static-content.springer.com10.1007... · Web viewThis study concludes that combining acc. data from...

Electronic Supplementary Material Table S1 Summary of key methodological issues related to GT3X/+ data collection protocols and data processing in

validation/calibration studies in preschoolers (the rest of studies are available on request)

References

Study design(days of assessment, protocol)[validation method]

Topics discussedAims

SubjectsN, mean±SD or age range

Acc. model(placement) Filter

Sampling frequency

Epoch

NWT definitiona

Valid dayValid week

Cut-Points/EE algorithm/MET equation/Sleep Algorithm Main findings/Conclusions

Preschoolers (n=24, Validation, calibration and comparison studies reviewed for objectives 1 and 2: n=11)

Butte et al. [1] Part 1:Lab/controlled

Part 2:Free-living observational(7 days, waking hours protocol)

[Criterion validity: room calorimetry and doubly labelled water]

SED/PA intensity classificationEE algorithmTo develop and validate cross-sectional time series and multivariate adaptive regression splines models based on accelerometry and heart rate for the prediction of EE using room calorimetry and doubly labelled water and established accelerometry cut points for PA levels

Part 1:50,4.5±0.8 y

Part 2:105,4.6±0.9 y

GT3X+ (right hip)

Normal NS

60 s

Part 1:Not used

Part 2:≥20-0-0

≥1000 min/day≥4 days/week

Cut-points:Butte SED, LPA/MPA, MPA/VPA VA and VM cut-points

Cross-sectional time series and multivariate adaptive regression splines models are acceptable for the prediction of EE in preschool-age children. Cut points were satisfactory for the classification of SED, LPA, and MVPA in preschoolers

Costa et al. [2] Part 1:Lab/controlled

Part 2:Lab/controlled

[Criterion validity: direct observation]

SED/PA intensity classificationTo calibrate and validate against direct observation (video recorded) the GT3X+ to measure PA and SED in preschool children

Part 1:18,2.8±0.5y

Part 2:38,2-3 y

GT3X+ (right hip)

LFE 80 Hz.

5 s and 15 s

Not used Cut-points:Costa SED and MVPA VA and VM cut-points

The 5 s VA cut-points showed smaller biases in estimated sedentary behaviour and PA time in 2-3 years. MVPA showed a high overestimation time

1

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Flynn et al. [3] Part 1Lab/controlled

Part 2Lab/controlled

Part 3Free-living observational(1 school-day -8:30 a.m. to 4:00 p.m.-)

[Calibration study: reliability]

Others: light sensor1. To assess reliability of the GT3X+ ambient light sensor2. To identify a lux threshold to accurately discriminate between indoor and outdoor activities in children3. To test the accuracy of the lux threshold in a free-living environment

Part 120 GT3X+

Part 218,7.0±2.3 y

Part 318,4.4±0.4. y

GT3X+(right hip)

NS 60 Hz.

60 s (only for part 3)

Part 1:Not used

Part 2:Not used

Part 3:Not used

≥4 h/dayNS

Not used In part 1, there was high interinstrument reliability for the light sensor.In part 2, the optimal lux threshold was determined to be 240 lux.In part 3, results of the school-day validation demonstrated the monitor was 97% accurate for overall detection of indoor and outdoor conditions.

Martin et al. [4]

Free-living observational(7 days, waking hours protocol)

[Criterion validity: ActivPAL]

SED/PA intensity classificationTo compare activPAL and GT3X monitors in free-living children.

23,4.5±0.7 y

GT3X (right hip)

ActivPAL

NS 30 Hz.

60 s

NS

≥6 h/day≥3 days/week

Cut-points:Reilly SED VA cut-point

Differences exist between activPAL and GT3X in SED. GT3X presented lower values compared to activPAL

2

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Janssen et al. [5]

Lab/controlled

[Criterion validity: room calorimetry and direct observation]

SED/PA intensity classificationEE algorithm1. To examine the predictive validity of ActiGraph EE equations against room calorimetry2. To compare the classification accuracy of ActiGraph cut-points for classifying SED and PA intensity in 4-6 years old against direct observation

40,4-6 y

GT3X(right hip)

NS 30 Hz.

15 s or 60 s depending on the cut-points used

Not used

Not used

Cut-points:Evenson, Pate, Puyau, Reilly, Sirard andVan Cauwenberghe SED, LPA and MVPA VA cut-points

EE algorithmPate and Puyau equations

EE MVPA-Pate equation.For the rest, authors did not recommend to apply Pate or Puyau due to underestimation.Evenson cut-points showed significantly higher classification accuracy for SED. Pate cut-point showed significantly higher accuracy for MVPA

Jimmy et al. [6]

Lab/controlled

[Criterion validity: indirect calorimetry]

SED/PA intensity classificationTo develop and validate cut-points for the VA and the VM in 5 to 9 year old against indirect calorimetry

325-9 y

GT3X(right hip)

Normal 30 Hz.

5 s

Not used Cut-points:Jimmy MVPA and VPA VA and VM cut-points

Current cut-points adequately reflect MVPA and VPA in young children. Cut-off points based on VM counts did not appear to reflect the intensity categories better than cut-off points based on VA counts alone

Johansson et al. [7]

Part 1: Lab/controlled

Part 2: Free living observational(7 days, waking hours protocol)


PlacementSED/PA intensity classificationTo calibrate and validate the GT3X+ for wrist-worn placement in young preschoolers against direct observation (video recorded)

38,15-36 month

GT3X+(left wrist)

Normal 30 Hz.

5 s

Part 1:Not used

Part 2:NS

≥11h/day of SED removed

Cut-points:Johansson SED, LPA, VPA VA and VM cut-points

The developed intensity thresholds appear valid in order to categorize sedentary and physical activity intensity categories in preschool children

3

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Kahan et al. [8]

Part 1: Lab/controlled(3 daily unstructured morning play periods)

Part 2: Lab/controlled(3 daily unstructured morning play periods)

[Convergent validity]

SED/PA intensity classificationTo evaluate convergent validity of accelerometry with direct observation using methods that are either consistent with current practise/recommendation

Part 1: 57,4.7±0.3 y

Part 2: 12, (NS)

GT3X (right hip)

NS 30 Hz.

Part 1:15 sPart 2:5 s

≥30-0-0

NS

Cut-points:Evenson, Pate, Sirard and Van Cauwenberghe SED and MVPA VA cut-points

Cut-points of Sirard are the best converged with the direct observation measure for SED and MVPA.Also, Sirard and Pate cut-points are more sensitive in detecting SED and MVPA, respectively

Meredith-Jones et al. [9]

Free-living observational(7 days, 24h protocol)

[Comparison study without a criterion]

SED/PA intensity classificationSleep algorithmTo compare the effect of 6 different sleep-scoring rules on PA and SED

291,4-8 y

GT3X(right hip)

Normal 30 Hz.

15 s

≥60-0-0

≥8 h/days≥3 days/week

Cut-points:Puyau SED, LPA and MVPA VA cut-points

Sleep Algorithm:Sadeh sleep algorithm for method 5

Different methods of removing sleep from 24h data markedly affect estimates of SED, yielding values ranging from 556 to 1145 min/day. Estimates of NWT (33–193 min), wear time (736–1337 min) and CPM (384–658) also showed considerable variation. By contrast, estimates of MVPA were similar, varying by less than 1 min/day

Pulakka et al. [10]

Free-living observational(7 days, 24h protocol)Two free play sessions recorded at home during these days.


SED/PA intensity classificationTest the feasibility and validity of the GT3X in measuring PA of rural Malawian toddlers against direct observation (video recorded)

56,16-18.5 months

GT3X(right hip)

Normal 30 Hz.

