To assess performance of existing wear/nonwear time classification algorithms for accelerometry data collected in the free-living environment using a wrist-worn triaxial accelerometer and a waist-worn uniaxial accelerometer in older adults.
Twenty-nine adults aged 76 to 96 years wore wrist accelerometers for ~24-h per day and waist accelerometers during waking for ~7 days of free-living. Wear and nonwear times were classified by existing algorithms (Alg[Actilife], Alg[Troiano] and Alg[Choi]) and compared with wear and nonwear times identified by data plots and diary records. Using bias and probability of correct classification, performance of the algorithms, two time-windows (60- and 90-min), and vector magnitude (VM) vs. vertical axis (V) counts from a triaxial accelerometer, were compared.
Automated algorithms (Alg[Choi] and Alg[Troiano]) classified wear/nonwear time intervals more accurately from VM than V counts. The use of 90-min time window improved wear/nonwear classification accuracy when compared with the 60-min window. The Alg[Choi] and Alg[Troiano] performed better than the manufacturer-provided algorithm (Alg[Actilife]), and Alg[Choi] performed better than Alg[Troiano] for wear/nonwear time classification using data collected by both accelerometers.
Triaxial wrist-worn accelerometer can be used for an accurate wear/nonwear time classification in free-living older adults. The use of 90-min window and VM counts improves performance of commonly used algorithms for wear/nonwear classification for both uniaxial and triaxial accelerometers.
nonwear categorization; physical activity assessment; accelerometry; sedentary behavior
Physical activity and sedentary behaviour among children should be measured accurately in order to investigate their relationship with health. Accelerometry provides objective and accurate measurement of body movement, which can be converted to meaningful behavioural outcomes. The aim of this study was to evaluate the best evidence for the decisions on data collection and data processing with accelerometers among children resulting in a standardized protocol for use in the participating countries.
This cross-sectional accelerometer study was conducted as part of the European ENERGY-project that aimed to produce an obesity prevention intervention among schoolchildren. Five countries, namely Belgium, Greece, Hungary, Switzerland and the Netherlands participated in the accelerometer study. We used three different Actigraph models-Actitrainers (triaxial), GT3Xs and GT1Ms. Children wore the device for six consecutive days including two weekend days. We selected an epoch length of 15 seconds. Accelerometers were placed at children's waist at the right side of the body in an elastic belt.
In total, 1082 children participated in the study (mean age = 11.7 ± 0.75 y, 51% girls). Non-wearing time was calculated as periods of more than 20 minutes of consecutive zero counts. The minimum daily wearing time was set to 10 hours for weekdays and 8 hours for weekend days. The inclusion criterion for further analysis was having at least three valid weekdays and one valid weekend day. We selected a cut-point (count per minute (cpm)) of <100 cpm for sedentary behaviour, <3000 cpm for light, <5200 cpm for moderate, and >5200 cpm for vigorous physical activity. We also created time filters for school-time during data cleaning in order to explore school-time physical activity and sedentary behaviour patterns in particular.
This paper describes the decisions for data collection and processing. Use of standardized protocols would ease future use of accelerometry and the comparability of results between studies.
To determine the feasibility of using an accelerometer to characterize physical activity patterns (PA) surrounding chronic obstructive pulmonary disease (COPD) exacerbations (AECOPD) in patients with COPD for 16 weeks.
Patients with COPD (n = 8) wore the RT3®, a triaxial accelerometer (Stayhealthy, Monrovia, CA) during waking hours and kept daily symptom diaries. The mean vector magnitude unit (VMU) per minute was calculated by dividing the total VMU for the day by the number of minutes the device was worn. Descriptive statistics were used and plots were made showing PA for each subject with AECOPD markers based on symptom diaries and health resource utilization.
Sample characteristics were: age 71 ± 4; 5 Females; forced expiratory volume in one second (FEV1)% predicted: 40% ± 16%; FEV1/forced vital capacity: 45 ± 7; and Medical Research Council dyspnea scale: 2.3 ± 0.9. Overall adherence to the monitoring protocol was 97.6% (Range 92%–100%) while adherence to wearing the device for at least 10 hours per day was 91.5% (Range 75%–99%). Mean vector magnitude units per minute was 117.8 ± 47 (Range 61.4–184.1). Seven exacerbations were captured over a total of 896 person-days of monitoring. There were substantial intra-individual fluctuations in daily PA during both the stable state and with outpatient treated exacerbations.
