Journal for the Measurement of Physical Behaviour, 2018, 1, 165-173
https://doi.org/10.1123/jmpb.2018-0022
© 2018 Human Kinetics, Inc.
ORIGINAL RESEARCH
The Accuracy of the Placement of Wearable Monitors to Classify
Sedentary and Stationary Time Under Free-Living Conditions
Alberto Flórez-Pregonero
Pontificia Universidad Javeriana
Matthew S. Buman and Barbara E. Ainsworth
Arizona State University
Background: Published accelerometer cut-points have limited accuracy in measuring sedentary (SED) and stationary time
(STA) despite hip or wrist placement. Few studies have evaluated established cut-points to measure SED and STA in free-living
settings. Methods: This study evaluated published uniaxial and triaxial cut-points of accelerometers and identified optimal cutpoints to measure SED and STA. Twenty participants, ages 18–65, wore three ActiGraph GT3X+ (one on each wrist and the
waist) and two GENEActiv accelerometers (one on each wrist) for one weekday and one weekend day during simultaneous direct
observation of movement. ActiGraph uniaxial cut-points (50, 100, 150, and 500 counts per minute [cpm]) and GENEActiv vector
magnitude cut-points (VMCP; 217 and 386 cpm) were compared against the criterion measure of direct observation. As
compared to the criterion, accuracy was determined with mean percent error, Bland-Altman plots, kappa coefficient, sensitivity,
and specificity. Receiver operating characteristic curves identified cut-points with greatest discrimination to detect SED and STA.
Results: For the GENEActiv, the 217 VMCP was most accurate for measuring SED and STA regardless of which arm wore the
monitor. The ActiGraph was most accurate worn on the right hip using 100 and 150 uniaxial cpm to measure STA and 50 cpm to
measure SED. Optimal ActiGraph VMCP cut-points to classify SED and STA were ActiGraph 2,000 cpm (left-wrist) and 63 cpm
(right hip), respectively. Conclusion: Accuracy of ActiGraph uniaxial cut-points and GENEActiv VMCP is limited in assessing
SED in free-living settings. Newer cut-points may increase the accuracy of measuring SED and STA from monitors in free-living
settings.
Keywords: accelerometers, activity classification, computational methods, sedentary behavior, validation
Sedentary behaviors are a risk factor for several chronic
diseases and mortality (Biswas et al., 2015; Dunstan, Howard,
Healy, & Owen, 2012). Interrupting periods of sedentary behaviors
with light-intensity physical activity may be beneficial for health
(Chastin, Egerton, Leask, & Stamatakis, 2015). Furthermore,
increasing standing time might attenuate the health risks of prolonged sitting (Katzmarzyk, 2014; van der Ploeg et al., 2014).
Sedentary behaviors are defined as any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting,
reclining, or lying posture (e.g., watching TV); alternatively,
stationary behaviors are any waking behavior done while lying,
reclining, sitting, or standing, with no ambulation, irrespective of
energy expenditure (e.g., standing) (Tremblay et al., 2017).
Accelerometer-based wearable monitors are used in sedentary behavior and public health research as they can be used to
measure total volume and breaks in sedentary time (Atkin et al.,
2012). Among these monitors, the activPAL has shown high
accuracy to measure sedentary time (Kozey-Keadle, Libertine,
Lyden, Staudenmayer, & Freedson, 2011; Sellers, Dall, Grant, &
Stansfield, 2016). This type of monitor attaches to the thigh using
adhesive tape which may increase participant burden when
Flórez-Pregonero is with the Departamento de Formación, Pontificia Universidad
Javeriana, Bogota, Colombia. Buman and Ainsworth are with the College of Health
Solutions, Arizona State University, Phoenix, AZ. Ainsworth (Barbara.ainsworth@
asu.edu) is corresponding author.
compared to wrist or hip mounted monitors (Carson, Saunders,
& Tremblay, 2016). Having to attach the activPAL to the thigh
may limit its use for some individuals. Compliance in wearing
monitors is improved by wearing a monitor on the wrist as
opposed to other parts of the body (Freedson, & John, 2013).
The ActiGraph and the GENEActiv are monitors that can be worn
on the wrist. The wrist-worn ActiGraph (Koster et al., 2016) and
GENEActiv (Pavey, Gomersall, Clark, & Brown, 2016) have
been evaluated to measure sedentary time but not stationary time.
Thus, it is important to confirm the accuracy of such monitors to
measure sedentary and stationary behaviors.
The most common method used to process accelerometerbased wearable monitors is to translate accelerometer output into
measures of metabolic equivalents (METs) expenditure that reflect
thresholds (cut-points) for specified levels of activity (Watson,
Carlson, Carroll, & Fulton, 2014). Cut-points are numerical values
for the acceleration of movement intensity (activity counts) that
reflect thresholds in the energy cost of movement; higher numerical
cut-points reflect higher energy costs and vice versa. Most cutpoints are derived from prediction equations in which accelerometer counts are regressed against energy expenditure values in
kilocalories or in oxygen uptake values (Crouter, Clowers, &
Bassett, 2006); other cut-points are proposed based upon researchers’ experiences (e.g., 100 or 150 counts per minute). Cut-points
for sedentary behaviors use activity counts that reflect activities
with an energy expenditure ≤1.5 METs and are commonly summarized using 60-second data-collection epochs. To date, several
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Flórez-Pregonero, Buman, and Ainsworth
uniaxial cut-points for classifying sedentary time with a hipmounted ActiGraph have been proposed including 50 (Crouter
et al., 2006), 100 (Matthews et al., 2008), and 150 counts per
minute (cpm) (Kozey-Keadle et al., 2011). Triaxial cut-points
(i.e., vector magnitude cut-points [VMCP]) that classify sedentary
time from a GENEActiv have been proposed including 217 and
386 cpm for left-wrist and right-wrist respectively (Esliger et al.,
2011). Despite their extensive use, cut-points reflecting sedentary
time have limitations as few cut-points have been tested in freeliving conditions (Healy et al., 2011; Judice, Santos, Hamilton,
Sardinha, & Silva, 2015; Pavey et al., 2016). Cut-points-derived
estimates of sedentary time may reflect little-to-no movement and/
or movements greater than 1.5 METs. Light intensity physical
activities such as dusting or washing dishes with little movement
may be classified as sedentary time (Kozey, Lyden, Howe,
Staudenmayer, & Freedson, 2008). Thus, it is likely that existing
uniaxial sedentary cut-points reflect stationary behaviors rather
than exclusively sedentary behaviors. Accordingly, the cut-points
can result in inaccurate estimates of sedentary time (Crouter et al.,
2006; Lyden, 2012). Likewise, as monitor cut-points are specific to
where they are worn, cut-points should be considered locationspecific; it is unlikely that a cut-point validated for the hip will lead
to accurate classifications at the wrist.