15 s

NS Cut-points:Pulakka SED/LPA and MVPA VA and VM cut-points

The acc. proved a feasible and valid method of assessing PA among Malawian toddlers

4

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Zakeri et al. [11]

Lab/controlled

[Criterion validity: room calorimetry]

EE algorithmTo develop cross-sectional time series and multivariate adaptive regression splines models for prediction of EE based on accelerometry, heart rate and calorimetry method and validate them against room calorimetry

69,3-5 y

GT3X+ (right hip)

Normal 30 Hz.

60 s

Not used

Not used

EE algorithm:cross-sectional time series and multivariate adaptive regression splines statistical models

Cross-sectional time series and multivariate adaptive regression splines models should prove useful in capturing the complex dynamics of EE and movement that are characteristics of preschoolers

Acc: accelerometer, BMI: body mass index, CPM: counts per minute, EE: energy expenditure, h: hours, Lab: laboratory condition, LFE: low-frequency extension, LPA: light physical

activity, MPA: moderate physical activity, MVPA: moderate-to-vigorous physical activity, NS: not specified, NWT: non-wear time, PA: physical activity, s: seconds, SED: sedentary

time, VA: vertical axis, VM: vector magnitude, VPA: vigorous physical activity

a NWT definition expressed as: minimum minutes of 0 CPM – minimum minutes for before and after allowance windows – maximum minutes of allowance

5


validation/calibration studies in children and adolescents (the rest of studies are available on request)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Children and adolescents (n=81, Validation, calibration and comparison studies reviewed for objectives 1 and 2: n=26)

Aibar et al. [12]


[Comparison study without a criterion]

Epoch lengthTo examine the effect of different epoch lengths (3-60 s) on MVPA, 10 minute bouts of MVPA and compliance with World Health Organization guidelines.

401,14.5±0.7 y

GT3X(right hip)

NS 30 Hz.

3 s5 s10 s15 s30 s45 s60 s

≥10-0-0

NS

Cut-points:Evenson MVPA VA cut-points

Significant epoch effect for time spent in MVPA, 10 minute bouts of MVPA and the extent of compliance with guidelines percentage of compliance of guidelines. Shorter epochs such as 5 s, 10 s and 15 s are proposed for comparative studies carried out with adolescents

Aittasalo et al. [13]

Lab/controlled

[Criterion validity: direct observationConvergent validity]

SED/PA intensity classificationTo compare mean amplitude deviation of raw acceleration signal and to develop cut-points based on the mean amplitude deviation in two different acc. brands.

20,14.2±0.7 y

GT3X(left hip)

AM13(right hip)

Not used 30 Hz.

5 s

Not used Cut-points:Aittasalo SED, LPA, MPA and VPA VA cut-points for raw data VM

The cut-points were almost identical in the two brands and indicate that it is possible to find a method, which classifies similarly the intensity of adolescents’ PA from raw acceleration data irrespective of acc. brand.

6

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Barreira et al. [14]


[Criterion validity: daily logs]

Sleep algorithm1. To add layers and features to a previously published fully automated algorithm designed to identify children’s nocturnal sleep and to exclude episodes of night time non-wear/wakefulness and potentially misclassified daytime sleep episodes2. To validate this refined sleep algorithm against daily logs

45,10.0±0.4 y

GT3X+(hip)

LFE 80 Hz.

60 s

Not used Sleep algorithm:Tudor-Locke refined sleep algorithm

Refined sleep algorithm total sleep episode time (36 min) was significantly different from Log sleep period time (24 min), but not different from Log + Acc. sleep period time (24 min). Significant and moderately high correlations were apparent between refined sleep algorithm determined variables and those using the other methods. There were no differences between refined sleep algorithm and Log + Acc. estimates of sleep onset or sleep offset and log wake time

Chandler et al. [15]

Lab/controlled


SED/PA intensity classificationPlacement1. To determine the cut-points for the GT3X+, non-dominant wrist-mounted acc. in children aged 8-12 y2. To compare classification accuracies among the acc.’s three axes and VM values against direct observation

45,8-12 y

GT3X+(non-dominant wrist)

Normal NS

5 s

Not used Cut-points:Chandler SED, LPA, MPA, VPA VA, axis 2, axis 3 and VM cut-points

Results found comparable activity intensity classification accuracies from the GT3X+ wrist-worn acc. to previously published studies. Based on ROC and regression analyses, activity intensities can be distilled from this acc. using the 3 axes or VM values with similar classification accuracy

7

(video recorded)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Crouter et al. [16]


Part 2:Free living observational (unstructured PA session)


SED/PA intensity classificationPlacementTo develop cut-points for classifying PA intensity through dominant wrist accelerations and validate the against indirect calorimetry in an unstructured PA session

181,12.0±1.5 y

GT3X/+(dominant wrist)

LFE 30 Hz.

5 s

Not used Cut-points:Crouter SED, LPA, MPA and VPA VA and VM cut-points determined by ROC curves and regression analyses

Compared to measured values, the VA and VM regression models developed on wrist acc. data had insignificant mean bias for child-METs and time spent in sedentary behavior, LPA, MPA and VPA; however, they had large individual errors

Crouter et al. [17]

Lab/controlled


EE algorithmTo examine the validity of seven child-specific equations compared with indirect calorimetry.

72,12±0.8 y

GT3X+(right hip)

LFE 30 Hz.

10 s

Not used EE algorithm:Crouter VMCrouter VAEvensonFreedsonTrostTreuthPuyau

Crouter 2-regression model and Puyau were the most accurate equations

8

Crouter et al. [18]

Lab/controlled


EE algorithmTo develop two new two-regression models for use in children, that estimate energy expenditure (EE) using the ActiGraph GT3X mean VM counts or VA counts against indirect calorimetry

59,11.0±1.7 y

GT3X(right hip)

LFE 30 Hz.

10 s

Not used EE algorithm:VA and VM two regression-models developed

The new 2 regression models in children with the GT3X provide a closer estimate of mean measured MET than other currently available prediction equations. In addition, they improve the individual prediction errors across a wide range of activity intensities

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Dowd et al. [19]

Lab/controlled


SED/PA intensity classification1. To investigate the criterion validity, the concurrent validity, perform and validate a value calibration of the activPAL2. To compare the estimating posture by activPAL with sedentary tresholds used with the GT3X

30,17.2±0.9 y

GT3X(right hip)

ActivPAL

NS 30 Hz.

15 s

Not used Cut-points:Ridgers SED VA cut-point

These findings suggest that the activPAL is a valid, objective measurement tool that can be used for both the measurement of PA and SED in an adolescent female population

9

Fairclough et al. [20]


[Convergent validity: GT3X+ and GENEA]

PlacementSED/PA intensity classification1. To explore children’s compliance to wearing wrist and hip-mounted acc.2. To compare children’s PA derived from wrist and hip raw accelerations3. To examine differences in raw and counts PA measured by hip-worn accelerometry

109,9-10 y.

GT3X+(right hip)

GENEA(Non-dominant wrist)

NS 100 Hz.

1 s

≥20-0-0


Not used Wrist placement promotes superior compliance than hip. Raw accelerations were significantly higher for wrist compared to hip, possibly due to placement location and technical differences between devices. Hip PA calculated from raw accelerations and counts differed substantially.

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Flynn et al. [3] Part 1Lab/controlled


Part 3Free-living observational(1 school-day -8:30 a.m. to 4:00 p.m.)

[Calibration study: reliability]

Others: light sensor1. To assess reliability of the GT3X+ ambient light sensor2. To identify a lux threshold to accurately discriminate between indoor and outdoor activities in children3. To test the accuracy of the lux threshold in a free-living environment

Part 120 GT3X+

Part 218,7.0±2.3 y

Part 318,4.4±0.4. y

GT3X+(right hip)

NS 60 Hz.

