University of Groningen
Validity of six activity monitors in chronic obstructivepulmonary diseaseVan Remoortel, Hans; Raste, Yogini; Louvaris,Zafeiris; Giavedoni, Santiago; Burtin, Chris;Langer, Daniel;Wilson, Frederick; Rabinovich, Roberto; Vogiatzis, Ioannis;Hopkinson,Nicholas SPublished in:PLoS ONE
DOI:10.1371/journal.pone.0039198
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Validity of Six Activity Monitors in ChronicObstructivePulmonary Disease: A Comparison withIndirectCalorimetryHans Van Remoortel1., Yogini Raste2., ZafeirisLouvaris3, Santiago Giavedoni4, Chris Burtin1,
Daniel Langer1, Frederick Wilson5, Roberto Rabinovich4, IoannisVogiatzis3, Nicholas S. Hopkinson2,
Thierry Troosters1*, on behalf of PROactive consortium"
1 Faculty of Kinesiology and Rehabilitation Sciences, Departmentof Rehabilitation Sciences, Katholieke Universiteit Leuven, andRespiratory Division, UZ Gasthuisberg,
Leuven, Belgium, 2 NIHR Respiratory Biomedical Research Unit atRoyal Brompton and Harefield NHS Foundation Trust and ImperialCollege, London, United Kingdom,
3 Thorax Foundation, Research Centre of Intensive and EmergencyThoracic Medicine, and Department of Physical Education and SportsSciences, National and
Kapodistrian University of Athens, Athens, Greece, 4 ELEGI ColtLaboratory, Centre for Inflammation Research, University ofEdinburgh, Edinburgh, Scotland, United
Kingdom, 5 Precision Medicine, Pfizer Worldwide Research andDevelopment, Sandwich, Kent, United Kingdom
Abstract
Reduced physical activity is an important feature of ChronicObstructive Pulmonary Disease (COPD). Various activitymonitors areavailable but their validity is poorly established. The aim was toevaluate the validity of six monitors in patientswith COPD. Wehypothesized triaxial monitors to be more valid compared touniaxial monitors. Thirty-nine patients (age6867years, FEV154618%predicted) performed a one-hour standardized activityprotocol. Patients wore 6 monitors (KenzLifecorder (Kenz),Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod(MiniMod), and SenseWear Armband(SenseWear)) as well as a portablemetabolic system (Oxycon Mobile). Validity was evaluated bycorrelation analysisbetween indirect calorimetry (VO2) and themonitor outputs: Metabolic Equivalent of Task [METs] (SenseWear,MiniMod),activity counts (Actiwatch), vector magnitude units(Actigraph, RT3) and arbitrary units (Kenz) over the whole protocolandslow versus fast walking. Minute-by-minute correlations werehighest for the MiniMod (r = 0.82), Actigraph (r = 0.79),SenseWear(r = 0.73) and RT3 (r = 0.73). Over the whole protocol, the meancorrelations were best for the SenseWear(r = 0.76), Kenz (r =0.52), Actigraph (r = 0.49) and MiniMod (r = 0.45). The MiniMod (r= 0.94) and Actigraph (r = 0.88)performed better in detectingdifferent walking speeds. The Dynaport MiniMod, Actigraph GT3X andSenseWear Armband(all triaxial monitors) are the most validmonitors during standardized physical activities. The DynaportMiniMod andActigraph GT3X discriminate best between differentwalking speeds.
Citation: Van Remoortel H, Raste Y, Louvaris Z, Giavedoni S,Burtin C, et al. (2012) Validity of Six Activity Monitors inChronic Obstructive Pulmonary Disease: AComparison with IndirectCalorimetry. PLoS ONE 7(6): e39198.doi:10.1371/journal.pone.0039198
Editor: Marco Idzko, University Hospital Freiburg, Germany
Received November 25, 2011; Accepted May 17, 2012; PublishedJune 20, 2012
Copyright: � 2012 Van Remoortel et al. This is an open-accessarticle distributed under the terms of the Creative CommonsAttribution License, which permitsunrestricted use, distribution,and reproduction in any medium, provided the original author andsource are credited.
