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COVID-19 Containment Measures—a Step Back for Walking Mobility? A 2-Year, 60-Country Analysis of the Apple Mobility Data
Francesco Luciano, Federica Crova, and Francesco Canella
Energy Cost of Common Activities in Children and Adolescents
Kate Lyden, Sarah Kozey Keadle, John Staudenmayer, Patty Freedson, and Sofiya Alhassan
Background:
The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values.
Purpose:
To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium.
Methods:
Thirty-two children (8−11 years old) and 28 adolescents (12−16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer.
Results:
In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1−3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3−2.5 METs lower)] and 3 TRDs [2.24 m·s-1, 1.56 m·s-1, and 1.34 m·s-1 (0.4−0.8 METs lower)].
Conclusion:
The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth.
Validity of the Apple iPhone®/iPod Touch® as an Accelerometer-Based Physical Activity Monitor: A Proof-of-Concept Study
Meaghan Nolan, J. Ross Mitchell, and Patricia K. Doyle-Baker
Background:
The popularity of smartphones has led researchers to ask if they can replace traditional tools for assessing free-living physical activity. Our purpose was to establish proof-of-concept that a smartphone could record acceleration during physical activity, and those data could be modeled to predict activity type (walking or running), speed (km·h−1), and energy expenditure (METs).
Methods:
An application to record and e-mail accelerations was developed for the Apple iPhone®/iPod Touch®. Twentyfive healthy adults performed treadmill walking (4.0 km·h−1 to 7.2 km·h−1) and running (8.1 km·h−1 to 11.3 km·h−1) wearing the device. Criterion energy expenditure measurements were collected via metabolic cart.
Results:
Activity type was classified with 99% accuracy. Speed was predicted with a bias of 0.02 km·h−1 (SEE: 0.57 km·h−1) for walking, –0.03 km·h−1 (SEE: 1.02 km·h−1) for running. Energy expenditure was predicted with a bias of 0.35 METs (SEE: 0.75 METs) for walking, –0.43 METs (SEE: 1.24 METs) for running.
Conclusion:
Our results suggest that an iPhone/iPod Touch can predict aspects of locomotion with accuracy similar to other accelerometer-based tools. Future studies may leverage this and the additional features of smartphones to improve data collection and compliance.
Occupational Physical Activity Was Associated With Disability Levels at 6-Month Follow-Up of Patients With Chronic Nonspecific Low Back Pain: A Prospective Cohort Study
Thalysi M. Hisamatsu, Crystian B. Oliveira, Fábio S. Lira, Priscila K. Morelhão, Bruna R. Azevedo, Ítalo R. Lemes, Márcia R. Franco, and Rafael Z. Pinto
Questionnaire (BPAQ). 19 This questionnaire contains 16 questions and each item is answered on a 5-point Likert scale ranging from 1 to 5 points. Total score considering the 3 physical activity domains (ie, occupational physical activity, exercise in leisure, and leisure activity and locomotion) is expressed
A Network Perspective on the Relationship Between Moderate to Vigorous Physical Activity and Fundamental Motor Skills in Early Childhood
Thaynã Alves Bezerra, Paulo Felipe Ribeiro Bandeira, Anastácio Neco de Souza Filho, Cain Craig Truman Clark, Jorge Augusto Pinto Silva Mota, Michael Joseph Duncan, and Clarice Maria de Lucena Martins
the performance of the skill for that trial or a “0” indicated the component was not present. The video analysis was performed by 2 expert evaluators, obtaining high intra- and interrater reliability (ICC: .93–.98). The locomotion and object control scores are based on the presence (one) or absence
School Children’s Physical Activity, Motor Competence, and Corresponding Self-Perception: A Longitudinal Analysis of Reciprocal Relationships
Jeffrey Sallen, Christian Andrä, Sebastian Ludyga, Manuel Mücke, and Christian Herrmann
into different subdomains (eg, locomotion, object control). Stodden et al 8 hypothesized that children can increasingly assess their actual MC more realistically with the transition from middle to late childhood. At these stages of development, actual MC drives PA not only directly but also indirectly
Physical Activity Type and Intensity Are Associated With Abdominal Muscle Area and Density: The Multiethnic Study of Atherosclerosis
Chantal A. Vella, Iva Miljkovic, Candice A. Price, and Matthew Allison
), muscles of locomotion (psoas muscle), and total abdominal muscle (oblique, rectus abdominis, paraspinal muscles, and psoas). For each muscle, area was determined by summing the number of pixels of 0 to 100 HU within that muscle’s corresponding fascial plane. Muscle density was the average HU measurement
Psychosocial Outcomes Associated With Types and Intensities of Physical Activity in People With Spinal Cord Injury: The Mediating Role of Self-Efficacy and Functionality
Alex Castan, Iván Bonilla, Andrés Chamarro, and Joan Saurí
whether it was LTPA or lifestyle activity. The degree of independence in the daily life activities of self-care, mobility, sphincter control, locomotion, communication, and social cognition was assessed using the Functional Independence Measure. 34 The 18 items of the Functional Independence Measure are
Questionnaires Measuring 24-Hour Movement Behaviors in Childhood and Adolescence: Content Description and Measurement Properties—A Systematic Review
Bruno Rodrigues, Jorge Encantado, Eliana Carraça, João Martins, Adilson Marques, Luís Lopes, Eduarda Sousa-Sá, Dylan Cliff, Romeu Mendes, and Rute Santos
-weighted average was computed; score based on time spend on sedentary behavior PAL; MVPA; lying down, sitting, standing or in locomotion Past day in weekday, weekend, holiday or day off from school Segmented day format (web-based) D; I; M peas@tees 74 Children and adolescents (9–10 y), England Self
Energy Expenditure of Level Overground Walking in Young Adults: Comparison With Prediction Equations
Jingjing Xue, Shuo Li, Rou Wen, and Ping Hong
Walsum TA , Siebert U , Halsey LG . Does the treadmill support valid energetics estimates of field locomotion . Integr Comp Biol . 2017 ; 57 ( 2 ): 301 – 319 . PubMed ID: 28859410 doi:10.1093/icb/icx038 10.1093/icb/icx038 28859410 45. Hatamoto Y , Yamada Y , Fujii T , Higaki Y
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