Estimating Bone Loading during Physical Activity:

Where Do We Go next?

Hannah Rice

Department of Physical Performance, Norwegian School of Sport Sciences, Sognsveien 220, 0863 Oslo, Norway

Keywords: Bone Stress, Overuse Injury, Internal Loading.

Abstract: Bone stress injuries affect athletic populations who undertake activities in which bones are repeatedly loaded.

In order to understand and reduce the risk of bone stress injuries, we need to quantify the loading experienced

by the bones during activities such as running. Bone loading is difficult to quantify as the magnitudes of stress

are influenced to a large extent by the magnitude of muscular forces acting on the bone. Musculoskeletal

modelling, ranging from very simple to very complex approaches, can be used to estimate the internal loading

experienced by the bone during human movement such as running. This has allowed us to explore factors

such as speed, slope, step width and step length and their influence on bone loading during running. However,

in order to truly understand risk of stress injuries this needs to be taken out of the lab and in-field. Today we

have access to rapidly improving technology and data processing capabilities. Could this facilitate the

estimation of bone loading in real time? What are the current limitations and challenges, and how might these

be overcome in the future?

1 BACKGROUND

Running is one of the most accessible and popular

forms of physical activity worldwide, yet healthy

people who run are at a high risk of injury,

particularly to the lower limbs (van Gent et al., 2007).

Bone stress injuries are problematic as they result in

several months of time loss. Stress fractures are the

most severe stress injury, and comprise up to 30% of

running-related injuries (Robertson & Wood, 2017).

The tibia is the most common site of stress injury

(Wood et al., 2014), followed by the second and third

metatarsals (Bennell et al., 1996; Fetzer & Wright,

2006; Gross & Bunch, 1989; Iwamoto & Takeda,

2003).

Bone stress injuries affect athletic populations

who undertake activities in which bones are

repeatedly loaded. During running, the bone is

typically loaded to stress magnitudes considerably

below the failure threshold (Burr et al., 1996;

Milgrom et al., 2000), however the repetitive nature

of the loading can result in risk of injury (Burr et al.,

1997; Warden et al., 2014) due to microdamage

accumulation. Excessive accumulation of

microdamage without sufficient recovery can lead to

increased risk of stress fractures (Burr, 2011).

During weight-bearing activities such as running,

the bone is subjected to external forces and muscular

forces, which contribute to both axial and bending

loading of the bone. Typically, the net external forces

bend the bone in one direction, whilst the net

muscular forces tend to counteract these and act in the

opposite direction (Pauwels, 1980). The bending

moments acting on the bone result in compression on

one surface of the bone and tension on the opposite

surface, and this tends to result in tension on the

anterior surface of the tibia (Derrick et al., 2016;

Meardon et al., 2015; Meardon & Derrick, 2014; H.

Rice et al., 2019) and the plantar surface of the

metatarsals (Arndt et al., 2002; Ellison, Kenny, et al.,

2020) during the majority of the stance phase of

running. The magnitude of compression is greater

overall than the magnitude of tension on the opposite

side, as the stress due to the bending is superimposed

with the stress due to longitudinal compression.

In order to understand and reduce the risk of bone

stress injuries, we need to be able to quantify the

loading experienced by the bones during activities

such as running. Bone loading is difficult to quantify,

as the magnitudes of stress are influenced to a large

extent by the magnitude of muscular forces acting on

the bone which cannot be directly measured. Existing

estimates of internal bone loading have included

Rice, H.

Estimating Bone Loading during Physical Activity: Where Do We Go next?.

DOI: 10.5220/0011598300003321

In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022), pages 5-9

ISBN: 978-989-758-610-1; ISSN: 2184-3201

invasive direct measurement approaches (Arndt et al.,

1999, 2002; Burr et al., 1996; Milgrom et al., 2000)

using strain gauges. Modelling approaches have been

increasingly used to address such research questions,

with varying degrees of complexity (Ellison, Akrami,

et al., 2020; Ellison, Kenny, et al., 2020; Firminger et

al., 2017; Gross & Bunch, 1989; H. Rice et al., 2019;

H. M. Rice et al., 2020; Stokes et al., 1979). These

approaches have allowed us to explore factors such as

speed, slope, step width and step length and their

influence on bone loading during running (Baggaley

et al., 2021; Edwards et al., 2009, 2010; Meardon et

al., 2015). Such understanding is valuable in helping

athletes and recreational runners to adapt their

training in order to minimise risk of a bone stress

injury, but current understanding is limited in two key

ways: 1) these approaches need to be taken out of the

lab and into the field; 2) the modelling approaches

require further evaluation and ultimately validation.

In addition, few findings have been replicated or

supported in similar populations, and this is essential

for scientific integrity.

2 IN-FIELD ASSESSMENT

Modern wearable devices can be worn in-field

during physical activity to collect data, including

kinematic and kinetic data. Recent advances in

wearable devices have made in-field data collection

more accessible (Willy, 2018). Examples of such

devices include wrist- or ankle-worn accelerometers

and gyroscopes, pressure-sensing insoles, and

electromyography electrodes. The potential benefits

of this are clear, but researchers should be careful

not to infer injury causation from single external

loading variables as these may not be representative

of internal loading (Ellison et al., 2021; Ellison,

Kenny, et al., 2020; Matijevich et al., 2019). For

example, according to subject-specific

musculoskeletal models, greater external forces

under the metatarsals during running do not simply

translate to proportionally greater internal peak

stresses (Ellison, Kenny, et al., 2020). However,

there is the potential to use combined information

from multiple wearable devices to estimate internal

loading, through machine learning. Matijevich et al.