Funding: The Proactive project is funded by the InnovativeMedicines Initiative Joint Undertaking (IMU JU) #115011 andadditional funds at each institute. Thefunders had no role in studydesign, data collection and analysis, decision to publish, orpreparation of the manuscript. Laura Jacobs (Respiratory Division,UZGasthuisberg, Leuven, Belgium) was involved in patientrecruitment and data collection, Y.R. (NIHR Respiratory BiomedicalResearch Unit, Royal Brompton,London, United Kingdom) was involvedin patient recruitment, data collection, data analysis andmanuscript preparation. Both were funded by IMI JU.
Competing Interests: Frederick Wilson is employed by Pfizer Ltdand holds share options in Pfizer Inc. There are no patents,products in development, ormarketed products to declare. This doesnot alter the authors’ adherence to all the PLoS ONE policies onsharing data and materials.
* E-mail: [emailprotected]
. These authors contributed equally to this work.
" Membership of the PROactive Consortium is provided in theAcknowledgments.
Introduction
Chronic Obstructive Pulmonary Disease (COPD) is a chronic
disease characterized by poorly reversible airflow limitationand
destruction of lung parenchyma. However, COPD is now
recognized as a systemic illness with significantextra-pulmonary
features such as muscle wasting and weakness [1]. Physical
inactivity is known to contribute to these extra-pulmonaryfeatures
[2,3]. A recent systematic literature review showed thatphysical
activity is reduced in patients with COPD [4].
Physical activity is defined as any bodily movement producedby
the contraction of skeletal muscle that increases energyexpendi-
ture above a basal level [5]. In the general population, lackof
physical activity is associated with the burden of chronicdisease.
Similarly, there is increasing evidence that reducedphysical
activity worsens the prognosis of patients with COPD. Hence,
inactivity is not only a manifestation of disease severity inCOPD,
but is intrinsic to disease progression [6].
Physical activity monitors are frequently used to estimatelevels
of daily physical activity. These devices use piezoelectricacceler-
ometers, which measure the body’s acceleration, in one, twoor
three axes (uniaxial, biaxial or triaxial activity monitors).The
signal can then be transformed into an estimate of energy
expenditure using one of a variety of algorithms, or summarizedas
activity counts or vector magnitude units (reflectingacceleration).
PLoS ONE | www.plosone.org 1 June 2012 | Volume 7 | Issue 6 |e39198
With the information obtained in the vertical plane orthrough
pattern recognition, steps or walking time can also be derivedby
some monitors. The availability of sophisticated physicalactivity
monitors has made the objective measurement of physicalactivity
in COPD patients possible in a number of contexts, including
assessment of the response to pharmacotherapy [7], during
rehabilitation programmes [8] and during inpatient admission
[9]. Most of the monitors currently available have beenvalidated
in healthy subjects, but not necessarily in patients withchronic
diseases. As such patients are less physically active and movemore
slowly than healthy subjects [10,11], the validity of thesemonitors
to detect movement in these patients needs to be evaluated.
The aim of this study was to validate six physical activity
monitors in COPD patients, against a gold standard ofindirect
calorimetry in the form of VO2 data from a portablemetabolic
system. Since triaxial accelerometers have previously been
reported to be more effective compared to uniaxialaccelerometers
[12], we hypothesized that triaxial activity monitors wouldbe
more valid tools compared to uniaxial activity monitors. Thework
described here forms part of the EU/IMI-funded PROactive
project to develop and validate a patient reported outcomefor
physical activity in COPD (www.proactivecopd.com).
Materials and Methods
Study SubjectsTen patients were recruited in each of the 4centres (Athens,
Edinburgh, Leuven and London) to give a total number of 40
patients. All patients were diagnosed with COPD ranging in
severity from mild to very severe according to the GlobalInitiative
for Chronic Obstructive Lung Disease (or GOLD) (stages I toIV)
[13]. They were clinically stable and free of exacerbations forat
least 4 weeks prior to the study. Patients were excluded if theyhad
other co-morbidities which would interfere with theirmovement
patterns (e.g. arthritis), or if they were on long-term orambulatory
oxygen therapy, as they could not have supplemental oxygen
whilst wearing the metabolic equipment. The protocol was
approved by the ethics committee of each centre; MedicalEthical
Board of the University Hospitals Leuven (Leuven, Belgium),
NRES Committee London - Bloomsbury (London, United
Kingdom), Sotiria Hospital Scientific and Ethics Commitee
(Athens, Greece) and Lothian Regional Ethics Committee
(Edinburgh, United Kingdom). Patients provided writteninformed
consent.
