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MINI REVIEW
published: xx June 2019
doi: 10.3389/fpubh.2019.00167
Frontiers in Public Health | www.frontiersin.org 1June 2019 | Volume 7 | Article 167
Edited by:
Daniel P. Bailey,
University of Bedfordshire Bedford,
United Kingdom
Reviewed by:
Anselm Ting Su,
Universiti Malaysia Sarawak, Malaysia
Jean-Frédéric Brun,
Inserm U1046 Physiologie Et
Médecine Expérimentale Du Coeur Et
Des Muscles, France
*Correspondence:
Frédéric Dutheil
frederic.dutheil@uca.fr
Specialty section:
This article was submitted to
Occupational Health and Safety,
a section of the journal
Frontiers in Public Health
Received: 16 October 2018
Accepted: 05 June 2019
Published: xx June 2019
Citation:
Boudet G, Chausse P, Thivel D,
Rousset S, Mermillod M, Baker JS,
Parreira LM, Esquirol Y, Duclos M and
Dutheil F (2019) How to Measure
Sedentary Behavior at Work?
Front. Public Health 7:167.
doi: 10.3389/fpubh.2019.00167
How to Measure Sedentary Behavior
at Work?
Gil Boudet 1, Pierre Chausse 2, David Thivel 3,4 , Sylvie Rousset 5, Martial Mermillod 4,6 ,
Julien S. Baker 7, Lenise M. Parreira 1, Yolande Esquirol 8, Martine Duclos 9and
Frédéric Dutheil 10,11
*
1Faculté de Médecine, Institut de Médecine du Travail, Université Clermont-Auvergne, Clermont-Ferrand, France, 2Cellule
d’Accompagnement Technologique–Department of Technological Accompaniment, CNRS, LaPSCo, Université Clermont
Auvergne, Clermont–Ferrand, France, 3Laboratory of the Metabolic Adaptations to Exercise Under Physiological and
Pathological Conditions (AME2P EA 3533), Université Clermont Auvergne, Clermont–Ferrand, France, 4Institut Universitaire
de France, Paris, France, 5Unité de Nutrition Humaine, INRA, Université Clermont Auvergne, Clermont-Ferrand, France,
6LPNC, CNRS, Université Grenoble Alpes, Université Savoie Mont Blanc, Grenoble, France, 7School of Science and Sport,
Institute of Clinical Exercise and Health Sciences, University of the West of Scotland, Hamilton, United Kingdom,
8Occupational and Preventive Medicine, INSERM UMR-1027, Université Paul Sabatier Toulouse 3, CHU Toulouse, Toulouse,
France, 9Sport Medicine and Functional Explorations, CRNH, INRA UMR-1019, University Hospital of Clermont–Ferrand,
Université Clermont Auvergne, CHU Clermont–Ferrand, Clermont-Ferrand, France, 10 LaPSCo, Physiological and
Psychosocial Stress, Preventive and Occupational Medicine, CNRS, University Hospital of Clermont–Ferrand, Université
Clermont Auvergne, CHU Clermont–Ferrand, WittyFit, Clermont–Ferrand, France, 11 Faculty of Health, School of Exercise
Science, Australian Catholic University, Melbourne, VIC, Australia
Background: Prolonged sedentary behavior (SB) is associated with increased risk for
chronic conditions. A growing number of the workforce is employed in office setting with
high occupational exposure to SB. There is a new focus in assessing, understanding and
reducing SB in the workplace. There are many subjective (questionnaires) and objective
methods (monitoring with wearable devices) available to determine SB. Therefore, we
aimed to provide a global understanding on methods currently used for SB assessment
at work.
Methods: We carried out a systematic review on methods to measure SB at work.
Pubmed, Cochrane, Embase, and Web of Science were searched for peer-reviewed
English-language articles published between 1st January 2000 and 17th March 2019.
Results: We included 154 articles: 89 were cross-sectional and 65 were longitudinal
studies, for a total of 474,091 participants. SB was assessed by self-reported
questionnaires in 91 studies, by wearables devices in also 91 studies, and simultaneously
by a questionnaire and wearables devices in 30 studies. Among the 91 studies using
wearable devices, 73 studies used only one device, 15 studies used several devices,
and three studies used complex physiological systems. Studies exploring SB on a large
sample used significantly more only questionnaires and/or one wearable device.
