Wearable monitors criterion validity for energy expenditure in sedentary and light activities

Florez-Pregonero Alerto,Mekes Nathanael,Buman Mathew,Barara E.Ainsworth

aDepartamento de Formación,Facultad de Educación,Pontifici Universidad Javeriana,Bogota,Distrito Capital 11001000,Colombia

bDepartment of Exercise and Sport Science,School of Behavioral and Applied Sciences,Azuza Pacifi University,Azuza,CA 91702,USA

cSchool of Nutrition and Health Promotion,Arizona State University,Phoenix,AZ 85004,USA

Wearable monitors criterion validity for energy expenditure in sedentary and light activities

Florez-Pregonero Albertoa,*,Meckes Nathanaelb,Buman Mathewc,Barbara E.Ainsworthc

aDepartamento de Formación,Facultad de Educación,Pontifici Universidad Javeriana,Bogota,Distrito Capital 11001000,Colombia

bDepartment of Exercise and Sport Science,School of Behavioral and Applied Sciences,Azuza Pacifi University,Azuza,CA 91702,USA

cSchool of Nutrition and Health Promotion,Arizona State University,Phoenix,AZ 85004,USA

Background:Wearable monitors(WMs)are used to estimate the time spent in sedentary behaviors(SBs)and light-intensity physical activities (LPAs)and their associated energy cost;however,the accuracy of WMs in measuring behaviors on the lower end of the intensity spectrum is unclear.The aim of this study was to assess the validity of 3 WMs(ActiGraph GT3X+;activPAL,and SenseWear 2)in estimating the intensity of SB and LPA in adults as compared with the criterion measure of oxygen uptake(VO2)measured by indirect calorimetry.

Methods:Sixteen participants(age:25.38±8.58 years)wore the ActiGraph GT3X+,activPAL,and SenseWear 2 devices during 7 sedentary-to-light activities.VO2(mL/kg/min)was estimated by means of a portable gas analyzer,Oxycon Mobile(Carefusion,Yorba Linda,CA,USA).All data were transformed into metabolic equivalents and analyzed using mean percentage error,equivalence plots,Bland-Altman plots,kappa statistics,and sensitivity/specificit.

Results:Mean percentage error was lowest for theactivPAL for SB(14.9%)and LPA(9.3%)compared with other WMs,which were>21.2%. None of the WMs fell within the equivalency range of±10%of the criterion mean value.Bland-Altman plots revealed narrower levels of agreement with all WMs for SB than for LPA.Kappa statistics were low for all WMs,and sensitivity and specificit varied by WM type.

Conclusion:None of the WMs tested in this study were equivalent with the criterion measure(VO2)in estimating sedentary-to-light activities; however,theactivPAL had greater overall accuracy in measuring SB and LPA than did the ActiGraph and SenseWear 2 monitors.

?2017 Production and hosting by Elsevier B.V.on behalf of Shanghai University of Sport.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Accelerometers;Accuracy;Low intensity;Metabolic estimations;Objective measurement;Sedentary behaviors

1.Introduction

Sedentary behavior(SB)is an important determinant of health.1Accurate assessment of this behavior is useful for epidemiologic research and to evaluate changes for interventions and programs.2Self-report has been the most common method of quantifying SB;however,its validity is still under assessment.3,4Therefore,objective measurement with sophisticated wearable monitors(WMs)has emerged to overcome selfreporting biases,yet many challenges accompany their use.5–9To date,the treatment and understanding of the data obtained from WMs is still very limited.5,10Furthermore,most of the available WMs have been extensively evaluated for accuracy in estimating moderate-to-vigorous physical activity(MVPA)and not SB or light-intensity physical activity(LPA).

Because many of the adults from developed and developing countries spend most of their time in SB and LPA,11it is critical to assess the validity of WMs in measuring SB and LPA.Early work in understanding energy expenditure(EE)has described the lack of ability of WMs to measure EE in the sedentary-tolight intensity spectrum.12More recently,Calabró et al.13assessed the validity of a variety of WMs in estimating EE during light-to-moderate intensity activities,findin a percentage error ranging from 9.5 to 30.5.Even though their work provides important information for considering whether to use a WM when there is interest in tracking low-intensity activities, several questions remain regarding which are the most valid and reliable objective wearable measures of SB and LPA.

