Measurement of osteogenic exercise - how to interpret accelerometric data?
ABSTRACT Bone tissue adapts to its mechanical loading environment. We review here the accelerometric measurements with special emphasis on osteogenic exercise. The accelerometric method offers a unique opportunity to assess the intensity of mechanical loadings. We present methods to interpret accelerometric data, reducing it to the daily distributions of magnitude, slope, area, and energy of signal. These features represent the intensity level of physical activities, and were associated with the changes in bone density, bone geometry, physical performance, and metabolism in healthy premenopausal women. Bone adaptations presented a dose- and intensity dependent relationship with impact loading. Changes in hip were threshold dependent, indicating the importance of high-impacts exceeding acceleration of 4 g or slope of 100 g/s as an osteogenic stimulus. The number of impacts needed was 60/day. We also present the daily impact score to describe the osteogenic potential of daily mechanical loading with a single score. The methodology presented here can be used to study musculoskeletal adaptation to exercise in other target groups as well.
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ABSTRACT: While it is widely acknowledged that bones adapt to the site-specific prevalent loading environment, reasonable ways to estimate skeletal loads are not necessarily available. For long bone shafts, muscles acting to bend the bone may provide a more appropriate surrogate of the loading than muscles expected to cause compressive loads. Thus, the aim of this study was to investigate whether mid-thigh muscle cross-sectional area (CSA) was a better predictor of tibial mid-shaft bone strength than mid-tibia muscle CSA in middle aged and older men. 181 Caucasian men aged 50-79 years (mean±SD; 61±7 years) participated in this study. Mid-femoral and mid-tibial bone traits cortical area, density weighted polar moment of area and muscle CSA [cm(2)] were assessed with computed tomography. Tibial bone traits were positively associated with both the mid-femur (r=0.44 to 0.46, P<0.001) and the mid-tibia muscle CSA (r=0.35 to 0.37, P<0.001). Multivariate regression analysis, adjusting for age, weight, physical activity and femoral length, indicated that mid-femur muscle CSA predicted tibial mid-shaft bone strength indices better than mid-tibia muscle CSA. In conclusion, the association between a given skeletal site and functionally adjacent muscles may provide a meaningful probe of the site-specific effect of loading on bone.Journal of musculoskeletal & neuronal interactions 09/2013; 13(3):235-44. · 2.40 Impact Factor
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ABSTRACT: Evidence strongly supports a positive, causal effect of physical activity on bone strength and suggests long-term benefits of childhood physical activity to the prevention of osteoporosis. The contribution of healthy bone development in youth is likely to be as important to fracture prevention as the amount of late adulthood bone loss. Families, schools (particularly physical education), and communities are key settings for health promotion focused on bone-enhancing physical activity. However, little research has explored the topic of health promotion and physical education as they pertain to bone health, so best practices are not known. Based on our understanding of the literature, we present the top 10 research questions in health promotion and physical education that should be answered to advance bone-enhancing physical activity in children and adolescents.Research quarterly for exercise and sport 03/2015; 86(1):5-12. · 1.26 Impact Factor
published: 18 October 2011
Measurement of osteogenic exercise – how to interpret
Timo Jämsä1,2*, RiikkaAhola1and Raija Korpelainen1,3,4,5
1Department of MedicalTechnology, Institute of Biomedicine, University of Oulu, Oulu, Finland
2Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
3Department of Sports and Exercise Medicine, Oulu Deaconess Institute, Oulu, Finland
4Unit of General Practice, Institute of Health Sciences, University of Oulu, Oulu, Finland
5Unit of General Practice, Institute of Health Sciences, University Hospital of Oulu, Oulu, Finland
Heikki OlaviTikkanen, University of
Saija Kontulainen, University of
Thorsten Rudroff, University of
Colorado at Boulder, USA
Timo Jämsä, Department of Medical
Technology, University of Oulu, P .O.
Box 5000, 90014 Oulu, Finland.
