Jan Christian Brønd

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

BackgroundMore 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.MethodsThe 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.ResultsThe AM7164 differed from the newer generations across frequencies (p < 0.05) in the mechanical setup. The AM7164 produced a higher output at the lower and at the highest intensities, whereas the output was lower at the middle intensities in the mid-range compared to the newer generations. The LFE option decreased the differences at the lower frequencies, but increased differences at the higher. In free living, the mean physical activity level (PA) of the GT3X was 18 counts per minute (CPM) (8%) lower compared to the AM7164 (p < 0.001). Time spent in sedentary intensity was 26.6 minutes (95% CI 15.6 to 35.3) higher when assessed by the GT3X compared to the AM7164 (p < 0.001). Time spend in light and vigorous PA were 23.3 minutes (95% CI 31.8 to 14.8) and 11.7 minutes (95% CI 2.8 to 0.7) lower when assessed by the GT3X compared to the AM7164 (p < 0.05). When enabling the LFE the differences in the sedentary and light PA intensity (<333 counts*10 sec-1) were attenuated (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.ConclusionWe 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.

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.

Experimental Peptide Identification Repository (EPIR) An Integrated Peptide-Centric Platform for Validation and Mining of Tandem Mass Spectrometry Data

LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence.

Do extra compulsory physical education lessons mean more physically active children - findings from the childhood health, activity, and motor performance school study Denmark (The CHAMPS-study DK)

BackgroundPrimarily, this study aims to examine whether children attending sports schools are more active than their counterpart attending normal schools. Secondary, the study aims to examine if physical activity (PA) levels in specific domains differ across school types. Finally, potential modifications by status of overweight/obesity and poor cardio-respiratory fitness are examined.MethodsParticipants were from the first part of the CHAMPS-study DK, which included approximately 1200 children attending the 0th - 6th grade. At the sports schools, the mandatory physical education (PE) program was increased from 2 to 6 weekly lessons over a 3-year period. Children attending normal schools were offered the standard 2 PE lessons. PA was assessed at two different occasions with the GT3X ActiGraph accelerometer, once during winter in 2009/10 and once during summer/fall in 2010. Leisure time organized sports participation was quantified by SMS track. Based on baseline values in 2008, we generated a high-BMI and a low-cardio-respiratory fitness for age and sex group variable.ResultsThere were no significant differences in PA levels during total time, PE, or recess between children attending sports schools and normal schools, respectively. However, children, especially boys, attending sports schools were more active during school time than children attending normal schools (girls: β=51, p=0.065; boys: β=113, p<0.001). However, in the leisure time during weekdays children who attended sports schools were less active (girls: β=-41, p=0.004; boys: β=-72, p<0.001) and less involved in leisure time organized sports participation (girls: β=-0.4, p=0.016; boys: β=-0.2, p=0.236) than children who attended normal schools. Examination of modification by baseline status of overweight/obesity and low cardio-respiratory fitness indicated that during PE low fit girls in particular were more active at sports schools.ConclusionNo differences were revealed in overall PA levels between children attending sports schools and normal schools. Sports schools children were more active than normal schools children during school time, but less active during leisure time. In girls, less organized sports participation at least partly explained the observed differences in PA levels during leisure time across school types. Baseline status of cardio-respiratory fitness modified school type differences in PA levels during PE in girls.

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

ActiGraph acceleration data are processed through several steps (including band-pass 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 spun in a mechanical setup. Finally, 20 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 band-pass filter. Frequencies above 5 Hz were eliminated totally. However, with other sampling frequencies, acceleration signals above 5 Hz escaped the band-pass filter to a varied degree and contributed to additional activity counts. Similar results were found for the spinning of the GT3X+ monitors, although the amount of activity counts generated was less, indicating that raw data stored in the GT3X+ monitor is processed. Between 600 and 1,600 more counts per minute were generated with the sampling frequencies 40 and 100 Hz compared with 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.