Conclusions: Available questionnaires are the most accessible method for studies on
large population with a limited budget. For smaller groups, SB at work can be objectively
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Boudet et al. Sedentary Behavior Measurement at Work
measured with wearable devices (accelerometers, heart-rate monitors, pressure meters,
goniometers, electromyography meters, gas-meters) and the results can be associated
and compared with a subjective measure (questionnaire). The number of devices worn
can increase the accuracy but make the analysis more complex and time consuming.
Keywords: occupational health, sedentary lifestyle, workplace, sedentary behavior measurement, work,
questionnaires, wearable devices, recommendations
INTRODUCTION
Sedentary behavior (SB), has been defined as sitting or lying with
low energy expenditure ≤1.5 METs (1) and is an independent risk
factor for numerous adverse health outcomes. In industrialized
modern societies, more and more time is spent for SB activities
during normal lifestyle behavior, such as working on computers,
traveling by car, and watching television during leisure time
(2,3). Further to this, more workers are now employed in low
activity jobs such as administrative work. Office workers can
have SB for more than ¾ of their working day (4). Chronic
disease and all-cause mortality have been linked with self-
reported time spent sitting (5–13). A dose response relationship
has been demonstrated between all-cause mortality and daily
total sitting, with a 2% increase in all-cause mortality per hour
seated per day (14). Even after adjustment on the quantity of
moderate or vigorous physical activity (15,16), the risk of death
persists, demonstrating that time spent sitting is a risk factor
independent of the level of physical activity. SB can be measured
by declarative methods (auto-administrate questionnaires) and
objective methods (observation, video, or technical instruments).
Descriptive parameters of physical activity and sedentary activity
used most often are duration, frequency, intensity, domain or
context (leisure, work, domestic, transport), and the type of
activity. Indicators combining these parameters can be calculated
globally or for each one of the domains individually. The most
common are the volume (time ×frequency) and the energy
expenditure (duration ×frequency ×intensity), the latter being
calculated to account for overall physical activity. Time spent in
front of a screen (television, video, video games, computer...) is
currently the most used sedentary indicator and in the majority
studies, is the time spent watching television measured by survey
techniques. Considering the public health impact of SB at work,
there is now a growing research interest about sedentariness at
work. However, SB is measured through a wide range of methods,
but no scientific articles provide a global overview on all methods
used to quantify sedentary behavior.
OBJECTIVE
The aim of this paper was to provide a global understanding on
methods currently available for SB assessment at work.
SEARCH STRATEGY
Published studies with measures of SB at work were retrieved
through a systematic search of the Pubmed, Cochrane, Embase,
and Web of Science databases. We selected articles published
between 1st January 2000 and 27th March 2019 because SB gained
momentum in recent years, with more diversity on assessing
SB at work, and because only recent articles distinguished
between SB and physical inactivity and their specific health effects
(6,17–19). The search strategy and keywords used are detailed in
Supplementary Material Appendix 1. We restricted our search
to articles in humans and written in English. We did not restrict
our search to specific countries or regions, nor on a minimal
sample size. Included articles had to describe tools used to
measure SB at work. The search strategy is displayed in Figure 1.
Three authors (GB, PC, FD) conducted all literature searches
and agreed on the final decision for articles inclusion. A fourth
author (MD) reviewed articles when no consensus was met.
Then, eligible articles were reviewed by all authors.
DATA EXTRACTION AND SYNTHESIS
We extracted the following information: type of study
(longitudinal, cross-sectional), category of material
(questionnaire, one common sensor, multiple sensors, complex
physiological system), number of subjects and the main measure
of sedentariness. Identified devices which assessed sedentary
behavior at work where tabulated to highlight the performance
and the usability of methods and devices to access sedentary
behavior at work (see Table S1 for the complete lists of included
articles with those details).