60 s (only for part 3)

Part 1:Not used

Part 2:Not used

Part 3:Not used

≥4 h/dayNS

Not used In part 1, there was high interinstrument reliability for the light sensor.In part 2, the optimal lux threshold was determined to be 240 lux.In part 3, results of the school-day validation demonstrated the monitor was 97% accurate for overall detection of indoor and outdoor conditions.

10

Hänggi et al. [21]

Lab/controlled


SED/PA intensity classificationOthers: inclinometerTo investigate comparability of the GT3X to the GT1M and to develop and validate activity intensity cut-points for the VM of the GT3X in children against indirect calorimetry

49,10.8±1.9

GT1MGT3X(right hip)

Normal 30 Hz.

1 s

Not used

Not used

Cut-points:Hänggi SED, LPA and MVPA VM cut-points

The anteroposterior and vertical counts of the GT1M and GT3X are not comparable in certain activities, although these differences should not affect the classification of vertical mean counts per seconds in different categories

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Hildebrand et al. [22]

Lab/controlled


PlacementSED/PA intensity classificationEE algorithmTo compare raw triaxial acc. output from GT3X+ and GENEActiv placed on the hip and the wrist and to develop regression equations for estimating energy expenditure against indirect calorimetry

Children: 30,8.9±0.9 y

Adults:30,34.2±10.7 y

2 GT3X+2 GENEA(right hip and non-dominant wrist)

NS 60 Hz.

1 s

Not used

Not used

Cut-points:Hildebrand LPA, MPA and VPA VM cut-points for wrist and hip from raw data

EE algorithm:Hildebrand equation for children and adults (hip and wrist)

Acc. outputs from GT3X+ and GENEActiv seem comparable when attached to the same body location in adults but not in children. Acceleration output from both GT3X+ and GENEActive explained a significant proportion of the variance (R2) in VO2. The R2 for wrist-monitors ranged between 71% and 78% in children and between 75% and 81% in adults, with consistently (4%–6% units) lower R2 for wrist compared with that for hip placements.

11

Hjorth et al. [23]



PlacementSleep algorithmTo examine the concordance between GT3X waist- and wrist-worn during nocturnal period with Sadeh and Cole algorithms

62,10.3±0.6 y

2 GT3X(right hip and non-dominant wrist)

Wrist: Normal

Waist: Normal and LFE

NS

60 s

Not used

Not used

Sleep algorithm:Cole-Kripke and Sadeh sleep algorithms filtered with diaries

Total sleep time and sleep efficiency is higher in waist than in wrist. Waist and wrist cannot be used interchangeably for the measurement of sleep

Jimmy et al. [6]

Lab/controlled


SED/PA intensity classificationTo develop and validate cut-points for the VA and the VM in 5 to 9 year old against indirect calorimetry

325-9 y

GT3X(right hip)

Normal 30 Hz.

5 s

Not used Cut-points:Jimmy MVPA and VPA VA and VM cut-points

Cut-off points based on VM counts did not appear to reflect the intensity categories better than cut-off points based on VA counts alone

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Meredith-Jones et al. [9]



SED/PA intensity classificationSleep algorithmTo compare the effect of 6 different sleep-scoring rules on PA and SED

291,4-8 y

GT3X(right hip)

Normal 30 Hz.

15 s

≥60-0-0


Cut-points:Puyau SED, LPA and MVPA VA cut-points

Sleep Algorithm:Sadeh sleep algorithm for method 5

Different methods of removing sleep from 24h markedly affect estimates of SED, yielding values ranging from 556 to 1145 min/day. Estimates of NWT (33–193 min), wear time (736–1337 min) and CPM (384–658) also showed variation. By contrast, estimates of MVPA were similar

12

Kim et al. [24] Lab/controlled


EE algorithmTo compare the validity of different equations for estimation of EE in children comparing with indirect calorimetry

59,9.9±1.8 y

GT3X(right hip)

LFE 30 Hz.

10 s

Not used EE algorithm:Crouter VACrouter VMFreedson 2005TrostPuyauTreuth

None of the 2-regression models and 1 regression-model were equivalent to indirect calorimetry. The 2 regression-models showed smaller individual-level errors than the 1 regression-model

Peterson et al. [25]

Lab/controlled


SED/PA intensity classificationOthers: InclinometerTo assess the degree of construct validity of the inclinometer function and VA and VM metrics of the GT3X in objectively measuring SED and PA against direct observation

28,18-20

GT3X+(right hip)

LFE 30 Hz.

10 s

Not used Cut-points:Kozey-keadle SED (VA and VM): <100, <150 CPM

Accuracy in measuring both SED and PA, acc. metrics (94.7–97.8%) outperformed the inclinometer (70.9%). 150 CPM in the VM performed the best. Inclinometer had advantages at detecting walking, biking, and standing

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


13

Romanzini et al. [26]

Lab/controlled


SED/PA intensity classificationTo establish cut-points for three different acc. models: ActiGraph GT3X, RT3 and Actical to accurately classify PA intensity levels in adolescents using indirect calorimetry as reference

79,10-15 y

GT3XRT3Actical(right or left hip)

Normal 30 Hz.

15 s

Not used Cut-points:Romanzini SED, LPA, MPA and VPA VA and VM cut-points

For all three acc. models, there was an almost perfect discrimination of SED and MVPA and an excellent discrimination of VPA observed. Areas under the ROC curves indicated better discrimination of MVPA by GT3X and Actical when compared to RT3. The cut-points developed in this study for the GT3X (VM), RT3 and Actical acc. models can be used to monitor PA level of adolescents.

Rowlands et al. [27]


Part 2Free-living observational(7 days, 24h protocol)


Sampling FrequencyTo establish the equivalence of output between two brands of monitor in a laboratory and in a free-living environment

Part 138,39.3±5.7 y

Part 258,10.7±0.8 y

GT3X+GENEA(right hip)

Not used Part 1:100 Hz.

Part 2:80 Hz.

Part 1Not used

Part 2NS

Part 1Not used

Part 2Not used

The strong relation between accelerations measured by the two brands suggests that habitual activity level and activity patterns assessed by the GENEA and GT3X+ may compare well if analyzed appropriately




SED/PA intensity classificationTo examine the concurrent validity of total activity and time spent at different PA intensities from GENEActiv and GT3X+

58,10.7±0.8 y

GT3X+ (right hip)

GENEA

Normal 80 Hz.

1 s

Default by GENEActiv

≥10 h/day≥1 day/week

Cut-points:Evenson (VA) and Hänggi (VM) SED, LPA, MPA and VPA cut-points

All output variables were strongly linear correlated between GT3X+ and GENEActiv. Using VM obtained better correlations (especially in SED and LPA)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


14

Santos-Lozano et al. [29]

Lab/controlled


SED/PA intensity classificationEE algorithm1. To compare EE equations with indirect calorimetry2. To define and validate new EE equations with GT3X against indirect calorimetry3. To define cut-points with VM according to the MET for PA intensity level classification

Young:31,14.7±1.0 y

Adults:31,47.1±3.5 y

Elderly:35,71.9±5.4 y

GT3X+(right hip)

Normal 30 Hz.

