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
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
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.
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
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.
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.
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.
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 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.
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.
Evidence suggests possible synergetic effects of multiple lifestyle behaviors on health risks like obesity and other health outcomes. A better insight in the clustering of those behaviors, could help to identify groups who are at risk in developing chronic diseases. This study examines the prevalence and clustering of physical activity, sedentary and dietary patterns among European adolescents and investigates if the identified clusters could be characterized by socio-demographic factors.
The study comprised a total of 2084 adolescents (45.6% male), from eight European cities participating in the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study. Physical activity and sedentary behavior were measured using self-reported questionnaires and diet quality was assessed based on dietary recall. Based on the results of those three indices, cluster analyses were performed. To identify gender differences and associations with socio-demographic variables, chi-square tests were executed.
Five stable and meaningful clusters were found. Only 18% of the adolescents showed healthy and 21% unhealthy scores on all three included indices. Males were highly presented in the cluster with high levels of moderate to vigorous physical activity (MVPA) and low quality diets. The clusters with low levels of MVPA and high quality diets comprised more female adolescents. Adolescents with low educated parents had diets of lower quality and spent more time in sedentary activities. In addition, the clusters with high levels of MVPA comprised more adolescents of the younger age category.
In order to develop effective primary prevention strategies, it would be important to consider multiple health indices when identifying high risk groups.
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
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
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 paper describes the application of best practice recommendations for using accelerometers in a physical activity (PA) intervention trial, and the concordance of different methods for measuring PA. A subsample (n=63; 26%) of the 239 healthy, sedentary adults participating in a PA trial (mean age=47.5; 82% women) wore the ActiGraph monitor at all 3 assessment time points. ActiGraph data were compared with self-report (i.e., PA weekly recall and monthly log) and fitness variables. Correlations between the PA recall and ActiGraph for moderate intensity activity ranged from 0.16–0.48 and from 0.28–0.42 for vigorous intensity activity. ActiGraph and fitness [estimated VO2(ml/kg/min)] had correlations of 0.15–0.45. The ActiGraph and weekly self-report were significantly correlated at all time points (correlations ranged from 0.23–0.44). In terms of detecting intervention effects, intervention groups recorded more minutes of at least moderate-intensity PA on the ActiGraph than the control group at 6 months (min=46.47, 95% CI=14.36–78.58), but not at 12 months. Limitations of the study include a small sample size and only 3 days of ActiGraph monitoring. To obtain optimal results with accelerometers in clinical trials, the authors recommend following best practice recommendations: detailed protocols for monitor use, calibration of monitors and validation of data quality, and use of validated equations for analysis. The ActiGraph has modest concordance with other assessment tools and is sensitive to change over time. However, until more information validating the use of accelerometry in clinical trials becomes available, properly administered self-report measures of PA should remain part of the assessment battery.
exercise; objective monitoring; best practice recommendations; ActiGraph
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.
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
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
Evidence suggests possible synergetic effects of multiple lifestyle behaviors on health risks like obesity and other health outcomes. Therefore it is important to investigate associations between dietary and physical activity behavior, the two most important lifestyle behaviors influencing our energy balance and body composition. The objective of the present study is to describe the relationship between energy, nutrient and food intake and the physical activity level among a large group of European adolescents.
The study comprised a total of 2176 adolescents (46.2% male) from ten European cities participating in the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study. Dietary intake and physical activity were assessed using validated 24-h dietary recalls and self-reported questionnaires respectively. Analyses of covariance (ANCOVA) were used to compare the energy and nutrient intake and the food consumption between groups of adolescents with different physical activity levels (1st to 3rd tertile).
In both sexes no differences were found in energy intake between the levels of physical activity. The most active males showed a higher intake of polysaccharides, protein, water and vitamin C and a lower intake of saccharides compared to less active males. Females with the highest physical activity level consumed more polysaccharides compared to their least active peers. Male and female adolescents with the highest physical activity levels, consumed more fruit and milk products and less cheese compared to the least active adolescents. The most active males showed higher intakes of vegetables and meat, fish, eggs, meat substitutes and vegetarian products compared to the least active ones. The least active males reported the highest consumption of grain products and potatoes. Within the female group, significantly lower intakes of bread and cereal products and spreads were found for those reporting to spend most time in moderate to vigorous physical activity. The consumption of foods from the remaining food groups, did not differ between the physical activity levels in both sexes.
It can be concluded that dietary habits diverge between adolescents with different self-reported physical activity levels. For some food groups a difference in intake could be found, which were reflected in differences in some nutrient intakes. It can also be concluded that physically active adolescents are not always inclined to eat healthier diets than their less active peers.
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
We aimed to examine whether time spent on different sedentary behaviours is associated with bone mineral content (BMC) in adolescents, after controlling for relevant confounders such as lean mass and objectively measured physical activity (PA), and if so, whether extra-curricular participation in osteogenic sports could have a role in this association.
Participants were 359 Spanish adolescents (12.5-17.5 yr, 178 boys,) from the HELENA-CSS (2006–07). Relationships of sedentary behaviours with bone variables were analysed by linear regression. The prevalence of low BMC (at least 1SD below the mean) and time spent on sedentary behaviours according to extracurricular sport participation was analysed by Chi-square tests.
In boys, the use of internet for non-study was negatively associated with whole body BMC after adjustment for lean mass and moderate to vigorous PA (MVPA). In girls, the time spent studying was negatively associated with femoral neck BMC. Additional adjustment for lean mass slightly reduced the negative association between time spent studying and femoral neck BMC. The additional adjustment for MVPA did not change the results at this site. The percentage of girls having low femoral neck BMC was significantly smaller in those participating in osteogenic sports (≥ 3 h/week) than in the rest, independently of the cut-off selected for the time spent studying.
The use of internet for non-study (in boys) and the time spent studying (in girls) are negatively associated with whole body and femoral neck BMC, respectively. In addition, at least 3 h/week of extra-curricular osteogenic sports may help to counteract the negative association of time spent studying on bone health in girls.
Bone health; Sedentary behaviours; Adolescents; Physical activity and extra-curricular participation in sports
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 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
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)