Pulmonary Function TestingAll pulmonary function measurementswere performed with
standardized equipment and according to American Thoracic
Society and European Respiratory Society guidelines [14].Post-
bronchodilator spirometry was measured and lung diffusion
capacity was determined by the single breath carbon monoxide
gas transfer method (DLCO). All variables are given asabsolute
values and expressed as percentages of the predictedreference
values [15,16].
Six-minute Walking TestFunctional exercise capacity wasdetermined by six minute
walking distance (6MWD) [17]. Values were related topreviously
published reference values [18].
Incremental Exercise TestingA symptom-limited incremental cycleergometer test according
to the ATS/ACCP statement on cardiopulmonary exercisetesting
[19], was used to assess the maximal exercise capacity (peakVO2).
The values of peak oxygen consumption were related topreviously
described reference values [20].
COPD-specific Health-related Quality of LifeQuestionnaires
The St.-George’s Respiratory Questionnaire (SGRQ) provides
a total score and three component scores for symptoms,activity
and impacts. Each score ranges from 0 (no impairment) to 100
(worst possible) [21].
The COPD Assessment Test (CAT) covers eight items (cough,
phlegm, chest tightness, breathlessness, going uphills/stairs,
activity limitations at home, confidence leaving home, sleepand
energy). Each item is scored from 0 to 5 giving a total scorerange
from 0 to 40, corresponding to the best and worst health statusin
patients with COPD, respectively [22].
The Medical Research Council (MRC) dyspnoea scale rates the
type and magnitude of dyspnoea according to five grades of
increasing severity [23].
Study DesignEach patient wore 6 activity monitors simultaneouslywhich
were selected as a result of a systematic review of theliterature.
These were two uniaxial activity monitors [Kenz Lifecorder
(Kenz), Actiwatch (Actiwatch)], three triaxial activitymonitors
[RT3, Actigraph GT3X (Actigraph), DynaPort MiniMod (Mini-
Mod)] and one multisensor activity monitor combining atriaxial
accelerometer with different sensors [SenseWear Armband(Sense-
Wear)]. More details about software, type, body location and
outputs of these monitors can be found in Table 1.
Patients also wore a portable metabolic system (JaegerOxycon
Mobile), an oxygen saturation finger probe and a Polar T31
(Polar) coded transmitter belt for heart rate monitoring.The
portable metabolic system was attached to the upper chest witha
harness and due to its low weight (950 g), caused minimal
discomfort. A face mask with a dead space of ,30 mL (HansRudolphInc, Kansas City MO/USA) was used. Location of
attachment for the Oxycon Mobile together with the sixactivity
monitors is shown in Figure 1. A two-point gas calibrationwascompleted prior to each test. Oxygen consumption (VO2),carbon
dioxide production (VCO2), heart rate, respiratory rate andtidal
volume were measured continuously. Breath-by-breath measure-
ments were averaged over one-minute intervals. After the
experiment, stored data were downloaded from the portable
metabolic device to a personal computer. VO2 values weredivided
by participants’ body weight and converted to Metabolic
Equivalents of Task (METs) [24]. Energy expenditureestimates
from the portable metabolic system (METs) were used as a
criterion measure for energy expenditure and were comparedwith
the following activity monitor outputs: Kenz - arbitrary units(AU);
Actiwatch - activity counts (AC); Actigraph and RT3 - vector
magnitude units (VMU); MiniMod and SenseWear - METs.
Patients were instructed to perform a strict schedule ofactivities
lasting 59 minutes (Table 2) which were chosen toberepresentative of everyday tasks (such as walking, stairclimbing
and sweeping the floor) that are reported as problematic byCOPD
patients [25]. Time was kept with both a stopwatch and alaptop
computer clock so that activities were completed in wholeminutes.
Statistical AnalysisMinute-by-minute data from all devices werecompiled for each
patient in one database and synchronisation was verified by
inspection of the curves to ensure the best fit between themonitors
on a patient-by-patient basis. Analyses were carried out asfollows
Validation of Six Activity Monitors in COPD
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(Pearson Product Moment Correlation was used for allcorrelation
analyses):
1. A minute-by-minute correlation between METs from the
portable metabolic system and each of the activity monitor
outputs was calculated for every patient. Correlationsbetween
minute-by-minute VO2 and activity monitor outputs were
reported as median with interquartile range. AKruskal-Wallis
test was used to compare results between different activity
monitors. A median correlation larger than 0.7 was defined a
priori as representing evidence of validity.
