COMPONENTS OF PHYSICAL FITNESS
Physical fitness components were assessed using the extended version of the Physical fitness and Health health-related physical fitness test battery 19. This battery has been shown to be valid, reliable, feasible, and safe for the assessment of the physical fitness components in children and adolescents 19.
Cardiorespiratory fitness was estimated by the 20-m shuttle-run test 20. This test was always performed at the end of the fitness battery testing session. The total number of completed laps were registered. Upper- and lower-body muscular fitness were assessed using the handgrip strength test and the standing long jump test, respectively. A digital hand dynamometer with an adjustable grip Takei, Tokyo, Japan) was used to assess upper-body muscular fitness. Each child performed the test twice, and the maximum scores of left and right hands were averaged and used as a measurement of absolute upper-body muscular fitness in kilograms (kg). The standing long jump test was performed three times and the longest jump was recorded in centimeters (cm) as a measurement of relative lower-body muscular fitness. In addition, we computed a relative-to-body weight measurement from upper body muscular fitness kg/body weight) and an absolute measurement from lower body muscular fitness according to previous research in children with obesity. Motor fitness was assessed using the 4 × 10-m shuttle-run test. Participants were required to run back and forth twice between two lines 10-m apart. Children were instructed to run as fast as possible and every time they crossed any of the lines, they were instructed to pick up the first time or exchange (second and third time) a sponge that had earlier been placed behind the lines. The test was performed twice and the fastest time was recorded in seconds. Since a longer completion time indicates a lower fitness level, for analysis purposes we inverted this variable by multiplying test completion time by 1. Thus, higher scores indicated higher motor fitness levels.
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Image preprocessing is able to sample features of the microstructural architecture of white matter 22. To quantify total metrics, we use fractional anisotropy and mean diffusivity, as two of the most common derived scalar metrics from DTI23. FA expresses the degree to which water diffuses preferentially along one axis, and has shown to increase with age 23 during development and to be lower in the context of various neurological and psychiatric diseases 24. MD is a scalar describing the average diffusion in all directions, with higher levels indicating relatively unimpeded diffusion correlated with .Functional MRI of the Brain Software Library was used to processed MRI data 26,27. First, images were adjusted for minor head motion 28, which included a Gaussian process for outlier replacement29. Then, the resulting transformation matrices were used to rotate the diffusion gradient direction table30,31. Non-brain tissue was removed using the FSL Brain Extraction Tool32. Lastly, the diffusion tensor was fit, and common scalar maps were subsequently computed.
Probabilistic fiber tractographyFully automated probabilistic fiber tractography was performed using the FSL plugin, “AutoPtx” Diffusion data were processed using the Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques accounting for two fiber orientations at each voxel 33,34. Then, for each subject, the FA map was aligned to the FMRIB-58 FA template image with the FSL nonlinear registration tool. Next, the inverse of this nonlinear warp field was computed, and applied to a series of predefined seed, target, exclusion, and termination masks provided by the Auto Ptx plugin 35. Probabilistic fiber tracking was then execute with the FSL Probtrackx module using these supplied tract-specific masks (i.E., seed, target, that were warped to the native diffusion image space of each subject 33. Lastly, the resulting path distributions were normalized to a scale from 0 to 1 using the total number of successful seed-to-target attempts and were subsequently thresholded to remove low-probability voxels likely related to noise.
White matter tract segmentation was performed by thresholding the normalized tract density images based on previously established values by de Groot et al.35 E., cingulate gyrus part of cingulum forceps major forceps minor (FMI): 0.01, inferior longitudinal fasciculus fasciculus Average FA and MD values were then computed for each fiber bundle. Connectivity distributions were estimated for the 7 large fiber bundles previously named and selected based on previous reports 36,37,38. Average of FA and MD in the left and right hemisphere was calculated in those tracts present in both hemispheres To assess whether physical fitness components fitness, muscular fitness, and motor fitness are related to global measures of white matter microstructure selected tracts were combined into a single factor (“global factor”). The global factor was computed by averaging all tracts and weighting this average by the size of the tracts.
Tract-based spatial statisticsTract-based spatial statistics was used to perform voxel-wise statistical analyses of the DTI data (A mean FA image was calculated and thinned to create a mean FA skeleton, which represents the center of white matter tracts. A threshold of FA was selected to exclude voxels not belonging to white matter. FA maps of each participant were then projected onto the skeleton. The same procedure was applied to the MD maps.