CHARACTERISTICS OF INCLUDED
ARTICLES
An initial search retrieved a possible 4,118 articles. Removing
duplicates and applying the selection criteria decreased the
number of articles reporting measures of SB at work to 154
articles (Figure 1). Among the 154 included articles, 89 were
cross-sectional studies, and 65 were longitudinal studies, for a
total of 474 091 participants. SB was assessed by self-reported
questionnaires in 91 studies, and by wearables devices in also
91 studies. Among those studies, 30 studies used simultaneously
a questionnaire and wearables devices. Among the 91 studies
using wearable devices, 73 studies used only one device, 15
studies used several devices, and three studies used complex
physiological systems. Studies exploring SB on a large population
used significantly more only questionnaires and/or one wearable
device. Complete list of included articles, with details on the
type of the study, number of participants, type of measures of
SB, and main outcomes are presented in Table S1. Methods of
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Boudet et al. Sedentary Behavior Measurement at Work
FIGURE 1 | Search strategy.
measuring SB retrieved in included articles are detailed below.
For practitioners and researchers who want to evaluate SB at the
workplace, we propose a strategy for the best options to evaluate
SB in the workplace, depending on several factors, including
comfort, number of subjects, duration of measures, accuracy, and
budget (Figures 2,3and Table 1).
METHODS OF MEASURING SEDENTARY
BEHAVIOR
Declarative Methods-Self-Reported
Questionnaires
These questionnaires are the most common method of
measuring SB, relying on recall ability of participants (20).
The commonly used self-report questionnaires for SB at work
assessment are: The Global Physical Activity Questionnaire
(GPAQ), International Physical Activity Questionnaire (IPAQ)
(21,22), Workforce Sitting Questionnaire (WSQ, Adapted from
the Marshall Questionnaire), Occupational Sitting and Physical
Activity Questionnaire (OSPAQ) (23) and European Physical
activity Questionnaire (EPAQ) (24). Questionnaires differed on
global characteristics of SB or PA (such as duration, intensity
or frequency), precision of data (habitual or recent, leisure, or
non-leisure activities), reporting data (such as time, calories,
or scores), time of recall (such as last week or over the 12
last months), and method for conducting the survey (such as
paper, computer, face-to-face) (25). Questionnaires have the
advantages of their low cost and low effort, both for responders
and researchers, rendering them accessible for studies in large
populations. However, self-reported SB at the workplace has
been demonstrated to be imprecise, biased in measurement of
light or moderate physical activity, and in the assessment of
energy expenditure. Severe others limitations are the dependency
on written language and external factors such as age, seasonal
variation, complexity of the questionnaire, and social desirability)
(26–30). Characteristics and performances of questionnaires for
SB assessment at work are presented in Table 2.
OBJECTIVE METHODS
Visual Observation (Direct or Videotaped)
SB at work can also be assessed by visual observation, either
recorded or on-site. Visual observation is still a classical method
used by ergonomics, occupational physicians, or researchers
(30). This method of assessment is often use for assessing body
postures at work in delimited space (e.g., work space). Contextual
information (such as location, clothing, or time) and details
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Boudet et al. Sedentary Behavior Measurement at Work
FIGURE 2 | Decision strategy for the best option to measure sedentary behavior at work.
FIGURE 3 | Categorization of wearable devices to measure sedentary
behavior depending on accuracy and complexity.
on SB (such as type or personal activities) could be assessed
with this method. However, direct observations are costly and
time consuming (31), and are therefore mostly adapted for
small populations and on short periods. Visual observations
are also dependent on observers who may rate differently the
same behavior (32). Observed workers may also modify their
behavior (observational bias) because of the logistic burden
associated with data collection. Videotaped monitoring at work
also needs the authorization of the employers and workers and
ethical consideration.
CARDIORESPIRATORY ASSESSMENT
Indirect Calorimetry (IC)
With IC, total energy expenditure is calculated from Weir’s
formula that takes into account oxygen consumption and carbon
dioxide production (33). This accurate and non-invasive method
can be used in routine but not in large epidemiological studies
nor for measures in an ecologic environment (outside of a
laboratory or a specific workplace setting). Moreover, materials
needed are costly. For data collection, the workers needs to
wear a facemask linked with a central unit. For ambulatory
measurements, the central unit could be worn in a backpack.
Thus by discriminating energy expenditure, SB is defined as
seated, reclining, or lying activities requiring low levels of energy
expenditure (i.e., ≤1.5 METs), light-intensity physical activity
(LPA) as standing is between 1.6 and 2.9 METs and Moderate-
to vigorous-intensity physical activity (MVPA) require energy
expenditure ≥3.0 METs). IC can evaluate sedentary time.