Currently,there are many types ofWM brands available(e.g., ActiGraph(ActiGraph,Pensacola,FL,USA);activPAL(PAL TechnologiesLtd.,Glasgow,UK);and SenseWear2(Body Media, Pittsburgh,PA,USA))to measure PA and SB that have been extensively evaluated foraccuracy in estimating MVPA.However, their ability to estimate EE on the lower end of the intensity spectrum,such as for SB and LPA,is less well known.For example,the ActiGraph,a triaxialaccelerometer,measures acceleration in 3 individual axes(vertical,anteroposterior,and mediolateral)and provides activity counts for separate and composite vector magnitude of these 3 axes;however,the primary determination of SB by the ActiGraph is often based on only 1 axis,using an intensity threshold of<100 counts per minute (cpm).There has been some concern about the accuracy of this threshold because it has underestimated sitting time by 5%. Although 150 cpmseemsto be a more accurate cutoffpointforthe ActiGraph WM,14there are several proposed cpm thresholds that classify SB in different studies:50 cpm,15100 cpm,16150 cpm,14and 500 cpm.17Another monitor is theactivPAL PA logger,a uniaxial accelerometer and inclinometer that identifie walking, sitting,standing,steps,and instantaneous cadence.18TheactivPAL has shown accuracy in distinguishing sitting or lying down from standing posturesand in classifying time stepping;14,19however,the estimated metabolic equivalent(MET)values from theactivPAL atvarious speeds(2–4 mph)are significanty different(p<0.0001)from the criterion of oxygen uptake(VO2).20A third example of a monitor to measure SB and LPA is the SenseWear 2,which integrates information from a biaxial accelerometer and other physiological sensors(heat flux temperature, and galvanic skin response)to provide estimates of EE using a proprietary algorithm.21This WM overestimates EE at various walking and running speedsranging from2 to 8 mph(p<0.0001) as compared with the criterion of oxygen uptake(VO2).22

The accuracy(validity)for each of these WMs in estimating EE during sedentary-to-light activities is unclear.One way to assess validity of the WM is to compare its outputs against a criterion measure(criterion validity).The criterion validity describes the relationship between WM outputs and physiological measures that reflec more directly the energy cost of the activity.Thus,the goal of this study was to examine the validity of 3 WMs(ActiGraph GT3X+,activPAL,and SenseWear 2)in estimating intensity for sedentary-to-light activities in adults as compared with VO2measured in mL/kg/min.We hypothesized that the validity of EE estimates made by the tested WM(ActiGraph,activPAL,and SenseWear 2)would be low because most of the WMs are validated for measuring MVPA but not SB or LPA.

2.Materials and methods

2.1.Participant information

A convenience sample of 16 participants(n=8 men,n=8 women)with an age range of 19–47 years(25.38±8.58 years), body mass index range of 18.8–35.0 kg/m2(24.6±4.6 kg/m2), no contraindications for exercise(assessed with the PA readiness questionnaire(PAR-Q)),23and ability to walk unassisted on a motorized treadmill at 2.0 mph participated in the study. Prior to participation,all participants read and signed an informed consent document approved by the Arizona State University Institutional Review Board.

2.2.Procedures

Participantswere instructed to avoid vigorousexercise the day before the testing and to eat their usual diet.Each participant performed 7 sedentary-to-light activities in a randomly assigned order.Activities close to the LPA threshold of 1.5 METs were selected based on values listed in the 2011 Compendium of PhysicalActivities.24Every activity wasperformed for7 min,with 4 min ofrestbetween activities.Participantswere instructed to be silent during the monitoring periods.The activities were performed twice,with at least 24 h between trials.Participants were instructed to perform the activities as follows:

1.Treadmill walking at 1.0 mph(0.45 m/s),1.5 mph (0.67 m/s),and 2.0 mph(0.90 m/s)—walk using their normal gate at each speed and not using the handrails for support.

2.Cleaning a kitchen(cleaning)—simulate cleaning a kitchen and dishes using a dry rag.Tasks included clearing dishes off a counter,simulating washing and drying dishes,placing dishes in a cupboard,and wiping the counter.

3.Standing while reading(reading)—stand in place and read a book silently.

4.Sitting while typing(typing)—sit at a computer to type a given paragraph.Participants were instructed to sit up straight and maintain that posture while typing.

5.Sitting while gaming(gaming)—be seated and quietly play a board game,which required the participant to put 5 objects in a define order.Participants also rolled a die and moved their game piece a certain number of spaces based on their score from ordering the objects.Participants competed against the researcher to more accurately simulate playing a board game.

