Accelerometer counts and raw acceleration output in relation to mechanical loading

The purpose of this study was to assess the relationship of accelerometer output, in counts (ActiGraph GT1M) and as raw accelerations (ActiGraph GT3X+ and GENEA), with ground reaction force (GRF) in adults. Ten participants (age: 29.4 ± 8.2 yr, mass: 74.3 ± 9.8 kg, height: 1.76 ± 0.09 m) performed eight trials each of: slow walking, brisk walking, slow running, faster running and box drops. GRF data were collected for one step per trial (walking and running) using a force plate. Low jumps and higher jumps (one per second) were performed for 20 s each on the force plate. For box drops, participants dropped from a 35 cm box onto the force plate. Throughout, three accelerometers were worn at the hip: GT1M, GT3X+ and GENEA. A further GT3X+ and GENEA were worn on the left and right wrist, respectively. GT1M counts correlated with peak impact force (r = 0.85, p < 0.05), average resultant force (r = 0.73, p < 0.05) and peak loading rate (r = 0.76, p < 0.05). Accelerations from the GT3X+ and GENEA correlated with average resultant force and peak loading rate irrespective of whether monitors were worn at the hip or wrist (r > 0.82, p < 0.05, r > 0.63 p < 0.05, respectively). In conclusion, accelerometer count and raw acceleration output correlate positively with GRF and thus may be appropriate for the quantification of activity beneficial to bone. Wrist-worn monitors show a similar relationship with GRF as hip-worn monitors, suggesting that wrist-worn monitors may be a viable option for future studies looking at bone health.     

Accuracy of StepWatch? and ActiGraph Accelerometers for Measuring Steps Taken among Persons with Multiple Sclerosis

Introduction

There has been increased interest in the objective monitoring of free-living walking behavior using accelerometers in clinical research involving persons with multiple sclerosis (MS). The current investigation examined and compared the accuracy of the StepWatch activity monitor and ActiGraph model GT3X+ accelerometer for capturing steps taken during various speeds of prolonged, over-ground ambulation in persons with MS who had mild, moderate, and severe disability.

Methods

Sixty-three persons with MS underwent a neurological examination for generation of an EDSS score and undertook two trials of walking on the GAITRite electronic walkway. Participants were fitted with accelerometers, and undertook three modified six-minute walk (6MW) tests that were interspersed with 10–15 minutes of rest. The first 6MW was undertaken at a comfortable walking speed (CWS), and the two remaining 6MW tests were undertaken above (faster walking speed; FWS) or below (slower walking speed; SWS) the participant''s CWS. The actual number of steps taken was counted through direct observation using hand-tally counters.

Results

The StepWatch activity monitor (99.8%–99.9%) and ActiGraph model GT3X+ accelerometer (95.6%–97.4%) both demonstrated highly accurate measurement of steps taken under CWS and FWS conditions. The StepWatch had better accuracy (99.0%) than the ActiGraph (95.5%) in the overall sample under the SWS condition, and this was particularly apparent in those with severe disability (StepWatch: 95.7%; ActiGraph: 87.3%). The inaccuracy in measurement for the ActiGraph was associated with alterations of gait (e.g., slower gait velocity, shorter step length, wider base of support).

Conclusions

This research will help inform the choice of accelerometer to be adopted in clinical trials of MS wherein the monitoring of free-living walking behavior is of particular value.     

Reliability of the Actigraph GT3X+ Accelerometer in Adults under Free-Living Conditions

Background

Reliability of the Actigraph GT3X+ accelerometer has not been determined under normal wear time criteria in a large sample of subjects and accelerometer units. The aim of this study was to assess contralateral hip difference and inter-instrument reliability of the Actigraph GT3X+ monitor in adults under long-term free-living conditions.

Methods

Eighty-seven adult subjects (28 men; mean (standard deviation) age 31.3 (12.2) years; body mass index 23.7 (3.1) kg/m²) concurrently wore two GT3X+ accelerometers (174 units in total) attached to contralateral hips for 21 days. Reliability was assessed using Bland-Altman plots, mixed model regression analyses and absolute measures of agreement for different lengths of data accumulation (single-day-, 7-day- and 21-day periods).