60 s

Not used Cut-points:Santos-Lozano LPA, MPA and VPA VM cut-points

EE algorithm:WETWET combined with Freedson VAWET combined with Sasaki VMSantos-Lozano new EE algorithm developed

The combined equation for MET estimation achieved better results that the rest of previous equations. Also, they defined a more accurate equation for young, adults and old people. According cut-points, they provided it for each group

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


15

Toftager et al. [30]

Free-living observational(7-8 days, waking hours protocol)


NWT definitionValid day and valid weekTo investigate how different data reduction criteria (number of valid days, daily wear time and non-wear time) changed the composition of the adolescent population retained in acc. data analysis

1348,11-14 y

GT3X(hip)

Normal 30 Hz.

30 s

≥10-0-0≥20-0-0≥30-0-0≥60-0-0≥90-0-0

≥6 h/day≥8 h/day≥9 h/day≥10 h/day≥12 h/day

≥1 day/week≥2 days/week≥3 days/week≥4 days/week≥5 days/week≥6 days/week=7 days/week

Cut-points:Evenson SED VA cut-point

Increasing daily wear time and number of valid days, and applying shorter non-wear time resulted in fewer adolescents retained in the dataset. Even small differences in acc. data reduction criteria can have substantial impact on sample size and PA and SED outcomes

Tudor-Locke et al. [31]

Free-living observational(7 days, waking hours and 24h protocols)

[Criterion validity: expert analysis on the data as criterion]

Sleep algorithmTo validate a refined and fully automated algorithm to determine bedtime and wake time and refine estimates of sleep-period time in children against expert analysis of minute-by-minute acc. data

30,9.9±0.2 y

GT3X+(right hip)

LFE 80 Hz.

60 s

Not used Algorithm 1: Sadeh

Algorithm 2: Sadeh + inclinometer

Algorithm 3: definition of BedTime & WakeTime (VA)

Algorithms 1 and 2 overestimated total sleep time by 43 and 90 min respectively. Algorithm 3 produced the smallest mean difference (2 min), and was not significantly different from expert criterion

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


16




Registration period protocolNWT definitionTo compare 24-h waist-worn acc., wear time characteristics of 9-11 y/o children in the ISCOLE to similarly aged U.S. children providing waking hours waist-worn accelerometry data in the NHANES project.

NHANES:586,10.4±0.1 y

ISCOLE:491,9.9±0.1 y

7164NHANES(waist)

GT3XISCOLE(waist)

NHANES:NS

ISCOLE: Normal

NHANES:NS

60 s

ISCOLE:30 Hz.

60 s

NHANES: NS

NS

ISCOLE:≥20-0-0


Not used Wear time characteristics were consistently higher in all ISCOLE study sites compared to the NHANES protocol

Zhu et al. [33] Lab/controlled


SED/PA intensity classificationTo develop cut-points for MVPA and VPA for Chinese children and youth, to validate it against indirect calorimetry and to compare the classification accuracy to that of a set of existing cut-points

367,9-17 y

GT3X(right hip)

NS 30 Hz.

60 s

Not used Cut-points:Zhu, Freedson, Puyau, TreuthMattocks, Evenson and Vanhelst MVPA and VPA VA cut-points

These new cut-points were more accurate in the sample of Chinese children that the other developed in previous studies

Zhu et al. [34] Lab/controlled


EE algorithmTo develop and cross-validate an equation based on GT3X output to predict children and youth’s EE of PA against indirect calorimetry

317,9-17 y

GT3X(right hip)

NS 30 Hz.

60 s

Not used EE algorithm:Zhu VMTrostFreedsonPuyauTreuthSchmitzMattocks

VM had a moderately high correlation with EE compared with indirect calorimetry

Acc: accelerometer, CPM: counts per minute, EE: energy expenditure, h: hours, Lab: laboratory condition, LFE: low-frequency extension, LPA: light physical activity, MET: metabolic

cost, MPA: moderate physical activity, MVPA: moderate-to-vigorous physical activity, NS: not specified, NWT: non-wear time, PA: physical activity, ROC: receiver operating

characteristic, s: seconds, SED: sedentary time, VA: vertical axis, VM: vector magnitude, VPA: vigorous physical activity, WET: work energy theorem equation

17


18


validation/calibration studies in adults (the rest of studies are available on request)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Adults (n=103, Validation, calibration and comparison studies reviewed for objectives 1 and 2: n=40)

Aadland et al. [35]


[Convergent validity and reliability]

PlacementSED/PA intensity classificationTo assess the contralateral hip difference and the inter-instrument reliability of the GT3X+ to determine PA level, intensity-specific PA and SED in adults under free-living conditions

87, 31.3±12.2 y

2 GT3X+(right and left hips)

NS 30 Hz.

10 s

≥60-0-2 (Troiano algorithm)

≥8 h/day≥3 d/week

Cut-points:Metzger (2008) SED, LPA, MPA and VPA VA cut-points

The GT3X+ is a reliable tool for measuring PA and SED in adults under free-living conditions. Inter-instrument reliability increased with a longer time-period (21 days).Contralateral hip differences were minimal. They suggest GT3X+ be attached to the right hip for consistency.

Aadland et al. [36]


[Reliability]

SED/PA intensity classificationValid day and valid weekTo determine the agreement of objectively assessed SEDand PA over 3 subsequent weeks in an adult population

87, 31.3±12.2 y

2 GT3X+(right and left hips)

NS 30 Hz.

10 s


≥8 h/day≥10 h/day≥12 h/day

Cut-points:Metzger (2008) SED, LPA, MPA and VPA VA cut-points

More than 7 days of measurement was needed to achieve a reliability (intra-class correlation coefficient –ICC-) of 0.80. Although we obtained a reliability of ICC>0.70 for 3 subsequent weeks of measurement, considerable week-by-week variability was found. The findings clearly demonstrated that wear time was of crucial importance to reliably assess SED.

19

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Anastasopoulou et al. [37]

Lab/controlled


EE algorithmTo determine which procedure is more accurate to determine the energy cost during the most common everyday life activities: GT3X (based on single regression model) or move II (based on activity-dependent calculation) compared with indirect calorimetry

19,30.4±9.0 y

GT3XMove II(right hip)

NS 30 Hz.

1 s

Not used EE algorithm:Sasaki (2011) VM

The activity monitor using activity-dependent calculation models (i.e. move II) is more appropriate for predicting EE in daily life than the activity monitor using a single regression model (i.e. GT3X)




Others: InclinometerTo compare free-living acc.-derived and posture-derived estimates of breaks in SED using GT3X+ and ActivPAL, respectively

15,27.5±2.5 y

GT3X+(hip)

ActivPAL

LFE 80 Hz.

60 s

NS

NS

Cut-points:<100 CPM VA for SED cut-point

Higher number of break in SED was observed in the GT3X+ compared to activPAL.

Brønd et al. [39]

Part 1:Lab-controlled




Sampling FrequencyTo investigate the effect of sampling frequency on the GT3X+ activity counts generated from a broad range of signal frequencies

Part 3:20,NS

GT3X+(right hip in part 3)

Normal 30 Hz.40 Hz.50 Hz.60 Hz.70 Hz.80 Hz.90 Hz.100 Hz.