Bone tissue adapts to its mechanical loading environment. We review here the accelero-
metric measurements with special emphasis on osteogenic exercise.The accelerometric
method offers a unique opportunity to assess the intensity of mechanical loadings. We
present methods to interpret accelerometric data, reducing it to the daily distributions of
magnitude, slope, area, and energy of signal.These features represent the intensity level of
physical activities, and were associated with the changes in bone density, bone geometry,
physical performance, and metabolism in healthy premenopausal women. Bone adapta-
tions presented a dose- and intensity dependent relationship with impact loading. Changes
in hip were threshold dependent, indicating the importance of high-impacts exceeding
acceleration of 4g or slope of 100g/s as an osteogenic stimulus.The number of impacts
needed was 60/day. We also present the daily impact score to describe the osteogenic
potential of daily mechanical loading with a single score.The methodology presented here
can be used to study musculoskeletal adaptation to exercise in other target groups as well.
Keywords: physical activity, exercise, accelerometer, bone, osteoporosis, BMD, mechanical loading, biomechanics
Bone is dynamic tissue that is able to adapt its structure and
strength to mechanical loading environment. Aging and related
changes in hormones, nutrition, muscle mass, and other factors
disturb this adaptation process, making bones weaker and fragile
(Seeman and Delmas, 2006). Due to aging population, osteo-
porosis and fragility fractures have become a major public health
Physical exercise as a non-pharmaceutical tool has been exten-
sively studied in preventing bone loss. It is well known that
mechanical loading increases and maintains bone mass and
strength. Exercises including high-impact loading have been
shown to be beneficial for bones, and high-impact activities are
most effective in improving femoral neck bone mineral density
(BMD) at the hip (Wolff et al., 1999; Wallace and Cumming,
2000), a common site of osteoporotic fracture. However, the
dose–response, i.e., the optimal amount, intensity, frequency, and
duration of bone exercise, is far less known, because of tech-
nological challenges for long-term evaluation of the osteogenic
features of exercise in population-based studies. Accelerometer-
based measurement of body movement is an accepted method
for monitoring physical activity (Mathie et al., 2004; Chen and
Bassett,2005). The method can also be used to assess the mechan-
ical loading of bones,and thus for optimizing exercises to prevent
Here we review the accelerometric measurement of motion
with special emphasis on osteogenic exercise, and the findings
obtained during our set of studies revealing dose–response and
the determinants of physical activity and exercise beneficial for
ACCELEROMETRIC MEASUREMENT OF HUMAN MOTION
Accelerometer sensors measure acceleration, i.e., the change in
speed with respect to time, expressed as (m/s2) or multiplies of
the acceleration of gravity (g =9.81m/s2). It is to be noted that
accelerometers do not measure static motion. The derivative of
acceleration with respect to time is jerk (m/s3) or (g/s).
onal dimensions (e.g., vertical, anterior–posterior, and medial–
both acceleration due to movement and acceleration due to grav-
ity. Accelerometers have become a valuable method of assessing
human motion in clinical research and everyday life (Mathie
et al., 2004; Plasqui and Westerterp, 2007). They are small,
unobtrusive, and light-weight, and they are also able to record
The magnitude of acceleration in human movements depends
on the measurement site and the activity that is being done. The
highest vertical values of up to 15g or even more are measured
close to the site of ground contact (Bhattacharya et al., 1980;
Gross and Nelson,1988;Moran and Marshall,2006). The signal is
attenuated greatly as the shock wave propagates up the body, and
attenuation is present even in the ankle joint (Gross and Nelson,
the muscles,bone,ligaments,and tendons.
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Jämsä et al. Measurement of osteogenic exercise
The place of attachment of the accelerometer on the human
body is of great importance. If the whole body is being studied,
waist or back give an estimate of the acceleration at the center of
mass. Sensors attached to the wrist, arm, or ankle provide infor-
mation on the movement of the extremities. Regarding sensor
attachment,subject compliance to wearing the sensor is crucial in
long-term conditions (Mathie et al., 2004; Trost et al., 2005).