Research suggests that VMCP may be an accurate method to
estimate time spent in sedentary behaviors in older adults in freeliving environments (Aguilar-Farias, Brown, & Peeters, 2014).
However, researchers have not yet developed VMCP to assess
sedentary and/or stationary behaviors with the ActiGraph in healthy
adults. While, sedentary VMCP have been developed under laboratory settings for the GENEActiv, the cut-points have not been
extensively tested in free-living settings (Esliger et al., 2011).
The distinction of sedentary and stationary behaviors may
have important implications in the relationships with health outcomes. As one’s cardiometabolic health may be enhanced differentially by interrupting prolonged sitting time with frequent brief
bouts of light-intensity activity and/or standing, valid measurement
of sedentary and stationary behaviors is important.
The aim of this study is twofold: (1) to test the accuracy of
published uniaxial and VMCP to classify sedentary and stationary
time in free-living conditions in different body locations (left/right
wrist and right hip) as compared to direct observation, and (2) to
develop optimal VMCP to classify sedentary and stationary time
based upon data collected under free-living conditions from the
ActiGraph and GENEActiv monitors.
Methods
Participants
A convenience sample of 20 adults enrolled in the study. Eligible
participants were (a) 18–65 years of age; (b) normal-to-overweight
body mass index (BMI) of 18.5 to 29.9 kg/m2; and (c) without
disease or disability that could inhibit daily physical activity,
assessed by completing the Physical Activity Readiness Questionnaire (PAR-Q; Shephard, 1988). Participants were recruited
through e-mail announcements and fliers placed on the university
campus. All enrolled participants provided informed consent prior
to participation and the University Office of Research Integrity and
Assurance approved the study protocol.
One author (AFP) visited each participant three times. The first
visit included a brief interviewer-administered questionnaire to
collect demographics, an agreement on the times and places for
visits two and three, and an explanation of the use of wearable
monitors. Visits 2 and 3 consisted of data collection of free-living
movement.
Wearable Monitors
The ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL, USA)
and the GENEActiv (Activinsights, Kimbolton, Cambridgeshire,
United Kingdom) wearable monitors were used in this study. The
ActiGraph is a triaxial wearable monitor capable of recording
accelerations in three axes (vertical, anteroposterior, and mediolateral). The ActiGraph GT3X+ measures accelerations ranging
from 30 Hz up to 100 Hz in response to a magnitude range of
±6 g. The GENEActiv is a triaxial wearable monitor capable of
recording accelerations in three axes; it measures accelerations
ranging from 10 Hz up to 100 Hz in response to a magnitude range
of ±8 g.
Before field data collection, the investigators tested the
monitors for inter-monitor reliability by placing them in the
same body location (left arm) during five different activities
performed in a 10-minute period. Intraclass correlation coefficients (ICC) were calculated to assess inter-monitor reliability
in the vertical axis using 60-seconds data-collection epochs
(ICC = 0.95 for ActiGraph, and 0.96 for GENEActiv wearable
monitors).
Before visits 2 and 3, study staff initialized the monitors at
100 Hz and downloaded the data to. csv files in 1-, 15-, and 60second epochs using ActiLife® software 6.11.5 and GENEActiv
software 2.9 for ActiGraph and GENEActiv, respectively.
During visits 2 and 3, participants wore the monitors simultaneously for 6 hours and were observed by trained staff members.
One staff member placed the monitors on participants. Participants
wore one ActiGraph GT3X+ on each wrist in the most proximal
position using the manufacturer’s adjustable wristband; the monitors were oriented so the participant could read the brand logo.
Participants wore another ActiGraph on the hip over the right
anterior superior iliac spine mounted with an elastic belt and with
the USB cap oriented towards the participant’s head. Participants
wore a GENEActiv on each wrist in the most distal position and
next to the ActiGraph with an orientation so the participants could
read the serial number.
Criterion Measure: Direct Observation
The investigators used direct observation with focal sampling and
duration coding to collect criterion data in real-time occurrence
in free-living conditions. They coded activities into six mutually
exclusive categories and/or three non-activities as described below.
Three non-activity categories included times when the participants’
needed privacy, when activities were unobserved, and when there
was an error in recording the data.
• Private: Time when participants required private time
(e.g., restrooms use). Direct observations resumed when the
participant finished the private activity.
• Unobserved: Time when participants were available for observations but out of sight of the observers (e.g., turning corners).
• Error: Time when observers made an error in coding or were
unable to determine an accurate code for an activity performed.
• Walking: Walking for all locomotion purposes, walking in
flat or inclined surfaces, and walking up or down stairs.
Incidental or incomplete steps that did not result in moving
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Wearable Monitors Classification
from one place to another were not included in this category
(e.g., weight shifting).
• Running: Continuous and short bouts of running or jogging
(e.g., jogging for exercising or short runs to cross the street).
• Sports and conditioning exercise: Playing sports or performing
continuous or intermittent conditioning exercises. Exercises
that differed from running or jogging were in this category
(e.g., weight lifting, yoga, Pilates, or gym classes).
• Household chores: Housekeeping activities such as dish washing, gardening, vacuuming, and doing the laundry.
• Standing: Standing with or without upper body movements
while bearing the body weight in one or both lower limbs.
Incidental or incomplete steps that did not result in moving
from one place to another were included in this category.