Results

There were no significant differences between contralateral hips (effect size ≤0.042; p ≥.213). Inter-instrument reliability increased with increased length of data-accumulation. For a 7-day measurement period (n = 232 weeks), limits of agreement were ±68 cpm (vertical axis) and ±81.3 cpm (vector magnitude) for overall physical activity (PA) level, ±51 min for sedentary time, ±18.2 min for light PA, ±6.3 min for moderate PA, ±3.5 min for vigorous PA, and ±6.7 min for moderate-to-vigorous PA.

Conclusions

The Actigraph GT3X+ accelerometer is a reliable tool for measuring PA in adults under free-living conditions using normal data-reduction criteria. Contralateral hip differences are very small. We suggest accelerometers be attached to the right hip and data to be accumulated over several days of measurement.     

Assessment of sleep in subjects with visual impairment: Comparison using subjective and objective methods

The objective of the present study was to verify the agreement between objective and subjective measures of sleep in people with and without visual impairment. Thirty-seven subjects with visual impairment participated in the study (19 blind without light perception and 18 low-vision), as well as 34 subjects with normal vision, with paired age and gender characteristics. For the subjective sleep evaluation, we used the Sleep Quality Index—PSQI and for the objective evaluation we used the ActiGraph GT3X+. Among the three analyzed groups, the blind was the only ones who presented differences between subjective and objective sleep duration (p = 0.021). Furthermore, the concordance between subjective and objective sleep duration (ICC = 0.388; p = 0.108) was not observed in blind subjects, and a greater variability of differences in sleep duration between the two methods was observed by the Bland Altman scatter plot. We concluded that the sleep duration obtained by PSQI did not show agreement for the objective sleep duration in blind subjects without light perception.     

Comparison of Self-Reported and Accelerometer-Assessed Physical Activity in Older Women

Background

Self-reported physical activity measures continue to be validated against accelerometers; however, the absence of standardized, accelerometer moderate-to-vigorous physical activity (MVPA) definitions has made comparisons across studies difficult. Furthermore, recent accelerometer models assess accelerations in three axes, instead of only the vertical axis, but validation studies have yet to take incorporate triaxial data.

Methods

Participants (n = 10 115) from the Women’s Health Study wore a hip-worn accelerometer (ActiGraph GT3X+) for seven days during waking hours (2011–2014). Women then completed a physical activity questionnaire. We compared self-reported with accelerometer-assessed MVPA, using four established cutpoints for MVPA: three using only vertical axis data (760, 1041 and 1952 counts per minute (cpm)) and one using triaxial data (2690 cpm).

Results

According to self-reported physical activity, 66.6% of women met the US federal physical activity guidelines, engaging in ≥150 minutes per week of MVPA. The percent of women who met guidelines varied widely depending on the accelerometer MVPA definition (760 cpm: 50.0%, 1041 cpm: 33.0%, 1952 cpm: 13.4%, and 2690 cpm: 19.3%).

Conclusions

Triaxial count data do not substantially reduce the difference between self-reported and accelerometer-assessed MVPA.     

An artificial neural network model of energy expenditure using nonintegrated acceleration signals.

Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that we felt had the potential to characterize EE intensity. Using these data, we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12 x 20 x 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 +/- 0.10 kcal/min), mean squared errors (0.23 +/- 0.14 kcal(2)/min(2)), and difference in total EE (21 +/- 115 kcal/day), compared with both the IDEEA (P < 0.01) and a regression model for the ActiGraph accelerometer (P < 0.001). Thus ANN combined with raw acceleration signals is a promising approach to link body accelerations to EE. Further validation is needed to understand the performance of the model for different physical activity types under free-living conditions.     