2.3.WMs

Each participant wore the 3 WMs under assessment and the criterion monitor simultaneously during the 7 selected activities.The criterion measure,VO2in mL/kg/min,was determined with the Oxycon Mobile portable metabolic unit(Carefusion, Yorba Linda,CA,USA);25the unit was calibrated before each test according to the manufacturer’s specifications

The ActiGraph was worn on an elastic belt on the right hip. The ActiGraph was initialized to collect data at 30 Hz.TheactivPAL was worn on the anterior and medial portion of the right thigh attached to the skin by hypoallergenic medical tape. The SenseWear 2 was worn on the left upper arm of the individual using the factory-provided elastic strap.

2.4.Data management and processing

Researchers kept a written record of the time when each activity was performed;for example,walking 1 mph was performed from 1:00 p.m.to 1:07 p.m.When data collection was complete,data were downloaded from each of the WMs to adesktop computer.Data from 2 trials performed by each of the 16 participants were included for data analysis,resulting in a maximum of 32 trials.

Table 1 Calculations used to obtain METs from monitors and the criterion measure.

To ensure that a steady state ofVO2had been attained during each activity and to avoid small discrepancies between start and stop times for each activity,the firs 2 min(Min 1–2)and the fina minute of data(Min 7)were dropped from the analysis. Accordingly,Min 3–6 of each activity were utilized to identify the activity intensity for each WM.This process yielded four 1 min epochs for each subject in each activity.

Capabilities for data summarizing and measurement units are different among the selected WMs;as a result,data output lengths were standardized to a 1 min epoch and the measurement units were standardized to METs.A MET is define as the energy cost of a specifi activity divided by a standard resting EE of 3.5 mL/kg/min.Table 1 summarizes how the measurement units for the criterion and the WM output values were transformed into METs.

2.5.Statistical analysis

Analyses were conducted by averaging the four 1 min epochs of each activity into 1 variable reflectin the average energy cost forthe activity.The variables were stratifie into 2 groups according to their MET values:SB(<1.5 METs:reading,typing,and gaming)and LPA(≥1.5 METs:walking 1 mph,walking 1.5 mph, walking 2 mph,and cleaning).Because each participant completed 2 trialsforeach activity,weperformed atest–retestreliability analysis(intraclass correlation coefficient ICC)for each WM prior to comparison to the criterion measure.

Mean percentage error(MPE)was calculated to assess the proportion of error for each of the 3 WMs relative to the criterion measure.MPE was calculated using the equation MPE=((measured score?true score)/true score)×100.The true score was the criterion value(VO2in METs),and the measured score was the MET value obtained from each WM.A positive MPE indicated a MET value overestimation for the WM,whereas a negative MPE indicated a MET value underestimation for the WM.26

Equivalency testing was used to examine whether the MET value for each of the WMs was statistically equivalent to the criterion MET value.Equivalence testing is an alternative approach to testing for significan differences between means.27Equivalence testing requires researchers to identify a clinically meaningful range(i.e.,equivalence zone)that permits comparisons between the values for WM and the criterion values in the equivalence zone.If the full 90%confidenc interval(CI)range of the WM lies within the equivalence zone,then it can be concluded(withα<0.05)that the WM value is equivalent to the criterion value.Based on previously published work,28we established±10%of the criterion mean MET value as the equivalence zone;by choosing the same values,we will facilitate comparisons when needed.

Bland-Altman plots29were used to show the distribution of the error and to assess systematic variation between the criterion MET value and each WM’s MET value.The Bland-Altman plot is a graphical method to compare 2 measurement techniques.In this method,the difference score between 2 measures (i.e.,criterion MET value minus the WM’s MET value)is plotted against the averages of the 2 measures.The error distribution can be observed within 3 horizontal reference lines that are drawn:mean difference(0 deviation line),upper limit of agreement(+1.96 SD),and lower limit of agreement(–1.96 SD).To provide a statistical reference for systematic bias between the criterion MET value and each WM’s MET value, the difference score between methods is regressed on the average of the 2 scores.Thus,the regression line provides information about whether the WM value becomes more or less accurate at varying levels of the criterion value.A fla regression line in the Bland-Altman plot indicates that the MET estimate of the WM varies in the same manner as the criterion value;a positive slope indicates that the WM is positively biased when compared with the criterion MET value;and a negative slope indicates that the WM is negatively biased when compared with the criterion MET value.The White test was used to examine the presence of heteroskedasticity.30

Kappa statistics was used to observe agreement between each WM and the criterion value for classifying activities while taking into account the agreement occurring by chance.31Data were dichotomous indicatorvariables forSB(0)or LPA(1).The kappa value interpretation is based on recommendations from Landis and Koch32as follows:0–0.2=slight agreement,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.