• Sitting and lying down: Includes various body positions in
which the participant’s buttocks, thighs, or back supported the
body weight. This category included sitting in a chair or
laboratory stool, reclining, and lying down in a supine and/
or prone position.
Simultaneously, two investigators directly observed a participant’s movement in their free-living environment for six hours
per day during two separate days; on a weekday (visit 2) and a
weekend day (visit 3). Each observer independently recorded
activities in an iPad tablet. Every time a participant changed
the activity, observers made an annotation reflecting the new
activity. Annotations were made with a commercially available
app, Timestamped Field Notes app (TFNA) (Neukadye, 2017).
TFNA allows configuration of colored buttons for predefined
observation categories. TFNA stores annotations in an offline
database that later can be downloaded as a. txt file containing
the timestamp and the observations made at each time point.
Tablets were time synchronized with the same computer that
initialized and downloaded the monitor data. Investigators downloaded and exported data from the tablet into a text file (.txt) at the
end of each direct observation period.
The criterion variable for sedentary behaviors was a dichotomous variable calculated by including sitting and lying down for
those observations in which both researchers had 100% agreement.
A minute was considered sedentary when most of its seconds were
sedentary (i.e., between 31–60 seconds per minute). The criterion
variable for stationary behaviors was calculated with the same
criteria except that standing activities were considered as stationary
in addition to sitting and lying down postures.
To address the second aim of this study, two dichotomous
15-second criterion variables (sedentary and stationary) and two
1-second criterion variables (sedentary and stationary) were
computed. The 15-second criterion variable for sedentary behaviors was a dichotomous variable calculated by including sitting
and lying down for those observations in which both researchers
had 100% agreement. A 15-second epoch was considered sedentary when most of its seconds were sedentary (i.e., between
8–15 seconds per minute). The 15-second criterion variable for
stationary behaviors was calculated with the same criteria noted
above except that standing activities were considered as stationary in addition to sitting and lying down postures. The 1-second
criterion variable for sedentary behaviors was a dichotomous
variable including the seconds of sitting and lying down. The
1-second criterion variable for stationary activities was calculated
with the same criteria as sedentary except that standing activities
were considered as stationary in addition to sitting and lying down
postures.
167
Observer Training
Two observers completed 24 hours of one-to-one supervised
training consisting of:
• Two hours to become familiarized with the study protocols
including thorough explanations of activity categories and use
of tablets to record data.
• Two hours of training in direct observation techniques designed not to disrupt, disturb, or modify the participant’s
natural behavior to every extent possible. This included how
the observer placed him- or herself in small spaces to observe
movement but not disturb the participant.
• Ten hours of direct observation practice using the tablets to
record observations while watching a set of training videos
made for this study with people doing various activities at
work and in home environments.
• Ten hours of direct observation practice using the tablets in
real-time occurrence with people doing their own activities at
work and home environments.
After the training, researchers completed a testing session of
direct observation of eight video clips with different activity
scenarios. The total duration of the video clips was 40 minutes
and referred to as the testing set. Two investigators (MSB and
BEA) coded the videos independently; they discussed and
modified the codes assigned as needed to assure full agreement.
To be able to collect field data, the two observers were required
to have high agreement with the testing set determined as an
ICC > 0.80.
Cut-points
To achieve the first aim of the study, ActiGraph uniaxial cut-points
of 50 (Crouter et al., 2006), 100 (Matthews et al., 2008), 150
(Kozey-Keadle et al., 2011), and 500 cpm (Silva, Aires, Santos,
Vale, Welk, & Mota, 2011) were tested for the accuracy of SED
and STA time at each wear location. For the GENEActiv, VMCP of
217 and 386 cpm were tested for accuracy of SED and STA time at
each wear location (Esliger et al., 2011). To achieve the second
study’s aim, analyses used receiver operating characteristic (ROC)
curves to estimate the cut-points for the greatest accuracy of SED
and STA time for the ActiGraph using 1-, 15-, and 60-second
epochs and for the GENEActiv using 1- and 15-second epochs at
each wear location.
Statistical Analysis
Descriptive statistics characterized the sample by sex, age, and
BMI. The ICC identified agreement between the observers’ field
observations. Several computations identified the accuracy (first
aim) of the tested cut-points used to classify an activity as sedentary
for each one of the aforementioned wearable monitors and locations. Percent error (PE) assessed the proportion of error for each of
the tested cut-points relative to the sedentary criterion. PE was
calculated as PE = [(Monitor Total Sedentary Minutes – Criterion
Sedentary Minutes) / Criterion Sedentary Minutes] × 100. A positive PE indicated an overestimation of sedentary time whereas a
negative PE indicated an underestimation of sedentary time.
Kappa was used to observe agreement between each cut-point
and the sedentary criterion value for classifying activities as
sedentary while taking into account the agreement occurring by
chance (Cohen, 1960). Landis and Koch published categories to
interpret the kappa values as follows: 0–0.2 = slight agreement,
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Flórez-Pregonero, Buman, and Ainsworth
0.2–0.4 = fair agreement, 0.4–0.6 = moderate agreement, 0.6–0.8 =
substantial agreement, and 0.8–1.0 = almost perfect agreement
(Landis & Koch, 1977). Kappa was calculated on a minute-byminute basis.
Sensitivity and specificity measure the accuracy of the tested
cut-points to classify an activity as sedentary or non-sedentary.
Sensitivity measured the accuracy of a cut-point to classify an
observed sedentary activity as sedentary (true positives proportion). Sensitivity was calculated using the formula Sensitivity =
True positives / (True positives + False negatives). A sensitivity
value close to 1.0 shows that a cut-point correctly classifies a high
proportion of observed sedentary activities as sedentary; a sensitivity value close to 0.0 indicates that the cut-point fails to classify
observed sedentary activities as sedentary. Specificity measures the
ability of a cut-point to classify observed non-sedentary activities
as non-sedentary (true negatives proportion). Specificity was
calculated using the formula Specificity = True negatives/(False
positives + True negatives). A specificity value close to 1.0 shows
that the cut-point correctly classify a high proportion of the
observed non-sedentary activities as non-sedentary. A specificity
value close to 0.0 indicates that the cut-point fails to classify
observed non-sedentary activities as non-sedentary. The investigators also used PE, kappa, sensitivity, and specificity calculate the
accuracy (first aim) of the tested cut-points to classify activities as
stationary for each wearable monitor worn at different locations on
the body as compared with the stationary behavior criterion.