60 s

Not used Not used The ActiGraph frequency bandpass filter eliminates most of the signals generated from movements corresponding to VPA using the 30 Hz default sampling frequency. A considerable amount of unwanted signals escapes the filter with other sampling frequencies and contributes to the activity counts generated

20

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Cain et al. [40] Free-living observational(3 days, waking hours protocol)


Others: Steps countTo compare step counts and PA collected with the 7164 and GT3X + using the normal filter and the LFE

25,32.8 y

GT1MGT3X+(right hip)

Normal

LFE

NS

60 s

≥60-0-0

≥8 h/day≥1 day/week

Cut-points:Freedson (1998) SED, MPA and VPA VA cut-points

Studies using the newer ActiGraphs should employ the LFE for greater sensitivity to lower intensity activity and more comparable activity results with studies using the older models. Newer generation ActiGraphs do not produce comparable step counts to the older generations with any filter

Calabró et al. [41]

Lab/controlled


EE algorithmTo determine the validity of different activity monitors for estimating EE of LPA, semi-structured activities against indirect calorimetry

40,18-53 y

GT3XSenseWearActiheartActivPAL(NS)

NS NS

60 s

Not used EE algorithm:WET combined with Freedson VA equation

SenseWear Mini provided more accurate estimates of EE during LPA semi-structured activities compared to other activity monitors

Cellini et al. [42]

Lab/controlled

[Criterion validity: polysomnography]

Sleep algorithmTo compare two commercially available acc. devices with concurrent polysomnography during a daytime nap

30,20.8±3.1 y

GT3X+(non-dominant wrist)

AW-64

Normal

LFE

NS

60 s

Not used Sleep algorithm:Sadeh sleep algorithm

GT3X+ overestimated total sleep time and efficiency and underestimated latency and awakenings. Epoch-by-epoch agreement better in GT3X+ with polysomnography.

Dannecker et al. [43]

Lab/controlled


EE algorithmTo determine the validity of EE estimation of a footwear PA monitor and

19,26.9±6.6 y

GT3X (right hip)

Actical

NS 30 Hz.

1 s

Not used EE algorithm:WET combined with Freedson VA equation

The GT3X significantly underestimated EE. Estimating EE based on classification of PA can be more accurate and

21

to compare with some PA monitors against indirect calorimetry

IDEEAFitbitFoot-wear

precise than estimating EE based on total PA

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Donaldson et al. [44]


[Reliability]

Valid weekTo evaluate variability of SED throughout a 7-day measurement period and to determine if < 7-days of SED measurement would be comparable to the typical 7-day measurement period

293,55±14 y

GT1M orGT3X/+(dominant hip)

NS 30 Hz. for GT3X and60 Hz. for GT3X+

60 s


≥10 h/days≥1 day/week

Cut-points:Kozey-keadle SED 100 CPM and 150 CMP VA cut-points

When assessed over a 7-day period, SED appears to be very stable from day-to-day, although there may be some small differences between men and women on weekend days. A measurement period as short as 4 days could provide comparable data (91% of variance) to a one-week assessment

Ellis et al. [45] Lab/controlled


SED/PA intensity classificationTo compare acc. worn on the wrist and hip, and the added value of heart rate data, for predicting PA type using machine learning

40,35.8±12.0 y

3 GT3X+(right hip, left hip, and non-dominant wrist)

NS NS

60 s

Not used

Not used

EE algorithm:Regression trees

Random forest classifier

The results demonstrate the validity of random forest classification and regression forests for PA type and MET prediction using acc.

Feito et al. [46]


Part 2Free-living observational(2 days, waking hours protocol)

Others: Steps count1. To compare the step counts of three generations of ActiGraph against direct observation in treadmill2. To compare the performance of 5 devices

56,28-31y

7164(right hip)GT1M(left hip)GT3X(right hip)

SW-200

Normal

LFE

30 Hz.

15 s

Not used. Monitors worn just when walking

Not used We demonstrated that the 7164 yields higher step counts than the GT1M and GT3X, when the LFE is not in place

22


on step counting in a free-living environment

StepWatchActivPAL

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Feito et al. [47]


Part 2Free-living observational(24h, walking time)

[Criterion validity: StepWatch3]

Others: Steps countTo examine the accuracy of the GT3X and GT1M with LFE to measure step counts

24,23.8±8.8 y

2 GT1M (belt 1, right hip)2 GT3X (belt 2, right hip)

StepWatch 3

Normal

LFE

30 Hz.

15 s

Not used Monitors worn just when walking.

≥1500 steps/day

Not used In lab, LFE increases the sensitivity of GTM1 and GT3X when measuring walking at speeds less than 67 m/min. In free-living, LFE significantly overestimates steps compared with StepWatch 3

Gatti et al. [48]

Lab/controlled


Others: Steps countTo investigate the reliability and validity (against direct observation) of step and pedal-revolution counts produced by the GT3X+ placed at different locations during running and bicycling

22,23.9±1.9 y

6 GT3X/+(2 shin,2 thigh and 2 hip)

NS 100 Hz.

1 s

Not used Not used Excellent reliability (intraclass correlation ≥ 0.99) and validity (Pearson ≥ 0.99) were found for measurements taken from acc. mounted at the waist and shank during running and at the thigh and shank during bicycling (intraclass correlation ≥ 0.99; Pearson ≥ 0.99)

Hickey et al. [49]

Lab/controlled

[Criterion validity: GT3X+]

SED/PA intensity classificationTo investigate a simple methodology to determine cut points based on ratios between SED and PA for a

12,28.5±4.6 y

wGT3X-BT(right hip)

PRO- DiaryTM

NS 30 Hz.

60 s

Not used Cut-points:Sasaki MPA and VPA VM cut-points

Count ratios (6.31, 7.68, 4.63, 3.96) were calculated to successfully determine cut-points for the new wrist worn wearable technology during SED (0–426) as well as LPA

23

new wrist worn device (PRO-DiaryTM) by comparing it to a validated and well characterised ‘gold standard’ (GT3X+)

(non-dominant wrist)

(427–803), MPA (804–2085)and VPA (≥2086)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Hildebrand et al. [22]

Lab/controlled


PlacementSED/PA intensity classificationEE algorithmTo compare raw triaxial acc. output from GT3X+ and GENEActiv placed on the hip and the wrist and to develop regression equations for estimating energy expenditure against indirect calorimetry

Children: 30,8.9±0.9 y

Adults:30,34.2±10.7 y

2 GT3X+2 GENEA(right hip and non-dominant wrist)

NS 60 Hz.

1 s

Not used

Not used

Cut-points:Hildebrand LPA, MPA and VPA VM cut-points for wrist and hip from raw data

EE algorithm:Hildebrand equation for children and adults (hip and wrist)

Acc. outputs from GT3X+ and GENEActiv seem comparable when attached to the same body location in adults but not in children. Acceleration output from both GT3X+ and GENEActive explained a significant proportion of the variance (R2) in VO2. The R2 for wrist-monitors ranged between 71% and 78% in children and between 75% and 81% in adults, with consistently (4%–6% units) lower R2 for wrist compared with that for hip placements.

24

Huberty et al. (2015) [50]

Free-living observational(7 days, 24 protocol)

[Reliability]

PlacementTo determine the feasibility of three widely used wearable sensors in research settings for 24h monitoring of sleep, SED, and active behaviors in middle-aged women

21,45.3±9.7 y

GT3X+(non-dominant hip moved to non-dominant wrist at bed time)

GENEA(wrist)

SenseWear(upper arm)

NS 40 Hz.

60 s

≥ 60-0-0

≥10 h/dayNS

Cut-points:Freedson (1998) SED, LPA, MPA, VPA and very VPA VA cut-points

Sleep algorithm:Sadeh sleep algorithm

Women felt the GENEActiv (94.7 %) and SenseWear Mini (90.0 %) were easier to wear and preferred the placement (68.4, 80 % respectively) as compared to the GT3X+ (42.9, 47.6 % respectively). 24h monitoring over seven consecutive days is a feasible approach in middle-aged women

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Korshøj et al. [51]

Lab/controlled

[Criterion validity: TrakStar]

Others: InclinometerTo validate the inclinometer measurements placed on upper arm and lower back by GT3X+ with Acti4

8,NS

3 GT3X+ (arm, upper back and deltoid)

NS 90 Hz.