Patients with COPD were able to adhere to a 16-week activity monitoring protocol and reported a willingness to wear such a device for an extended period of time if the data yield important and useful information for themselves and their health provider. Future work will need to focus first, on validating other promising devices that produce higher quality PA data and second, replicate this monitoring protocol with a larger sample of COPD patients over a longer period.
physical activity; accelerometry; COPD; exacerbations
The aim of this study was to compare physical activity measured using GT1M ActiGraph and GT3X ActiGraph accelerometers in free living conditions.
Twenty-five adults wore GT1M and GT3X Actigraph accelerometers simultaneously during a typical weekday of activity. Data were uploaded from the monitor to a computer at the end of test (one day). Previously established thresholds were used for defining time spent at each level of physical activity, physical activity was assessed at varying intensities comparing data from the two accelerometers by ANOVA and Bland and Altman statistical analysis. The concordance correlation coefficient between accelerometers at each intensity level was 0.99. There were no significant differences between accelerometers at any of the activity levels. Differences between data obtained in minutes with the GT1M accelerometer and the GT3X monitor were to 0.56, 0.36, 0.52 and 0.44% for sedentary, light, moderate and vigorous, respectively. The Bland and Altman method showed good agreement between data obtained for the two accelerometers.
Findings suggest that the two accelerometers provided similar results and therefore the GT3X may be used in clinical and epidemiological studies without additional calibration or validation studies.
Accelerometry; Physical activity; Assessment; Equivalence
Physical activity and sedentary behavior are central components of lifetime weight control; however, our understanding of dimensions of these behaviors in childhood is limited. This study investigated free-living activity pattern characteristics and the individual variability of these characteristics in 84 lean and obese Chinese children (7–9 y) during the school day and over the weekend. Activity pattern characteristics were established from triaxial accelerometry (StayHealthy RT3). Results indicated that children's free-living activity is characterized by many short-duration, low-intensity bouts of movement. Obese children take longer rest intervals between bouts and engage in fewer activity bouts both at school and at home. Intraindividual variability in activity patterns was low during school days but high for the rest intervals between bouts and number of activity bouts per day at the weekend. Finding ways to reduce the rest time between bouts of movement and increase the number of movement bouts a child experiences each day is an important next step.
To investigate the association between physical activity and health, we need accurate and detailed free-living physical activity measurements. The determination of energy expenditure of activity (EEACT) may also be useful in the treatment and maintenance of nutritional diseases such as diabetes mellitus. Minute-to-minute energy expenditure during a 24-h period was measured in 60 sedentary normal female volunteers (35.4 ± 9.0 years, body mass index 30.0 ± 5.9 kg/m2), using a state-of-the-art whole-room indirect calorimeter. The activities ranged from sedentary deskwork to walking and stepping at different intensities. Body movements were simultaneously measured using a hip-worn triaxial accelerometer (Tritrac-R3D, Hemokentics, Inc., Madison, Wisconsin) and a wrist-worn uniaxial accelerometer (ActiWatch AW64, MiniMitter Co., Sunriver, Oregon) on the dominant arm. Movement data from the accelerometers were used to develop nonlinear prediction models (separately and combined) to estimate EEACT and compared for accuracy. In a subgroup (n = 12), a second 24-h study period was repeated for cross-validation of the combined model. The combined model, using Tritrac-R3D and ActiWatch, accurately estimated total EEACT (97.7 ± 3.2% of the measured values, p = 0.781), as compared with using ActiWatch (86.0 ± 4.7%, p < 0.001) or Tritrac-R3D (90.0 ± 4.6%, p < 0.001) alone. This model was also accurate for all intensity categories during various physical activities. The subgroup cross-validation also showed accurate and reproducible predictions by the combination model. In this study, we demonstrated that movement measured using accelerometers at the hip and wrist could be used to accurately predict EEACT of various types and intensity of activities. This concept can be extended to develop valid models for the accurate measurement of free-living energy metabolism in clinical populations.
Accelerometers are promising tools for characterizing physical activity (PA) patterns in free-living persons. To date, validation of energy expenditure (EE) predictions from accelerometers has been restricted to short laboratory or simulated free-living protocols. This study seeks to determine the capabilities of eight previously published regression equations for three commercially available accelerometers to predict summary measures of daily EE.