Raw acceleration data can be described with basic statistics,
such as mean, root-mean-square (RMS) value, or variance (God-
frey et al., 2008), but a common way to express physical activity
is to use counts. Counts are arbitrary and not universal,since they
depend on signal processing (Chen and Bassett,2005) and also on
sensor properties. Counts can be determined for example as (1)
the number of times the acceleration signal exceeds a pre-defined
threshold, (2) the maximum acceleration value during the time
period (epoch), or (3) the integral of the signal for the epoch
(Mathie et al., 2004; Chen and Bassett, 2005). Several other sig-
nal analysis tools can also be applied, such as pattern recognition,
wavelets, classification trees, or neural networks (Godfrey et al.,
2008). Standardized procedures to calculate physical activity from
minute,physical activity is described as minutes spent in different
son et al., 1998; Janz et al., 2004). Counts per minute can also be
calibrated to energy expenditure, i.e., metabolic energy equiva-
lents (MET; Trost et al., 2005; Plasqui and Westerterp, 2007). The
equations used (Ward et al., 2005).
MEASUREMENT OF OSTEOGENIC EXERCISE
Despite of vast literature on the use of accelerometers in mea-
suring physical activity,studies using accelerometers to determine
optimal exercise for the bone are few. In terms of bone loading,
activity counts or MET levels do not directly provide information
on mechanical loading or strain levels. Bone can adjust to a short
bout of mechanical loading, and a load of high strain with only
a few repetitions may optimize the osteogenic response (Turner,
1998; Kato et al., 2006). A surrogate measure of bone loading can
be obtained with accelerometers.
For a bone undergoing an impact with the ground,the acceler-
ation a is related to the external force F according to the effective
mass m involved (F=ma). Furthermore, stress σ is the force
applied to the cross-sectional area A and strain ε is the stress
divided by the elastic modulus E. It follows that
Thus, there is an association between acceleration a and strain
ε. Consequently, strain rate is related to the rate of acceleration
An accelerometer-based bone exercise recorder has been devel-
vertical acceleration peaks caused by impacts. The distribution of
daily impacts according to acceleration levels is recorded, which
enables analysis of the number of loadings, and also the various
features of the activities in terms of magnitude, slope (jerk), area,
and energy of the acceleration signal.
A combined parameter to describe the total effect of daily
impact activity would be valuable. Previously, daily stress stim-
ulus (Carter et al., 1987) and osteogenic index (Turner, 1998)
theories were suggested to describe the osteogenic potential of
exercise. These theories consider exponential or logarithmic rela-
tionships between loading numbers and magnitude. Inspired by
these theories, we developed and tested the daily impact score
(DIS) to describe the overall daily exercise using a single score
of accelerations in two ways, using exponential (DISExp) or log-
arithmic (DISLog) relationship between acceleration magnitude
and number of loading cycles:
in which ajis the jth acceleration level of impacts and Njis the
factor for the relative importance of the magnitude.
LONG-TERM ACCELERATION DATA FROM AN EXERCISE
Acceleration data were obtained from a 12-month population-
based randomized controlled exercise intervention (Vainionpää
et al., 2005, 2006) in healthy women (35–40years, N =34 in the
high-impact exercise group and N =30 in the control group).
Average (SD) height was 162.9 (SD 6.0)cm and BMI was 25.5
(4.6)kg/m2. Originally, the study sample was chosen to represent
a clinically potential target group for early prevention of osteo-
porosis. The individual daily bone loading was assessed with the
waist-worn bone exercise recorder (Newtest Ltd., Oulu, Finland).
The subjects were asked to carry the black-box recorder close to
recording time was several weeks.
eration levels from 0.3 to 9.9g (acceleration of gravity subtracted,
i.e.,0g equal to standing). The distribution of average daily num-
ber of impacts with different magnitudes,slopes (jerk),areas,and
signal energies was analyzed. Figure 1 shows the distribution of
average daily number of impacts at different acceleration levels in
the high-impact exercise group and controls. The average DISExp
and DISLogwere also calculated.
Exercise-induced bone changes during the 12-month study
were analyzed with respect to the acceleration data. BMD was
measured at the proximal femur and lumbar spine with dual X-
ray absorptiometry (Hologic Delphi QDR, Bedford, MA, USA),
ral computed tomography (Siemens Somatom Emotion,Munich,
Frontiers in Physiology | Clinical andTranslational Physiology
October 2011 | Volume 2 | Article 73 | 2
Jämsä et al. Measurement of osteogenic exercise
FIGURE 1 |The average daily distribution of impacts during the
12-month high-impact exercise intervention in premenopausal
women. HIE, high-impact exercise group (N =34); C, control group
(N =30). Modified from Jämsä et al. (2006).