Sensitivity and specificit were calculated to measure the accuracy of the WM in classifying an activity as SB or LPA. Sensitivity is the proportion of true positives(i.e.,correct MET category for the WM and the criterion value)that are correctly identifie by the WM(true positive proportion).Sensitivity was calculated using the formula:sensitivity=true positives/(true positives+false negatives).33A sensitivity value close to 1 shows that the WM is able to accurately classify a high proportion of the activities into the correct category;a sensitivity value close to 0 indicates that the WM fails to classify activities into the correct category.Specificit refers to the proportion of true negatives(i.e.,correct exclusion of the WM and the criterion value from the incorrect category)that are correctly classifie by the WM(true negative proportion).Specificit was calculated using the formula:specificit=true negatives/(false positives+true negatives).33A specificit value close to 1 shows that the WM is able to exclude a high proportion of the activities from being classifie into the incorrect category.A specificit value close to 0 indicates that the WM is unable to exclude activities from being classifie into the incorrectcategory.Significanc was set at the p<0.05 probability level. All analyses were performed using SPSS Version 21(IBM Corporation,Armonk,NY,USA)and SAS Version 9.3(SAS Institute Inc.,Cary,NC,USA).

3.Results

ICC test–retest values were high for all WMs(0.94,0.97, 0.99,and 0.85 for Oxycon Mobile,ActiGraph,activ PAL,and SenseWear 2,respectively).Table 2 present means,SDs,and 95%CIs in METs for the criterion and all WMs under assessment for SB and LPA,respectively.MPE is presented for the ActiGraph,activ PAL,and SenseWear 2 WMs referenced to the criterion value.For both SB and LPA,MPEs were lowest for the activ PAL and highest for the SenseWear 2 monitors.When SB and LPA were combined,MPEs were lowest for the ActiGraph and highest for the SenseWear 2.

Based on the equivalence plots listed in Fig.1,none of the WMs(and their associated CIs)fell within the equivalencyrange of±10%for the criterion mean.The ActiGraph fell above the equivalence zone for SB and below the zone for LPA;the activ PAL provided estimates closest to the equivalency range for both SB and LPA;and the SenseWear 2 was over the equivalence range for both SB and LPA.

Table 2 Measured MET values and MPE for SB and LPA(both<1.5 METs).

Fig.1.Equivalence plots for sedentary behavior(SB)and light-intensity physical activity(LPA)compared with the criterion measure.Gray areas represent±10%for the criterion mean(equivalence zone),and black bars represents 90%confidenc interval for the test monitor.METs=metabolic equivalents.

Bland-Altman plots(Fig.2)revealed narrower levels of agreement for the WM when measuring SB(0.56,0.55,and 1.62 METs forActiGraph,activ PAL,and SenseWear 2,respectively)than when measuring LPA(1.42,1.31,and 2.20 METs for ActiGraph,activ PAL,and SenseWear 2,respectively).For SB,the ActiGraph and the activ PAL had no pronounced variation across the intensity range,but the SenseWear 2 showed a slight cluster of data points below the mean difference line.The variation for LPA was greater for all the devices compared with the variation observed in SB;the ActiGraph had greater variation at higher intensity levels,with a negative slope indicating a negative bias for EE as the intensity levels increased. Heteroskedasticity was found for the activ PAL(p=0.11)and SenseWear 2(p=0.30)for SB but not for LPA.

Table 3 shows the kappa statistics for agreement between the WM and the criterion measure to classify SB and LPA as well as results for sensitivity and specificit.There was a slight overall agreement among the instruments for measuring SB. For LPA,the agreement was fair for the ActiGraph and moderate for the activ PAL.When data for SB and LPA were combined,the agreement increased markedly.For SB,both the ActiGraph and the activ PAL had high sensitivity but low specificit.For LPA,both the ActiGraph and the activ PAL had fair sensitivity and good specificity the SenseWear 2 had good sensitivity but low specificit.

4.Discussion

Fig.2.Bland-Altman plots for sedentary behavior(SB)metabolic equivalents(METs)(A–C)and light-intensity physicalactivity(LPA)values(D–F)compared with the criterion value.