Influence of Accelerometer Type and Placement on Physical Activity Energy Expenditure Prediction in Manual Wheelchair Users

PurposeTo assess the validity of two accelerometer devices, at two different anatomical locations, for the prediction of physical activity energy expenditure (PAEE) in manual wheelchair users (MWUs).MethodsSeventeen MWUs (36 ± 10 yrs, 72 ± 11 kg) completed ten activities; resting, folding clothes, propulsion on a 1% gradient (3,4,5,6 and 7 km·hr^-1) and propulsion at 4km·hr^-1 (with an additional 8% body mass, 2% and 3% gradient) on a motorised wheelchair treadmill. GT3X+ and GENEActiv accelerometers were worn on the right wrist (W) and upper arm (UA). Linear regression analysis was conducted between outputs from each accelerometer and criterion PAEE, measured using indirect calorimetry. Subsequent error statistics were calculated for the derived regression equations for all four device/location combinations, using a leave-one-out cross-validation analysis.ResultsAccelerometer outputs at each anatomical location were significantly (p < .01) associated with PAEE (GT3X+-UA; r = 0.68 and GT3X+-W; r = 0.82. GENEActiv-UA; r = 0.87 and GENEActiv-W; r = 0.88). Mean ± SD PAEE estimation errors for all activities combined were 15 ± 45%, 14 ± 50%, 3 ± 25% and 4 ± 26% for GT3X+-UA, GT3X+-W, GENEActiv-UA and GENEActiv-W, respectively. Absolute PAEE estimation errors for devices varied, 19 to 66% for GT3X+-UA, 17 to 122% for GT3X+-W, 15 to 26% for GENEActiv-UA and from 17.0 to 32% for the GENEActiv-W.ConclusionThe results indicate that the GENEActiv device worn on either the upper arm or wrist provides the most valid prediction of PAEE in MWUs. Variation in error statistics between the two devices is a result of inherent differences in internal components, on-board filtering processes and outputs of each device.     

Validity of Physical Activity Intensity Predictions by ActiGraph,Actical, and RT3 Accelerometers

Objective: 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). Results: 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. Discussion: 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.     

Predictive Validity and Classification Accuracy of ActiGraph Energy Expenditure Equations and Cut-Points in Young Children

Objectives

Evaluate the predictive validity of ActiGraph energy expenditure equations and the classification accuracy of physical activity intensity cut-points in preschoolers.

Methods

Forty children aged 4–6 years (5.3±1.0 years) completed a ∼150-min room calorimeter protocol involving age-appropriate sedentary, light and moderate-to vigorous-intensity physical activities. Children wore an ActiGraph GT3X on the right mid-axillary line of the hip. Energy expenditure measured by room calorimetry and physical activity intensity classified using direct observation were the criterion methods. Energy expenditure was predicted using Pate and Puyau equations. Physical activity intensity was classified using Evenson, Sirard, Van Cauwenberghe, Pate, Puyau, and Reilly, ActiGraph cut-points.

Results

The Pate equation significantly overestimated VO₂ during sedentary behaviors, light physical activities and total VO₂ (P<0.001). No difference was found between measured and predicted VO₂ during moderate-to vigorous-intensity physical activities (P = 0.072). The Puyau equation significantly underestimated activity energy expenditure during moderate-to vigorous-intensity physical activities, light-intensity physical activities and total activity energy expenditure (P<0.0125). However, no overestimation of activity energy expenditure during sedentary behavior was found. The Evenson cut-point demonstrated significantly higher accuracy for classifying sedentary behaviors and light-intensity physical activities than others. Classification accuracy for moderate-to vigorous-intensity physical activities was significantly higher for Pate than others.

Conclusion

Available ActiGraph equations do not provide accurate estimates of energy expenditure across physical activity intensities in preschoolers. Cut-points of ≤25counts⋅15 s⁻¹ and ≥420 counts⋅15 s⁻¹ for classifying sedentary behaviors and moderate-to vigorous-intensity physical activities, respectively, are recommended.     

Comparison of Several Algorithms to Estimate Activity Counts with Smartphones as an Indication of Physical Activity Level

Background

Dedicated devices like GT3X+, Actical or ActivPal have been widely used to measure physical activity (PA) levels by using cut-points on activity counts. However, the calculation of activity counts relies on proprietary software. Since smartphones incorporate accelerometers they are suitable candidates to determine PA levels in a wider population.

