Accurate estimates of free-living physical activity (PA) and sedentary behavior are critical for many different research applications. However, accuracy is particularly important for research with clinical populations since it is important to be able to monitor the dose of activity performed and to
Laura D. Ellingson, Paul R. Hibbing, Gregory J. Welk, Dana Dailey, Barbara A. Rakel, Leslie J. Crofford, Kathleen A. Sluka and Laura A. Frey-Law
Dale W. Esliger, Jennifer L. Copeland, Joel D. Barnes and Mark S. Tremblay
The unequivocal link between physical activity and health has prompted researchers and public health officials to search for valid, reliable, and logistically feasible tools to measure and quantify free-living physical activity. Accelerometers hold promise in this regard. Recent technological advances have led to decreases in both the size and cost of accelerometers while increasing functionality (e.g., greater memory, waterproofing). A lack of common data reduction and standardized reporting procedures dramatically limit their potential, however. The purpose of this article is to expand on the utility of accelerometers for measuring free-living physical activity. A detailed example profile of physical activity is presented to highlight the potential richness of accelerometer data. Specific recommendations for optimizing and standardizing the use of accelerometer data are provided with support from specific examples. This descriptive article is intended to advance and ignite scholarly dialogue and debate regarding accelerometer data capture, reduction, analysis, and reporting.
Hotaka Maeda, Chris C. Cho, Young Cho and Scott J. Strath
Valid measurement methods are essential for understanding people’s free-living physical activity (PA) behaviors. Recently, accelerometer-based devices, or PA monitors, have become a popular method in assessing free-living PA. A PubMed search of (accelerometer) AND (“physical activity” OR exercise
Greg Petrucci Jr., Patty Freedson, Brittany Masteller, Melanna Cox, John Staudenmayer and John Sirard
Purpose: Determine the sensitivity of the Misfit Shine™ (MS) to detect changes in physical activity (PA) measures (steps, “points,” kCals) in laboratory (LAB) and free-living (FL) conditions. Methods: Twenty-one participants wore the MS and ActiGraph GT3X+™ accelerometer (AG) at the hip and dominant-wrist during three, one-hour LAB sessions: sedentary (SS), sedentary plus walking (SW), and sedentary plus jogging (SJ). Direct observation (DO) of steps served as the criterion measure. Devices were also worn during two FL conditions: 1) active week (ACT) and 2) inactive week (INACT). For LAB and FL, significant differences were examined using paired t-tests and linear mixed effects models, respectively. Linear mixed effects models were used to estimate differences between MS estimated steps and DO (α ≤ 0.05). Results: For all hip-worn MS measures and wrist-worn MS estimates of steps and “points,” there was a significant increase (p < .05) from SS to SJ. However, wrist-worn MS kCal estimates were greater for SJ, compared to SS and SW, which were similar to each other (95% CI [95.5, 152.8] and [141.1, 378.9], respectively). Compared with DO, MS hip significantly underestimated steps by 3.5%, while MS wrist significantly overestimated steps by 4.2%. During FL conditions, all MS measures were sensitive to changes between ACT and INACT (p < .0001). Conclusion: Although there were systematic errors in step estimates from the MS, it was sensitive to changes during LAB and FL, and may be a useful tool for interventionists where tracking changes in PA is an important exposure or outcome variable.
Alison L. Innerd and Liane B. Azevedo
The aim of this study is to establish the energy expenditure (EE) of a range of child-relevant activities and to compare different methods of estimating activity MET.
27 children (17 boys) aged 9 to 11 years participated. Participants were randomly assigned to 1 of 2 routines of 6 activities ranging from sedentary to vigorous intensity. Indirect calorimetry was used to estimate resting and physical activity EE. Activity metabolic equivalent (MET) was determined using individual resting metabolic rate (RMR), the Harrell-MET and the Schofield equation.
Activity EE ranges from 123.7± 35.7 J/min/Kg (playing cards) to 823.1 ± 177.8 J/min/kg (basketball). Individual RMR, the Harrell-MET and the Schofield equation MET prediction were relatively similar at light and moderate but not at vigorous intensity. Schofield equation provided a better comparison with the Compendium of Energy Expenditure for Youth.
This information might be advantageous to support the development of a new Compendium of Energy Expenditure for Youth.
