Effects of Overweight and Obesity on Running Mechanics in Children

Bradley J Bowser; Kristen Roles

Disclosures

Med Sci Sports Exerc. 2021;53(10):2101-2110. 

In This Article

Methods

Participants

The Physical Activity Readiness Questionnaire, an injury history questionnaire, and informed assent and consent waivers as approved by the Institutional Human Subjects Review Board were completed by the participant and participant's guardian before participation. All participants had to be deemed healthy and free of injury during the previous 3 months to be eligible. An a priori power analysis using pilot data was used to determine the sample size needed to achieve statistical significance. Based on the power analysis, 42 participants were needed to adequately power this study (effect size = 0.80, α = 0.05, β = 0.20). Forty-two children between 8 and 12 yr of age were recruited to participate in this study. Participants were recruited from the local community via word of mouth, flyers placed in public areas, and flyers e-mailed to various youth clubs and organizations. Upon completion of participation in the study, each child received a $40 Amazon gift card. Participants included 16 OW/OB participants (BMI ≥85th percentile) and 26 HW participants (BMI < 85th percentile). Participant demographics are displayed in Table 1.

Instrumentation

Twenty-seven reflective markers and two cluster markers were used to identify anatomical landmarks of the lower extremities using a modified Helen Hayes marker set. The inclusion of iliac crest and greater trochanter markers as well as thigh and shank clusters was used to limit skin movement artifact for the OW/OB children. Three-dimensional marker coordinates were collected using an eight-camera (Oqus-3) Qualisys motion capture system (Qualisys, Gothenburg, Sweden) at a sampling frequency of 200 Hz. Ground reaction forces (1000 Hz) were collected using an AMTI force platform (AMTI, Newton, MA) embedded in a 15-m runway.

Procedures

Participants underwent a single 2-h testing session at a university biomechanics laboratory. After assent and consent, the participant's name, date of birth, and sex were recorded. Height (m) and weight (lb) were measured using a stadiometer and an AMTI force plate (AMTI), respectively. Both height and weight were used in calculating BMI percentile via the CDCP's BMI percentile calculator, which uses height, weight, age, and gender in its calculations.[23] All participants wore standardized footwear (Nike Pegasus) to control for the effect of footwear on running mechanics. Participant's leg length was measured bilaterally from the anterior superior iliac spine to the medial malleolus. Retroreflective markers were placed on the anterior, posterior, and lateral portions of the shoe; lateral and medial malleolus; lateral and medial condyles of the knee; greater trochanter; anterior superior iliac spine; superior border of the iliac crest; and lumbosacral section of the spine. Clusters of four markers on rigid base plates were attached to the thigh and shank (Figure 1). A 5-min warm-up that included light jogging and stretching was performed after the placement of reflective markers. A static calibration trial was then collected while the participant stood on a single force platform in the center of the capture volume. After static calibration, anatomical markers were removed from the participant, leaving only the tracking markers on the participant during the movement trials. Next, participants ran across a 15-m runway, embedded with a ground reaction force platform, at a given speed of 3.5 m·s−1 ± 5% (Figure 1). Two to three practice trials were performed to help familiarize participants with the correct running speed, to help establish starting position, and to ensure participants contacted the forceplate with the correct foot. Participants then repeated the running trials 8–10 times. After each running trial, participants walked back to the starting position and were provided a minimum of 60–120 s of rest before starting their next trial. No participants reported being tired and did not appear winded for any of the trials. Trials were excluded and repeated if the participant (a) did not strike the force plate entirely with their dominant foot, (b) ran outside of the accepted speed range during the set speed trials, (c) adjusted their running mechanics based on force plate location, and/or (d) sped up or slowed down while crossing the forceplate. The first three to five trials that met each of the above criteria were used for analysis. Foot dominance was defined as the foot the participant would use to kick a ball. Running speed was monitored using a photocell timing system.

Figure 1.

Participant with tracking markers completing a running trial.

Data Reduction

The CDCP's BMI percentile calculator was used to determine participant placement into the OW/OB or HW groups.[23] Participants classified ≥85th percentile were placed into the OW/OB group, whereas participants ≥5th and <85th percentile were placed into the HW group. BMI percentile was used because of its wide acceptance among clinicians and researchers as a valid and reliable tool to screen children for overweight and obesity.

Reflective markers were labeled then digitized using Qualisys Track Manager Software (Qualisys). The digitized markers were used to calculate joint motion using Visual 3-D (C-Motion, Inc., Germantown, MD). Functional hip joint centers were calculated using the method outlined by Hicks and Richards.[24] Marker data were filtered with a recursive fourth-order Butterworth filter at 5 Hz.[25] Kinematic variables of interest included sagittal and frontal plane joint angles and excursions of the hip, knee, and ankle joints. Excursions for early stance were calculated from foot strike (FS) to vertical impact peak (VIP) and FS to FZmax. Total joint excursion was calculated as the difference between the maximum and the minimum joint angles during stance.

Ground reaction force data were filtered with a recursive fourth-order Butterworth filter with a cutoff frequency of 50 Hz. Kinetic variables of interest from the ground reaction force data during running included VIP, average vertical load rate, instantaneous vertical load rate, FZmax, peak braking force, and peak propulsive force. Three-dimensional joint and segment angles were calculated with Visual 3-D software (C-Motion, Inc.) using an X, Y, Z Euler angle rotation sequence.[26] Segment inertial properties were used to calculate internal joint moments.[27,28] Customized software (LabVIEW 18.0; National Instruments, Austin, TX) was used to extract and calculate all the variables of interest from the Visual 3-D motion files.

Because OW/OB children have a more mass than HW children, it would be expected that OW/OB children would display significantly greater unscaled ground reaction forces and joint moments than HW children. However, as body mass increases, there is not a proportionate increase in bone density, joint surface area, and/or muscle mass to accommodate for the increased load.[29] Greater unscaled force distributed over a similar, or slightly larger joint surface area, would likely result in greater overall stress at that joint. For this reason, both scaled and unscaled ground reaction force and joint moment variables are reported. For the scaled variables, ground reaction forces were scaled to body mass, and joint moments were scaled to body mass and height.

Statistical Analysis

For all variables of interest, the average of three to five trials was used for statistical comparisons. Kinematic variables of interest included stance time, step length, and frontal and sagittal plane joint angles and excursions at the hip, knee, and ankle. Kinetic variables of interest included ground reaction forces and peak hip, knee, and ankle moments in the sagittal and frontal planes. A one-way ANOVA (group as factor) was used to compare group differences for all variables of interests using SPSS software (Version 25.0; IBM® SPSS® Statistics, Chicago, IL). Effect sizes were calculated using Cohen's d with 0.2, 0.5, and 0.8 considered small, medium, and large effects, respectively.[30] Box plot analyses were used to identify and remove outliers. The level of significance was set at P < 0.05. Data are presented as mean and SD.

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