Elsevier

Journal of Biomechanics

Volume 71, 11 April 2018, Pages 217-224
Journal of Biomechanics

A comparison of methods to determine center of mass during pregnancy

https://doi.org/10.1016/j.jbiomech.2018.02.004Get rights and content

Abstract

Balance changes during pregnancy likely occur because of mass gains and mass distribution changes. However, to date there is no way of tracking balance through center of mass motion because no method is available to identify of the body center of mass throughout pregnancy. We compared methods for determining segment masses and torso center of mass location. The availability of a method for tracking these changes during pregnancy will make determining balance changes through center of mass motion an option for future pregnancy balance research. Thirty pregnant women from eight weeks gestation until birth were recruited for monthly anthropometric measurements, motion capture analysis of body segment locations, and force plate analysis of center of pressure during quiet standing and supine laying. From these measurements, we were able to compare regression, volume measurement, and weighted sum methods to calculate body center of mass throughout pregnancy. We found that mass changes around the trunk were most prevalent as expected, but mass changes throughout the body (especially the thighs) were also seen. Our findings also suggest that a series of anthropometric measurements first suggested by Pavol et al. (2002), in combination with quiet standing on a force plate, can be used to identify the needed components (segment masses and torso center of mass location in three dimensions) to calculate body center of mass changes during pregnancy. The results of this study will make tracking of center of mass motion a possibility for future pregnancy balance research.

Introduction

Pregnant women have been reported to fall at a rate similar to elderly (Dunning et al., 2010), with approximately 25% reporting at least one fall during the 40-week span of pregnancy (Dunning et al., 2003, Kuo et al., 2007), and some experiencing multiple falls. This prevalence is cause for concern, particularly given falls during pregnancy can lead to traumatic injury to the mother and harm to the fetus (Kuo et al., 2007). There is an increase in mass during pregnancy, mostly around the trunk (torso and pelvis), averaging about 15 kg (Ochsenbein-Kolble et al., 2007). Numerous studies indicate increased mass can cause balance changes (Catena et al., 2010, Cieslinska-Swider et al., 2017), and standing balance changes have been identified to occur during pregnancy (McCrory et al., 2010, Opala-Berdzik et al., 2010). Yet it is not well understood how the mass (amount and distribution) changes specific to pregnancy affect dynamic balance given there is no method to date that identifies the pregnant body center of mass (COM).

A validated model is needed to locate the COM from segment COMs in order to track dynamic balance during functional activities. Duel-energy X-ray absorptiometry scanning, a standard for identifying segment COMs, is not recommended with pregnant women because of low-dose radiation exposure. Magnetic resonance imaging is impractical for frequent use to track changes due to cost. A weighted sum of segment COMs is a practical and appropriate methodology for determining pregnancy related center of mass change, given it is both cost effective and non-invasive.

Using volume data and assumed densities from a sample of women throughout pregnancy, Jensen et al. reported a rate of change in mass distribution and moment of inertia of 16 body segments (Jensen et al., 1996). Rate of change for the lower trunk was the only significant change found through pregnancy. However, the investigators combined pelvis and lower torso, making the model difficult to use in future investigations of balance, given it is expected that the two segments would move with respect to each other during most functional activities.

Pavol et al. developed a method to calculate participant specific segment masses and COMs in an obese population using a set of anthropometric measurements (Pavol et al., 2002). Their identification of segment COMs relative to joint centers and also finding separate pelvis, lower torso, and upper torso segments allows this method to be applied to motion capture data for locating the body COM during functional activities. As of yet, Pavol’s (2002) method has not been validated in a pregnant population.

Alternatively, force plates can be used to identify the body COM location. The average location of the body center of gravity, a two dimensional projection of the COM in the transverse plane, will identify the location of the body COM (Opala-Berdzik et al., 2010). However, the use of a standing force plate analysis of center of pressure in this study could not identify vertical changes in the COM. Combining this with supine analysis of center of pressure spanning two force plates could be used to identify the vertical location of the COM and segment COMs (Park et al., 1999).

Using the three described methods above as a basis, we had two objectives in this study: (1) to compare the use of Jensen’s (1996) data for segment mass determination (fit to linear regression specific to pregnant women) to Pavol’s (2002) method (volume measurements and density assumptions) and (2) to determine pregnancy torso COM from Pavol’s method (based on the weighted sum of five portions of a torso) vs. a force plate method for calculating the torso COM location. Additionally, this study will be the first to report a sample of changes in segment COMs throughout pregnancy. This information is important for measuring balance changes during pregnancy, given pregnant women are encouraged to be more physically active during pregnancy (Leavitt, 2008), but also told (ACOG, 2015, CDC, 2017) to reduce or avoid activities with an elevated risk of falling.

Section snippets

Materials and methods

Pregnant women (n = 30) were recruited from local obstetrical clinics for participation in this study (Table 1). Criteria to participate included being 18–40 years of age; not been categorized as high-risk pregnancy by a medical professional; and not having previously experienced recent lower extremity injury, vertigo, imbalances, or loss of consciousness. Participants were tested once a month until birth, beginning at eight weeks gestation.

IRB approved consent was obtained before each testing

Superficial anthropometry

Anthropometry associated with changes in the trunk demonstrated the greatest linear correlation with gestation (Table 3). BMI, torso depth halfway between nipple level and L3-4 level, torso depth at L3-4 level, and waist circumference (measured at L3-4 level) had average R-values all above 0.9, indicating that some values related to volume change within the torso fit well to a constant linear change through gestation. Torso width halfway between nipple level and L3-4 level and pelvis

Body and segment masses

Our findings suggest that Pavol’s method for segment mass calculations based on external anthropometric measurements is a better alternative than fitting to Jensen’s regression equations for tracking segment masses throughout pregnancy. This determination is based on the assumption that body mass should be the sum of segment masses, and the resultant difference to that measured via scale is zero. When segment masses are added together, Pavol’s method remains consistently close to the true mass,

Conclusions

The goals of our study were to compare two methods for tracking segment masses, and two methods for tracking the torso COM throughout pregnancy. Our results indicate that the Pavol method is a viable option for tracking segment mass changes. It is also appropriate for tracking superior-inferior torso COM shifts through pregnancy. A quiet standing trial on a force plate, and use of the center of pressure is a better option to track torso COM in the anterior and lateral directions. Being able to

Author statement

All authors were fully involved in the study and preparation of the manuscript and the material within has not been and will not be submitted for publication elsewhere.

Acknowledgements

This research was funded by a WSU New Faculty Award and a College of Education Faculty Funding Award.

Conflict of interest

There are no conflicts of interest associated with this research.

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