To investigate whether correlations became weaker with
decreasing six minute walking distance (i.e. slower overall
walking speed), and therefore if a monitor’s performance
worsened as patients moved more slowly, the relationship
between these minute-by-minute correlations and the six-
minute walking distance (6 MWD) was tested. Correlation
coefficients (minute-by-minute) in patients with mild to
moderate COPD (GOLD I/II) were compared to those with
severe to very severe COPD (GOLD III/IV).
2. The mean METs level for the 59 minutes was calculated and
correlated to the mean activity monitor output over thisperiod.
A statistically significant relationship was judged a priorias
indicative of validity.
3. The response of monitors to slow and fast walking wasassessed
by evaluating the correlation between changes in METs and
changes in activity monitor outputs at the different speeds.For
this analysis the first minute of each walking phase was
excluded; stronger correlations were taken to indicategreater
validity. A sub-analysis included the response to inclinedversus
flat treadmill walking in triaxial compared to uniaxialmonitors
(paired t-test). Bland regression analyses [26] wereperformed
to test the agreement between indirect calorimetry (METs)and
the activity monitor outputs. The 95% prediction limits ofthe
METs at the mean (of the different activity monitor outputs)
were calculated.
4. To investigate the total variance potentially explained bythe
most valid monitor(s), their output information was inserted
into different linear regression models to investigate thetotal
(R2) and partial variance (pR2) in mean VO2 explained byeach
activity monitor.
Statistical analyses were performed with SAS software(version
9.2). A p-value ,0.05 was considered to be statisticallysignificant.Figure construction was performed with GraphPad PrismVersion
4.0.
Results
Patient characteristics are listed in Table 3. One patientwasexcluded from the analysis due to a technical problem withthe
collection of breath-by-breath data, leaving 39 patients in thefinal
analysis. Due to technical problems, data collected from theRT3
in one centre could not be used, leaving 29 patients with RT3in
the final analysis. None of the included patients reported
significant co-morbidities and all had normal exerciseelectrocar-
diograms.
An example of one patient’s data is provided Figure S1. ThemeanVO2 for all activities during the experiment was
8.561.5 ml*min21*kg21 which corresponded to a moderateintensityof activity (i.e. approximately 50% of VO2peak).
Minute-by-minute correlations between metabolic cost (METs)
and activity monitor output are shown in Figure 2.Strongcorrelations (R.0.7 [interquartile range, IQR]) were foundwiththe MiniMod (0.82 [IQR 0.72 to 0.85]), Actigraph (0.79 [IQR
0.74 to 0.85]), SenseWear (0.73 [IQR 0.63 to 0.81]) and RT3
(0.73 [IQR 0.64 to 0.79]) compared to the Actiwatch (0.53[IQR
0.41 to 0.62]) and Kenz (0.57 [IQR 0.39 to 0.65]). Thisdifference
was also statistically significant (p,0.05).These individualminute-by-minute correlations [95% confi-
dence interval (95% CI)] were moderately but significantlyrelated
to the 6MWD for the Actiwatch (r = 0.60 [95% CI 0.35–0.77]),
MiniMod (r = 0.51 [95% CI 0.23–0.71]), SenseWear (r = 0.48
[95% CI 0.19–0.69]) and Actigraph (r = 0.47 [95% CI 0.18–
0.69]). No differences were observed for minute-by-minute
correlations in mild to moderate COPD (GOLD I/II) compared
to severe and very severe COPD (GOLD III/IV) as showed in
Table 4.
The mean correlation between metabolic cost (METs) and
activity monitor outputs over the whole protocol was, fromhighest
Table 1. Details of type, location and output of the sixactivity monitors.