NS

Not used Not used The GT3X+ acc. is a valid tool to measure arm and trunk inclinations

25

Kozey-Keadle et al. [52]

Free-living longitudinal(2 periods of 7 days, waking hours protocol)


SED/PA intensity classificationTo examine the validity of commercially available monitors to assess sedentary behavior

20,46.5 yOverweight

GT3X(right hip)

LFE 30 Hz.

1 s

NS

NS

Cut-points:SED (CPM in VA): <50, <100, <150, <200, <250

When the ActiGraph monitor is used, 150 CPM may be the most appropriate cut point to define sedentary behavior

Lee et al. [53] Part 1: Lab/controlled



Others: Steps countTo evaluate the step count validity of different pedometers and acc. in controlled and free-living conditions

43,20.9±2.0 y

GT3X+ (right hip)

Polar activeOmron

Normal 30 Hz.

NS

NS

NS

Not used The Omron pedometer provide the most reliable and valid steps compared to GT3X+ and Polar Active acc. both in controlled and free-living conditions

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Lee et al. [54] Lab/controlled


EE algorithmTo examine the validity of EE estimates from different consumer-based and PA monitors against indirect calorimetry

60,26.4±5.7 y

GT3X+ (hip)

Armband DirectLifeFitbit one

NS NS Not used

Not used

EE algorithm:Sasaki VM algorithm (2011)

The Armband monitor showed the best agreement with the indirect calorimetry estimate (within the 10% equivalence zone). The GT3X+ showed a root mean squared error of 47.1

26

Fitbit ZipJawbone UpBasis B1 Band

kcal compared with the indirect calorimeter

Lyden et al. [55]

Free-living(2 periods of 10 hours, waking hours protocol)


SED/PA intensity classificationFilterTo validate the GT3X for measuring SED with normal and LFE filters in free-living against direct observation

13,24.8±5.2 y

GT3X(hip)

ActivPAL

Normal

LFE

30 Hz.

1 s

Not used Cut-pointsKozey-Keadle SED <100 CPM and SED <150 CPM VA cut-points

Expressing breaks from SED as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the ActivPAL is a valid tool to measure SED in free-living environments

Ozemek et al. [56]

Lab/controlled

[Reliability]

PlacementTo test the GT3X+’s reliability in measuring activity recorded by multiple axes (axis 1, 2, 3 and VM) between monitors worn on the wrist, hip, and ankle while subjects perform activities of daily living

40,40 y

6 GT3X+ (2 at hip, 2 at dominant wrist and 2 at right ankle)

Normal 100 Hz.

60 s

Not used Not used These data suggest that GT3X+ acc. measurements made from the hip, wrist and ankle sites are reliable during activities of daily living across all axes and VM

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


27

Peeters et al. [57]



NWT definitionTo compare three methods for assessing wear time from acc. data: automated, log-books and a combination

45,31-53 y

GT3X+(right hip)

Own filter30 Hz.

60 s

≥20-0-0≥60-0-0≥90-0-0≥20-0-2≥60-0-2 (Troiano algorithm)≥90-0-2


Not used Automated filters are as accurate as a combination of automated filters and log-books for filtering wear time from acc. data. Automated filters based on 90-min of consecutive zero counts without interruptions are recommended

Peterson et al. [25]

Lab/controlled


SED/PA intensity classificationOthers: InclinometerTo assess the degree of construct validity of the inclinometer and VA and VM metrics of the GT3X in measuring SED and PA against direct observation

28,18-20

GT3X+(right hip)

LFE 30 Hz.

10 s

Not used Cut-points:Kozey-keadle SED (VA and VM): <100, <150 CPM

Accuracy in measuring both SED and PA, acc. metrics (94.7–97.8%) outperformed the inclinometer (70.9%). 150 CPM in the VM performed the best. Inclinometer had advantages at detecting walking, biking, and standing

Ried-Larsen et al. [58]


Part 2:Free living observational(1 day)


FilterTo study the responses of four generations of the ActiGraph in a mechanical setup and free living with normal and LFE filters

20,37.8±8.0 y

7164GT1MGT3XGT3X+(right hip)

Normal

LFE

30 Hz.

10 s

NS

NS

Cut-points:Freedson (1998) SED, LPA, MPA, VPA and MVPA VA cut-points

Significant differences between the 7164 and the newer generations of AG in a mechanical set-up and free-living. Enabling the LFE attenuated the differences in mean PA

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


28

Rosenberger et al. [59]

Free-living observational(1 day, 24h protocol)


SED/PA intensity classificationSleep algorithmTo compare nine devices for accuracy in 24-hour activity measurement. Criterion measurements were: GT3X+ for LPA and MVPA; Z-machine for sleep; ActivPAL for SED; and Omron for steps

40,21-76 y

GT3X+(right hip and right wrist at bed time)

Z-machineActivPALOmronFitbit OneGENEAJawbone upLumobackFuelband

NS NS

60 s

Not used Cut-points:Kozey-Keadle SED and Freedson (1998) MVPA VA cut-points

Sleep algorithm:Sadeh sleep algorithm

Error rates ranged from 8.1-16.9% for sleep; 9.5-65.8% for SED; 19.7-28.0% for LPA; 51.8-92% for MVPA; and 14.1-29.9% for steps. Equivalence testing indicated only two comparisons were significantly equivalent to standards: the LUMOback for sedentary behavior and the GT3X+ for sleep. Bland-Altman plots indicated GT3X+ had the closest measurement for sleep, and steps



Part 2Free-living observational(7 days, 24h protocol)


Sampling frequencyTo establish the equivalence of output between two brands of monitor in a laboratory and in a free-living environment

Part 138,39.3±5.7 y

Part 258,10.7±0.8 y

GT3X+GENEA(right hip)

Not used Part 1:100 Hz.

Part 2:80 Hz.

Part 1Not used

Part 2NS

Part 1Not used

Part 2Not used

The strong relation between accelerations measured by the two brands suggests that habitual activity level and activity patterns assessed by the GENEA and GT3X+ may compare well if analyzed appropriately

Ryde et al. [60]

Free-living observational(60 min)


Others: InclinometerTo measure desk based sitting time and transitions, against camera derived direct observation, and to compare the data with GT3X+ inclinometer

13,30±6.5 y

GT3X+(NS)

NS 30 Hz.

1 s

Not used Not used Agreement between the camera and the GT3X+ was poor for both sitting time and transitions

References Study design(days of assessment, protocol)[validation method]


SubjectsN, mean±SD or age

Acc. model(placement)

Filter Sampling frequency

Epoch

NWT definitiona

Valid day

Cut-Points/EE algorithm/MET equation/Sleep Algorithm

Main findings/Conclusions

29

range Valid week


Lab/controlled



Young:31,14.7±1.0 y

Adults:31,47.1±3.5 y

Elderly:35,71.9±5.4 y

GT3X+(right hip)

Normal 30 Hz.

60 s




Sasaki et al. [61]

Lab/controlled


SED/PA intensity classificationTo compare activity counts from GT3X to those from the GT1M during treadmill walking/running. Develop tri-axial VM cut-points to classify PA intensity

32,28.0±9.0 y

GT1MGT3X(non-dominant hip)

Normal 30 Hz.

60 s

Not used Cut-points:Sasaki MPA, VPA and very VPA VM cut-points

MET equation:Sasaki MET equation

Comparisons of data obtained with GT1M and the GT3X should be avoided when using more than just the VA. VM cut-points may be used to classify PA in future studies

References Study design(days of assessment, protocol)[validation method]


SubjectsN, mean±SD or age



Epoch

NWT definitiona

Valid day

Cut-Points/EE algorithm/MET equation/Sleep Algorithm


30

range Valid week

Schneller et al. [62]

Lab/controlled


EE algorithmTo compare the accuracy of five objective methods, including two newly developed methods combining accelerometry and activity type recognition (Acti4), against indirect calorimetry, to estimate total EE of different activities in semi-standardized settings

14,20-40 y

2 GT3X(right hip and thigh)ActivPALActiHeart

NS 60 Hz.