Table 3 Kappa statistics,sensitivity,and specificit for monitor MET values compared with criterion MET values.

The aim of the present study was to examine the accuracy of 3 WMs(ActiGraph GT3X+,activ PAL,and SenseWear 2)in estimating EE during SB and LPA in adults as compared with VO2measured by indirect calorimetry.The results showed overall low accuracy of the 3 WMs in estimating EE in METs. These finding emphasize the need for more refinement in the low spectrum of the EE measurements given the necessity of accurately estimating SB and/or LPA in relation to mortality and chronic diseases.34The analyses are relevant,because estimations made by the tested WMs are often used in PA research and commonly used to quantify behaviors in the lower range of intensity.Although the WM validity and reliability has been demonstrated in the moderate-to-vigorous EE spectrum,the tested WMs showed considerable limitations in measuring the metabolic cost of SB and LPA.For example, Calabró et al.13examined the validity of EE estimates during sedentary-to-moderate intensity activities for different monitors compared with the Oxycon Mobile.They reported a 25.5%and 22.2%underestimation for the ActiGraph and the activ PAL monitors,respectively.Their magnitude of underestimation is similar to what we found in the current study for the ActiGraph(21.2%)but differs for the activ PAL(9.3%).The discrepancies may be explained by the fact that their protocol included less-structured activities,which could have increased the amount of error for the activ PAL monitor.Similarly, Kozey-Keadle et al.14conducted a study to examine the validity of 2 monitors in classifying SB against direct observation as the criterion.They found that both the ActiGraph and the activ PAL monitors underestimated time spent in SB by 4.9% and 2.8%,respectively.They also tested the monitors for their ability to detect changes between sedentary and active pursuits.They found that the activ PAL was more precise in measuring time in SB and more sensitive in detecting reductions in sitting time.Even though the Kozey-Keadle et al. study14and the current study used different metrics(percentage bias vs.MPE)and outcomes(time spent in SB vs.EE),the results are similar in the sense that the activ PAL monitor performed best when estimating SB.

Possible explanations for the measurement error observed in the current study are that arm movements related to certain activities might cause the SenseWear 2 not to differentiate arm movements during activities such as typing from free ambulation.In addition,a small range of motion for the hip while walking at slow speeds on the treadmill may cause the ActiGraph to misclassify some LPAs as SBs.Another possible explanation of error measurement for the ActiGraph relates to the characteristics of the Freedson prediction equation used to estimate EE values.35The Freedson equation was validated using treadmill walking and running activities ranging from 3 to 9 mph.The EE estimates may be less accurate with lowerintensity activities.We acknowledge the availability of other equations to estimate METs from the ActiGraph cpm in adults;36–41however,we are unaware of an equation to estimate EE during SB-to-LPA for the ActiGraph.Thus,we chose Freedson’s equation35given its common use in the fiel and its validity(R2=0.82;standard error of the estimate (SEE)=±1.12 METs).

Among the 3 assessed WMs,only the activ PAL was located in the anteromedial thigh.The activ PAL showed the lowest amount of error compared with the other WMs(MPE=14.89, MPE=9.30 for SB and LPA,respectively).Thus,locating WMs in the lower limbs seemed to be more suitable when measuring SB and LPA.These finding need further confi mation by means of an WM placed in even more distal locations, such as the ankle.Alternatively,the activ PAL may include a more sensitive transducer than the ActiGraph or SenseWear 2 WMs that may make it more suitable for measuring SB and LPA.42However,this assertion has to be tested,because we investigated the validity of the equations of each monitor not the transducer itself.

Interestingly,less error distribution was observed in the tested WMs for SB than for LPA.This was demonstrated by the Bland-Altman plots,which revealed narrower levels of agreement for SB than LPA.In particular,the lowest limits of agreement were for the activ PAL as compared with the other WMs (0.55 METs for the SB and 1.31 METs for LPA).On the other hand,the heteroskedasticity test revealed that the variance of the residuals was not homogeneous for the activ PAL and SenseWear 2 in SB.These results suggest a systematic bias in the 2 WMs for assessing SB.In other words,the activ PAL and SenseWear 2 WMs seem to have positive bias with heterogeneous error variation when assessing SB.Future research should be conducted to assess the sources of variability on the measurement of SB.

