Mechanical and free living comparisons of four generations of the Actigraph activity monitor.

BACKGROUND: More studies include multiple generations of the Actigraph activity monitor. So far no studies have compared the output including the newest generation and investigated the impact on the output of the activity monitor when enabling the low frequency extension (LFE) option. The aims were to study the responses of four generations (AM7164, GT1M, GT3X and GT3X+) of the Actigraph activity monitor in a mechanical setup and a free living environment with and without enabling the LFE option. METHODS: The monitors were oscillated in a mechanical setup using two radii in the frequency range 0.25-3.0 Hz. Following the mechanical study a convenience sample (N = 20) wore three monitors (one AM7164 and two GT3X) for 24 hours. RESULTS: The AM7164 differed from the newer generations across frequencies (p  0.05 for differences between generations) thus attenuated the difference in mean PA (p > 0.05) when the LFE option was enabled. However, it did not attenuate the difference in time spend in vigorous PA and it introduced a difference in time spend in moderate PA (+ 3.0 min (95% CI 0.4 to 5.6)) between the generations. CONCLUSION: We observed significant differences between the AM7164 and the newer Actigraph GT-generations (GT1M, GT3X and GT3X+) in a mechanical setup and in free-living. Enabling the LFE option attenuated the differences in mean PA completely, but induced a bias in the moderate PA intensities.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Accelerometer data reduction in adolescents: effects on sample retention and bias

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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

Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children

Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mg's), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15-sec and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n=29) wore a hip- and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15-sec were computed for each axis and as vector magnitude. Main Results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15-sec (mean bias 4-43 counts/15-sec across axes) for both the hip- and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r=0.46-0.63; wrist: r=0.26-0.55) and were greater for hip- versus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4-99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate

GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines

Validity of the Sedentary Behaviour Questionnaire in European Older Adults using English, Spanish, German and Danish versions

The main aim of this study was to assess the criterion validity of the Sedentary Behavior Questionnaire (SBQ) to measure SB in community-dwelling older adults using thigh-measured accelerometry as the criterion method. 801 participants (75.6 ± 6.1 years old, 57.6% females) provided valid thigh-based accelerometer data (activPAL/Axivity) and completed the SBQ. Criterion validity was assessed using Spearman’s Rho coefficients. Bland–Altman plots, including 95% limits of agreement and Intraclass Correlation Coefficient (ICC), were used to assess the agreement between self-report and device-measured daily SB time. Strength of the association was examined using multiple linear regression. There was a weak correlation (Rho = 0.25, p < .001) between self-reported and device-based SB measures. The SBQ under-estimated daily SB time compared to accelerometry. Our results highlighted an overall weak-to-moderate correlation between measures, with significant differences between each country’s version. Researchers should be cautious when using the SBQ to provide an estimation of SB time in older adults

Bidirectional associations between adiposity and physical activity: a longitudinal study from pre-puberty to early adulthood

ObjectiveThis study aimed to investigate directional influences in the association between adiposity and physical activity (PA) from pre-puberty to early adulthood.MethodsIn the Calex-study, height, weight, body fat and leisure-time physical activity (LTPA) were measured at age11.2-years, 13.2-years and 18.3-years in 396 Finnish girls. Body fat was measured by dual-energy X-ray absorptiometry, calculating fat mass index (FMI) as total fat mass in kilograms divided by height in meters squared. LTPA level was evaluated using a physical activity questionnaire. In the European Youth Heart Study (EYHS), height, weight and habitual PA were measured at age 9.6-years, 15.7-years and 21.8-years in 399 Danish boys and girls. Habitual PA and sedentary behaviour were assessed with an accelerometer. Directional influences of adiposity and PA were examined using a bivariate cross-lagged path panel model.ResultsThe temporal stability of BMI from pre-puberty to early adulthood was higher than the temporal stability of PA or physical inactivity over the same time period both in girls and boys. In the Calex-study, BMI and FMI at age 11.2-years were both directly associated with LTPA at age 13.2-years (β = 0.167, p = 0.005 and β = 0.167, p = 0.005, respectively), whereas FMI at age 13.2-years showed an inverse association with LTPA at age 18.3-years (β = - 0.187, p = 0.048). However, earlier LTPA level was not associated with subsequent BMI or FMI. In the EYHS, no directional association was found for physical inactivity, light-, moderate-, and vigorous-PA with BMI during the follow-up in girls. In boys, BMI at age 15.7-years was directly associated with moderate PA (β = 0.301, p = 0.017) at age 21.8-years, while vigorous PA at age 15.7-years showed inverse associations with BMI at age 21.8-years (β = - 0.185, p = 0.023).ConclusionOur study indicates that previous fatness level is a much stronger predictor of future fatness than level of leisure-time or habitual physical activity during adolescence. The directional associations between adiposity and physical activity are not clear during adolescence, and may differ between boys and girls depending on pubertal status

A new approach to define and diagnose cardiometabolic disorder in children.

The aim of the study was to test the performance of a new definition of metabolic syndrome (MetS), which better describes metabolic dysfunction in children. Methods. 15,794 youths aged 6-18 years participated. Mean z-score for CVD risk factors was calculated. Sensitivity analyses were performed to evaluate which parameters best described the metabolic dysfunction by analysing the score against independent variables not included in the score. Results. More youth had clustering of CVD risk factors (>6.2%) compared to the number selected by existing MetS definitions (International Diabetes Federation (IDF) < 1%). Waist circumference and BMI were interchangeable, but using insulin resistance homeostasis model assessment (HOMA) instead of fasting glucose increased the score. The continuous MetS score was increased when cardiorespiratory fitness (CRF) and leptin were included. A mean z-score of 0.40-0.85 indicated borderline and above 0.85 indicated clustering of risk factors. A noninvasive risk score based on adiposity and CRF showed sensitivity and specificity of 0.85 and an area under the curve of 0.92 against IDF definition of MetS. Conclusions. Diagnosis for MetS in youth can be improved by using continuous variables for risk factors and by including CRF and leptin.Peer Reviewe

Sampling frequency affects the processing of ActiGraph raw acceleration data to activity counts.

ActiGraph acceleration data are processed through several steps (including bandpass filtering to attenuate unwanted signal frequencies) to generate the activity counts commonly used in physical activity research. We performed three experiments to investigate the effect of sampling frequency on the generation of activity counts. Ideal acceleration signals were produced in the MATLAB software. Thereafter, ActiGraph GT3X+ monitors were spinned in a mechanical setup. Finally, twenty subjects performed walking and running wearing GT3X+ monitors. Acceleration data from all experiments were collected with different sampling frequencies and activity counts were generated with the ActiLife software. With the default 30 Hz (or 60 Hz, 90 Hz) sampling frequency, the generation of activity counts was performed as intended with 50 % attenuation of acceleration signals with a frequency of 2.5 Hz by the signal frequency bandpass filter. Frequencies above 5 Hz were eliminated totally. However, with other sampling frequencies, acceleration signals above 5 Hz escaped the bandpass filter to a varied degree and contributed to additional activity counts. Similar results were found for the spinning of the GT3X+ monitors, although the amont of activity counts generated was less, indicating that raw data stored in the GT3X+ monitor is processed. Between 600 and 1600 more counts per minute were generated with the sampling frequencies 40 Hz and 100 Hz compared to 30 Hz during running. Sampling frequency affects the processing of ActiGraph acceleration data to activity counts. Researchers need to be aware of this error when selecting sampling frequencies other than the default 30 Hz