10 s

Not used EE algorithm:Crouter (2010)

This study concludes that combining acc. data from a thigh-worn ActiGraph GT3X+ with activity type recognition improved the accuracy of activity specific EE estimation against indirect calorimetry in semi-standardized settings compared to previously validated methods using CPM

Slater et al. [63]

Lab/controlled


PlacementSleep algorithmTo examine the validity of wrist and hip acc. measured sleep-related behaviors against polysomnography

108,22.7±0.2 y

2 GT3X+ (right hip and non-dominant wrist)

Normal 30 Hz.

60 s

Not used Sleep algorithm:Sadeh sleep algorithm

A wrist worn GT3X+ provided more valid measures of sleep but with only moderate capability to detect periods of wake during the sleep period

Staudenmayer et al. [64]

Lab/controlled


SED/PA intensity classificationTo develop algorithms to estimate: MET-hours, minutes in PA intensities, minutes in SED vs not and minutes in locomotion vs not. And validate these algorithms against indirect calorimetry

20,24.1±4.5 y

GT3X+(dominant wrist)

GT3X(right hip)

NS GT3X+:80 Hz.

15 s

GT3X:30 Hz.

60 s

Not used Cut-points:New algorithms developed andFreedson (1998) SED, LPA, MPA, VPA and very VPA VA cut-points

The wrist models, applied to 15 s epoch, estimated METs better than a previously developed model that used counts per minute measured at the hip. In a separate set of comparisons, the simpler decision trees classified PA, SED, and locomotion time nearly as well or better than the machine-learning approaches

References Study design(days of assessment, protocol)


SubjectsN, mean±SD



NWT definitiona

Cut-Points/EE algorithm/MET equation/


31

[validation method]or age range Epoch

Valid dayValid week Sleep Algorithm

Stec et al. [65] Lab/controlled


PlacementEE algorithmTo estimate the energy cost and the optimal placement during resistance exercise

30,21.7±1.0 y

3 GT3X(right wrist, hip and ankle)

ActiTrainer

NS 30 Hz.

1 s

Not used EE algorithm:Stec equation developed in this study

Hip-worn GT3X can be used to estimate resistance exercise energy expenditure

Steeves et al. [66]




Others: InclinometerTo compare sitting, standing and stepping classifications from thigh-worn ActiGraph and activPAL monitors and validate it against direct observation in lab

36,32.0±10.3 y

GT3X+ (midlineof the right thigh)

activPAL

NS 80 Hz.

1 s

Part 1:Not used

Part 2:NS

Not used GT3X+ showed more sensitive to free-living upright motions than activPAL



Part 2:Free-living observational(7 days, 24h protocol)


Others: Steps countTo compare step outputs obtained from waist and wrist acc. (with direct observation) attachment sites under laboratory and free-living

15,27.5±2.5 y

GT3X+ (right hip and non-dominant wrist)

Normal

LFE

NS

60 s

Not used.

≥1500 steps/day

Not used Step outputs obtained from waist- and wrist-wornacc. attachment sites are generally not comparable in either laboratory or free-living conditions (and with both filters)

References Study design(days of assessment,


SubjectsN,



NWT definitiona

Cut-Points/EE algorithm/


32

protocol)[validation method]

mean±SD or age range Epoch

Valid dayValid week

MET equation/Sleep Algorithm

Vähä-Ypyä et al. [68]

Lab/controlled


SED/PA intensity classificationTo devise a universal and physically meaningful analysis algorithm for accurate classification of PA by intensity using the raw data obtained from waist-mounted, tri-axial acc.

21,42±11 y

GT3X(hip)

NS 30 Hz.

5 s

Not used Cut-points:New algorithms developed

Irrespective of the acc. brand, a simply calculable mean amplitude deviation with universal cut-off limits provides a universal method to evaluate PA and SED using raw acc. data

Van Nassau et al. [69]



Others: InclinometerTo determine the criterion validity against activPAL and responsiveness to change of two activity monitors (GT3X and activPAL) and two questionnaires

42,38±11 y

GT1M or GT3X(right hip)

NS NS

NS

≥60-0-0


Cut-points:Troiano SED, LPA, MPA and VPA VA cut-points

The hip-worn ActiGraph was unable to distinguish between occupational sitting and standing time, when using uniaxial data and traditional cut-points for sedentary time and LPA

Zinkhan et al. [70]

Lab/controlled


Sleep algorithmTo examine the agreement between GT3X and SOMNOWatch placed in right-hip and non-dominant wrist, respectively, and agreement of them with polysomnography

100, 50.0±13.0 y

GT3X(right hip)

Somnowatch

Normal 80 Hz.

60 s

Not used Sleep algorithm:Cole-Kripke sleep algorithm

Sleep prediction limited from waist monitors. Mean difference of total sleep time from polysomnography compared to GT3X was 81.1 min.

33

Acc: accelerometer, BMI: Body Mass Index, CPM: counts per minute, EE: energy expenditure, h: hours, Lab: laboratory condition, LFE: low-frequency extension, LPA: light physical

activity, MET: metabolic cost, MPA: moderate physical activity, MVPA: moderate-to-vigorous physical activity, NS: not specified, PA: physical activity, s: seconds, SED: sedentary

time, VA: vertical axis, VM: vector magnitude, VPA: vigorous physical activity


34


validation/calibration studies in older adults (the rest of studies are available on request)

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Older adults (n=51, Validation, calibration and comparison studies reviewed for objectives 1 and 2: n=10)

Aguilar-Farías et al. [71]



SED/PA intensity classificationTo determine SED cut-point with VM in older adults against ActivePAL

37,73.5±7.3 y

GT3X+(right hip)

ActivePAL

LFE 30 Hz.

1 s15 s60 s

≥90-0-0


Cut-pointsAguilar-Farías SED VM cut-points for 1, 5 and 60 s epochs

Cut-points are dependent on unit of analyses (i.e. epoch length and axes); cut-points for a given epoch length and axis cannot be extrapolated to others



[Criterion validity: NL-1000]

FilterOthers: Steps countTo compare steps/day derived from the GT3X+ using the normal filter and the LFE with a NL-1000 pedometer

15,61-82 y

GT3X+(right hip)

Normal

LFE

80 Hz.

60 s

>60-0-2 (Troiano algorithm)


Not used Regardless the filter, GT3X+ did not provide comparable pedometer estimates of steps/day in this older adult sample

Choi et al. [73]



NWT definitionTo assess the performance of wear/non-wear time classification algorithms for accelerometry data collected in the free-living environment using a wrist-worn triaxial acc. and a waist-worn uniaxial acc. in older adults

29,76-96 y

GT1M(Hip)

GT3X(dominant wrist)

NS 30 Hz.

60 s

Default by ActiLife90-30-260-0-2

NS

Not used Triaxial wrist-worn acc. can be used for an accurate wear/NWT classification in free-living older adults. The use of the 90-min window and the VM counts improves the performance of commonly used algorithms for wear/non-wear classification for both uniaxial and triaxial acc.