These analyzers are now portable like the Cosmed K5 (34) or
Metamax Cortex (35). Their use over a long period can be
difficult to support depending on the activity of the worker
but are still feasible. Because of the relatively slight differences
in energy expenditure between sitting and standing posture
(36,37), assessment of energy expenditure does not provide
reliable information about the body posture. So, measurement
of body posture is also required for assessment of SB at work.
Conversely, most of body positions at work can be assessed by
wearable devices. The use of multiple devices may also inform on
anatomical location of movements.
Holter-Electrocardiography (Holter-ECG)
A linear relationship between cardiorespiratory response and
energy expenditure, and thus with activity intensity has been
clearly demonstrated (38). Heart rate (HR) can therefore be used
to estimate energy expenditure. Coupling HR monitoring and
accelerometers leads to a better accuracy in the assessment of SB
and physical activity (30,39). Historically, electrical HR sensors
detect the electric impulses that are linked with the myocardial
contraction. The signal allows detection of all heartbeats, and
therefore of the HR. In clinical setting, the gold-standard for
electrocardiographic assessment is a 12-lead ECG. In an ecologic
environment (outside of hospital), a portable 3 or 5-lead Holter-
ECG is commonly used for scientific research. It allows abnormal
heart rhythms and cardiac symptoms detection and is considered
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Boudet et al. Sedentary Behavior Measurement at Work
TABLE 1 | Instrument, raw unit, cost, and environment of tools to measure sedentary behavior at work.
Instrument Measure/raw unit Cost Environment
Questionnaire Response quote qualitative Negligible Possible at work but take time
Video observation Video qualitative 50 to 500efor a camera May need authorization especially at work
Smartphone All sensors (XYZ g, m/s, position, direction, brightness
illuminance lux …)
300 to 1000e+costs of
applications
Easy to wear and common
Accelerometer g or count (on X,Y,Z axis 3D, position, direction, brightness
illuminance lux)
50 to 400eEasy to wear even at work
Heart rate monitor Beat/minute 50 to 300eEasy to wear even at work
Holter-ECG RR interval from ECG 300 to 2000eEasy to wear even at work
Gas analyser O2 CO2 consumption/production (liter, m3…) 20 to 30000eFor a short period on few individuals
Less comfortable
as a medical device. Commercially wearables Holter-ECG are
often based on simply a 1- or 2-lead ECG. Despite its accuracy
and validity, measures with 1- or 2-lead Holter-ECG are more
susceptible to artifacts because of external factors, and therefore
are not consider as a medical device. Major causes are motion,
physical and muscle activity, or detachment of electrodes (40,41).
To allow better diagnostic accuracy, the worker can place time
markers for specific activities or events at the workplace. Data
can be stored directly into a specific memory into the device or in
a digital storage media (e.g., SD cards). Data are downloaded and
analyzed with specific softwares by a cardiologist, a physician, or
a researcher.
Heart-Rate Monitors
There are two different types of technology used by HR
monitors: the electrical signal (chest belt) and optical sensor
(wristwatch or armband) (42). Chest belts detect electrical
signals sent through the heart each time it contracts (ECG-
based detection of RR interval). Sometimes, chest belts can
transmit HR data on a wristwatch providing a feedback (pulse
monitoring) to the user. The Optical HR measurement is based
on photoplethysmography (PPG). The Optical HR devices use
integrated LED and light sensors to detect HR through rhythmic
changes in blood flow occurring at each systole (blood volume
pulse) (43). These sensors are cheap, discrete, and comfortable.
They are mostly placed on wrists and arms, and sometimes
ear lobes or fingertips. Main limitations are artifacts because of
motion and a decreased sensitivity with some skin texture (44).
ECG-based chest belts still offer the most reliable, consistent,
and accurate way to monitor HR thanks to higher sampling
rates and the position of the electrodes closer to the heart
(45). However, many people prefer the comfort and convenience
of optical sensors built into watches, such as Applewatch. HR
monitors are able to capture energy expenditure during working
activities and to categorize levels of physical activity. Moreover,
they can estimate the energy expenditure even with no vertical
trunk displacement that is not taking into account by most
accelerometers and pedometers (46). HR monitors are less
accurate to estimate energy expenditure particularly at very high
and low intensities (47), because the relationship between HR
and energy expenditure is not linear for high intensity of physical
activity or at rest and low-intensity (with confounding factors
such as body position, stress, or caffeine affecting the HR—
energy expenditure relationship) (47). Others factors also affect
this linear relationship or reduce its accuracy, such as age, sex,
body composition and muscle mass, or fitness level (48).