Methods and Procedures:
Study participants were outfitted with ActiGraph, Actical, and RT3 accelerometers, while measurements were simultaneously made during overnight stays in a room calorimeter, which provided minute-by-minute EE measurements, in a diverse subject population (n = 85). Regression equations for each device were used to predict the minute-by-minute metabolic equivalents (METs) along with the daily PA level (PAL).
Two RT3 regressions and one ActiGraph regression were not significantly different from calorimeter measured PAL. When data from the entire visit were divided into four intensity categories—sedentary, light, moderate, and vigorous—significant (P < 0.001) over- and underpredictions were detected in numerous regression equations and intensity categories.
Most EE prediction equations showed differences of <2% in the moderate and vigorous intensity categories. These differences, though small in magnitude, may limit the ability of these regressions to accurately characterize whether specific PA goals have been met in the field setting. New regression equations should be developed if more accurate prediction of the daily PAL or higher precision in determining the time spent in specific PA intensity categories is desired.
To determine the reliability and validity of the Multimedia Activity Recall for Children and Adults (MARCA) in people with chronic obstructive pulmonary disease (COPD).
People with COPD and their carers completed the Multimedia Activity Recall for Children and Adults (MARCA) for four, 24-hour periods (including test-retest of 2 days) while wearing a triaxial accelerometer (Actigraph GT3X+®), a multi-sensor armband (Sensewear Pro3®) and a pedometer (New Lifestyles 1000®).
Self reported activity recalls (MARCA) and objective activity monitoring (Accelerometry) were recorded under free-living conditions.
24 couples were included in the analysis (COPD; age 74.4±7.9 yrs, FEV1 54±13% Carer; age 69.6±10.9 yrs, FEV1 99±24%).
Main Outcome Measure(s)
Test-retest reliability was compared for MARCA activity domains and different energy expenditure zones. Validity was assessed between MARCA-derived physical activity level (in metabolic equivalent of task (MET) per minute), duration of moderate to vigorous physical activity (min) and related data from the objective measurement devices. Analysis included intra-class correlation coefficients (ICC), Bland-Altman analyses, paired t-tests (p) and Spearman's rank correlation coefficients (rs).
Reliability between occasions of recall for all activity domains was uniformly high, with test-retest correlations consistently >0.9. Validity correlations were moderate to strong (rs = 0.43–0.80) across all comparisons. The MARCA yields comparable PAL estimates and slightly higher moderate to vigorous physical activity (MVPA) estimates.
In older adults with chronic illness, the MARCA is a valid and reliable tool for capturing not only the time and energy expenditure associated with physical and sedentary activities but also information on the types of activities.
To test a field-based protocol using intermittent activities representative of children's physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.
Twenty-eight children (46% boys) aged 10–11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children's play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.
Cut-points of ≤372, >2160 and >4806 counts•min−1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96–97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89–99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.
The development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.
We explored using the ActiGraph accelerometer to differentiate activity levels between participants in a physical activity (PA, n=54) or `successful aging' (SA) program (n = 52). The relationship between a PA questionnaire for older adults (CHAMPS) and accelerometry variables was also determined. Individualized accelerometry count thresholds (THRESHIND) measured during a 400-m walk were used to identify “meaningful activity.” Participants then wore the ActiGraph for 7 days. Results indicated more activity bouts·d−1 ≥ 10 min above THRESHIND in the PA group compared to SA group (1.1 ± 2.0 vs 0.5 ± 0.8, p = 0.05) and more activity counts·d−1 above THRESHIND for the PA group (28,101 ± 27,521) compared to the SA group (17,234 ± 15,620, p = 0.02). Correlations between activity counts·hr−1 and CHAMPS ranged from 0.27 – 0.42, p<0.01. The ActiGraph and THRESHIND may be useful for differentiating PA levels in older adults at risk for mobility disability.
Accelerometers are considered to be the most promising tool for measuring physical activity (PA) in free-living young children. So far, no studies have examined the feasibility and validity of accelerometer measurements in children under 3 years of age. Therefore, the purpose of the present study was to examine the feasibility and validity of accelerometer measurements in toddlers (1- to 3-year olds).