P. Margaret Grant, Malcolm H. Granat, Morag K. Thow and William M. Maclaren
This study measured objectively the postural physical activity of 4 groups of older adults (≥65 yr). The participants (N = 70) comprised 3 patient groups—2 from rehabilitation wards (city n = 20, 81.8 ± 6.7 yr; rural n = 10, 79.4 ± 4.7 yr) and the third from a city day hospital (n = 20, 74.7 ± 7.9 yr)—and a healthy group to provide context (n = 20, 73.7 ± 5.5 yr). The participants wore an activity monitor (activPAL) for a week. A restricted maximum-likelihood-estimation analysis of hourly upright time (standing and walking) revealed significant differences between day, hour, and location and the interaction between location and hour (p < .001). Differences in the manner in which groups accumulated upright and sedentary time (sitting and lying) were found, with the ward-based groups sedentary for prolonged periods and upright for short episodes. This information may be used by clinicians to design appropriate rehabilitation interventions and monitor patient progress.
Peter Collins, Yahya Al-Nakeeb and Mark Lyons
Active school commuting is widely regarded as a key opportunity for youth to participate in physical activity (PA). However, the accurate measurement of the commute home from school and its contribution to total free-living moderateto- vigorous PA (MVPA) is relatively unexplored.
Seventy-five adolescents (38 males, 37 females) wore an integrated GPS and heart rate device during after-school hours for 4 consecutive weekdays.
Active commuters were significantly more active (11.72 minutes MVPA) than passive commuters (3.5 minutes MVPA) during their commute home from school (P = .001). The commute home of walkers and cyclists on average contributed 35% of their total free-living PA. However, there was no significant difference in the overall free-living PA levels of passive and active commuters (P > .05). A total 92.7% of the youth living within 1.5 miles of the school actively commuted, compared with 16.7% of the youth who lived further away. Socioeconomic differences in commuting patterns were also evident.
The findings highlighted the significant proportion of total free-living PA that was attributed to active commuting home from school. The study demonstrates the usefulness of utilizing GPS and heart rate data to accurately track young people’s after-school PA. Demographic influences and implications for future research are discussed.
Scott E. Crouter, Diane M. DellaValle, Jere D. Haas, Edward A. Frongillo and David R. Bassett
The purpose of this study was to compare the 2006 and 2010 Crouter algorithms for the ActiGraph accelerometer and the NHANES and Matthews cut-points, to indirect calorimetry during a 6-hr free-living measurement period.
Twenty-nine participants (mean ± SD; age, 38 ± 11.7 yrs; BMI, 25.0 ± 4.6 kg·m-2) were monitored for 6 hours while at work or during their leisure time. Physical activity (PA) data were collected using an ActiGraph GT1M and energy expenditure (METs) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared with the Cosmed for METs and time spent in sedentary behaviors, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA).
The 2010 Crouter algorithm overestimated time spent in LPA, MPA, and VPA by 9.0%−44.5% and underestimated sedentary time by 20.8%. The NHANES cut-points overestimated sedentary time and LPA by 8.3%−9.9% and underestimated MPA and VPA by 50.4%−56.7%. The Matthews cut-points overestimated sedentary time (9.9%) and MPA (33.4%) and underestimated LPA (25.7%) and VPA (50.1%). The 2006 Crouter algorithm was within 1.8% of measured sedentary time; however, mean errors ranged from 34.4%−163.1% for LPA, MPA, and VPA.
Of the ActiGraph prediction methods examined, none of them was clearly superior for estimating free-living PA compared with indirect calorimetry.
Ian Cook, Marianne Alberts and Estelle V. Lambert
We describe the effect of 2 different accelerometer cut-points on physical activity (PA) patterns in rural and urban black South African women.
Hip-mounted uni-axial accelerometers were worn for 6 to 7 days by rural (n = 272) and urban (n = 16) participants. Twenty-hour (4 AM to 12 AM) PA counts (cts) and volumes (min·day−1) were extracted: sedentary (SED, <100 cts·min−1), light (100–759 cts·min−1), moderate-1 (MOD1, 760–1951 cts·min−1), moderate-2 to vigorous (MOD2VG, ≥1952 cts·min−1), and bouts ≥10 min for ≥760 cts·min−1 (MOD1VGbt) and ≥1952 cts·min−1 (MOD2VGbt).
Valid data were obtained from 263 rural women and 16 urban women. Total counts and average counts were higher (+80,399 cts·day−1, +98 cts·min−1.day−1) (P < .01), SED lower (−61 min·day−1, P = .0042), MOD1 higher (+65 min·day−1, P < .0001), and MOD1VGbt higher (+19 min·day−1, P = .0179) in rural women compared with urban women. Estimated adherence (≥30 min·day−1 for 5 days·wk−1) was 1.4-fold higher in rural women than urban women for MOD-1VGbt, but 3.3-fold higher in urban women than rural women for MOD2VGbt.
Rural women accumulate greater amounts of PA than urban women within a particular count band. Depending on which moderate PA cut-point was used to estimate PA public health adherence, rural women could be classified as less physically active than urban women.
Meaghan Nolan, J. Ross Mitchell and Patricia K. Doyle-Baker
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).
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.
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.
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.