Accelerometers
Accelerometers are currently used to measure and quantify the
physical activity intensity category related to SB and have become
the method of choice for measuring SB. Accelerometers are easy
to use, accurate, and able to capture large amounts of data,
particularly in large studies. These devices detect movement in
real time and measure acceleration (counts) in three orthogonal
planes (anteroposterior, mediolateral, and vertical) (49). The
postulate is that the acceleration detected is proportional to the
force produced by the muscles engaged in motion, and therefore
related to energy expenditure. Time of SB is assessed by two
different ways to detect body posture (standing, sitting, or lying):
(1) posture by tri-axial sensors using gravitational components,
or (2) spinal curvature by three uni-axial gyroscopes orthogonally
aligned. Some accelerometers fail to differentiate walking
intensity or body position (such as standing or sitting)
(50). New accelerometers have a better validity than older
models, compared to energy expenditure measured by doubly
labeled water (DLW). However, accelerometers cannot provide
contextual information (such as type of activity and setting)
and induce a reactivity bias (51). Accuracy to determine SB
depends on the threshold chosen for each count (count cut-
point) (52). Most of the time, the acceleration counts characterize
sedentary (absence of movement) and active behavior. The
most commonly used cut-points for adult populations are <100
counts/min for SB, 100–1,951 counts/min for light-intensity
physical activity (LPA), and ≥1,952 counts/min for moderate-
to vigorous-intensity physical activity (MVPA) for the ActiGraph
accelerometer (53,54). However, these cut-points were developed
in specific populations and during strict, laboratory-based
protocols. Other studies validating the ActiGraph have found
vastly different cut-points for SB (range 50–250 counts/min)
and MVPA (191–2,691 counts/min) in adults, depending on the
population and type of validation setting (55,56). The cut-point
method has several limitations; it cannot differentiate standing
from sitting/lying, but standing is considered LPA because it
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TABLE 2 | Characteristics of self-report questionnaires to measure sedentary behavior at work.
Measure Period(s) of interest Categories of activity included Input Output Special notes
GPAQ Typical week 16 items; PA at work, Moderate to
vigorous, Transportation, Leisure-time
MET-min per week Time spend in moderate or vigorous
PA, Job-related PA, Total physical
activity, Time spend sitting
For adults of both sex. For face-to-face
interviews conducted by trained
interviewers. Many domains explored.
Quantifies exposure. Cross cultural
application. 20min.
IPAQ-S (short) Past week 7 items; moderate or vigorous PA,
walking, sitting, including time spend
at work
Duration (min per week) Duration in each PA domain and
sitting, Job-related
For adults of both sex. Self-administered.
Many settings and in different languages.
Cross cultural application. Shorter than
IPAQ-S. 10 min.
IPAQ-L (Long) Past week 24 items; moderate or vigorous PA,
walking, sitting, including Job-related
PA, house work, transportation PA,
and weekend
Duration (min per week) Duration in each PA domain and
sitting, Job-related, house work,
leisure
For adults of both sex. Self-administered.
Many settings and in different languages.
Cross cultural application. 30min.
WSQ (Workforce Sitting
Questionnaire)
Past week Duration of work. Total and
domain-specific sitting time based on
work and non-workdays,
transportation. Time spend watching
TV, computer, others leisure
Duration (min per week) Duration of work. Time spend sitting
at work and in non-workdays. Time
spend in transportation, in screen
watching and other leisure
For adults of both sex. Self-administered.
For measuring sitting time at work on a
work-day and for assessing total sitting
time based on work and non-workdays.
Cross cultural application.
OSPAQ Past five working days 7 items; Work time spent sitting,
standing, walking, and doing heavy
labor, as well as the total length of
time worked in the past five working
days
Duration (min per week) Time spend sitting, standing and
walking, and doing heavy labor and
total length of working
For adults of both sex. Self-administered.