Forty-seven toddlers (25 boys; 20 ± 4 months) wore a GT1M ActiGraph accelerometer for 6 consecutive days and parental perceptions of the acceptability of wearing the monitor were assessed to examine feasibility. To investigate the validity of the ActiGraph and the predictive validity of three ActiGraph cut points, accelerometer measurements of 31 toddlers (17 boys; 20 ± 4 months) during free play at child care were compared to directly observed PA, using the Observational System for Recording Physical Activity in Children-Preschool (OSRAC-P). Validity was assessed using Pearson and Spearman correlations and predictive validity using area under the Receiver Operating Characteristic curve (ROC-AUC).
The feasibility examination indicated that accelerometer measurements of 30 toddlers (63.8%) could be included with a mean registration time of 564 ± 62 min during weekdays and 595 ± 83 min during weekend days. According to the parental reports, 83% perceived wearing the accelerometer as 'not unpleasant and not pleasant' and none as 'unpleasant'. The validity evaluation showed that mean ActiGraph activity counts were significantly and positively associated with mean OSRAC-P activity intensity (r = 0.66; p < 0.001; n = 31). Further, the correlation among the ActiGraph activity counts and the OSRAC-P activity intensity level during each observation interval was significantly positive (ρ = 0.52; p < 0.001; n = 4218). Finally, the three sedentary cut points exhibited poor to fair classification accuracy (ROC-AUC: 0.56 to 0.71) while the three light PA (ROC-AUC: 0.51 to 0.62) and the three moderate-to-vigorous PA cut points (ROC-AUC: 0.53 to 0.57) demonstrated poor classification accuracy with respect to detecting sedentary behavior, light PA and moderate-to-vigorous PA, respectively.
The present findings suggest that ActiGraph accelerometer measurements are feasible and valid for quantifying PA in toddlers. However, further research is needed to accurately identify PA intensities in toddlers using accelerometry.
ActiGraph; Observational System for Recording Physical Activity in Children-Preschool (OSRAC-P); Feasibility; Criterion validity; Predictive validity; Accelerometer cut points; Toddlers
Purpose. To critically review the validity of accelerometry-based prediction models to estimate activity energy expenditure (AEE) in children and adolescents.
Methods. The CINAHL, EMBASE, PsycINFO, and PubMed/MEDLINE databases were searched. Inclusion criteria were development or validation of an accelerometer-based prediction model for the estimation of AEE in healthy children or adolescents (6–18 years), criterion measure: indirect calorimetry, or doubly labelled water, and language: Dutch, English or German.
Results. Nine studies were included. Median methodological quality was 5.5 ± 2.0 IR (out of a maximum 10 points). Prediction models combining heart rate and counts explained 86–91% of the variance in measured AEE. A prediction model based on a triaxial accelerometer explained 90%. Models derived during free-living explained up to 45%.
Conclusions. Accelerometry-based prediction models may provide an accurate estimate of AEE in children on a group level. Best results are retrieved when the model combines accelerometer counts with heart rate or when a triaxial accelerometer is used. Future development of AEE prediction models applicable to free-living scenarios is needed.
This study assessed and compared the daily step counts recorded by two different motion sensors in order to estimate the free-living physical activity of 135 adolescent girls. Each girl concurrently wore a Yamax pedometer and an ActiGraph accelerometer (criterion measure) every day for seven consecutive days. The convergent validity of the pedometer can be considered intermediate when used to measure the step counts in free-living physical activity; but should be considered with caution when used to classify participants’ step counts into corresponding physical activity categories because of a likelihood of ‘erroneous’ classification in comparison with the accelerometer.
pedometer; accelerometer; validity; monitoring; measurement; step counter; categorization of physical activity
Accelerometry is increasingly being recognized as an accurate and reliable method to assess free-living physical activity (PA) in children and adolescents. However, accelerometer data reduction criteria remain inconsistent, and the consequences of excluding participants in for example intervention studies are not well described. In this study, we investigated how different data reduction criteria changed the composition of the adolescent population retained in accelerometer data analysis.
Accelerometer data (Actigraph GT3X), anthropometric measures and survey data were obtained from 1348 adolescents aged 11–14 years enrolled in the Danish SPACE for physical activity study. Accelerometer data were analysed using different settings for each of the three key data reduction criteria: (1) number of valid days; (2) daily wear time; and (3) non-wear time. The effects of the selected setting on sample retention and PA counts were investigated and compared. Ordinal logistic regression and multilevel mixed-effect linear regression models were used to analyse the impact of differing non-wear time definitions in different subgroups defined by body mass index, age, sex, and self-reported PA and sedentary levels.