Name, Manufacturer (software) Type Location Measured outputEstimated output
Kenz Lifecorder Plus Suzuken CoLtd., Nagoya, Japan (PhysicalActivityAnalysis Software)
Uniaxial accelerometer Waist (left) Steps, activity score EE,activity intensity level
Actiwatch, MiniMitterCo,Sunriver,OR, USA (Respironics Actiware5)
Uniaxial accelerometer Wrist (left) AC
RT3, Stayhealthy Inc. Monrovia, CA,USA (Stayhealthy RT3 AssistVersion1.0.7)
Triaxial accelerometer Waist (right) AC, VMU EE
Actigraph GT3X, Actigraph LLCPensacola, FL (Actilife 5)
Triaxial accelerometer Waist (right) Steps, AC EE, activityintensity level
DynaPortH MiniMod, McRoberts BV,The Hague, The Netherlands
Triaxial accelerometer Waist (lower back) Steps, movementIntensity,different body positions
EE
SenseWear Armband, Bodymedia,Pittsburgh, PA, USA(SenseWearProfessional 6.0)
Multisensor device: triaxialaccelerometer + sensors (heatflux,galvanic skin response andskin temperature)
Upper left arm at triceps Steps, activity intensity level EE
AC; activity counts, VMU: vector magnitude unit, EE; energyexpenditure.doi:10.1371/journal.pone.0039198.t001
Validation of Six Activity Monitors in COPD
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to lowest; SenseWear (r = 0.76 [95% confidence interval (95%CI)
0.54–0.91]), Kenz (r = 0.52 [95% CI 0.27–0.73]), Actigraph
(r = 0.49 [95% CI 0.28–0.64]), MiniMod (r = 0.45 [95% CI
0.21–0.61]), Actiwatch (r = 0.37 [95% CI 0.17–0.56]), allp,0.05and RT3 (r = 0.35 [95% CI 20.04–0.48], p = 0.06) (Figure3).
Patients changed their walking speed by 1.31 km/h from the
slow (3.2760.47 km/h) to the fast (4.6561.28 km/h) walking
phase. As expected, the change in walking speed correlatedwith
the change in VO2 determined by the metabolic equipment
(r = 0.65) and rose from 3.1160.74 METs to 3.9361.23 METs.Allmonitors detected this increase in energy expenditure during
fast walking compared to slow walking via their outputs. The
highest correlations were reported for the MiniMod (r = 0.94[95%
confidence interval (CI) 0.89–0.97]) and Actigraph (r = 0.88[95%
Figure 1. Location of attachment for the Oxycon Mobile and thesix activity monitors.doi:10.1371/journal.pone.0039198.g001
Validation of Six Activity Monitors in COPD
PLoS ONE | www.plosone.org 4 June 2012 | Volume 7 | Issue 6 |e39198
CI 0.77–0.93]) compared to the RT3 (r = 0.69 [95% CI 0.42–
0.85]), Actiwatch (r = 0.59 [95% CI 0.34–0.76]), Kenz (r =0.57
[95% CI 0.31–0.76]) and SenseWear (r = 0.52 [95% CI 0.25–
0.72]), all p,0.0001. As one would expect, walking on aninclineon the treadmill (3.9260.92 METs) expended more energythanwalking on the flat (3.4860.75 METs), as measured byindirectcalorimetry (p,0.0001), but no differences between thetwoactivities were detected by any of the activity monitors (Figure4).
Bland regression analyses demonstrated significantrelationships
(p,0.05) between METs derived from indirect calorimetry andthedifferent activity monitor outputs, except for the RT3
(p = 0.06) (Figure 5). The 95% limits of prediction forMETs(derived from indirect calorimetry) at the mean (3.59 METs)were
from lowest to highest: 61.13 METs (SenseWear), 61.15METs(Actigraph), 61.17 METs (MiniMod), 61.25 METs (Actiwatch),61.28METs (Kenz) and 61.41 METs (RT3).
The Sensewear, Actigraph and MiniMod monitors together
explained 62% of the variance in mean VO2. Partial variancesby
different sequences of activity monitors are presented in Table5.Most variance in mean VO2 was explained by the SenseWear
(58%) compared to the Actigraph (24%) and the MiniMod (21%).
Little variance in mean VO2 was explained over and above the
SenseWear, when it was introduced into the regression modelfirst.
In fact, introduction of the SenseWear further improved
prediction models from other monitors (range 34 to 41%)
compared to Actigraph (range 0 to 7%) and MiniMod (range 3
to 4%).
Discussion
The validity of six commercially available activity monitorswas
investigated by comparing activity monitor outputs for each
monitor to actual VO2 measured with indirect calorimetry.