Only Job-related PA, excluding
transportation, and leisure time. 10 min.
EPAQ Typical week 21 items; Sitting and standing,
moderate PA in leisure and working
time, heavy labor at work
Duration (min per week) Time spend standing, sitting, doing
moderate PA at work and in
non-workdays, in house work, and
leisure and heavy labor at work
For adults of both sex. Self-administered.
Do not distinguish moderate and vigorous
PA, but focus on moderate PA. Assessed
walking and bicycle separately.
MET, Metabolic equivalent of task (1 MET represents 3.5 ml/kg/min oxygen consumption); PA, Physical Activity; Questionnaires, GPAQ Global activity Questionnaire, IPAQ International Physical Activity Questionnaire, IPAQ-S (Short
version), IPAQ-L (Long version), WSQ (Workforce Sitting Questionnaire), EPAQ (European Physical Activity Questionnaire).
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elicits different physiologic responses and has different long-
term health consequences than sitting/lying (57,58). Thus, the
interpretation of what is considered to be active behavior is
consequently different and makes the comparison between the
studies difficult. Obese people spend more time in SB than
normal weight individuals (59,60). Thus, cut-points have to be
more accurate to show difference among and between normal-
weight and obese populations. Accelerometers worn on the right
thigh achieve high accuracy for classification of three distinct
physical activity intensity categories (SB, LPA, and MVPA) as well
as breaks in SB in a semi-structured setting. Wrist accelerometers
also have high accuracy for assessment of SB but have some
misclassifications of LPA and MVPA, with interestingly better
accuracy when they are worn on the left wrist compared to
the right wrist (or hip). These findings support the use of
accelerometers worn at the thigh to assess the time spent in SB
and different categories of physical activity intensity. Alternately,
for researchers using wrist-worn accelerometers to assess physical
activity, wear on the non-dominant wrist is likely to allow
for higher measurement accuracy than wear on the dominant
wrist (61). Due to limitations of the cut-point approach to
measure categories of physical activity intensity, researchers
have utilized modelization technics to improve accuracy of
physical activity measurement from accelerometers worn on
various body locations (62,63). An accelerometer does not give
the position information of the subject. It will be completed
by a gyroscope (measuring orientation and angular velocity)
(Samsung Gear S3) and a magnetometer (detecting Erath’s
magnetic three perpendicular axes X, Y, Z) (Actigraph GT9X)
(64). The ActivPal is an alternative tri-axial accelerometer thigh-
worn. The thigh position allows the determination of step counts,
stepping speed, and start-end of each period spent sitting, lying,
standing, or stepping, as well as breaks in SB and postural
transitions. The ActivPAL is a monobloc system that is discrete,
easily used by individuals, without calibration, and reliable for the
measurement of SB (65,66). Therefore, ActivPAL is increasingly
used in ecological environment outside laboratories.
Global Positioning System (GPS)
Global Positioning System (GPS) can complete this variety
of sensors by giving the geographical position (latitude and
longitude) and time of each geographical position, but mainly
outside building. Newer GPS can also deliver information such
as speed (retrieved from time between different geographical
positions), elevation, and indoor/outdoor activities. However,
most workers spend a high proportion of their time indoors,
and unfortunately GPS are only able to receive indoor signal
from small buildings with a wooden roof or high buildings
with large windows. GPS are unable to determine room-level of
indoor location (67). However, even if GPS is mostly for outdoor
activities, newest GPS can also track SB indoors. Moreover, some
devices also include useful tools such as a brightness sensor
to access sleep quality. These wearable lightweight GPS devices
are easily forgotten by users. The researcher should take care
to check the sampling frequency, resolution, and the maximum
amplitude of the device. In order to make long observations, it
is also necessary to check the device battery and storage space.
Recent smart-phones or smartwatches are equipped with all the
mentioned sensors.
Smartwatches and Smartphones
Smartwatches are wrist-worn computerized devices with
extensive communication capabilities. They are linked to
one mobile operating system. In perpetual development,
manufacturers continue to implement new features, such as
GPS, fitness/health tracking, or waterproof frames (16). The
gestures of the hands, such as smoking, are now accessible thanks
to the addition of reliable and sensitive inertial sensors (17).