Increasing the minimum requirements for daily wear time and the number of valid days and applying shorter non-wear definitions, resulted in fewer adolescents retained in the dataset. Moreover, the different settings for non-wear time significantly influenced which participants would be retained in the accelerometer data analyses. Adolescents with a higher BMI (OR:0.93, CI:0.87-0.98, p=0.015) and older adolescents (OR:0.68, CI:0.49-0.95, p=0.025) were more likely to be excluded from analysis using 10 minutes of non-wear compared to longer non-wear time periods. Overweight and older adolescents accumulated more daily non-wear time if the non-wear time setting was short, and the relative difference between groups changed depending on the non-wear setting. Overweight and older adolescents did also accumulate more sedentary time, but this was not significant correlated to the non-wear setting used.
Even small differences in accelerometer data reduction criteria can have substantial impact on sample size and PA and sedentary outcomes. This study highlighted the risk of introducing bias with more overweight and older adolescents excluded from the analysis when using short non-wear time definitions.
Physical activity; Measurements; Adolescents; Overweight; Accelerometer; Bias
Surveillance of physical activity (PA) is increasingly based on accelerometry. However, data management guidelines are lacking. We propose an approach for combining accelerometry and diary based PA information for assessment of PA in adolescents and provide an example of this approach using data from German adolescents.
The 15-year-old participants comprised a subsample the GINIplus birth cohort (n = 328, 42.4% male). Data on PA was obtained from hip-worn accelerometers (ActiGraph GT3X) for seven consecutive days, combined with a prospective activity diary. Major aspects of data management were validity of wear time, handling of non-wear time and diary comments. After data cleaning, PA and percentage of adolescents meeting the recommendations for moderate-to-vigorous activity (MVPA) per day were determined.
From the 2224 recorded days 493 days (25%) were invalid, mainly due to uncertainties relating to non-wear time (322 days). Ultimately, 269 of 328 subjects (82%) with valid data for at least three weekdays and one weekend day were included in the analysis. Mean MVPA per day was 39.1 minutes (SD ±25.0), with boys being more active than girls (41.8±21.5 minutes vs. 37.1±27.8 minutes, p<0.001). Accordingly, 24.7% of boys and 17.2% of girls (p<0.01) met the WHO recommendations for PA. School sport accounted for only 6% of weekly MVPA. In fact, most MVPA was performed during leisure time, with the majority of adolescents engaging in ball sports (25.4%) and endurance sports (19.7%). Girls also frequently reported dancing and gymnastics (23%).
For assessment of PA in adolescents, collecting both accelerometry and diary-based information is recommended. The diary is vital for the identification of invalid data and non-compliant participants. Preliminary results suggest that four out of five German adolescents do not meet WHO recommendations for PA and that school sport contributes only little to MVPA.
The preschools that children attend influence their physical activity level. But, little is known about which characteristics of a preschool may influence the physical activity of children. The purpose of this study was to examine policies and characteristics of preschools and the extent to which they influence the physical activity of 3- to 5-year-old children during the preschool day.
A total of 299 children from 20 preschools wore ActiGraph (Pensacola, FL) accelerometers an average of 8.1 hours (SD=1.5) per day for 5.5 days (SD=2.1). A researcher completed the Early Childhood Environment Rating Scale-Revised Edition (ECERS-R) for each preschool to access quality. Classrooms and playgrounds were measured, and the preschool director was interviewed about physical activity policies. For each policy or characteristic, preschools were divided into two groups based on whether or not the characteristic/policy was presumed to promote physical activity (PPA) or not promote physical activity (NPA).
Children spent fewer minutes per hour in sedentary activity and more minutes per hour in moderate-to-vigorous physical activity (MVPA) in preschools that had higher quality scores, less fixed playground equipment, more portable playground equipment, lower electronic media use, and larger playgrounds. Five preschools had all five of these characteristics, and children in those preschools had significantly higher MVPA minutes per hour and lower sedentary minutes per hour compared to children in the other preschools.