Physical activity, sedentary behavior, and long-term cardiovascular risk in young people: A review and discussion of methodology in prospective studies

The long-term effects of physical activity (PA) or sedentary behavior on cardiovascular health in young people are not well understood. In this study, we use a narrative format to review the evidence for a prospective association with adiposity and other well-established biological cardiovascular risk factors in healthy young people, considering only studies with at least 2 years of follow-up. PA appears to elicit a long-term beneficial effect on adiposity and particularly markers of cardiovascular health. With adiposity, however, a few studies also reported that higher levels of PA were associated with higher levels of adiposity. Time spent sedentary does not appear to be related to adiposity or markers of cardiovascular health independent of PA. We then discuss the uncertainties in the underlying causal chain and consider a number of alternative modeling strategies, which could improve our understanding of the relationship in future studies. Finally, we consider the current methodology for assessing PA and sedentary time.

Measuring Children's Physical Activity: Compliance Using Skin-taped Accelerometers

Introduction Accelerometer-based physical activity monitoring has become the method of choice in many large-scale physical activity (PA) studies. However, there is an ongoing debate regarding the placement of the device, the determination of device wear time, and how to solve a lack of participant compliance. The aim of this study was to assess the compliance of Axivity AX3 accelerometers taped directly to the skin of 9- to 13-yr-old children. Methods Children in 46 school classes (53.4% girls, age 11.0 ± 1.0 yr, BMI 17.7 ± 2.8 kg·m−1) across Denmark wore two Axivity AX3 accelerometers, one taped on the thigh (n = 903) and one on the lower back (n = 856), for up to 10 consecutive days. Participants were instructed not to reattach an accelerometer should it fall off. Simple and multiple linear regressions were used to determine associations between accelerometer wear time and age, sex, BMI percentiles, and PA level. Results More than 65% had >7 d of uninterrupted, 24-h wear time for the thigh location and 59.5% for the lower back location. From multiple linear regressions, PA levels showed the strongest association with lower wear time (thigh: &bgr; = −0.231, R2 = 0.066; lower back: &bgr; = −0.454, R2 = 0.126). In addition, being a boy, being older (only for lower back), and having higher BMI percentile were associated with lower wear time. Conclusion Using skin-taped Axivity accelerometers, we obtained 7 d of uninterrupted accelerometer data with 24-h wear time per day with a compliance rate of more than 65%. Thigh placement resulted in higher compliance than lower back placement. Achieving days with 24-h wear time reduces the need for arbitrary decisions regarding wear time validation and most likely improves the validity of daily life PA measurements.

Generating Actigraph Counts from Raw Acceleration Recorded by an Alternative Monitor

Purpose This study aimed to implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. Methods The aggregation method, including the frequency band-pass filter, was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-h free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared with counts generated with ActiLife from ActiGraph GT3X+ data. Results An optimal band-pass filter was fitted resulting in a root-mean-square error of 25.7 counts per 10 s and mean absolute error of 15.0 counts per second across the full frequency range. The mechanical evaluation of the proposed aggregation method resulted in an absolute mean ± SD difference of −0.11 ± 0.97 counts per 10 s across all rotational frequencies compared with the original ActiGraph method. Applying the aggregation method to the 24-h free-living recordings resulted in an epoch level bias ranging from −16.2 to 0.9 counts per 10 s, a relative difference in the averaged physical activity (counts per minute) ranging from −0.5% to 4.7% with a group mean ± SD of 2.2% ± 1.7%, and a Cohen’s kappa of 0.945, indicating almost a perfect agreement in the intensity classification. Conclusion The proposed band-pass filter and aggregation method is highly valid for generating ActiGraph counts from raw acceleration data recorded with alternative devices. It would facilitate comparability between studies using different devices collecting raw acceleration data.

Analysis of large-scale MS data sets: the dramas and the delights

Abstract The biotechnology and pharmaceutical industries are faced with the serious challenge of consolidating the enormous quantities of data that have been generated from high-throughput proteomic applications. The bottleneck of data validation and placement of the information obtained into sound biological context urgently needs to be addressed. Here, we review the issues that arise when analysing large quantities of data generated by liquid chromatography mass spectrometry, offer potential solutions for data management and predict the future direction of large-scale data analysis by mass spectrometry.

Validation of SenseWear Armband in children, adolescents, and adults

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi‐structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist‐worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%‐32%) and for basketball and biking (19%‐35%), compared to walking and running (8%‐20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%‐74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research.