Although allWMshad high test–retestreliability(ICC=0.94, ICC=0.97,ICC=0.99,and ICC=0.85 for the Oxycon Mobile, ActiGraph,activ PAL,and SenseWear 2,respectively),all WMs showed low agreement with the criterion measure in classifying activities as either SBor LPA.When SBand LPAwere combined into 1 category,the agreementofthe WMstended to be betterthan when evaluated separately(see kappa statistics in Table 3).In a similar manner,the accuracy of the devices seemed to improve when SB and LPA were combined(see sensitivity and specificit results in Table 3).This may have been due to having more data points with a greater range of values from low-to light-intensity EE.It may also be due to reduced variation in movement when performing SBs as compared with LPAs.These results highlight the importance ofrefinin WMaccuracy forthe lowerspectrum of the EE(sedentary to light).

When assessing the validity of WMs in measuring SB, researchers should pay special attention to criterion selection regarding the complexity of the behavior measured.Anywaking behavior is characterized by an EE≤1.5 METs while in a sitting or reclining posture.43As a consequence,and whenever possible,a combination of criterion measures should be considered(e.g.,VO2for EE and direct observation for postural allocation).In the current study we aimed to examine the accuracy of WMs in estimating EE of sedentary-to-light activities; thus,a criterion such as the VO2was needed to detect small differences between SB and LPA.However,a more comprehensive approach in classifying SB and LPA should also include the assessment of posture to fully address the ability of a device to detect SB.Among the selected activities for the current study there was 1 activity,standing while reading,that we classifie as an SB.We considered that the very low EE of 1.13 METs and the lack of motion made this activity more of an SB than an LPA.However,this assertion may not be applicable to everybody,because the EE for standing may differ according to individual characteristics.44

There were several strengths to this study.First,participants wore the 3 WMs and the Oxycon Mobile simultaneously so each activity could be monitored within a laboratory setting.Second, activities were randomized to prevent systematic bias in the measurement,which allowed the results to improve in accuracy.Last, activities were selected to be near the light-intensity activity threshold of>1.5 METs.This insured that activities performed would aid in understanding the accuracy of estimating EE in the lower end of the spectrum(sedentary to light).

With respect to the results for EE estimations for this study,it should be noted that the participants’resting metabolic rates were not measured,and we used the standard MET value of 3.5 mL/kg/min to estimate resting EE units.This may have introduced error,resulting in an overestimation ofresting EE(10%and 15%for men and women,respectively)as reported in a recent systematic review.45We also observed that the MET values in the lower levels of the intensity spectrum for SB activities were quite homogeneous,which implies the need to reduce the variance to obtain substantial agreement.Additionally,we would like to note thatthe7 sedentary-to-lightactivitiesmay notbe representativeof the whole spectrum of sedentary-to-light EE;however,they were thoughtfully selected and randomly assigned to avoid introducing systematicerror.We acknowledge thattheparticipantscomprised a convenience sample of healthy adults and that data were obtained in a laboratory setting with staged activities,limiting generalization of the results to other populations(e.g.,older adults).Missing data were caused by problems with WM initialization and an inability to save data to a spreadsheet.Although missing dataare alimitation,thedatalosswasrandom and did not represent a systematic bias.

5.Conclusion

As growing evidence demonstrates the associations between SB and morbidity and mortality,more research and refinement in EE estimations and in the ability of WMs to record SB-to-LPA is needed.Based on equivalency testing,none of the WMs tested in this study was equivalent with the criterion measure of VO2for estimating EE in SB-to-LPA.However,among the WMs tested,the activ PAL had the highest overall criterion validity to measure both SB and LPA as compared with the ActiGraph and SenseWear 2 WMs.This study provides support for the use of activ PAL in studying SB and LPA in healthy adults.

Acknowledgment

The authors would like to thank the participants of this study. There are no funding sources to state.

Authors’contributions

FPA contributed to data analysis and drafted the manuscript; MN,BM,and BEA contributed to design of the study,data collection,data analysis and drafted the manuscript.All authors have read and approved the fina version of the manuscript,and agree with the order of presentation.

Competing interests

None of the authors declare competing financia interests.

1.Tremblay MS,Colley RC,Saunders TJ,Healy GN,Owen N.Physiological and health implications of a sedentary lifestyle.Appl Physiol Nutr Metab 2010;35:725–40.

2.Pate RR,O’Neill JR,Lobelo F.The evolving definitio of“sedentary”. Exerc Sport Sci Rev 2008;36:173–8.