All preschools can encourage physical activity by providing inexpensive portable playground equipment, limiting the number of children using fixed equipment and the number of children on the playground at one time, and limiting electronic media use. Children in the top five physical activity promoting preschools accumulated more than 60 minutes per day of MVPA as compared to the children in the other preschools who accumulated less than 60 minutes per day of MVPA.
children; preschool; childcare; physical activity; accelerometer
Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity energy expenditure in youth remains unexplored in the research literature.
To develop and test artificial neural networks (ANNs) to predict physical activity (PA) type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents.
100 participants between the ages of 5 and 15 y completed 12 activity trials that were categorized into 5 PA types: sedentary, walking, running, light intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip and VO2 was measured using the Oxycon Mobile portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, features were extracted from 10, 15, 20, 30, and 60 s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE).
As window size increased from 10 to 60 s, accuracy for the PA type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30–40% lower than conventional regression-based approaches.
ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.
objective assessment; validity; children; adolescents; pattern recognition
This study aimed to develop a translation equation to enable comparison between Actical and ActiGraph GT3X accelerometer counts recorded minute by minute.
Five males and five females of variable height, weight, body mass index and age participated in this investigation. Participants simultaneously wore an Actical and an ActiGraph accelerometer for two days. Conversion algorithms and R2 were calculated day by day for each subject between the omnidirectional Actical and three different ActiGraph (three-dimensional) outputs: 1) vertical direction, 2) combined vector, and 3) a custom vector. Three conversion algorithms suitable for minute/minute conversions were then calculated from the full data set.
The vertical ActiGraph activity counts demonstrated the closest relationship with the Actical, with consistent moderate to strong conversions using the algorithm: y = 0.905x, in the day by day data (R2 range: 0.514 to 0.989 and average: 0.822) and full data set (R2 = 0.865).
The Actical is most sensitive to accelerations in the vertical direction, and does not closely correlate with three-dimensional ActiGraph output. Minute by minute conversions between the Actical and ActiGraph vertical component can be confidently performed between data sets and might allow further synthesis of information between studies.
Accelerometry; Actical; ActiGraph; Translation equations
The National Association for Sport and Physical Education (NASPE) guidelines for preschoolers recommend 120 minutes of physical activity daily. Two issues, however, create a situation whereby substantial variation in estimated prevalence rates of (in)active preschoolers are reported. First, NASPE guidelines have been interpreted in multiple ways. Second, objective monitoring via accelerometry is the most widely accepted measure of preschoolers' physical activity, yet multiple cut points provide vastly different estimates of physical activity. This study aimed to estimate the prevalence of preschoolers meeting NASPE guidelines and illustrate the differences among rates, given guideline interpretations, and cut points.
PATIENTS AND METHODS:
Three- to 5-year-old children (n = 397) wore ActiGraph accelerometers for an average of 5.9 days. NASPE guidelines were expressed in 3 ways: 120 minutes daily of light-to-vigorous physical activity; 120 minutes daily of moderate-to-vigorous physical activity; and 60 minutes daily of moderate-to-vigorous physical activity. Estimates of 120 minutes daily of light-to-vigorous physical activity, 120 minutes daily of moderate-to-vigorous physical activity, and 60 minutes daily of moderate-to-vigorous physical activity were calculated on the basis of 4 common accelerometer cut points for preschoolers: Pate, Reilly and Puyau, Sirard, and Freedson.
Prevalence rates varied considerably, with estimates ranging from 13.5% to 99.5%, 0.0% to 95.7%, and 0.5% to 99.5% for 120 minutes daily of light-to-vigorous physical activity, 120 minutes daily of moderate-to-vigorous physical activity, and 60 minutes daily of moderate-to-vigorous physical activity, respectively.
The variation in NASPE guidelines, coupled with different accelerometer cut points, results in disparate estimates of (in)active preschoolers. This limits the ability to estimate population prevalence levels of physical activity that can be used to guide public health policy. Development of new guidelines should focus on an explicit delineation of physical activity and attempt to standardize the measurement of preschoolers' physical activity.
moderate to vigorous; children; adolescents; benchmark
This study aimed to compare the levels of objectively-measured sedentary behavior in children attending Montessori preschools with those attending traditional preschools.