Image quality assurance
Raw image quality was assessed via visual inspection. In addition, the sum-of-squares error maps from the tensor estimation were calculated and visually inspected for structured noise 12. Image quality was rated using a 4-point scale, with 1 = “excellent”, 2 = “minor”, 3 = “moderate”, and 4 = “severe”. Datasets determined to be of insufficient quality for statistical analyses were excluded n = 2. Lastly, probabilistic tractography data were inspected visually. First, the native space FA map registration was inspected to ensure images were all properly aligned to the template (masks were properly mapped to native space). Second, all tracts were visualized to ensure accurate path reconstruction.
CovariatesBody weight and height were performed with participants having bare feet and wearing underclothes; weight was measured with an electronic scale SECA 861, Hamburg, Germany) and height (cm) with a stadiometer Both measurements were performed twice, and averages were used. BMI was expressed in kg/m2. PHV is a common indicator of maturity in children and adolescents 40. PHV was obtained from anthropometric variables (weight, height and/or seated height) using Moore’s equations 41. The total composite IQ was assessed by the Spanish version of the Kaufman Brief Intelligence Test , a validated and reliable instrument 42. This test consists of vocabulary and matrices subtests which provided indicators of crystallized intelligence and fluid intelligence, respectively. The typical punctuation of both, crystallized and fluid indicators of intelligence, were computed and a total intelligence score was obtained from the sum of them. Parental education was assessed by the educational level of mother and father reported (i.E., no elementary school, elementary school, middle school, high school and university completed). Parent answers were combined into a trichotomous variable (i.E., none of the parents had a university degree, one of the parents had a university degree and both parents had a university degree). Lastly, the Behavior Assessment System for Children level-2 for children aged 6–12 years old, was used to assess behavioral and emotional functioning. A total behavioral symptoms index (including aggressively, hyperactivity, attention problems, atypical behaviors,was extracted from the questionnaire .
Statistical analysis
All analyses, with the exception of analyses, were performed using the Statistical Package for Social Sciences Statistics for Windows, version 22.0, Armonk, The characteristics of the study sample are presented as means and standard deviations or percentages. In addition, we tested the correlation of BMI with global DTI metrics and physical fitness components. Interaction analyses of sex with physical fitness variables were also performed. No significant interactions with sex were found (P ≥ 0.10) and therefore analyses are presented for the whole sample. In addition, we explored the association of several confounders i.E., sex, PHV, BMI, IQ, parental education, and emotional and behavioral problems) with tractography-derived white matter variables using a Pearson's bivariate correlation analysis (data no shown). Among all of the potential confounders, parental education, socioeconomic status, and emotional and behavioral problems were not significantly related to white matter microstructure (all P values > 0.1) and were therefore excluded from the subsequent analyses.
Separate linear regression analyses adjusted for sex, PHV, BMI and IQ were performed to examine the association between physical fitness components and global-extracted DTI scalar metrics (i.E., global FA and MD). Each regression model examined separately the relationships between a single physical fitness component and a single DTI scalar metric.
Then, in order to determine whether the association of physical fitness with white matter microstructure was indeed only global or restricted to a particular set of white matter bundles, and to facilitate comparison with future studies, we applied two commonly used methodologies: (1) probabilistic tractography of large, commonly studied white matter tracts and (2) TBSS, which is a voxel-based approach. For probabilistic tractography analyses, false discovery rate (FDR. Benjamini–Hochberg method) was used to adjust for multiple comparisons 44. Correction for multiple comparisons was based on 7 tracts, 2 DTI metrics and 6 physical fitness components for a total of 84 tests. For TBSS analyses, the association between physical fitness components and DTI scalar metrics were tested voxel-wise using general linear models, including sex, PHV, BMI and IQ as covariates. A permutation-based statistical approach (5,000 permutations) within FSL Randomise 39 was performed including the threshold-free cluster enhancement (TFCE) multiple comparison correction method. Significance was set at P < 0.05, corrected for family-wise error.
