Overweight and obesity in children and adolescents is seen as a global health challenge and a priority for prevention (1). To solve such a health issue, we need full understanding of the related ...health behaviors (and underlying beliefs), and understanding of the biological mechanisms that cause or can prevent the issue. However, for overweight and obesity, drawing a full picture of the exact problem (and the subsequent solution) is difficult. In this paper, we describe how we used Intervention Mapping to develop a theory and evidence-based prevention program targeting overweight and obesity and how we investigated the 1-year efficacy of this program on body composition and physical activity of adolescents. A helpful tool, theoretical, and methodological lessons learned are given from our attempt to contribute to solving the obesity problem.
Background
In this study, the main hypothesis is that heavier people enjoy strength exercises more than normal-weight people, mediated by fat-free mass and muscle strength. Further, it is ...hypothesized that heavier people are better in strength exercises and enjoy strength exercises more compared to aerobic exercises.
Methods
In a cross-sectional study, height, weight, body composition (i.e., fat mass and fat-free mass by underwater weighing), muscle strength (i.e., one-repetition maximal strength for the leg press and chest press), maximal aerobic exertion (VO
2
max) during cycle ergometry, and psychological determinants (i.e., attitudes, intentions, and self-determined motivations for strength exercises and aerobic exercises using questionnaires) were measured in 68 participants (18–30 years).
Results
Significant correlations between weight/BMI and fat-free mass (index) (
r
values = .70–.80,
p
values < .001), fat-free mass and muscle strength (
r
values = .35–.55,
p
values < .05), and muscle strength and attitudes, intentions, and motivation for strength exercises were found (
r
values = .29–.43,
p
values < .05); BMI was related to psychological determinants via fat-free mass and muscle strength. Furthermore, participants with a higher BMI are significantly better in strength exercises, more intrinsically motivated, and less motivated to do strength exercises compared to aerobic exercises (all
p
values < .05). Trends in the same direction were found for the following variables: instrumental attitude, experiential attitude, and intention (
p
values < .1).
Conclusions
Strength exercises could be more appropriate for heavier people and might therefore be a valuable component in physical activity programs for people who are overweight or obese.
This study assessed the cardiorespiratory capacity, anaerobic speed reserve, and anthropometric and spatiotemporal variables of a 75-year-old world-class middle-distance runner who previously ...obtained several European and world records in the age categories of 60-70 years, achieved 13 European titles and 15 world champion titles, and also holds several European records for the 75-year-old category.
Heart rate, oxygen uptake, carbon dioxide production, ventilation, step frequency, contact time, and velocity at maximal oxygen uptake (VO2max) were measured during treadmill running. Maximal sprinting speed was assessed during track sprinting and used to compute anaerobic speed reserve. Body fat percentage was assessed using air displacement plethysmography.
Body fat percentage was 8.6%, VO2max was 50.5 mL·kg-1·min-1, maximal ventilation was 141 L·min-1, maximum heart rate was 164 beats·min-1, maximum respiratory exchange ratio was 1.18, and velocity at VO2max was 16.7 km·h-1. The average stride frequency and contact time during the last 30 seconds of the 4-minute run at 10 km·h-1 were 171 steps·min-1 and 241 ms and 187 steps·min-1 and 190 ms in the last 40 seconds at 17 km·h-1, respectively. The anaerobic speed reserve was 11.4 km·h-1, corresponding to an anaerobic speed reserve ratio of 1.68.
This 75-year-old runner has an exceptionally high VO2max and anaerobic speed reserve ratio. In addition, his resilience to injuries, possibly due to a relatively high volume of easy runs, enabled him to sustain regular training since his 50s and achieve international performance in his age group.
BackgroundAn increasing number of commercially available wearables provide real‐time feedback on running biomechanics with the aim to reduce injury risk or improve performance.ObjectiveInvestigate ...whether real‐time feedback by wearable insoles (ARION) alters running biomechanics and improves running economy more as compared to unsupervised running training. We also explored the correlation between changes in running biomechanics and running economy.MethodsForty recreational runners were randomized to an intervention and control group and performed ~6 months of in‐field training with or without wearable‐based real‐time feedback on running technique and speed. Running economy and running biomechanics were measured in lab conditions without feedback pre and post intervention at four speeds.ResultsTwenty‐two individuals (13 control, 9 intervention) completed both tests. Both groups significantly reduced their energetic cost by an average of −6.1% and −7.7% for the control and intervention groups, respectively. The reduction in energy cost did not significantly differ between groups overall (−0.07 ± 0.14 J∙kg∙m−1, −1.5%, p = 0.63). There were significant changes in spatiotemporal metrics, but their magnitude was minor and did not differ between the groups. There were no significant changes in running kinematics within or between groups. However, alterations in running biomechanics beyond typical session‐to‐session variation were observed during some in‐field sessions for individuals that received real‐time feedback.ConclusionAlterations in running biomechanics as observed during some in‐field sessions for individuals receiving wearable‐based real‐time feedback did not result in significant differences in running economy or running biomechanics when measured in controlled lab conditions without feedback.