The participants in this study were preschool children aged 4 years old who were enrolled in Montessori and traditional preschools. The preschool children wore ActiGraph accelerometers. Accelerometers were initialized using 15-second intervals and sedentary behavior was defined as <200 counts/15-second. The accelerometry data were summarized into the average minutes per hour spent in sedentary behavior during the in-school, the after-school, and the total-day period. Mixed linear regression models were used to determine differences in the average time spent in sedentary behavior between children attending traditional and Montessori preschools, after adjusting for selected potential correlates of preschoolers’ sedentary behavior.
Children attending Montessori preschools spent less time in sedentary behavior than those attending traditional preschools during the in-school (44.4. min/hr vs. 47.1 min/hr, P = 0.03), after-school (42.8. min/hr vs. 44.7 min/hr, P = 0.04), and total-day (43.7 min/hr vs. 45.5 min/hr, P = 0. 009) periods. School type (Montessori or traditional), preschool setting (private or public), socio-demographic factors (age, gender, and socioeconomic status) were found to be significant predictors of preschoolers’ sedentary behavior.
Levels of objectively-measured sedentary behavior were significantly lower among children attending Montessori preschools compared to children attending traditional preschools. Future research should examine the specific characteristics of Montessori preschools that predict the lower levels of sedentary behavior among children attending these preschools compared to children attending traditional preschools.
Sedentary behavior; Preschool; Montessori; Accelerometer
There is general consensus that physical activity is important for preserving functional capacities of older adults and positively influencing quality of life. While accelerometry is widely accepted and applied to assess physical activity in studies, several problems with this method remain (e.g., low retest reliability, measurement errors). The aim of this study was to test the intra-instrumental retest reliability of a wrist-worn accelerometer in a 3-day measurement of physical activity in older adults and to compare different estimators. A sample of 123 older adults (76.5 ± 5.1 years, 59 % female) wore a uniaxial accelerometer continuously for 1 week. The data were split into two repeated measurement values (week set) of 3 days each. The sum, the 80–99th quantiles and the 80–99th trimmed sums were built for each week set. Retest reliability was assessed for each estimator and graphically demonstrated by Bland–Altman plots. The intraclass correlation of the retest reliability ranged from 0.22 to 0.91. Retest reliability increases when a more robust estimator than the overall sum is used. Therefore, the trimmed sum can be recommended as a conservative estimate of the physical activity level of older adults.
Aged; Reproducibility of results; Activities of daily living; Bias (epidemiology)
Reduced physical activity is an important feature of Chronic Obstructive Pulmonary Disease (COPD). Various activity monitors are available but their validity is poorly established. The aim was to evaluate the validity of six monitors in patients with COPD. We hypothesized triaxial monitors to be more valid compared to uniaxial monitors. Thirty-nine patients (age 68±7years, FEV1 54±18%predicted) performed a one-hour standardized activity protocol. Patients wore 6 monitors (Kenz Lifecorder (Kenz), Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod (MiniMod), and SenseWear Armband (SenseWear)) as well as a portable metabolic system (Oxycon Mobile). Validity was evaluated by correlation analysis between indirect calorimetry (VO2) and the monitor outputs: Metabolic Equivalent of Task [METs] (SenseWear, MiniMod), activity counts (Actiwatch), vector magnitude units (Actigraph, RT3) and arbitrary units (Kenz) over the whole protocol and slow versus fast walking. Minute-by-minute correlations were highest for the MiniMod (r = 0.82), Actigraph (r = 0.79), SenseWear (r = 0.73) and RT3 (r = 0.73). Over the whole protocol, the mean correlations were best for the SenseWear (r = 0.76), Kenz (r = 0.52), Actigraph (r = 0.49) and MiniMod (r = 0.45). The MiniMod (r = 0.94) and Actigraph (r = 0.88) performed better in detecting different walking speeds. The Dynaport MiniMod, Actigraph GT3X and SenseWear Armband (all triaxial monitors) are the most valid monitors during standardized physical activities. The Dynaport MiniMod and Actigraph GT3X discriminate best between different walking speeds.
Previous research on the environment and physical activity has mostly focused on macro-scale environments, such as the neighborhood environment. There has been a paucity of research on the role of micro-scale and proximal environments, such as that of the home which may be particularly relevant for younger adolescents who have more limited independence and mobility. The purpose of this study was to describe associations between the home environment and adolescent physical activity, sedentary time, and screen time.