(Matijevich et al., 2020) demonstrated that multi-

sensor algorithms can improve estimates of

musculoskeletal loads, such as tibial forces,

compared with existing approaches.

In order to quantify internal bone loading in-field

using modelling approaches, we must be able to

measure the necessary input variables with the

required accuracy. The input variables required for

the model are dependent upon the complexity of the

model. The ‘correct’ level of complexity is therefore

the simplest model that can provide valid answers to

the specific research question being addressed. Bone

geometry is understood to be an important contributor

to bone stress magnitude (Ellison, Kenny, et al.,

2020) and to risk of stress injury (Nunns et al., 2016)

yet their input into musculoskeletal models can add

considerable financial and computational expense,

due to the requirement for magnetic resonance images

or computer tomographic scans if density is also to be

considered. Whether sufficiently valid estimates of

bone loading can be obtained using generic scaled

geometry warrants further investigation. It is likely

that such modelling approaches could provide more

robust estimates of within-person change in bone

stress magnitude (for example in two different

footwear conditions) than between-participant

differences (for example identifying runners who

display higher stress than average).

Whilst information about participant-specific

bone geometry and density are difficult to obtain, they

do not need to be collected in real-time and therefore

do not limit the possibility of accurately estimating

bone loading in real-time. The real-time inputs into

the model include kinematic and kinetic data during

the stance phase of running, which can be estimated

using wearable devices with almost instantaneous

feedback. One of the greatest challenges with

musculoskeletal modelling of bone loading in real-

time is estimating muscular forces, often done using

static optimization constrained to joint moments

(Baggaley et al., 2021; Derrick et al., 2016; Edwards

et al., 2010; Meardon & Derrick, 2014; H. Rice et al.,

2019). This requires non-negligible computation time

and relies on accurate joint moment estimates, two

aspects which challenge the ability to quantify bone

loading in real-time during running.

In order for the potential advances in estimating

bone loading in-field to be realised, technology must

be sufficiently robust and accurate for the research

question. For example, when considering running in-

field, there is a need for wearable technologies that

have longevity (e.g. over the course of a training

intervention), sufficient battery life and resistance to

different weather conditions, amongst other practical

considerations.

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

3 VALIDATION OF MODELLING

APPROACHES

Validation of musculoskeletal modelling is another of

the greatest challenges and limitations in this field.

There is not a clear ‘gold standard’ measurement

approach to validate against, as the in vivo

measurements have considerable limitations of their

own. Currently, many approaches are evaluated by

comparison with existing approaches, providing

convergent validity but no direct validation. More

robust validation is essential to advance the field. A

first step towards achieving this would be to obtain

baseline data from participants during running, such

that the bone stresses could be estimated using a

variety of approaches with a range of complexity.

These participants would then be followed up over

time to quantify changes in markers of bone stress

reactions or injury outcomes. With sufficient

statistical power, it would then be possible to quantify

the ability of each modelling approach to predict bone

stress outcomes. Furthermore, the simplest model that

can be used to achieve a reasonable prediction of bone

stress outcomes could be identified, allowing this

approach to be developed to provide real-time

feedback.

4 TECHNOLOGY AND THE

FUTURE

Since running is such an accessible form of physical

activity that is popular worldwide, it can play an

important role in maintaining physical activity levels

throughout the lifespan. In today’s ageing society it is

particularly crucial that adults can maintain healthy

physical activity levels whilst minimising the risk of

stress-related injuries. Not only is the burden of lower

limb stress injuries well-documented in adults who

run, stress injuries also present a major problem in

military populations (Beck, 1998; Knapik et al., 2004;

Milgrom et al., 1985; Orr et al., 2014), in adolescent

athletes (Field et al., 2011) and in postmenopausal

women (Pegrum et al., 2012).

The possibility of quantifying tissue loading in

real-time using wearable devices can be exploited by

wearable device manufacturers for the benefit of

many users. For example, apps for use with mobile

devices could be developed that would collect

synchronised data from wearable devices worn by the

user and estimate bone stresses in real-time. These

apps could record cumulative internal loading in each

training session, as well as providing real-time

feedback - for example via an audible signal - when

certain thresholds are exceeded. This has the potential

to transform how people structure and plan their

running, whether they run for healthy physical

activity, to train for competitions, or as part of

military training. Ultimately, this could result in

reduced stress injury occurrence. Similarly, there is

potential for footwear manufacturers to develop

footwear that reduces internal loading on structures

and therefore injury risk.

There is increasing acknowledgement that the

existing approach of inferring injury risk from a

single externally-measured variable can result in

misleading recommendations. By combining the

ability to estimate internal loading using wearable

devices in-field with an improved understanding of

how this translates to tissue loading, there is the

potential for important societal impact.

5 CONCLUSION

There is enormous potential for bone loading to be

quantified in real-time, in-field, in the near future.

This has important implications and potential to

reduce the risk of overuse bone stress injuries, but it

is important that the understanding is not outpaced by

the development of technology. We must improve our

understanding of the validity of the measures as they

are taken into the field.

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Estimating Bone Loading during Physical Activity: Where Do We Go next?