This study focused on developing a new method to assess VO2max outside laboratory conditions and without the need for maximal exertion. We hypothesized that the combined use of accelerometry and HR ...monitoring, under daily life conditions, could provide a good estimate of physical fitness.
Twenty-six healthy subjects (15 women, 11 men), aged 28 +/- 7 yr, performed a maximal incremental test on a bicycle ergometer to determine VO2max. Body composition was measured with underwater weighing and deuterium dilution using a three-compartment model. A triaxial accelerometer (Tracmor) and an HR monitor were worn for seven consecutive days under free-living conditions. The ratio of HR to activity counts per minute (ACM) was used as a fitness index (HR.ACM(-1)).
As hypothesized, HR.ACM(-1) was significantly correlated with VO2max. Using fat-free mass (FFM) (P < 0.0001), age (P = 0.025), and HR.ACM(-1) (P = 0.021) as the independent variables, the explained variation in VO2max was 76% (P < 0.0001, SEE = 363 mL x min(-1)). In order to generate a prediction formula that is applicable in the field when no data on body composition are available, the same analysis was done with body mass and gender in the model instead of FFM. HR.ACM(-1) was significantly (P = 0.023) correlated with VO2max. The total explained variation of the model was 71%, with a SEE of 409 mL x min(-1), or 13.7% of the average VO2max.
After correction for body composition, VO2max was significantly related to HR.ACM(-1). It is, to our knowledge, the first tool that yields a measure of VO2max by monitoring people in their daily life activities without the need for a specific protocol or for maximal exertion, and therefore is applicable to a large variety of subjects.
Treadmill assessments are often performed at a fixed speed. Feedback-controlled algorithms allow users to adjust the treadmill speed, hereby potentially better resembling natural self-paced ...locomotion. However, it is currently unknown whether the energetics and biomechanics of self-paced differ from fixed-paced treadmill walking. Such information is important for clinicians and researchers using self-paced locomotion for assessing gait.
To investigate whether energy cost and biomechanics are different between self-paced and matched-speed fixed-paced locomotion.
18 healthy participants (9 males/9 females, mean ± standard deviation age 24.8 ± 3.3 years, height 1.71 ± 0.81 m, weight 65.9 ± 8.1 kg) walked at four different self-paced speeds (comfortable, slow, very slow, fast) in randomized order on an instrumented treadmill while three-dimensional motion capture and gas exchange were measured continuously. The average walking speed during the last 2 min of the self-paced trials was used to match the speed in fixed-paced conditions. Linear mixed models were used to assess differences in mean values and within-subject variations between conditions (self-paced and fixed-paced) and speeds. Statistical Parametric Mapping was used to assess differences in kinematics of the lower limb between conditions.
Although self-paced walking consistently resulted in a 4–6% higher net cost of walking, there were no significant differences in the net cost of walking between conditions. Further, there were also no differences of clinical relevance in spatiotemporal outcomes and sagittal-plane lower-limb kinematics between the self-paced and fixed-paced conditions. Within-trial variability was also not significantly different between conditions.
Self-paced and fixed-paced treadmill walking yield similar energetics and kinematics in healthy young individuals when mean values or linear measures of variation are of interest.
•Spatiotemporal parameters are similar in self-paced and fixed speed treadmill walking•Energetic cost of walking does not differ between self-paced and fixed speed walking•Self-paced treadmill walking does not increase gait variability
Introduction
Accurate determination of total daily energy expenditure (TDEE) in athletes is important for optimal performance and injury prevention, but current approaches are insufficiently ...accurate. We therefore developed an approach to determine TDEE in professional cyclists based on power data, basal metabolic rate (BMR), and a non‐exercise physical activity level (PAL) value, and compared energy expenditure (EE) between multi‐day and single‐day races.
Methods
Twenty‐one male professional cyclists participated. We measured: (1) BMR, (2) the relationship between power output and EE during an incremental cycling test, which was used to determine EE during exercise (EEE), and (3) TDEE using doubly labeled water (DLW). A non‐exercise PAL‐value was obtained by subtracting EEE from TDEE and dividing this by BMR.
Results
Measured BMR was 7.9 ± 0.8 MJ/day, which was significantly higher than predicted by the Oxford equations. A new BMR equation for elite endurance athletes was therefore developed. Mean TDEE was 31.7 ± 2.8 and 27.3 ± 2.8 MJ/day during the Vuelta a España and Ardennes classics, while EEE was 17.4 ± 1.8 and 10.1 ± 1.4 MJ/day, respectively. Non‐exercise PAL‐values were 1.8 and 2.0 for the Vuelta and Ardennes classics, respectively, which is substantially higher than currently used generic PAL‐values.
Conclusion
We show that the proposed approach leads to a more accurate estimation of non‐exercise EE than the use of a generic PAL‐value in combination with BMR predictive equations developed for non‐elite athletes, with the latter underestimating non‐exercise EE by ~28%. The proposed approach may therefore improve nutritional strategies in professional cyclists.
Background
An increasing number of commercially available wearables provide real‐time feedback on running biomechanics with the aim to reduce injury risk or improve performance.