3.Lynch BM,Friedenreich CM,Khandwala F,Liu A,Nicholas J,Csizmadi I.Development and testing of a past year measure of sedentary behavior: the SIT-Q.BMC Public Health 2014;14:899.doi:10.1186/1471-2458-14-899

4.Marshall AL,Miller YD,Burton NW,Brown WJ.Measuring total and domain-specifi sitting:a study of reliability and validity.Med Sci Sports Exerc 2010;42:1094–102.

5.Ward DS,Evenson KR,Vaughn A,Rodgers AB,Troiano RP. Accelerometer use in physical activity:best practices and research recommendations.Med Sci Sports Exerc 2005;37(Suppl.11):S582–8.

6.Healy GN,Clark BK,Winkler EA,Gardiner PA,Brown WJ,Matthews CE. Measurement of adults’sedentary time in population-based studies.Am J Prev Med 2011;41:216–27.

7.Lyden K,Kozey SL,Staudenmeyer JW,Freedson PS.A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.Eur J Appl Physiol 2011;111:187–201.

8.Troiano RP,Berrigan D,Dodd KW,Masse LC,Tilert T,McDowell M. Physical activity in the United States measured by accelerometer.Med Sci Sports Exerc 2008;40:181–8.

9.Chen KY,Bassett Jr DR.The technology of accelerometry-based activity monitors:current and future.Med Sci Sports Exerc 2005;37(Suppl.11): S490–500.

10.Freedson P,Bowles HR,Troiano R,Haskell W.Assessment of physical activity using wearable monitors:recommendations for monitor calibration and use in the field Med Sci Sports Exerc 2012;44(Suppl.1):S1–4.

11.Dempsey PC,Owen N,Biddle SJ,Dunstan DW.Managing sedentary behavior to reduce the risk of diabetes and cardiovascular disease.Curr Diab Rep 2014;14:522.doi:10.1007/s11892-014-0522-0

12.Montoye HJ,Washburn R,Servais S,Ertl A,Webster JG,Nagle FJ. Estimation of energy expenditure by a portable accelerometer.Med Sci Sports Exerc 1983;15:403–7.

13.Calabró M,Lee JM,Saint-Maurice PF,Yoo H,Welk GJ.Validity of physical activity monitors for assessing lower intensity activity in adults. Int J Behav Nutr Phys Act 2014;11:119.doi:10.1186/s12966-014-0119-7

14.Kozey-Keadle S,Libertine A,Lyden K,Staudenmayer J,Freedson PS. Validation of wearable monitors for assessing sedentary behavior.Med Sci Sports Exerc 2011;43:1561–7.

15.Crouter SE,Clowers KG,Bassett Jr DR.A novel method for using accelerometer data to predict energy expenditure.J Appl Physiol 2006;100:1324–31.

16.Matthews CE,Chen KY,Freedson PS,Buchowski MS,Beech BM,Pate RR,et al.Amount of time spent in sedentary behaviors in the United States, 2003–2004.Am J Epidemiol 2008;167:875–81.

17.Silva P,Aires L,Santos RM,Vale S,Welk G,Mota J.Lifespan snapshot of physical activity assessed by accelerometry in Porto.J Phys Act Health 2011;8:352–60.

18.Grant PM,Ryan CG,Tigbe WW,Granat MH.The validation of a novel activity monitor in the measurementof posture and motion during everyday activities.Br J Sports Med 2006;40:992–7.

19.Hart TL,McClain JJ,Tudor-Locke C.Controlled and free-living evaluation of objective measures of sedentary and active behaviors.J Phys Act Health 2011;8:848–57.

20.Harrington DM,Welk GJ,Donnelly AE.Validation of MET estimates and step measurement using the ActivPAL physical activity logger.J Sports Sci 2011;29:627–33.

21.Johannsen DL,Calabro MA,Stewart J,Franke W,Rood JC,Welk GJ. Accuracy of armband monitors for measuring daily energy expenditure in healthy adults.Med Sci Sports Exerc 2010;42:2134–40.

22.King GA,Torres N,Potter C,Brooks TJ,Coleman KJ.Comparison of activity monitors to estimate energy cost of treadmill exercise.Med Sci Sports Exerc 2004;36:1244–51.

23.Shephard RJ.PAR-Q,Canadian home fitnes test and exercise screening alternatives.Sports Med 1988;5:185–95.