In a recent meta-analysis (68) the most popular smartwatches
(connected devices) on the market were compared: from
Apple, Fitbit, Garmin, Lumo, Misfit, Samsung Gear, and
TomTom. Generally, smartwatches tend to underestimate energy
expenditure compared to laboratory reference measurements
(Oxycon Mobile, CosMed K4b2, or MetaMax 3B). Moreover,
while smartwatches get better to estimate energy expenditure
with an increased intensity, validity becomes poorer with
low intensity, and sedentary measures. Because everyone has
a smartphone, they are an alternative to smartwatches or
other wearable devices. Now, all smartphones combine many
sensors, such as GPS or Global Navigation Satellite System
(GLONASS), accelerometer, e-compass, gyroscope, proximity
sensor, or ambient light sensor. Conveniently, smartphones can
be linked with an HR belt, a smartwatch, or even a gas analyzer.
However, all wrist and forearm devices have a tendency for
underestimating HR, especially for exercises at high intensity
and with amplitude of arm movement (such as exercising
on a treadmill or an elliptical machine)—and conversely,
measures of HR are more accurate at rest or for exercise without
movement of arms (such as on a cycle ergometer). While HR
is underestimated for high intensity of physical activity, step
count on the opposite is underestimated for slower walking
speeds and in free-living conditions. Smartphones are also
particularly attractive for context awareness and phone-based
personal information (69). The recognition of some activities
are dependent on position-attachment of the phone on the
body (70). For example, to recognize a specific activity, the
smartphone should be placed on the major members involved
within the activity. Unfortunately, a smartphone placed onto
the body can also be non-compatible with some activities in
an ecological environment (free-living conditions). Algorithm
used for long recording periods can quickly consume the battery
power, and may need a power supply. Another point consists of
choosing the accurate available application.
Mobile Applications
Smartphone applications experienced a boom in medical science.
In 2016, the Play Store displayed 105,000 and the Apple Store
126,000 health or fitness-related apps (71). These applications
propose physical exercises and fitness programs with or without
connected objects such as wristband, pedometer, scale, HR
monitor, smartphone, and smartwatch. When the mobile
applications integrate the use of sensors (accelerometer, HR
monitor, GPS), they inform the user of steps, distance, energy
expenditure, speed, and heart frequency. The three most popular
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applications are Fitbit, Noom, and AppleHealth (Table 3). These
special features are welcomed by the users. Conversely, most of
the applications are not scientifically validated.
WellBeNet (eMouve) and IntellilifePro were two applications
recently scientifically validated to assess accurately time spent
in SB, LPA, MVPA, and the total energy expenditure associated.
These two applications were specially developed to discriminate
SB from LPA, such as standing or slow walking. Accelerometry
data are collected via smartphones [WellBeNet (eMouve)] or via
both a smartphone and smartwatch (IntellilifePro).
E-Move
E-move (Android) application detects leg movements as the
smartphone is worn in a front pants pocket. Different algorithms
were designed for normal and overweight/obese adults. The
TABLE 3 | Characteristics and physical activity parameters evaluated by the three
most downloaded mobile applications.
Application Operating
system
Wearable monitor Measured
parameters
Fitbit Android
iOS
Web
Accelerometer
(wristband)
Manual input
Number of steps or
stairs
Intensity
Distance
Calories burnt
Noom Android
iOS
Smartphone sensors
GPS
HR monitor
Distance
Calories burnt
Speed
Apple iHealth iOS RunKeeper (GPS)
Moves (GPS and
smartphone sensors)
Manual input
Distance
Calories burnt
Number of steps
Duration of activities
total energy expenditure and time spent for each category
of physical activity given by the E-Mouve algorithms were
compared with reference method or device: either Armband or
indirect calorimetry (FitmatePro, Cosmed). Absolute error of the
total energy expenditure and activity estimates are 5.6 and 5.0%,
respectively in normal weight volunteers, and 8.6 and 5.0% in
overweight/obese participants (72,73).
IntellilifePro
IntellifePro is based on the simultaneous use of a smartphone
and a smartwatch (Android or Apple) to detect both leg
and wrist movements. IntellifePro can discriminate passive
from active sitting when in a sitting posture, while the arm,
the wrist and/or the hand are engaged in the movement.