A total of 613 parent-adolescent dyads were included in these analyses from two ongoing cohort studies. Parents completed a Physical Activity and Media Inventory (PAMI) of their home environment. Adolescent participants (49% male, 14.5 ± 1.8 years) self-reported their participation in screen time behaviors and wore an ActiGraph accelerometer for one week to assess active and sedentary time.
After adjusting for possible confounders, physical activity equipment density in the home was positively associated with accelerometer-measured physical activity (p < 0.01) among both males and females. Most of the PAMI-derived measures of screen media equipment in the home were positively associated with adolescent female's screen time behavior (p ≤ 0.03). In addition, the ratio of activity to media equipment was positively associated with physical activity (p = 0.04) in both males and females and negatively associated with screen time behavior for females (p < 0.01).
The home environment was associated with physical activity and screen time behavior in adolescents and differential environmental effects for males and females were observed. Additional research is warranted to more comprehensively assess the home environment and to identify obesogenic typologies of families so that early identification of at-risk families can lead to more informed, targeted intervention efforts.
We sought to develop procedures for computerized analysis of long-term, high-resolution activity monitoring data that allow accurate assessment of the time course of activity levels suitable for use in chronic obstructive pulmonary disease (COPD) patients. Twenty-two COPD patients utilizing long-term oxygen recruited from 5 sites of the COPD Clinical Research Network wore a triaxial accelerometer (RT3, Stayhealthy, Monrovia, CA) during waking hours over a14 day period. Computerized algorithms were composed allowing minute-by-minute activity data to be analyzed to determine, for each minute, whether the monitor was being worn. Temporal alignment allowed determination of average time course of activity level, expressed as average vector magnitude units (VMU, the vectorial sum of activity counts in three orthogonal directions) per minute, for each hour of the day. Mid-day activity was quantified as average VMU/minute between 10AM and 4PM for minutes the monitor was worn. Over the 14 day monitoring period, subjects wore the monitor an average of 11.4±3.0 hours·day−1. During midday hours, subjects wore the monitor 76.3% of the time and generated an average activity level of 112±55 VMU·min−1. Increase in precision of activity estimates with longer monitoring periods was demonstrated. This analysis scheme allows a detailed temporal pattern of activity to be defined from triaxial accelerometer recordings and has the potential to facilitate comparisons among subjects and between subject groups. This trial is registered at ClinicalTrials.gov (NCT00325754).
Chronic Obstructive Pulmonary Disease; Activity Monitor; Tri-axial Accelerometry; Daily Activities; Computerized Algorithms; Temporal Alignment
Objective quantification of physical activity (PA) is needed to understand PA and sedentary behaviors in bariatric surgery patients, yet it is unclear whether PA estimates produced by different monitors are comparable and can be interpreted similarly across studies. We compared PA estimates from the Stayhealthy RT3 triaxial accelerometer (RT3) and the Sensewear Pro2 Armband (SWA) at both the group and individual participant level.
Bariatric surgery candidates were instructed to wear the RT3 and SWA during waking hours for seven days. Participants meeting valid wear time requirements (≥4 days of ≥8 hours/day) for both monitors were included in the analyses. Time spent in sedentary (<1.5 METs), light (1.5–2.9 METs), moderate-to-vigorous (MVPA; ≥3.0 METs), and total PA (TPA; ≥1.5 METs) according to each monitor was compared.
Fifty-five participants (BMI: 48.4±8.2 kg/m2) met wear time requirements. Daily time spent in sedentary (RT3: 582.9±94.3; SWA: 602.3±128.6 min), light (RT3: 131.9±60.0; SWA: 120.6±65.7 min), MVPA (RT3: 25.9±20.9; SWA: 29.9±19.5 min), and TPA (RT3: 157.8±74.5; SWA: 150.6±80.7 min) was similar between monitors (p>0.05). While the average difference in TPA between the two monitors at the group level was 7.2±64.2 minutes; the average difference between the two monitors for each participant was 45.6±45.4 minutes.
At the group level, the RT3 and SWA provide similar estimates of PA and sedentary behaviors; however concordance between monitors may be compromised at the individual level. Findings related to PA and sedentary behaviors at the group level can be interpreted similarly across studies when either monitor is used.
Physical activity; exercise; bariatric surgery; severely obese