To develop VMCP (second aim), the investigators randomly
divided the observations into training (50%) and testing (50%)
datasets. ROC curves were created using the training sedentary and
stationary criterion dataset. To determine the cut-points, the investigators used the minimum distance method which identifies the
closest value to the optimal point at the upper-left corner of the
ROC plot where Sensitivity = 1 and 1 − Specificity = 0. The area
under the ROC curve (AUC), defined as an index of the accuracy of
the ROC curve was calculated for each of the estimated cut-points
(Hajian-Tilaki, 2013). An AUC of 1.0 indicates an estimated
cut-point is perfect in the classification of activities. An AUC of
0.5 indicates an estimated cut-point is no better than chance in the
classification activities. An AUC of 0.0 indicates an estimated cutpoint incorrectly classifies all activities. To test the accuracy of the
estimated cut-points further, the investigators computed PE, simple
kappa coefficient, sensitivity, and specificity in the testing dataset
to compare the classification of sedentary and stationary behaviors
obtained from the estimated cut-points with direct observation.
ROC curve analyses were conducted using ROCPLOT macro for
SAS (2005). The investigators used SAS version 9.4 to analyze
the data.
Results
All 20 participants completed the study. Fifty percent of the
participants were female. The mean age and BMI were 30.25
(±6.43) years and 22.7 (±3.1) kg/m2, respectively. All participants
enrolled in the study were right-handed. Due to an error in the
GENEActiv worn on the right wrist, 5.99 hours of movement for
one participant was missing for this device and wrist placement.
The observers directly recorded 241.32 hours of free-living
data. The average length of free-living observation sessions was
5.97 ± 0.26 hours. Table 1 displays the distribution of direct
observation classification categories and movement contexts.
There was a substantial agreement between researchers’ direct
observation codes (ICC = 0.76, 95% CI = 0.75–0.77).
Cut-points Accuracy
To assess the first study aim, investigators tested the accuracy of
published uniaxial ActiGraph and vector magnitude GENEActiv
cut-points to classify SED and STA as compared to direct observation of time spent in sedentary (sitting and lying down) and
stationary (sitting, lying, and standing) behaviors in different body
locations. Tables 2 and 3 show percent error, kappa coefficient,
Table 1 Minutes (Standard Deviation [SD]) and Percent of the Observation Period Stratified by Activity Categories,
Context, and Days of the Week
Total observation time (minutes)
Average observation time
per participant (minutes)
Activity categories
Sitting/lying down
Standing
Other non-sedentary
Unobserved
Private time
Context
Sports/conditioning
Household
Transportation
Occupation
Leisure
Non-agreement
Weekdays
Weekends
Combined
7,180
359
7,314
365.7
14,494
362.35
Minutes (SD)
187.4 (102.1)
64.6 (68.4)
28.9 (27.5)
5.8 (6.8)
4.9 (6.0)
Minutes (SD)
15.9 (38.4)
2.3 (8.8)
16.4 (27.1)
241.9 (115.1)
28.6 (42.5)
Minutes (SD)
67.4 (68.7)
% (SD)
52.3 (28.8)
18.1 (19.8)
8.0 (7.5)
1.6 (1.8)
1.3 (1.6)
% (SD)
4.4 (10.5)
0.66 (2.5)
4.7 (7.6)
67.4 (31.9)
8.0 (12.0)
% (SD)
18.5 (19.0)
Minutes (SD)
140.5 (69.1)
79.0 (51.2)
23.8 (22.2)
3.6 (7.5)
8.0 (13.9)
Minutes (SD)
19.8 (44.4)
2.9 (13.2)
21.2 (25.8)
88 (140.9)
148.8 (120.2)
Minutes (SD)
110.9 (70.5)
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% (SD)
38.5 (19.0)
21.6 (13.7)
6.5 (6.0)
1.0 (2.0)
2.2 (3.8)
% (SD)
5.2 (11.4)
0.8 (3.6)
5.8 (7.0)
23.9 (38.4)
41.1 (33.6)
% (SD)
30.3 (19.4)
Minutes (SD)
163.9 (89.3)
71.7 (60.1)
26.4 (24.8)
4.7 (7.2)
6.4 (10.7)
Minutes (SD)
17.9 (41.0)
2.6 (11.1)
18.8 (26.1)
164.9 (148.9)
88.7 (107.9)
Minutes (SD)
89.1 (72.2)
% (SD)
45.4 (25.1)
19.8 (16.9)
7.26 (6.7)
1.3 (1.9)
1.8 (2.9)
% (SD)
4.7 (10.9)
0.8 (3.1)
5.2 (7.3)
45.7 (41.3)
24.7 (30.0)
% (SD)
24.5 (19.8)
Wearable Monitors Classification
169
Table 2 Percent Error, Simple Kappa, Sensitivity, and Specificity for Published Sedentary Cut-Points as Compared
to the Sedentary Criterion
Monitor
Location
ActiGraph
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
ActiGraph
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrista
Right-wrista
Right-hip
Right hip
Right hip
Right hip
Tested
Cut-point
Number
of Axis
Percent
Error
Kappa (95% CI)
50
100
150
500
217
386
50
100
150
500
217
386
50
100
150
500
1
1
1
1
3
3
1
1
1
1
3
3
1
1
1
1
−73.04
−66.66
−61.16
−22
−0.66
39.16
−72.05
−65.01
−58.96
−25.85
−15.73
28.38
18.37
35.53
45.54
72.71
0.06 (0.05 to 0.07)
0.08 (0.07 to 0.10)
0.10 (0.09 to 0.12)
0.24 (0.23 to 0.26)
−0.29 (−0.31 to −0.28)
−0.36 (−0.37 to −0.34)
0.08 (0.06 to 0.09)
0.10 (0.09 to 0.11)
0.13 (0.11 to 0.14)
0.26 (0.24 to 0.27)
−0.26 (−0.28 to −0.25)
−0.36 (−0.38 to −0.35)
0.27 (0.26 to 0.29)
0.29 (0.28 to 0.31)
0.30 (0.28 to 0.31)
0.25 (0.24 to 0.26)
Sensitivity (95% CI)
0.15
0.19
0.23
0.48
0.61
0.82
0.17
0.21
0.25
0.47
0.53
0.78
0.69
0.78
0.83
0.93
(0.15
(0.18
(0.22
(0.47
(0.60
(0.81
(0.16
(0.20
(0.24
(0.46
(0.52
(0.77
(0.68
(0.77
(0.82
(0.92
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
0.16)
0.20)
0.24)
0.50)
0.62)
0.83)
0.17)
0.22)
0.26)
0.49)
0.54)
0.79)
0.70)
0.79)
0.84)
0.94)
Specificity (95% CI)
0.90
0.88
0.87
0.76
0.68
0.53
0.91
0.88
0.87
0.78
0.74
0.58
0.59
0.52
0.48
0.34
(0.90
(0.88
(0.86
(0.75
(0.67
(0.52
(0.90
(0.88
(0.86
(0.77
(0.73
(0.57
(0.58
(0.51
(0.47
(0.33
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
0.91)
0.89)
0.88)
0.76)
0.69)
0.54)
0.91)
0.89)
0.88)
0.79)
0.75)
0.59)
0.60)
0.53)
0.49)
0.35)
a
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Due to device malfunctioning there was 5.99 missing hours on this device; accordingly analyses include only 235.33 hours.