Absolute error of the total energy expenditure and activity
estimates are 3.1, 2.8, 1.5, and 0.04%, for SB, light, moderate,
and vigorous intensity, respectively. The absolute error for
total energy expenditure was lower than 5% in free living
conditions (74).
Pressure Sensors
Another alternative to assess SB is via pressure sensors. Sensors
can be placed in a sock, a shoe, or a chair. In a sock or shoe, a
high pressure measured by the sensor is related to standing, and
a low pressure is related to sitting or lying. On a chair, pressure
sensors (sitting pad) are generally binary: active when the user is
sitting, and inactive when nobody is sitting on the sensor (75).
Current technologies and attachment on the body are presented
in Figure 4.
Characteristics of Sedentary Behavior
Total daily duration of SB is commonly used to study the
effects on health of SB. However, characteristics of SB are of
major importance on health. Particularly, continuous prolonged
FIGURE 4 | Current technologies to measure sedentary behavior and attachment on the body.
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SB may be more deleterious on health outcomes than shorter
bouts of SB but with the same duration (76,77). The need
for a definition of a sedentariness has also been proposed (78).
Investigations of SB at work should not only assess total daily
duration of SB, but also the patterns and durations of SB and
non-SB periods. The context of SB is also important (what, where,
why, when, and with whom).
Limitations
Smart clothing (such as shirts with sensors measuring HR, socks
or shoes combining pressure and accelerometers, or helmets and
caps with a camera and GPS), goniometers (measuring an angle
and angular position), electromyography meters (measuring the
electrical activities of muscles EMG), and wearable camera have
been voluntary excluded of the presented devices because still in
development and not yet used to assess SB at work.
CONCLUSION
We proposed a systematic review on tools available to measure SB
at work. SB was mainly assessed by self-reported questionnaires
or by only one wearable device. Studies using several devices
were less common, and rarely studies used complex physiological
systems. The wide range of wearable devices offer a variety
of methods to evaluate SB at work. It is not an easy task to
select the optimal device and the right measurement strategy
for a particular study purpose. The main factors of work
(inside or outside, working movements, and postures) and
study population (i.e., number, age, gender, body mass index,
and comorbidities) may also affect the choice. To assess SB
at work, four determinants factors should be considered to
choose the appropriate method: (1) quality of measure (e.g.,
time spent on SB or energy expenditure), (2) objectivity of the
data and burden of workers (e.g., time/effort for measures), (3)
cost/burden for the researcher, and (4) specific limitations due
to environment and working activities. Available questionnaires
are the most accessible method for a large population with a
limited budget. SB at work (time sitting) is accessible from some
specific items. It is also possible to deduct SB in measuring
PA at work that is easily measurable. Assessments of SB need
both measures of energy expenditure and of body posture
(dual or multiple wearable devices with sensors). Accurate
measure of SB at work need a sufficient number of subjects
affected to the same assigned task and an objective measure
coupled to a questionnaire (mixed approach method). For a
restrictive group, SB at work can be objectively measured with
wearable devices (accelerometers, heart-rate monitors, pressure
meters, goniometers, electromyography meters, gas-meters) and
can be associated with subjective measures (questionnaires).
The number of devices worn increase the accuracy but
make the analysis complex and time consuming. Furthers
studies are necessary to improve the relative strengths and
weakness of subjective or objective methods to assess SB
at work.
AUTHOR CONTRIBUTIONS
FD: idea of the article. GB, PC, DT, MD, and FD: drafting the
article. All authors: critical revision of the article.
ACKNOWLEDGMENTS
This work was supported by Université Clermont Auvergne,
Institute of Occupational Medicine, by the Physiological and
Psychosocial Stress team from the Laboratory of Social and
Cognitive Psychology (LaPSCo, CNRS), by the laboratory of
the Metabolic Adaptations to Exercise under Physiological and
Pathological conditions (AME2P EA 3533), and by INRA, UNH,
Unité de Nutrition Humaine (UNH, INRA).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpubh.
2019.00167/full#supplementary-material
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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.
Copyright © 2019 Boudet, Chausse, Thivel, Rousset, Mermillod, Baker, Parreira,
Esquirol, Duclos and Dutheil. This is an open-access article distributed under the
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