Wearable with different characteristics making the choice of the

motion sensors (accelerometers and pedometers) are popular tools for objective
assessment. Pedometers are used to measure steps and distance while
accelerometers measure acceleration and movement (Strath
et al., 2013). Pedometers are motion sensors that record movement in
terms of steps taken. Early forms of pedometers used mechanical sensors that
identified steps based on the force generated during walking. Nowadays, with
the advancement of technologies, they use microelectromechanical systems to
identify steps which considerably increased their accuracy. Most of them are
hip-worn, but it is suggested that the more accurate position should be the
ankle. Furthermore, some recent models also allow measurement of energy
expenditure, acceleration, and sleep (Plasqui, Bonomi
& Westerterp, 2013). Accelerometers provide information about type,
frequency, intensity, and duration of physical activity, and, thus, they are
commonly used in research studies. Similar to
pedometers, they are typically hip-worn, but can also be fixed to ankles or
wrists. It is proposed that the more accurate position to wear accelerometers
is the lower back or hip, i.e. closer to the centre of the mass. They rely on
microelectromechanical systems to record acceleration and objectively capture
body movements. Thanks to the technology advancements, they can detect types of
physical activity and energy expenditure. There are many commercially available
accelerometers with different characteristics making the choice of the most
suitable accelerometer very difficult. (Plasqui et al.,
2013; Ainsworth et al., 2015)

only that the use of accelerometers increases in recent years, but with
technology improvements, there is a tendency to insert them into smartphones as they are regularly used in everyday lives,
especially among adolescents. It is proposed that designed application for
mobile phones should be used with other objective assessment monitors, which
will improve the quality of collected data (Dunton et
al., 2014; Shoaib, Bosch, Incel, Scholten & Havinga, 2014). On the other
hand, there is a high inaccuracy of smartphone pedometer applications, which
suggests caution in the interpretation of smartphone application data (Orr et
al., 2015).

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and disadvantages of motion sensors

use of wearable motion sensors, such as pedometers and accelerometers, in
physical activity assessment increases in research and clinical assessment.
However, the choice of the most adequate monitor will depend on several
factors: research goal, target population, physical activity characteristics,
cost-efficiency, and required measurement precision (Ainsworth
et al., 2015).

are inexpensive and present a low burden for participants. Further, they can be
used in studies with many participants and data obtained from pedometers are
easily processed.  But pedometers do not
measure intensity or duration of physical activity and are not accurate for
assessment of energy expenditure (Strath et al., 2013).
Pedometers also
fail to be accurate at slower walking speeds or when worn at pockets or wrists
and they cannot detect sedentary activities, posture, and energy expenditure
(Ainsworth et al., 2015).

of using accelerometers include detailed data about intensity, frequency, and
duration of physical activity, they are relatively inexpensive, small, and
non-invasive. The memory capacity increases nowadays, so data can be collected
over longer period of time. However, they are not suitable for all physical
activities, especially those that require the activity of the upper body parts.
Also, data are not measured in commonly used units and transformation of units
is time demanding (Strath et al., 2013). One of
the important advantages of accelerometers is the possibility to detect seated
postures and transitions between seated and standing postures. Yet, only few of
them can measure light-intensity physical activity and sedentary behaviour
(Ainsworth et al., 2015).

is a number of motion sensors commercially available for the assessment of
physical activity. Plasqui et al. (2013) compared validity of accelerometers
used in 15 different validation studies and proposed the necessity of
validation of accelerometers against doubly labelled water method. Although
accelerometers provide daily data in the assessment of physical activity and
doubly labelled water provides a measure of energy expenditure over a period of
time and both methods are prone to the error, for the most accurate measures of
physical activity both methods should be used complementarily (Plasqui et al., 2013).
In the study of Lee et al. (2014), eight different types
of motion sensors were investigated for the accuracy to estimate energy
expenditure. Participants wore all of them at the same time during activity
routine of 13 different activities categorized into sedentary, walking, running
and moderate-to vigorous activities. Devices were validated against ActiGraph, as the one most commonly used and almost
all of them showed good potential for the assessment of physical activity (Lee, Kim and Welk, 2014).