Table 3 Percent Error, Simple Kappa, Sensitivity, and Specificity for Published Sedentary Cut-Points as Compared
to the Stationary Criterion
Kappa
Monitor
Location
ActiGraph
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
ActiGraph
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrista
Right-wrista
Right-hip
Right-hip
Right-hip
Right-hip
Tested
Cut-point
Number
of Axis
Percent
Error
(95% CI)
50
100
150
500
217
386
50
100
150
500
217
386
50
100
150
500
1
1
1
1
3
3
1
1
1
1
3
3
1
1
1
1
−81.4
−77
−73.15
−46.2
−31.35
−41.8
−80.74
−75.9
−71.61
−48.84
−3.96
−11.33
−18.26
−6.38
0.44
19.25
0.02 (0.01 to 0.03)
0.03 (0.02 to 0.04)
0.04 (0.03 to 0.05)
0.10 (0.09 to 0.12)
−0.14 (−0.15 to −0.12)
−0.11 (−0.12 to −0.09)
0.01 (0.00 to 0.01)
0.02 (0.01 to 0.03)
0.03 (0.02 to 0.04)
0.09 (0.08 to 0.10)
−0.18 (−0.19 to −0.16)
−0.16 (−0.17 to −0.14)
0.20 (0.18 to 0.21)
0.25 (0.23 to 0.26)
0.28 (0.26 to 0.29)
0.31 (0.30 to 0.33)
Sensitivity
Specificity
(95% CI)
0.13
0.16
0.19
0.40
0.50
0.42
0.13
0.17
0.20
0.37
0.70
0.65
0.61
0.70
0.75
0.88
(0.12
(0.16
(0.19
(0.39
(0.49
(0.41
(0.12
(0.16
(0.19
(0.36
(0.70
(0.64
(0.60
(0.69
(0.75
(0.87
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
0.14)
0.17)
0.20)
0.41)
0.51)
0.43)
0.14)
0.17)
0.21)
0.38)
0.71)
0.65)
0.62)
0.71)
0.76)
0.89)
(95% CI)
0.89
0.87
0.86
0.73
0.65
0.69
0.88
0.86
0.84
0.73
0.51
0.54
0.60
0.55
0.52
0.40
(0.88
(0.87
(0.85
(0.72
(0.63
(0.68
(0.87
(0.85
(0.83
(0.72
(0.50
(0.52
(0.59
(0.54
(0.51
(0.39
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
0.90)
0.88)
0.87)
0.74)
0.66)
0.70)
0.89)
0.87)
0.85)
0.75)
0.52)
0.55)
0.62)
0.57)
0.54)
0.42)
a
Due to device malfunctioning there was 5.99 missing hours on this device; accordingly analyses include only 235.33 hours.
sensitivity, and specificity for the tested cut-points for the sedentary
and stationary criteria, respectively.
When compared to the sedentary criterion (sitting and lying
down), none of the cut-points tested had outstanding accuracy (PE
ranging from −73% to 73%; kappa <0.30; sensitivity <0.53; and
specificity <0.91). Overall, the ActiGraph hip cut-points showed
better accuracy than the wrist cut-points. The left-wrist cut-points
tended to be less accurate than the right-wrist and right-hip cutpoints. Further, the left-wrist cut-points tended to have high negative percent error (except for the GENEActiv 217 and GENEActiv
386), slight agreement (except for ActiGraph 500), low-to-moderate
sensitivity (except for GENEActiv 217 and GENEActiv 386), and
high specificity (except for GENEActiv 217 and GENEActiv 386).
With some exceptions, the right-wrist cut-points tended to have
JMPB Vol. 1, No. 4, 2018
170
Flórez-Pregonero, Buman, and Ainsworth
high negative percent error in excess of −25%, slight agreement
with kappa’s <0.17 (except for ActiGraph 500), low-to-moderate
sensitivity <0.53 (except for GENEActiv 386), and high specificity
>0.74 (except for GENEActiv 386). The right-hip cut-points tended
to have moderate positive percent error (except for ActiGraph 500),
fair agreement, high sensitivity, and moderate specificity.