Objective
Investigate ...whether real‐time feedback by wearable insoles (ARION) alters running biomechanics and improves running economy more as compared to unsupervised running training. We also explored the correlation between changes in running biomechanics and running economy.
Methods
Forty recreational runners were randomized to an intervention and control group and performed ~6 months of in‐field training with or without wearable‐based real‐time feedback on running technique and speed. Running economy and running biomechanics were measured in lab conditions without feedback pre and post intervention at four speeds.
Results
Twenty‐two individuals (13 control, 9 intervention) completed both tests. Both groups significantly reduced their energetic cost by an average of −6.1% and −7.7% for the control and intervention groups, respectively. The reduction in energy cost did not significantly differ between groups overall (−0.07 ± 0.14 J∙kg∙m−1, −1.5%, p = 0.63). There were significant changes in spatiotemporal metrics, but their magnitude was minor and did not differ between the groups. There were no significant changes in running kinematics within or between groups. However, alterations in running biomechanics beyond typical session‐to‐session variation were observed during some in‐field sessions for individuals that received real‐time feedback.
Conclusion
Alterations in running biomechanics as observed during some in‐field sessions for individuals receiving wearable‐based real‐time feedback did not result in significant differences in running economy or running biomechanics when measured in controlled lab conditions without feedback.
Spatiotemporal metrics such as step frequency have been associated with running injuries in some studies. Wearables can measure these metrics and provide real‐time feedback in‐field, but are often ...not validated. This study assessed the validity of commercially available wireless instrumented insoles (ARION) for quantifying spatiotemporal metrics during level running at different speeds (2.78–5.0 m s−1,) and slopes (3° and 6° up/downhill) to an instrumented treadmill. Mean raw, percentage and absolute percentage error, and limits of agreement (LoA) were calculated. Agreement was statistically quantified using four thresholds: excellent, <5%; good, <10%; acceptable, <15%; and poor, >15% error. Excellent agreement (<5% error) was achieved for stride time across all conditions, and for step frequency across all but one condition with good agreement. Contact time and swing time generally showed at least good agreement. The mean difference across all conditions was −0.95% for contact time, 0.11% for stride time, 0.6% for swing time, −0.11% for step frequency, and −0.09% when averaged across all outcomes and conditions. The accuracy at an individual level was generally good to excellent, being <10% for all but two conditions, with these conditions being <15%. Additional experiments among four runners showed that step length could also be measured with an accuracy of 1.76% across different speeds with an updated version of the insoles. These findings suggests that the ARION wearable may not only be useful for large‐scale in‐field studies investigating group differences, but also to quantify spatiotemporal metrics with generally good to excellent accuracy for individual runners.
Fatigue is a major complaint in patients with multiple sclerosis (pwMS) 1. Previous research identified walking fatigability in pwMS by assessing the change in distance walked between minute 6 and 1 ...of the 6-Minute Walk Test (6MWT) 2. Further, pwMS show lower limb gait deficits 3, resulting in decreased gait stability compared to healthy controls 4. Additionally, upper limb movements can be altered in pwMS due to direct MS lesions 5, which have an important role during gait 6.
Therefore, the aim was to assess to what extent change in walking speed in pwMS is associated by changes in gait stability and arm swing from minute 6 to 1 of the 6MWT.
Participants were included if they had: MS, age between 18–65, disease severity score from 1 to 5.5 on Expanded Disability Status Scale, ability to walk without walking aids. Participants were excluded if they had: a relapse 3 months, lower limb fracture 12 months, or lower limb botulinum toxin 6 months prior to the study.
Participants performed the 6MWT on the CAREN (Motek), equipped with the Human Body lower limb and trunk model, including extra markers for arm swing (acromion and ulnar styloid). Participants walked as fast as possible using self-paced mode. Two familiarization rounds of 3min, incl. breaks, were provided. Step width and variability of spatiotemporal parameters (i.e. step width, -length & -time) were used to assess gait stability 7. Arm swing length was calculated as the difference between maximum anterior and posterior hand position. Most affected side was taken into account and defined as the side with greatest motor impairment (i.e. spasticity and/or weakness). Difference scores between minute 6 and 1 of the 6MWT were used for analyses.
First, one-tailed Pearson correlations between gait stability measures & arm swing, and walking speed during the 6MWT were tested. Then one-tailed partial correlations were assessed to determine whether gait stability measures influenced walking speed when taking arm swing into account. Finally, significant factors were used in generalized estimation equations (GEE) to determine the extent of their effect on walking speed and possible interactions.
Preliminary results included data of 11 pwMS(Table1/T1). Walking speed was significantly related to step length variability, step time variability and arm swing(T1). Partial correlation of step length variability and step time variability remained significant when controlling for arm swing(T1). GEE determined interaction effects between step length variability, step time variability and arm swing on walking speed(T1). Display omitted
Results indicate that both gait stability and arm swing are significantly associated to walking speed during 6MWT in pwMS. These outcomes have a separate effect on walking speed as well as an interaction effect. Future studies could investigate whether gait stability and arm swing might be underlying factors driving walking fatigability in pwMS.
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