technology development provides opportunities to improve physical activity
assessment methods and overcome disadvantages of current methods. Pedometers
and most of accelerometers detect movements in the vertical plane. But some
accelerometers are sensitive to two or three planes and able to detect
different physical activities (McCarthy & Grey,
2015). Triaxial accelerometers show a high
sensitivity for sitting, standing, walking, running, and cycling (Skotte,
Korshøj, Kristiansen, Hanisch & Holtermann, 2014). Gatti et al. (2015)
found excellent reliability and validity of a triaxial accelerometer placed at
the waist and shank during running and pedal-revolution counts during bicycling
(Gatti, Stratford, Brenneman & Maly, 2015). They also have a
potential to be used to measure upper extremity physical activity, especially
if worn on wrists. That way they monitor arm usage and even detect differences
in slow arm movements, suggesting the importance in their usage during
rehabilitation (Lawinger, Uhl, Abel & Kamineni,
2015). Still, Pediši? and Bauman (2014) suggest that the use of motion sensors
is general population studies is still limited due to different study designs,
validity, between-study comparability and simplicity. Further
problem that could occur with motion sensors is limitation in cooperation with
participants. Participants could easily forget or refuse to wear them, and they
usually remove them during sleep and water-related activities (Dunton et al.,


use of motion sensors in clinical studies

behaviour increases the risk of chronic diseases and it is now identified as
one of the leading causes of global mortality. For this reason, physical
activity has important benefits in the general population and the World Health
Organisation (WHO) recognises its importance in health. Research in this area
provides important information about the dose-response relationship between
physical activity and health. This, together with the valid methods for the
assessment of physical activity, offers necessary information to make an
intervention plan to reduce sedentary behaviour (WHO, 2010). It is required to
address physical inactivity and develop specific interventions and implement
them at the national levels to increase physical activity among population and,
thus, decrease the burden of disease (Bauman, Merom,
Bull, Buchner & Fiatarone Singh, 2016).

the consequence of lifestyle and not only genetic factors in the development of
many diseases, current recommendations for their prevention include physical
activity. Motion sensors can be used to examine at which levels physical
activity can affect metabolic changes in
diabetic patients and be clinically beneficial (Herzig
et al. 2013). By using a motion sensor among patients with diabetes, low
levels of physical activity in patients, in term of total energy expenditure,
number of steps, and duration of physical activity, are observed (Fagour et al., 2013). Similarly, low levels physical
activity are detected among people with depressive and anxiety disorders,
measured by using accelerometer. Grounding the results on accelerometer
measures, it is recommended that for this type of patients, light physical
activity is more efficient than high-intensity physical activity in reducing
the disorders manifestation (Helgadóttir, Forsell &
Ekblom, 2015).

By recognizing the consequences of sedentary behaviour
in the development of diseases and the importance of physical activity to
improve health outcomes, motion sensors become very important monitoring and
interventional tools. It is reported that they can be used as intervention to
improve glucose metabolism with increase in physical activity in diabetic
patients (Miyazaki & Kotani, 2015).
Pedometer-driven physical activity is used as an intervention to increase
physical activity and consequently improve health. This is confirmed for
several diseases, such as diabetes (Guglani, Shenoy and
Sandhu, 2014), obesity (Cai et al., 2016),
mental illness (Helgadóttir et al., 2015), musculoskeletal
diseases (Mansi et al., 2014), and chronic
obstructive pulmonary disease (Mendoza et al., 2014).
Still, future studies are required for further clarification.



understanding the effect of physical inactivity on health, there is a need for
validated methods that measure physical activity and inactivity. There is no
gold standard for motion sensors and the choice of the optimal motion sensor is
complex. Motion sensors eliminate the problems of subjective methods, but they
are more money and time consuming and as discussed, they have their own
(dis)advantages. Motion sensors have the advantage of cost, non-invasiveness
and clear data. Still, there are lot limitations and it is suggested to use
them simultaneously with other physical assessment methods to improve the data
quality. A large heterogeneity in assessment of different types of motion
sensors across studies exists and data need to be interpreted with a caution. Yet,
they provide very important data in clinical studies. Not only that motion
sensors can be used in monitoring, but also in health intervention. The valid
interpretation of data in these studies can help in minimizing sedentary
behaviour and improve the assessment of health outcomes associated with
increased physical activity. Further research is necessary to support the use
of motion sensors interventions as long term interventions for chronic