When stationary behaviors (standing, sitting and lying down)
were included in the criterion variable, the ActiGraph hip cutpoints were more accurate than the wrist cut-points. When compared to the wrist cut-points (PE −81% to −46%; kappa 0.01 to
0.10; sensitivity 0.13 to 0.40; and specificity 0.73 to 0.89), the
ActiGraph hip cut-points had a lower percent error and higher
values for kappa, sensitivity, and specificity (PE −18% to 19%;
kappa 0.20 to 0.31; sensitivity 0.61 to 0.88; and specificity 0.40 to
0.60). The right-wrist GENEActiv cut-points (PE −11% to −4%;
kappa −0.18 to −0.16; sensitivity 0.65 to 0.70; and specificity 0.51
to 0.54) were more accurate that the left-wrist cut-points. Among
the cut-points tested, the ActiGraph 150 cpm right hip was the most
accurate stationary uniaxial cut-point (PE 0.4%; kappa 0.28;
sensitivity 0.75; and specificity 0.52). Regardless the location
(left or right) or the criterion (sedentary, stationary) all of the
kappa values for GENEactiv were negative., This is less agreement
than would have been expected by chance.
Downloaded by on 02/04/19
Developing Vector Magnitude Cut-points
To address the second aim, the investigators estimated VMCP to
classify SED based on the sedentary criterion (sitting + lying down)
and STA based on the stationary criterion (standing + sitting +
lying down). Estimated cut-points included a 60-seconds epoch for
the ActiGraph worn on the left-wrist, right-wrist, and right hip. The
investigators also estimated a 15-second and a 1-second epoch for
the ActiGraph worn on the left-wrist, right-wrist, and right hip and
for the GENEActiv worn on the left- and right-wrist. Tables 4 and 5
show values for AUC, PE, kappa, sensitivity, and specificity for the
estimated SED and STA cut-points, respectively. The Online
Supplementary Material presents all ROC graphs.
For the vector magnitude cut-point (VMCP) estimated from
the sedentary criterion (sitting + lying down), overall accuracy
metrics were better for the 60-seconds epoch (AUC 0.702 to
0.729; PE 13% to 20%; kappa 0.33 to 0.37; sensitivity 0.69 to
0.75; and specificity 0.62 to 0.64) as compared to the 1-second and
15-seconds epochs (AUC 0.646 to 0.699; PE 3% to 61%; kappa
−0.30 to 0.31; sensitivity 0.69 to 0.75; and specificity 0.40 to 0.63).
Overall accuracy metrics also were better for wrist- worn monitor
cut-points (AUC 0.647 to 0.723; PE 3% to 19%; kappa −0.30 to
0.35; sensitivity 0.62 to 0.71; and specificity 0.58 to 0.66) as
compared to the ActiGraph hip worn monitor.
The overall SED accuracy metrics were higher for all of the
estimated VMCP (PE 3% to 61%; kappa −0.30 to 0.37; sensitivity
0.62 to 0.88; and specificity 0.40 to 0.66) as compared to the
published uniaxial cut-points (PE −73% to 73%; kappa −0.36 to
0.30; sensitivity 0.15 to 0.93; and specificity 0.34 to 0.91). Among
the estimated VMCP, the ActiGraph 2000 cpm worn on the left
wrist was the most accurate SED cut-point. The Supplementary
Material [available online] presents all of the AUC plots.
The VMPC estimated from the stationary criterion (standing +
sitting + lying down) accuracy metrics were similar across the
different time epoch lengths ranging from 1 minute to 1 second.
The overall accuracy metrics were best for the estimated VMCP
(PE −20% to 12%; kappa −0.16 to 0.25; sensitivity 0.59 to 0.81;
and specificity 0.42 to 0.56) as compared to the uniaxial cut-points
(PE −81% to 19%; kappa −0.18 to 0.31; sensitivity 0.13 to 0.88;
and specificity 0.40 to 0.89). Among the estimated VMCP, the
ActiGraph 63 cpm worn on the right hip was the most the accurate
STA cut-point (AUC = 0.638). The Online Supplementary
Material presents all of the AUC plots.
Discussion
This study had two aims: (1) to test the accuracy of published cutpoints to classify sedentary and stationary time in free-living
conditions and (2) to develop VMCP to classify sedentary and
stationary time. Results showed four observations. First, there was
Table 4 Percent Error, Kappa, Sensitivity and Specificity for Estimated Vector Magnitude Cut-Points Sedentary
Criterion
Kappa
Monitor
Location
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrista
Right-wrista
Right-hip
Right-hip
Right-hip
VMCP
Epoch
Length (sec)
2000
455
5
65
2
2358
495
8
61
3
249
15
0
60
15
1
15
1
60
15
1
15
1
60
15
1
AUC
Percent
Error
(95% CI)
0.702
0.672
0.647
0.685
0.664
0.723
0.689
0.666
0.686
0.661
0.729
0.699
0.646
12.98
16.17
16.39
19.25
14.41
13.86
11.88
11.77
3.08
17.6
19.8
16.72
61.05
0.33 (0.30 to 0.35)
−0.27 (−0.28 to −0.26)
0.26 (0.26 to 0.26)
−0.29 (−0.30 to −0.28)
−0.25 (−0.25 to −0.25)
0.35 (0.33 to 0.37)
−0.30 (−0.31 to −0.29)
0.29 (0.28 to 0.29)
−0.28 (−0.29 to −0.27)
−0.25 (−0.26 to −0.25)
0.37 (0.35 to 0.39)
0.31 (0.30 to 0.32)
0.27 (0.26 to 0.27)
Sensitivity
Specificity
(95% CI)
0.69
0.67
0.67
0.70
0.65
0.71
0.67
0.66
0.62
0.67
0.75
0.70
0.88
(0.68
(0.67
(0.67
(0.69
(0.65
(0.70
(0.66
(0.66
(0.61
(0.67
(0.74
(0.69
(0.88
to
to
to
to
to
to
to
to
to
to
to
to
to
0.71)
0.68)
0.67)
0.71)
0.66)
0.73)
0.68)
0.67)
0.63)
0.67)
0.77)
0.71)
0.88)
(95% CI)
0.63
0.60
0.59
0.59
0.60
0.64
0.63
0.63
0.66
0.58
0.62
0.62
0.40
(0.62
(0.59
(0.59
(0.59
(0.59
(0.63
(0.62
(0.62
(0.66
(0.58
(0.61
(0.61
(0.40
to
to
to
to
to
to
to
to
to
to
to
to
to
0.65)
0.61)
0.60)
0.60)
0.60)
0.66)
0.64)
0.63)
0.67)
0.59)
0.64)
0.62)
0.40)
a
Due to device malfunctioning there was 5.99 missing hours on this device; accordingly analyses include only 235.33 hours. VMCP = Vector Magnitude Cut-point;
AUC = Area Under the Curve.
