| 1. |
- Arvidsson, Daniel, 1974, et al.
(författare)
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A Longitudinal Analysis of the Relationships of Physical Activity and Body Fat With Nerve Growth Factor and Brain-Derived Neural Factor in Children
- 2018
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Ingår i: Journal of Physical Activity & Health. - : Human Kinetics. - 1543-3080 .- 1543-5474. ; 15:8, s. 620-625
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Tidskriftsartikel (refereegranskat)abstract
- Background: Nerve growth factor (NGF) and brain-derived neural factor (BDNF) are important for brain function and detectable in the blood. This study explored the longitudinal associations of physical activity and body fat with serum NGF and BDNF in children. Methods: Two waves of measurements were performed 2 years apart in 8- to 11-year-old children, including physical activity using the ActiGraph model 7164, body composition by dual-energy X-ray absorptiometry, and serum NGF and BDNF determined by multiplex immunoassay. The first wave included 248 children. Full information maximum likelihood estimation with robust standard errors was applied in structural equation modeling. Results: Vigorous physical activity showed a direct positive longitudinal relationship with NGF (standardized coefficient beta = 0.30, P = .01) but not with BDNF (beta = 0.04, P = .84). At the same time, body fat percentage was positively related to both NGF (beta = 0.59, P < .001) and BDNF (beta = 0.17, P = .04). There was an indication of an indirect relationship of vigorous physical activity with NGF (product of unstandardized coefficient beta = -0.18, P = .02) and BDNF (beta = -0.07, P = .05) through the negative relationship with body fat percentage (beta = -0.36, P < .001). Conclusions: Vigorous physical activity is directly related to serum NGF and indirectly through the level of body fat. The relationships with serum BDNF are more complex.
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| 2. |
- Brond, J. C., et al.
(författare)
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Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor
- 2017
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Ingår i: Medicine and Science in Sports and Exercise. - : Ovid Technologies (Wolters Kluwer Health). - 0195-9131. ; 49:11, s. 2351-2360
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Brond, J. C., et al.
(författare)
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Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents
- 2020
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Ingår i: Children. - : MDPI AG. - 2227-9067. ; 7:7
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Tidskriftsartikel (refereegranskat)abstract
- Background:The objective and accurate assessment of children's sedentary and physical behavior is important for investigating their relation to health. The purpose of this study is to validate a simple and robust method for the identification of sitting, standing, walking, running and biking performed by preschool children, children and adolescents in the age from 3 to 16 years from a single thigh-worn accelerometer.Method:A total of 96 children were included in the study and all subjects followed a structured activity protocol performed in the subject's normal kindergarten or school environment. Thigh acceleration was measured using the Axivity AX3 (Axivity, Newcastle, UK) device. Method development and accuracy was evaluated by equally dividing the subjects into a development and test group.Results:The sensitivity and specificity for identifying sitting and standing was above 99.3% and for walking and running above 82.6% for all age groups. The sensitivity and specificity for identifying biking was above 85.8% for children and adolescents and above 64.8% for the preschool group using running bikes.Conclusion:The accurate assessment of sitting, standing, walking, running and biking from thigh acceleration and with children in the age range of 3 to 16 is valid, although not with preschool children using running bikes.
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| 4. |
- Lopez, A. Garcia, et al.
(författare)
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Validation of SenseWear Armband in children, adolescents, and adults
- 2018
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Ingår i: Scandinavian Journal of Medicine and Science in Sports. - : Wiley. - 0905-7188 .- 1600-0838. ; 28:2, s. 487-495
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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