Admiral William McRaven is well known for his 2014 commencement speech at the University of Texas, in which he preaches about how the small, mundane task of making your bed (to perfection) upon waking can fuel an accomplishment-filled day — reinforcing the principle that the little things in life matter. One highly effective aspect of the maxim to make your bed is that it is relatable to all who hear it and demonstrates how completing even one familiar feat early in the day can be a catalyst for more. The underlying components of this lesson are solid, but many people take it too literally. In fact, I believe making your bed is a waste of time. In the five minutes it takes to make your bed with perfect hospital corners, you could have contributed to a meaningful goal. Someone who's looking to improve physical fitness could have done 10 (or 100) pushups and a two-minute plank exercise; a student who's expanding their education could have read two pages of a book; a budding artist could have drawn a sketch. As long as you don't take it literally, making your bed remains an easy piece of advice worth sharing. Here are three other quick lessons that you can apply to facilitate a win every day.
Never Be Late
No matter what the reason is, tardiness is not fashionable. Never. In fact, it can be downright insulting to those you’ve left waiting. Being late to a meeting implies a variety of negative aspects about your character and abilities, such as that you don't prioritize the event and have an inability to effectively plan and maintain a schedule. If you cannot make it to a meeting on time, can you be trusted to deliver work on time? For a leader, modeling sound time-management skills is critically important for developing accountability in your subordinates. In the military, “five minutes early is on time, and on time is late.” In combat, failing to keep precise time can mean the difference between winning a well-synchronized battle and losing. Sometimes being late is literally the difference between life and death.
Of course, when you're not in a critical situation, there are emergencies in life that can cause you to reasonably arrive late. But by being late for an event, regardless of the reason, you have signaled to others that the event was not a priority for you. If an event is the most important part of your day, there is no excuse. When consecutive events have a timing conflict, you signal which one is your priority by which event you choose to appear at on time. In life, I've found that 90% of success is showing up, so be on time, prepared and looking the part.
Look Like A Winner
On that note, it is important to arrive looking presentable and suitably dressed for the occasion — whether it’s a casual event, professional meeting or formal affair. Just as you can create momentum by completing a task early, you can “set the stage” of how others see you by projecting a strong image. If you show up to a meeting with scuffed shoes, a button missing and wrinkled pants and expect others not to judge you, it’s already too late to make a first impression.
When you lead an organization, how you dress gives you an opportunity to influence the culture. Looking polished sets an example for others to follow and signals to everyone that you value attention to detail. How you dress will set a tone that can permeate throughout the organization. What do you want that atmosphere to be?
Do What You Say You Will Do
We have a tendency to agree to things we are not committed to doing to avoid the discomfort of saying no. This tendency can quickly morph into a bad habit of agreeing to do things without having a plan or intent to actually fulfill the commitment. Once a person gains a reputation for not keeping their word, their reputation is lost.
No matter how small an action it is, people will remember that you said you would do it. Failing to honor commitments kills trust. Leaders need both the integrity to follow through with commitments and the fortitude to say no when they cannot commit. Once you have agreed to do something, it becomes your responsibility to track and report the completion of that assignment. This is such a simple concept, but it can be difficult to execute. When you follow through with your commitments as a leader, you let your team know you expect the same from them. Leaders who shirk commitments frequently send a clear message to the organization that task accomplishment is not a priority.
As a leader, every single action you take and decision you make communicates to your staff, customers, vendors and partners. Being punctual, polished and personally accountable are some of the most important
Physical Activity
The “Global Strategy on Diet, Physical Activity and Health”, adopted by the World Health Assembly in 2004, describes the actions needed to increase physical activity worldwide. The Strategy urges stakeholders to take action at global, regional and local levels to increase physical activity.
The "Global Recommendations on Physical Activity for Health", published by WHO in 2010, focus on primary prevention of NCDs through physical activity. It proposes different policy options to reach the recommended levels of physical activity globally, such as:
To measure physical activity in adults, WHO has developed the Global Physical Activity Questionnaire This questionnaire helps countries monitor insufficient physical activity as one of the main NCD risk factors. The GPAQ has been integrated into the WHO STEPwise approach, which is a surveillance system for the main NCD risk factors.
A module to assess insufficient physical activity among schoolchildren has been integrated into the Global school-based student health survey. The GSHS is a WHO/US CDC surveillance project designed to help countries measure and assess the behavioural risk factors and protective factors in 10 key areas among young people aged 13 to 17 years.
In 2013, the World Health Assembly agreed on a set of global voluntary targets which include a 25% reduction of premature mortality from NCDs and a 10% decrease in insufficient physical activity by 2025. The “Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020” guides Member States, WHO and other UN Agencies on how to effectively achieve these targets. A sector specific toolkit is under development by WHO to assist Member States implement actions and achieve the targets.
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