Measurement of peak impact loads differ between accelerometers – Effects of system operating range and sampling rate

A wide variety of accelerometer systems, with differing sensor characteristics, are used to detect impact loading during physical activities. The study examined the effects of system characteristics on measured peak impact loading during a variety of activities by comparing outputs from three separate accelerometer systems, and by assessing the influence of simulated reductions in operating range and sampling rate. Twelve healthy young adults performed seven tasks (vertical jump, box drop, heel drop, and bilateral single leg and lateral jumps) while simultaneously wearing three tri-axial accelerometers including a criterion standard laboratory-grade unit (Endevco 7267A) and two systems primarily used for activity-monitoring (ActiGraph GT3X+, GCDC X6-2mini). Peak acceleration (gmax) was compared across accelerometers, and errors resulting from down-sampling (from 640 to 100 Hz) and range-limiting (to ±6 g) the criterion standard output were characterized. The Actigraph activity-monitoring accelerometer underestimated gmax by an average of 30.2%; underestimation by the X6-2mini was not significant. Underestimation error was greater for tasks with greater impact magnitudes. gmax was underestimated when the criterion standard signal was down-sampled (by an average of 11%), range limited (by 11%), and by combined down-sampling and range-limiting (by 18%). These effects explained 89% of the variance in gmax error for the Actigraph system. This study illustrates that both the type and intensity of activity should be considered when selecting an accelerometer for characterizing impact events. In addition, caution may be warranted when comparing impact magnitudes from studies that use different accelerometers, and when comparing accelerometer outputs to osteogenic impact thresholds proposed in literature.     

Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts

Background

Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA).

Methods

We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV.

Results

Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample) returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to −32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute) were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001).

Conclusions

Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children’s studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models.     

The Associations of Objectively Measured Physical Activity and Sedentary Time with Cognitive Functions in School-Aged Children

Low levels of physical activity among children have raised concerns over the effects of a physically inactive lifestyle, not only on physical health but also on cognitive prerequisites of learning. This study examined how objectively measured and self-reported physical activity and sedentary behavior are associated with cognitive functions in school-aged children. The study population consisted of 224 children from five schools in the Jyväskylä school district in Finland (mean age 12.2 years; 56% girls), who participated in the study in the spring of 2011. Physical activity and sedentary time were measured objectively for seven consecutive days using the ActiGraph GT1M/GT3X accelerometer. Self-reported moderate to vigorous physical activity (MVPA) and screen time were evaluated with the questions used in the “WHO Health Behavior in School-aged Children” study. Cognitive functions including visual memory, executive functions and attention were evaluated with a computerized Cambridge Neuropsychological Test Automated Battery by using five different tests. Structural equation modeling was applied to examine how objectively measured and self-reported MVPA and sedentary behavior were associated with cognitive functions. High levels of objectively measured MVPA were associated with good performance in the reaction time test. High levels of objectively measured sedentary time were associated with good performance in the sustained attention test. Objectively measured MVPA and sedentary time were not associated with other measures of cognitive functions. High amount of self-reported computer/video game play was associated with weaker performance in working memory test, whereas high amount of computer use was associated with weaker performance in test measuring shifting and flexibility of attention. Self-reported physical activity and total screen time were not associated with any measures of cognitive functions. The results of the present study propose that physical activity may benefit attentional processes. However, excessive video game play and computer use may have unfavorable influence on cognitive functions.     

Criterion and Concurrent Validity of the activPAL? Professional Physical Activity Monitor in Adolescent Females

Background

The activPAL has been identified as an accurate and reliable measure of sedentary behaviour. However, only limited information is available on the accuracy of the activPAL activity count function as a measure of physical activity, while no unit calibration of the activPAL has been completed to date. This study aimed to investigate the criterion validity of the activPAL, examine the concurrent validity of the activPAL, and perform and validate a value calibration of the activPAL in an adolescent female population. The performance of the activPAL in estimating posture was also compared with sedentary thresholds used with the ActiGraph accelerometer.

