The influences of foot placement on lumbopelvic rhythm during trunk flexion motion

Kinematic adaptations to restricting spine motion during symmetrical lifting

When lifting an object from the ground a person has many possible whole-body movement solutions to accomplish the task. It is unclear why lifters use most of their available lumbar spine flexion range-of-motion despite many ergonomic guidelines advising against doing so. Experimentally restricting spine motion and observing compensatory movement strategies is one approach to address this knowledge gap. A kinematic analysis was performed on 16 participants who completed symmetrical lifting tasks with and without wearing a spine motion-restricting device. Sagittal trunk, lumbar spine, and lower extremity kinematics, along with stance width and foot orientation in the transverse plane were evaluated between restricted and unrestricted lifting conditions. Restricting spine motion required greater ankle dorsiflexion (p < 0.001), knee flexion (p < 0.001), and hip flexion (p < 0.001) motion in comparison to unrestricted lifting. Motion was reallocated such that hip flexion showed the largest increase in restricted lifting, followed by ankle dorsiflexion, then knee flexion compared to unrestricted lifting. Trunk inclination decreased (i.e., more upright) in restricted compared to unrestricted lifting (p < 0.001). Neither stance width (p = 0.163) nor foot orientation (p = 0.228) were affected by restricting spine motion. These adaptive movements observed indicate lower extremity joint motion must be available and controlled to minimize lumbar spine flexion during lifting.

Effects of Custom-made Insoles on Plantar Biomechanics and Upper Extremity Muscle Performance

OBJECTIVE

To investigate the effectiveness of molding custom-made insoles for female patients with foot pain.

METHODS

The study included 20 patients whose insoles were prescribed according to biomechanical evaluations and molded by repositioning the subtalar joint in its neutral position using a simple set of tools. Plantar biomechanics were measured under the following conditions: static stand, walking at self-comfortable walking speed (CWS) barefoot, walking in patient owned running shoes, and walking in running shoes plus insoles. Each patient's upper arm isometric muscle strength and subjective feelings before and after the insole intervention were assessed.

RESULTS

The molded insoles increased plantar contact area both during static standing and walking at CWS compared to the barefoot condition. The insoles also had more evenly distributed plantar contact area and loading rate, with the changes in the medial arch area being most significant. Moreover, the custom-made insole intervention immediately increased maximum resistance and duration of bilateral upper arms, as well as improved foot comfort, especially at the medial arch area during single leg squat tests.

CONCLUSION

Molding custom-made insoles by repositioning the subtalar joint in its neutral position can be accomplished with a simple set of tools, making this method highly applicable for a majority of less developed regions. Insoles molded using this method are effective in immediately improving plantar biomechanics disorders and enhancing isometric upper muscle performance in female patients with foot pain.

Does Plantar Pressure Distribution Influence the Lumbar Multifidus Muscle Thickness in Asymptomatic Individuals? A Preliminary Study

BACKGROUND

Atrophy can occur in the lumbar multifidus (LM) muscle quickly as a result of various musculoskeletal problems. Knowing factors influencing muscle thickness of the LM will provide important clues about lumbopelvic stability.

OBJECTIVES

Although there are several studies in the literature investigating the adverse effects of foot-ankle postural disorders on the lumbopelvic region, to our knowledge there has been no investigation of plantar pressure distribution (PPD) as a factor influencing muscle thickness of the LM. The aim of this study was to determine whether PPD could affect LM muscle thickness.

METHODS

This observational study consisted of 25 asymptomatic individuals. Ultrasonographic imaging was used to determine the thickness of the LM. All participants were subjected to PPD analysis using the Digital Biometry Scanning System and Milletrix software in 9 different plantar pressure zones. The Pearson product-moment correlation coefficients were used to examine the correlations between the LM muscle thickness and other variables. Stepwise multiple linear regression analysis was used to determine the variables with the greatest influence on LM muscle thickness.

RESULTS

Peak pressures of medial and lateral zones of the heel were the significant and independent factors influencing static LM thickness, with 39.5% of the variance; moreover, the peak pressures of heel medial and fourth metatarsal bone were the significant and independent factors influencing dynamic LM thickness, with 38.7% of the variance.

CONCLUSIONS

Plantar pressure distribution could be an important factor influencing LM thickness, although further research is required. Examining foot-ankle biomechanics may provide information about the stability of the LM.

A machine-learning method for classifying and analyzing foot placement: Application to manual material handling

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler's foot placement strategies with regards to the performed task.

Influence of Anthropometry, Age, Sex, and Activity Level on the Hand Reach Star Excursion Balance Test

The influence of anthropometric measurements, age, sex, and activity level have been found to influence tests of dynamic postural control such as the star excursion balance test (SEBT). The hand reach star excursion balance test (HSEBT) measures different aspects of dynamic postural control. The purpose of the present study was to explore the influence of these factors on the HSEBT. A convenience sample of 223 subjects performed four horizontal (L45, R45, L135, and R135) and two rotational (LROT and RROT) reaches. The influence of anthropometric measurements (height, arm length, leg length, and wingspan) on reach measurements were assessed using stepwise multiple linear regression. Influence of age (young: <20 years; adult: >20 years), sex (male; female) and activity level (athletes; recreational) on reach measurements were analyzed using independent samples t-test (p < 0.05) and interpreted using effect size (Cohens d) and established values of minimal detectable change (MDC). Wingspan explained a significant portion of the variance of only R45 (34.6%) and L45 (11.7%) reach measurements and normalized (percentage of wingspan). A medium effect of age, sex, and activity level was observed for normalized L45 and R45 reaches (d = 0.50–72). Group differences greater than MDC values and a medium effect for age (d = 0.55) and activity level (d = 0.75) were observed for the R135 reach. L45 and R45 reaches should be normalized to wingspan, but not the other reaches. Between individual or group comparisons should consider age, activity level and sex as potential covariates.