Germany). Calcaneal ultrasound attenuation and velocity were
assessed with quantitative ultrasound (Hologic Sahara, Bedford,
MA, USA). High-impact training effects on bone metabolism,
physical performance, and risk factors of cardiovascular diseases
were also evaluated.
favored bone formation,increased BMD in weight-bearing bones,
especially at the hip, and led to geometric adaptations by increas-
ing periosteal circumference of the femur (Vainionpää et al.,2005,
2006, 2007a). Bone adaptations had a dose- and intensity depen-
dent relationship with measured impact loading. The number of
ionpää et al., 2006). Impact exercise also had a favorable effect
on bone metabolism, physical performance, and cardiorespira-
tory risk factors by increasing maximal oxygen uptake, dynamic
leg strength, and decreasing serum basal parathormone levels,
low-density lipoproteins, and waist circumference (Vainionpää
et al., 2007b, 2009). These changes were dose-dependent at wide
The magnitude, slope, area, and energy of the acceleration sig-
et al., 2006; Heikkinen et al., 2007). Changes in proximal femur
were threshold dependent, indicating the importance of high-
impacts exceeding acceleration of 4g as an osteogenic stimulus,
the hip was 100g/s. Similarly,acceleration peak area of 2m/s,and
signal energy of 75m2/s3were found to be thresholds for BMD
changes at the hip. These high intensity impacts can be achieved
during exercise including fast movements such as running and
jumping. The study also showed that even lower intensity exercise
at levels above 1g was osteogenic, the number of impacts being
the most significant predictor of change in bone circumference
(Vainionpää et al., 2007a).
Daily impact score calculated in either of the ways was sig-
nificantly higher in the exercise group than in the control group
(Ahola et al., 2010). DISExpand DISLogwere strongly correlated
changes at the hip and geometry changes at the mid-femur in the
for objective assessment of physical activity. Since bone adapts to
mechanical loadings, the accelerometric method offers a unique
ings. We presented a method to interpret accelerometric data,
reducing it to the daily distributions of the magnitude,slope,area,
and energy of the acceleration signal. All these features appeared
to represent the intensity level of physical activities, and were
associated with the changes in bone density,bone geometry,phys-
ical performance, and metabolism. Bone adaptations presented a
dose- and intensity dependent relationship with measured impact
loading. Changes in proximal femur were threshold dependent,
indicating the importance of high-impacts exceeding acceleration
of 4g or acceleration slope of 100g/s as an osteogenic stimulus.
The number of impacts needed to achieve this stimulation was
We also presented the DIS to describe the osteogenic poten-
tial of daily mechanical loading with a single score. DIS offers a
a single score in healthy premenopausal women.
There are challenges related to accelerometer compliance, data
the set of studies presented here, the impacts were measured on a
accelerometers. Future studies should aim to integrate the differ-
ent characteristics of loading into a single model to reveal which
component of loading best predicts bone changes.
The results obtained using quantification of physical activ-
ity provide new information for designing optimal and feasible
training programs that can prevent lower extremity and lumbar
bone loss in healthy premenopausal women. The methodology
presented here can be used to study bone adaptation to physical
exercise in other age groups as well. It also offers a possibility to
examine the dose–response of other tissues sensitive to mechani-
cal loading,such as articular cartilage. Since the memory capacity
of accelerometers has expanded rapidly, it is today possible to
record several weeks of raw data without data reduction during
data collection. The current approach can thus be extended to
study the dose–response in further details, e.g., the restoration of
mechanosensitivity during rest periods between bouts of impacts.
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 01 July 2011; paper pending
published: 26 July 2011; accepted: 03
October 2011; published online: 18 Octo-
Citation: Jämsä T, Ahola R and Kor-
pelainen R (2011) Measurement of
osteogenic exercise – how to interpret
accelerometric data? Front. Physio. 2:73.
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