Methodologies

Thirty adolescent females (15 developmental; 15 cross-validation) aged 15–18 years performed 5 activities while wearing the activPAL, ActiGraph GT3X, and the Cosmed K4B2. A random coefficient statistics model examined the relationship between metabolic equivalent (MET) values and activPAL counts. Receiver operating characteristic analysis was used to determine activity thresholds and for cross-validation. The random coefficient statistics model showed a concordance correlation coefficient of 0.93 (standard error of the estimate = 1.13). An optimal moderate threshold of 2997 was determined using mixed regression, while an optimal vigorous threshold of 8229 was determined using receiver operating statistics. The activPAL count function demonstrated very high concurrent validity (r = 0.96, p<0.01) with the ActiGraph count function. Levels of agreement for sitting, standing, and stepping between direct observation and the activPAL and ActiGraph were 100%, 98.1%, 99.2% and 100%, 0%, 100%, respectively.

Conclusions

These findings suggest that the activPAL is a valid, objective measurement tool that can be used for both the measurement of physical activity and sedentary behaviours in an adolescent female population.     

Validity of six activity monitors in chronic obstructive pulmonary disease: a comparison with indirect calorimetry

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±7 years, FEV(1) 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 (VO(2)) 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.     

Patterns of Objectively Measured Physical Activity in Normal Weight,Overweight, and Obese Individuals (20–85 Years): A Cross-Sectional Study

Background

The magnitude of the association between physical activity (PA) and obesity has been difficult to establish using questionnaires. The aim of the study was to evaluate patterns of PA across BMI-defined weight categories and to examine the independent contribution of PA on weight status, using accelerometers.

Methods

The study was a cross-sectional population-based study of 3,867 adults and older people aged 20–85 years, living in Norway. PA was assessed for seven consecutive days using the ActiGraph GT1M accelerometer. Anthropometrical data was self-reported and overweight and obesity was defined as having a body mass index (BMI) of 25–<30 and ≥30 kg/m², respectively.

Results

Overweight and obese participants performed less overall PA and PA of at least moderate intensity and took fewer steps, compared to normal weight participants. Although overall PA did not differ between weekdays and weekends, an interaction between BMI category and type of day was present, indicating a larger difference in overall PA between BMI categories on weekends compared to weekdays. Obese participants displayed 19% and 25% lower overall physical activity compared to normal weight participants, on weekdays and weekends, respectively. Participants in the most active quintile of overall PA had a 53% lower risk (OR 0.47, 95% CI: 0.37 to 0.60) for having a BMI above or below 25 kg/m², and a 71% lower risk (OR: 0.29, 95% CI: 0.20 to 0.44) for having a BMI above or below 30 kg/m².

Conclusions

Overweight and obese participants engaged in less overall PA and moderate and vigorous PA compared with normal weight individuals. The weight related differences in overall PA were most pronounced on the weekend and the risk of being overweight or obese decreases across quintiles of PA.     

Associations of Physical Activity,Sports Participation and Active Commuting on Mathematic Performance and Inhibitory Control in Adolescents

Objectives

To examine objectively measured physical activity level, organized sports participation and active commuting to school in relation to mathematic performance and inhibitory control in adolescents.

Methods

The design was cross-sectional. A convenient sample of 869 sixth and seventh grade students (12–14 years) was invited to participate in the study. A total of 568 students fulfilled the inclusion criteria and comprised the final sample for this study. Mathematic performance was assessed by a customized test and inhibitory control was assessed by a modified Eriksen flanker task. Physical activity was assessed with GT3X and GT3X+ accelerometers presented in sex-specific quartiles of mean counts per minute and mean minutes per day in moderate-to-vigorous physical activity. Active commuting and sports participation was self-reported. Mixed model regression was applied. Total physical activity level was stratified by bicycling status in order to bypass measurement error subject to the accelerometer.