JMPB Vol. 1, No. 4, 2018
Wearable Monitors Classification
171
Table 5 Percent Error, Kappa, Sensitivity and Specificity for Estimated Vector Magnitude Cut-Points Stationary
Criterion
Kappa
Monitor
Location
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
GENEActiv
GENEActiv
ActiGraph
ActiGraph
ActiGraph
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Left-wrist
Right-wrist
Right-wrist
Right-wrist
Right-wrista
Right-wrista
Right-hip
Right-hip
Right-hip
VMCP
Epoch
Length (sec)
2365
523
6
77
3
2411
630
18
91
4
423
63
0
60
15
1
15
1
60
15
1
15
1
60
15
1
AUC
Percent
Error
(95% CI)
0.611
0.603
0.600
0.620
0.613
0.601
0.598
0.599
0.602
0.597
0.645
0.638
0.626
−13.31
−14.85
−18.37
−10.34
−11
−20.13
−15.51
−16.39
−10.89
−11.44
−5.5
−2.2
11.77
0.19 (0.17 to 0.21)
−0.15 (−0.16 to −0.14)
0.17 (0.17 to 0.18)
−0.16 (−0.17 to −0.15)
−0.15 (−0.15 to −0.15)
0.17 (0.15 to 0.19)
−0.15 (−0.16 to −0.14)
0.17 (0.17 to 0.17)
−0.15 (−0.16 to −0.14)
−0.14 (−0.14 to −0.14)
0.25 (0.22 to 0.27)
0.22 (0.21 to 0.23)
0.24 (0.24 to 0.25)
Sensitivity
Specificity
(95% CI)
0.64
0.62
0.60
0.65
0.64
0.59
0.61
0.61
0.64
0.63
0.71
0.72
0.81
(0.63
(0.61
(0.59
(0.64
(0.64
(0.57
(0.60
(0.61
(0.64
(0.63
(0.70
(0.71
(0.81
to
to
to
to
to
to
to
to
to
to
to
to
to
0.65)
0.62)
0.60)
0.66)
0.64)
0.60)
0.62)
0.61)
0.65)
0.63)
0.72)
0.72)
0.81)
(95% CI)
0.56
0.56
0.59
0.54
0.53
0.59
0.56
0.58
0.53
0.53
0.54
0.51
0.42
(0.54
(0.55
(0.59
(0.53
(0.53
(0.58
(0.55
(0.57
(0.53
(0.52
(0.52
(0.50
(0.42
to
to
to
to
to
to
to
to
to
to
to
to
to
0.58)
0.57)
0.59)
0.55)
0.54)
0.61)
0.57)
0.58)
0.54)
0.53)
0.56)
0.52)
0.43)
Downloaded by on 02/04/19
a
Due to device malfunctioning there was 5.99 missing hours on this device; accordingly analyses include only 235.33 hours. AUC = Area Under the Curve; VMCP = Vector
Magnitude Cut-point.
an overall lack of accuracy for the published sedentary uniaxial cutpoints regardless of the accelerometer location and cut-points used.
Second, the ActiGraph 150 cpm worn on the right hip demonstrated moderate accuracy to differentiate STA (PE 0.44%; kappa
0.28; sensitivity 0.75; and specificity 0.52) but not SED (PE 46%;
kappa 0.30; sensitivity 0.83; and specificity 0.48). Third, established ActiGraph uniaxial cut-points used to measure SED were
more accurate when the monitor was worn on the right hip had than
when the monitor worn on the left- and right wrist. Fourth, the
estimated VMCP had the highest accuracy in measuring SED and
STA regardless of the monitor location and cut-point used.
Overall, accuracy of established uniaxial cut-points to classify
SED and STA was low regardless of the location worn and the cutpoint used. The low accuracy supports the ascertainment that there
is not an ideal uniaxial cut-point to measure SED. Interestingly;
uniaxial cut-points for the right wrist were more accurate than the
left wrist. This might be an effect of handedness; however, we
could not test this hypothesis, as all of the participants were righthanded. Future studies that test handedness on the accuracy of a
wrist mounted wearable monitors should recruit participants with
right- and left-handed dominance.
The ActiGraph 100 cpm and ActiGraph 150 cpm uniaxial cutpoints for the monitors worn on the right hip were most accurate to
assess STA (standing, sitting, and lying down) but not SED (sitting
and lying down). As wearable monitors measure body movements
using changes in acceleration that are used to estimate the intensity
of physical activities over time (Chen, & Bassett, 2015), these
findings are not surprising and suggest caution interpreting wearable monitors-derived measures of SED and its associations to
health-related outcomes. In 2011, Kozey-Keadle et al. (2011)
reported that the ActiGraph 100 cpm and ActiGraph 150 cpm
cut-points for monitors worn on the right hip had a similar error
magnitude in measuring SED. We observed the same cut-points
to be best for measuring STA, but not SED. It is possible that
the metrics used and methodological differences between KozeyKeadle et al and the current study explain some differences in the
results. For example, Kozey-Keadle et al used the low-frequency
extension for the ActiGraph; we did not apply additional filters
to the wearable monitors’ signal. It should be noted that the use of
the low-frequency extension in ActiGraph monitors may overestimate the number of steps taken throughout the day (Feito, Garner,
& Bassett, 2015), which may introduce error in the measurement of
sedentary and stationary behaviors as some incidental movements
without ambulation may be misclassified as light intensity physical
activity when they are stationary behaviors. Kozey-Keadle et al
derived their criterion measure from observations of a single
researcher whereas the in the current study, two observers coded
the free-living data. Regardless of the differences in the methods
and observations of Kozey-Keadle et al.’s and the current study, the
results contribute to the ongoing debate about what is most accurate
uniaxial cut-point to use in classifying SED from the ActiGraph
and whether the cut-points approach is more reflective of stationary
types of behaviors versus sedentary behaviors. To increase consistency of results, we suggest future studies compare the accuracy
of existing cut-points to measure SED and STA under free-living
conditions.