Triaxial activity monitors were judged to be more validcompared
to uniaxial activity monitors according to a number ofpre-defined
criteria. Correlations between minute-by-minute outputs andVO2were the highest with the Dynaport MiniMod, Actigraph GT3X,
SenseWear Armband and RT3, all exceeding the a priorithreshold
of 0.7. Similarly the average monitor output over the 59minute
assessment was related to the average VO2 with the best
correlations reported for three triaxial activity monitors(Sense-
Wear Armband (r = 0.76), Actigraph GT3X (r = 0.49) and
Dynaport MiniMod (r = 0.45)) and one uniaxial activitymonitor
(Kenz Lifecorder (r = 0.52)). All monitors were able todetect
modest changes in walking speed but two triaxial activitymonitors
had the strongest correlations (MiniMod (r = 0.94) andSenseWear
(r = 0.88)). Walking on an incline was more intense comparedto
flat walking when assessed with indirect calorimetry but no
differences were detected by any of the monitors. Allactivity
monitor outputs showed similar variability in predictingenergy
expenditure. The 95% prediction limits for mean METs
(3.59 METs) varied between 61.13 METs (SenseWear) and61.41 METs,(RT3). This implies that, from a clinical perspec-
Table 2. Schedule of physical activities in thestandardizedprotocol.
Activity Duration (minutes)
Standing 1
Lying 3
Sitting 2
Standing 2
Slow walk* 6
Sitting 2
Standing 2
Fast walk* 6
Sitting 2
Standing 2
Sweeping 2
Sitting 2
Standing 2
Lifting 2
Sitting 2
Walking/standing 1
Stairs 1
Sitting 5
Walking/standing 1
Walking on treadmill (flat)** 4
Standing 2
Walking on treadmill (4% incline)** 4
Walking/standing 1
Sitting 2
*These walking activities were performed in a 30 m corridor.Speeds were self-selected. During fast walking, patients wereinstructed to walk as fast aspossible.**Participants walked at 85%of their fast walking speed, first on the flat andthen at anincline of 4%. Participants were instructed not to support theirarmsduring treadmillwalking.doi:10.1371/journal.pone.0039198.t002
Table 3. Characteristics of the 39 patients.
Variable COPD patients (n = 39)
Age (years) 67.967.4
Gender (male/female, n) 25/14
FEV1 (L) 1.4360.60
FEV1 (%predicted) 54618
FVC (L) 2.9760.85
FVC (%predicted) 90616
GOLD stage I/II/III/IV (n) 4/18/14/3
BMI 26.265.2
6 MWD (m) 4386115
6 MWD (%predicted) 70618
VO2peak (ml*min21*kg21) 16.965.5
VO2peak (%predicted) 79631
MRC 2.660.7
CAT 1568
SGRQ
Total Score 42618
Activities 60624
Impacts 30617
Symptoms 44623
Data are expressed as mean 6 std. FEV1; forced expiratory volumein 1 s, FVC;forced vital capacity, 6 MWD; six-minute walkingdistance, MRC; MedicalResearch Council, CAT; COPD Assessment Test,SGRQ; St George’sRespiratoryQuestionnaire.doi:10.1371/journal.pone.0039198.t003
Validation of Six Activity Monitors in COPD
PLoS ONE | www.plosone.org 5 June 2012 | Volume 7 | Issue 6 |e39198
tive, predicting oxygen consumption directly from differentactivity
monitor outputs is not accurate. However, when patientsengage
in activity, monitors are highly capable of detecting theincrease in
physical activity levels within a range of 1 to 1.5 METs.
This is the first multi-center trial where several activitymonitors
have been validated against VO2 in different stages of COPD.
Until now, only the Dynaport MiniMod and SenseWear Armband
had been validated against VO2 in patients with COPD. Inthese
devices, similar correlation coefficients were previouslyreported
between activity monitor outputs and total energyexpenditure
(r = 0.75 and r = 0.93 for the SenseWear Armband [27,28], r =0.7
for the Dynaport Minimod [27]). During a set of 5 dailyactivities,
a high level of agreement between SenseWear Armband
(22.767 kcal) and indirect calorimetry (21.067.9 kcal)wasobserved [29]. In a similar protocol, a fair agreementbetween
energy expenditure estimate from the SenseWear Armband and
energy expenditure measure from indirect calorimetry was
reported (mean difference of 20.2 METs with a limit ofa*greementof 1.3 METs) [30].