Results

Non-cyclists in the 2^nd quartile of counts per minute displayed a higher mathematic score, so did cyclists in the 2^nd and 3^rd quartile of moderate-to-vigorous physical activity relative to the least active quartile. Non-cyclists in the 3^rd quartile of counts per minute had an improved reaction time and cyclists in the 2^nd quartile of counts per minute and moderate-to-vigorous physical activity displayed an improved accuracy, whereas non-cyclists in the 2^nd quartile of counts per minute showed an inferior accuracy relative to the least active quartile. Bicycling to school and organized sports participation were positively associated with mathematic performance.

Conclusions

Sports participation and bicycling were positively associated with mathematic performance. Results regarding objectively measured physical activity were mixed. Although, no linear nor dose-response relationship was observed there was no indication of a higher activity level impairing the scholastic or cognitive performance.     

Physical Activity in German Adolescents Measured by Accelerometry and Activity Diary: Introducing a Comprehensive Approach for Data Management and Preliminary Results

Introduction

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.

Methods

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.

Results

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%).

Conclusion

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.     

Quality Control Methods in Accelerometer Data Processing: Defining Minimum Wear Time

Background

When using accelerometers to measure physical activity, researchers need to determine whether subjects have worn their device for a sufficient period to be included in analyses. We propose a minimum wear criterion using population-based accelerometer data, and explore the influence of gender and the purposeful inclusion of children with weekend data on reliability.

Methods

Accelerometer data obtained during the age seven sweep of the UK Millennium Cohort Study were analysed. Children were asked to wear an ActiGraph GT1M accelerometer for seven days. Reliability coefficients(r) of mean daily counts/minute were calculated using the Spearman-Brown formula based on the intraclass correlation coefficient. An r of 1.0 indicates that all the variation is between- rather than within-children and that measurement is 100% reliable. An r of 0.8 is often regarded as acceptable reliability. Analyses were repeated on data from children who met different minimum daily wear times (one to 10 hours) and wear days (one to seven days). Analyses were conducted for all children, separately for boys and girls, and separately for children with and without weekend data.

Results

At least one hour of wear time data was obtained from 7,704 singletons. Reliability increased as the minimum number of days and the daily wear time increased. A high reliability (r = 0.86) and sample size (n = 6,528) was achieved when children with ≥ two days lasting ≥10 hours/day were included in analyses. Reliability coefficients were similar for both genders. Purposeful sampling of children with weekend data resulted in comparable reliabilities to those calculated independent of weekend wear.

Conclusion

Quality control procedures should be undertaken before analysing accelerometer data in large-scale studies. Using data from children with ≥ two days lasting ≥10 hours/day should provide reliable estimates of physical activity. It’s unnecessary to include only children with accelerometer data collected during weekends in analyses.     

Activities of Five Different Sialyltransferases in Fish and Rat Brains

Abstract: To investigate the role of Sialyltransferases in the metabolism of brain gangliosides, we examined activities of five different Sialyltransferases (GM3-, GD3-, GT3-, GD1a-, and GT1a-synthase) using total membrane preparations from cichlid fish and Sprague-Dawley rat brains, and analyzed the relationship between the enzyme activities and the ganglloside compositions. The patterns of sialyltransferase activities in fish and rat brains differed from each other. In fish brain, the GM3-synthase activity was lower than GD3-synthase activity, whereas the opposite relationship was observed in rat brain. The GT3-synthase reaction with fish brain membranes produced radiolabeled GM3, GD3, and a ganglioside that was identified as GT3 based on mobility on TLC using two different solvent systems. No GT3-synthase activity was detected in rat brain. The GD1a-and GT1a-synthase activities in fish brain were higher than those in rat brain. Although GT1a was a single radiolabeled ganglioside in fish GT1a-synthase reaction, this ganglioside could not be detected in rat brain. The ratios of GM3-, GD3-, GT3-, GD1a-, and GT1a-synthase activities in fish and rat brain were 23:31:4:28:14 and 61:21:0:18:0, respectively. Ganglioside analysis showed that fish brain was enriched with c-series gangliosides including GT3 and polysialo-species, whereas a-and b-se-ries gangliosides were major components in rat brain. These results suggest that the species-specific expression of gangliosides in brain tissues may be regulated, at least in part, at the level of sialyltransferase activities.