasks in children

We investigated the associations of age, sex, body size, body composition, and maturity with measures of physical activity (PA) intensity in children. PA intensity was assessed using VO₂ as % of VO_2reserve or VO₂ at ventilatory threshold (VT), muscle activity measured by textile electromyography, mean amplitude deviation (MAD) measured by accelerometry, and metabolic equivalent of task (MET) during laboratory activities.Age, stature, and muscle mass were inversely associated with VO2 as % of VO_2reserve and % of VT, during walking or running on a treadmill for 4, 6, and 8 km/h (Spearman r = -0.645 to -0.358). Age was inversely associated with MAD during walking on treadmill for 4 km/h (r = -0.541) and positively associated with MAD during running on a treadmill for 8 km/h, playing hopscotch, and during self-paced running (r = 0.368 to 0.478). Fat mass was positively associated with VO2 as % of VO_2reserve and VO2 as % of VO2 at VT and waist circumference was positively associated with VO2 as a % of VO_2reserve and muscle activity during stair climbing (r = 0.416 to 0.519).Fixed accelerometry cut-offs used to define PA intensities should be adjusted for age, sex, body size, and body composition.

Balancing time use for children's fitness and adiposity: Evidence to inform 24-hour guidelines for sleep, sedentary time and physical activity

PURPOSE

Daily time spent on one activity cannot change without compensatory changes in others, which themselves may impact on health outcomes. Optimal daily activity combinations may differ across outcomes. We estimated optimal daily activity durations for the highest fitness and lowest adiposity.

METHODS

Cross-sectional Child Health CheckPoint data (1182 11-12-year-olds; 51% boys) from the population-based Longitudinal Study of Australian Children were used. Daily activity composition (sleep, sedentary time, light physical activity [LPA], moderate-to-vigorous physical activity [MVPA]) was from 8-day, 24-hour accelerometry. We created composite outcomes for fitness (VO2max; standing long jump) and adiposity (waist-to-height ratio; body mass index; fat-to-fat-free log-ratio). Adjusted compositional models regressed activity log-ratios against each outcome. Best activity compositions (optimal time-use zones) were plotted in quaternary tetrahedrons; the overall optimal time-use composition was the center of the overlapping area.

RESULTS

Time-use composition was associated with fitness and adiposity (all measures p<0.001). Optimal time use differed for fitness and adiposity. While both maximized MVPA and minimized sedentary time, optimal fitness days had higher LPA (3.4 h) and shorter sleep (8.25 h), but optimal adiposity days had lower LPA (1.0 h) and longer sleep (10.9 h). Balancing both outcomes, the overall optimal time-use composition was (mean [range]): 10.2 [9.5; 10.5] h sleep, 9.9 [8.8; 11.2] h sedentary time, 2.4 [1.8; 3.2] h LPA and 1.5 [1.5; 1.5] h MVPA.

CONCLUSION

Optimal time use for children's fitness and adiposity involves trade-offs. To best balance both outcomes, estimated activity durations for sleep and LPA align with, but for MVPA exceed, 24-h guidelines.