In the present study, validity was assessed usingcorrelation
analysis rather than a measure of agreement (e.g. Bland andAltman
analysis), as most of the activity monitor outputs are indifferent
units (activity counts, vector magnitude units, etc.) to eachother,
and to the VO2 data. It is not possible to convert all monitoroutputs
to energy expenditure. Several prediction equations areavailable to
convert some outcomes (e.g. VMU) to energy expenditure and
energy expenditure can also be derived from the VO2 data fordirect
comparison. However, whilst the prediction equations used bythe
Actigraph (7164 model and GT1M), Actiwatch, Kenz and RT3 are
publicly available, [31,32,33,34], the SenseWear and MiniModuse
proprietary algorithms developed by the devicemanufacturers.
Moreover, the goal of the study was to assess the validity ofthe
devices rather than their prediction equations. Therefore,compar-
ing the raw data from the activity monitor with the VO2(derived
from the portable metabolic kit) by using correlation analysiswas
the most appropriate statistical approach.
In addition, energy expenditure is driven to a large extent bya
number of other factors. Whilst specific factors such asbody
weight, age, and height can be incorporated into prediction
equations, it is more difficult to include non-specific factorssuch as
mechanical efficiency, especially in patients with COPD.Patients
with COPD have a larger active energy expenditure [35] even
though it is well recognized that they are moving less. Thisis
consistent with findings of reduced mechanical efficiency inthese
patients compared to healthy controls [36,37]. An activitymonitor
cannot be expected to incorporate such a complex change inan
estimate of energy expenditure, so perhaps greater weightshould
be placed upon direct monitor outputs (steps, activitycounts,
vector magnitude units, etc.). In essence, this means thatactivity
monitors are most appropriately used for the assessment ofthe
activities of patients in terms of amount and/or intensity andit
should be acknowledged that the derivation of energyexpenditure
is imperfect. Therefore, the use of derived energy expenditureis
also not appropriate when assessing monitor performance.
Figure 2. Minute-by-minute correlations (R) between activitymonitor outputs and metabolic equivalents of task (METs) perpatient(white dots). MM; MiniMod, AG; Actigraph, SW; SenseWear, AW;Actiwatch, VMU; vector magnitude unit, AC, activity count, AU;arbitrary unit.Dotted line corresponds to a correlation of 0.7,defined a priori as supporting monitor validity. Median(interquartile range) correlation for eachactivity monitor isreflected by cross bars,*p,0.05.doi:10.1371/journal.pone.0039198.g002
Table 4. Minute-by-minute correlations betweenindirectcalorimetry (METs) and activity monitor output in mildtomoderate COPD (GOLD I/II) and severe to very severe COPD(GOLDIII/IV).
Activity monitor output GOLD I/II (n = 22) GOLD III/IV (n =17)
MiniMod (METs) 0.82 [0.81–0.86] 0.77 [0.68–0.83]
SenseWear (METs) 0.78 [0.68–0.83] 0.65 [0.59–0.75]
Actigraph (VMUs) 0.81 [0.74–0.86] 0.77 [0.74–0.83]
ActiWatch (Activity counts) 0.58 [0.46–0.64] 0.45[0.28–0.53]
RT3 (VMUs) 0.69 [0.34–0.78] 0.76 [0.64–0.79]
Kenz (Arbitrary units) 0.54 [0.42–0.64] 0.59 [0.38–0.65]
Data are expressed as median [interquartile range]. METs;Metabolic Equivalentsof Task, VMUs; Vector MagnitudeUnits.doi:10.1371/journal.pone.0039198.t004
Validation of Six Activity Monitors in COPD
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The ability of an activity monitor to pick up a differencein
walking speed of 1.31 km/hr is clinically relevant. Patientswith
COPD do walk more slowly than healthy subjects, which is
reflected, for example, by their reduced six minute walking
distance [38,39]. In our study, all monitors were able todetect
these modest changes in walking speed.
A limitation of this study was that inter- and intra-device
reliability of the different activity monitors was notevaluated.