All the established uniaxial cut-points for the ActiGraph
placed on the hip showed better accuracy for SED than those
for wrist locations regardless the cut-point used. This finding may
be related the participants’ arms movements during sedentary
activities (e.g., typing) that increased false negative classification
of SED. Investigators developed the ActiGraph uniaxial cut-points
with the monitor worn on the waist (Matthews et al., 2008); hence,
it is likely the cut-points are specific to hip displacement as opposed
to arm movement. In support of this latter explanation, the wristmounted GENEActiv VMPC cut-points developed for the wristworn monitor had a lower PE than the wrist-mounted ActiGraph
uniaxial cut-points developed for a waist-worn monitor (Esliger
et al., 2011). The poor accuracy of the wrist-mounted ActiGraph
wearable monitors to measure SED is an issue that may have
impact in physical activity and sedentary behaviors surveillance
and requires further research. For example, the U.S. NHANES is
using data from wrist-mounted ActiGraph wearable monitors to
estimate sedentary time at the population level (Fulton et al., 2016).
JMPB Vol. 1, No. 4, 2018
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172
Flórez-Pregonero, Buman, and Ainsworth
As compared to uniaxial cut-points, the ActiGraph estimated
VMCP improved the accuracy of measuring SED and STA considerably by reducing the overall PE and increasing kappa, sensitivity,
and specificity values. Among the estimated VMCP, the ActiGraph
2000 cpm left-wrist was most accurate in measuring SED. On the
other hand, the ActiGraph 63 cpm worn on the right hip was most
accurate in measuring STA. Having cut-points that accurately
differentiate standing, sitting and lying down from other types of
activities may be of interest for some researchers depending on the
goal of their research. Despite the limited accuracy of using cutpoints to assess SED, the cut-point approach remains the method of
choice for many researchers and practitioners due to its simplicity
and relatively low cost. Moreover, until machine learning-based
approaches that require computational skills and resources for the
researchers than current methods, are easily accessible to researchers
and practitioners to score wearable monitors data; investigators
should use the most accurate cut-points available.
When selecting a monitor to assess sedentary or stationary
time, researchers should consider several aspects to compute a
summary score. The cut-points approach is the most common
method used to assess sedentary behaviors and is easy to use
and understand. However, its use encompasses some limitations
that need to be addressed. The most common cut-point, 100 cpm,
was not empirically derived (Atkin et al., 2012); however, there are
several cut-points to measure sedentary behaviors with 150 cpm
considered to be the most accurate (Kozey-Keadle et al., 2011).
The 100 cpm cut-point was proposed for use with data from
accelerometers worn at the right hip, however, it is being used
in large scale studies while worn on the wrist. This approach,
although proposed as a way to increase monitors wear compliance,
may be inaccurate to detect sedentary behaviors as one can mimic
movement with their arms during sedentary behaviors (e.g., seated
while typing) (Freedson, & John, 2013). Accordingly, the most
accurate, location-specific cut-points should be used when measuring sedentary behaviors.
Monitors such as the activPAL worn on the thigh have shown
high accuracy to measure sedentary and stationary time. Accordingly, the activPAL is the optimal device if the primary goal of a
research study is to estimate sedentary and/or stationary time.
However, if the location of the activPAL as attached to the leg
using adhesive tape increases participant burden, its use may be
limited. Therefore, researchers should consider other monitors or
approaches to measure sedentary or stationary time (Calabró, Lee,
Saint-Maurice, Yoo, & Welk, 2014). Machine learning techniques
offer promising approaches to estimating sedentary or stationary
behaviors with increased precision (Dutta, Ma, Buman, & Bliss,
2016; Ellis, Kerr, Suneeta, Staudenmayer, & Lanckriet, 2016; Lyden,
Kozey-Keadle, Staudenmayer, & Freedson 2014; Staudenmayer, He,
Hickey, Sasaki, & Freedson, 2015; Zhang, Rowlands, Murray, &
Hurst, 2012). Algorithms have been developed and validated; however, such methods are complex and not practical for most researchers and practitioners to measure sedentary behaviors at this time.
A strength of the current study is the observers’ intensive
training that resulted in a substantial agreement between their field
observations. This agreement yielded a valid criterion with high
agreement between observers. In addition, the participants’ movements were observed in free-living settings for two days (weekday
and weekend day) allowing observers to capture a broad range of
observations in different contexts. An important limitation of the
current study is was the lack of a criterion measure for energy
expenditure, such as indirect calorimetry to classify SED as
≤1.5 METs that could have led to erroneous activity classifications.
In addition, when observers agreed on an observation coding it is
not possible to know whether each observer coded incorrectly,
which could be a source of error in the criterion. The study sample
included healthy right-handed young adults; this limits generalization of the results to other populations (e.g., left-handed and/or
older adults). Last, we lost data due to a monitor error resulting in a
GENEActiv right-wrist recording only 235.33 hours of movement
from all study participants as compared to the other monitors that
recorded 241.32 hours. To assure good external validity, additional
studies should evaluate the cut-points from this study in different
population groups.
Conclusion
This study showed that the ActiGraph uniaxial cut-points of 50, 100,
150, and 500 cpm and the GENEActiv VMCP of 217 and 386 cpm
had limited accuracy to assess SED in free-living settings (PE
ranging from −73% to 73%; kappa 0.01 to 0.10; sensitivity 0.13
to 0.40; and specificity 0.73 to 0.89). The ActiGraph 100 cpm and
the ActiGraph 150 cpm uniaxial cut-points worn on the right hip
demonstrated the best accuracy in differentiating STA (standing,
sitting, and lying down; PE 0.4%; kappa 0.28; sensitivity 0.75; and
specificity 0.52) but not SED (sitting and lying down). The estimated
60-seconds epoch ActiGraph 2000 cpm VMCP with the monitor
worn on the left wrist and the 15-seconds epoch ActiGraph 63 cpm
VMCP with the monitor worn on the right hip were most accurate in
classifying SED and STA in free-living settings, respectively.
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