24.Ainsworth BE,Haskell WL,Herrmann SD,Meckes N,Basset Jr DR, Tudor-Locke C,et al.2011 Compendium of Physical Activities:a second update of codes and MET values.Med Sci Sports Exerc 2011;43:1575–81.

25.Perret C,Mueller G.Validation of a new portable ergospirometric device (Oxycon Mobile)during exercise.Int J Sports Med 2006;27:363–7.

26.Hartsell H,Fitzpatrick D,Brand R,Frantz R,Saltzman C.Accuracy of a custom-designed activity monitor:implications for diabetic foot ulcer healing.J Rehabil Res Dev 2002;39:395–400.

27.Schuirmann DJ.A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability.J Pharmacokinet Biopharm 1987;15:657–80.

28.Lee JM,Kim Y,Welk GJ.Validity of consumer-based physical activity monitors.Med Sci Sports Exerc 2014;46:1840–8.

29.Bland JM,Altman DG.Statistical methods for assessing agreement between two methods of clinical measurement.Lancet 1986;1:307–10.

30.White H.A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.Econometrica 1980;48:817–38.

31.Cohen J.A coefficien of agreement for nominal scales.Educ Psychol Meas 1960;20:37–46.

32.Landis JR,Koch GG.The measurement of observer agreement for categorical data.Biometrics 1977;33:159–74.

33.Parikh R,Mathai A,Parikh S,Chandra Sekhar G,Thomas R. Understanding and using sensitivity,specificit and predictive values. Indian J Ophthalmol 2008;56:45–50.

34.Owen N,Healy GN,Matthews CE,Dunstan DW.Too much sitting:the population health science of sedentary behavior.Exerc Sport Sci Rev 2010;38:105–13.

35.Freedson PS,Melanson E,Sirard J.Calibration ofthe ComputerScience and Applications,Inc.accelerometer.Med Sci Sports Exerc 1998;30:777–81.

36.Crouter SE,Kuffel E,Haas JD,Frongillo EA,Bassett Jr DR.Refine two-regression model for the ActiGraph accelerometer.Med Sci Sports Exerc 2010;42:1029–37.

37.Hendelman D,Miller K,Baggett C,Debold E,Freedson P.Validity of accelerometry for the assessment of moderate intensity physical activity in the field Med Sci Sports Exerc 2000;32(Suppl.9):S442–9.

38.Swartz AM,Strath SJ,Bassett Jr DR,O’Brien WL,King GA,Ainsworth BE.Estimation of energy expenditure using CSA accelerometers at hip and wrist sites.Med Sci Sports Exerc 2000;32(Suppl.9):S450–6.

39.Leenders NY,Nelson TE,Sherman WM.Ability of different physical activity monitors to detect movement during treadmill walking.Int J Sports Med 2003;24:43–50.

40.Yngve A,Nilsson A,Sjostrom M,Ekelund U.Effect of monitor placement and of activity setting on the MTI accelerometer output.Med Sci Sports Exerc 2003;35:320–6.

41.Brooks AG,Gunn SM,Withers RT,Gore CJ,Plummer JL.Predicting walking METs and energy expenditure from speed or accelerometry.Med Sci Sports Exerc 2005;37:1216–23.

42.Chen KY,Janz KF,Zhu W,Brychta RJ.Redefinin the roles of sensors in objective physical activity monitoring.Med Sci Sports Exerc 2012; 44(Suppl.1):S13–23.

43.Sedentary Behaviour Research Network.Letter to the editor:standardized use of the terms“sedentary”and“sedentary behaviours”.Appl Physiol Nutr Metab 2012;37:540–2.

44.Miles-Chan JL,Sarafia D,Montani JP,Schutz Y,Dulloo A. Heterogeneity in the energy cost of posture maintenance during standing relative to sitting:phenotyping according to magnitude and time-course. PLoS One 2013;8:e65827.doi:10.1371/journal.pone.0065827

45.McMurray RG,Soares J,Caspersen CJ,McCurdy T.Examining variations of resting metabolic rate of adults:a public health perspective.Med Sci Sports Exerc 2014;46:1352–8.

Received 10 November 2015;revised 8 April 2016;accepted 5 July 2016 Available online 21 October 2016

Peer review under responsibility of Shanghai University of Sport.

*Corresponding author.

E-mail address:alberto.florez.p@gmail.co (F.-P.Alberto)

http://dx.doi.org/10.1016/j.jshs.2016.10.005

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