This is important when physical activity levels of patientsare
followed over time. Several studies have shown moderate tohigh
inter-device reliability (intra-class correlation coefficient(rICC) = 0.99
for the Actigraph Model 7164 [40], rICC = 0.95 for the Kenz
Figure 3. Relation between the activity monitor outputs andindirect calorimetry (METs). Data points represent mean values overthewhole protocol. MM; MiniMod, AG; Actigraph, SW; SenseWear, AW;Actiwatch, VMU; vector magnitude unit, AC; activity count, AU;arbitrary unit.doi:10.1371/journal.pone.0039198.g003
Validation of Six Activity Monitors in COPD
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Lifecorder [41] and rICC = 0.75 for the RT3 [42]) as well asintra-
device reliability (rICC = 0.98 for the Actiwatch [43], rICC =0.97 for
the SenseWear Armband [44] and rICC = 0.86–0.99 for theDynaport
Minimod [45]). Besides the concepts of validity and reliability,other
factors like size and scope of the study, usability and cost ofthe activity
monitor need to be taken into consideration when selectingan
activity monitor for use in clinical trials.
The validation of these activity monitors in a laboratorysetting
(validation against VO2) can be considered as an important stepin
ascertaining their validity. An essential next step will be toconfirm
their validity in a field setting.
In conclusion, this study found that three triaxial activity
monitors (Dynaport MiniMod, Actigraph GT3X and SenseWear
Armband) were the best monitors to assess standardized and
common physical activities in the range of intensity relevantto
Figure 4. METs (derived from indirect calorimetry (IC)) anddifferent activity monitor outputs during flat and inclined walkingon atreadmill (both at the same speed (85% of their fastest walkingspeed during 6 MWT). MM; MiniMod, SW; SenseWear, AG; Actigraph,AW;Actiwatch. Symbols represent the mean, error bars the standarderror of the mean.doi:10.1371/journal.pone.0039198.g004
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Figure 5. Bland regression analysis between METs derived fromindirect calorimetry (IC) and different activity monitoroutputs.Solid lines represent regression lines, dotted linesrepresent 95% limits ofprediction.doi:10.1371/journal.pone.0039198.g005
Validation of Six Activity Monitors in COPD
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patients with COPD. Changes in walking speed are most
accurately registered by the Dynaport MiniMod and Actigraph,
which are both devices that are worn on the hip. This shouldguide
users in choosing valid activity monitors for research or forclinical
use in patients with chronic diseases such as COPD.
Supporting Information
Figure S1 Example of one patient’s experiment; data ofthe OxyconMobile (VO2 (METs)) and the differentactivity monitoroutputs.(TIF)
Acknowledgments
The authors would like to acknowledge the members of thePROactive
consortium for the outstanding contribution to this work.
PROactive consortium: Chiesi Farmaceutici S.A.: CaterinaBrindicci,
Tim Higenbottam; Katholieke Universiteit Leuven: ThierryTroosters,
Fabienne Dobbels; Glaxo Smith Kline: Margaret X. Tabberer;University
of Edinburgh, Old College South Bridge: Roberto A Rabinovich,William
McNee; Thorax Research Foundation, Athens: Ioannis Vogiatzis;Royal
Brompton and Harefield NHS Foundation Trust: Michael Polkey,Nick
Hopkinson; Municipal Institute of Medical Research, Barcelona:Judith
Garcia-Aymerich; Universität Zürich, Zürich: Milo Puhan, AnjaFrei;
University Medical Center, Groningen: Thys van der Molen, CorinaDe
Jong; Netherlands Asthma Foundation, Leusden: Pim de Boer;British
Lung Foundation, UK: Ian Jarrod; Choice Healthcare Solution, UK:Paul
McBride; European Respiratory Society, Lausanne: Nadia Kamel;Pfizer:
Katja Rudell, Frederick J. Wilson; Almirall: Nathalie Ivanoff;Novartis:
Karoly Kulich, Alistair Glendenning; AstraZeneca AB: Niklas X.Karlsson,
Solange Corriol-Rohou; UCB: Enkeleida Nikai; BoehringerIngelheim:
Damijen Erzen.
C.B. is a doctoral fellow of the Research Foundation Flanders.D.L. is a
post doctoral fellow of the Research Foundation Flanders.
N.S.H. is supported by the NIHR Respiratory BiomedicalResearch
Unit at Royal Brompton and Harefield NHS Foundation Trustand
Imperial College, London, United Kingdom.
Author Contributions
Conceived and designed the experiments: TT. Performed theexperiments:
HVR YR ZL SG CB DL FW RR IV NSH TT. Analyzed the data: HVR
YR ZL SG CB DL FW RR IV NSH TT. Wrote the paper: HVR YR.
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