35

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Donaldson et al. [44]


[Reliability]

Valid weekTo evaluate variability of SED throughout a 7-day measurement period and to determine if < 7-days of SED measurement would be comparable to the typical 7-day measurement period

293,55±14 y

GT1M orGT3X/+(dominant hip)

NS 30 Hz. for GT3X and60 Hz. for GT3X+

60 s


≥10 h/days≥1 day/week

Cut-points:Kozey-keadle SED 100 CPM and 150 CMP VA cut-points

When assessed over a 7-day period, SED appears to be very stable from day-to-day, although there may be some small differences between men and women on weekend days. A measurement period as short as 4 days could provide comparable data (91% of variance) to a one-week assessment

Hall et al. [74] Lab/controlled


SED/PA intensity classificationTo measure the MET of walking activities in older adults, to examine the relationship between acc. output and METs and validate it against indirect calorimetry

20,60-90 y

GT3X(right hip)

Normal 30 Hz.

1 s

Not used MET equations:Hall developed in current study (VA)Miller, Hendelmal, Freedson and Ainsworth MET VA equations

Standard METs were 71% lower than the measured METs across all walking activities. This study identifies the need for equations and cut points specific measure EE in older adults

Keadle et al. [75]



NWT definitionTo compare the impact of wear-time assessment methods and using either VA or VM cut-points on acc. output

7650,71.1±5.8 y

GT3X+(right hip)

Normal NS

60 s

LogsTroiano VA: ≥60-0-2Troiano VM: ≥60-0-2Choi VA:≥90-30-2Choi VM:≥90-30-2

Cut-points:Matthews (2008) SED and Freedson LPA and MVPA VA cut-pointsAguilar-Farías SED and Sasaki LPA and MVPA VM cut-points

Log+Choi algorithm was optimal to identify wear time. PA in different intensities were not comparable between VA and VM cut-points

36

NS

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week



Lab/controlled



Young:31,14.7±1.0 y

Adults:31,47.1±3.5 y

Elderly:35,71.9±5.4 y

GT3X+(right hip)

Normal 30 Hz.

60 s




37

Webber et al. [76]

Lab/controlled


Others: Steps countTo compare step count accuracy of an acc. (GT3X+), a mechanical pedometer (Yamax SW200) and a piezoelectric pedometer (SC-StepMX)

13,81.5±5.0 y

2 GT3X+ (right and left hip)

YamaxStepMX

NS 100 Hz.

1 s

Not used Not used No significant differences were found among monitors for those who walked without aids. However, for individuals who used walking aids, the SC-Step MX demonstrated a significantly lower percentage of error than the other devices

References





Sampling frequency

Epoch

NWT definitiona

Valid dayValid week


Wanner et al. [77]



FilterTo compare GT3X data collected using the different filter selections simultaneously during 8 days

65,60.0±9.9 y

GT3X(right hip)

Normal

LFE

30 Hz.

60 s

≥60-0-0

NS

Metzger SED, LPA and MVPA VA cut-points

Results for all acc. outcomes differed according the filter option used, thus results are not comparable between filters. It is important to carefully evaluate the suitable filter option and to specify the filter choice in publications. The correction factors can be used to make data assessed using the LFE comparable to data using the Normal filter

Zisko et al. [78]

Lab/controlled


SED/PA intensity classificationTo use different fitness levels to create new relative intensity thresholds for light,

95,70-77 y

GT3X+(right hip)

NS NS

10 s

Not used Cut-points:Zisko SED, LPA, MPA, VPA and very VPA VA and VM cut-points developed

VM-model was found to be a better predictor of PA intensity than VA-model (p < 0.05). Established thresholds for MPA (46−63 % of VO2max) ranged from 669–3367 and 834–4048

38

moderate and vigorous PA for elderly men and women

CPM and VPA (64−90 % of VO2max) from 1625–4868 and 2012-5423 CPM, for women and men, respectively

Acc: accelerometer, CPM: counts per minute, EE: energy expenditure, h: hours, Lab: laboratory condition, LFE: low-frequency extension, LPA: light physical activity, MET: metabolic

cost, MVPA: moderate-to-vigorous physical activity, NS: not specified, PA: physical activity, s: seconds, SED: sedentary time, VA: vertical axis, VM: vector magnitude.

a NWT definition expressed as: minimum minutes of 0 CPM – minimum minutes for before and after allowance windows – maximum minutes of allowance.

39

References

1. Butte NF, Wong WW, Lee JS, et al. Prediction of energy expenditure and physical activity in preschoolers. Med Sci Sports Exerc. 2014;46:1216–26.

2. Costa S, Barber SE, Cameron N, et al. Calibration and validation of the ActiGraph GT3X+ in 2-3 year olds. J Sci Med Sport. 2013;17:617–22. doi:10.1016/j.jsams.2013.11.005

3. Flynn JI, Coe DP, Larsen CA, et al. Detecting indoor and outdoor environments using the ActiGraph GT3X+ light sensor in children. Med Sci Sports Exerc. 2014;46:201–6.

4. Martin A, McNeil M, Penpraze V, et al. Objective measurement of habitual sedentary behavior in pre-school children: comparison of activPAL with ActiGraph monitors. 2011;468–76.

5. Janssen X, Cliff DP, Reilly JJ, et al. Predictive validity and classification accuracy of ActiGraph energy expenditure equations and cut-points in young children. PLoS One. 2013;8(11): e79124.

6. Jimmy G, Seiler R, Mäder U. Development and validation of GT3X accelerometer cut-off points in 5- to 9-year-old children based on indirect calorimetry measurements. Schweizerische Zeitschrift fur Sport und Sport. 2013;61:37–43.

7. Johansson E, Ekelund U, Nero H, et al. Calibration and cross-validation of a wrist-worn ActiGraph in young preschoolers. Pediatr Obes. 2014;1–6.

8. Kahan D, Nicaise V, Reuben K. Convergent validity of four accelerometer cutpoints with direct observation of preschool children’s outdoor physical activity. Res Q Exerc Sport. 2013;84:59–67. doi:10.1080/02701367.2013.762294

9. Meredith-Jones K, Williams S, Galland B, et al. 24 h accelerometry: impact of sleep-screening methods on estimates of sedentary behaviour and physical activity while awake. J Sports Sci. 2015;414:1–7. doi:10.1080/02640414.2015.1068438

10. Pulakka A, Cheung YB, Ashorn U, et al. Feasibility and validity of the ActiGraph GT3X accelerometer in measuring physical activity of Malawian toddlers. Acta Paediatr Int J Paediatr. 2013;102:1192–8.

11. Zakeri IF, Adolph AL, Puyau MR, et al. Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers. J Nutr. 2013;143:114–22.

12. Aibar A, Bois JE, Zaragoza J, et al. Do epoch lengths affect adolescents’ compliance with physical activity guidelines? J Sports Med Phys Fitness. 2014;54:255–63.

13. Aittasalo M, Vähä-Ypyä H, Vasankari T, et al. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;1–7. doi:10.1186/s13102-015-0010-0

14. Barreira TV, Schuna JM, Mire EF, et al. Identifying children’s nocturnal sleep using 24-h waist accelerometry. Med Sci Sports Exerc. 2015. doi:10.1249/MSS.0000000000000486

15. Chandler JL, Brazendale K, Beets MW, et al. Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children. Pediatr Obes. 2015;11(2):120–7. doi:10.1111/ijpo.12033

16. Crouter SE, Flynn JI, Bassett DR. Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc. 2015;47:944–7.

17. Crouter SE, Horton M, Bassett DR. Validity of ActiGraph child-specific equations during various physical activities. Med Sci Sports Exerc. 2013;45:1403–9.

18. Crouter SE, Horton M, Bassett DR. Use of a two-regression model for estimating energy expenditure in children. Med Sci Sports Exerc. 2012;44:1177–85.

19. Dowd KP, Harrington DM, Donnelly AE. Criterion and concurrent validity of the activPAL(TM) professional physical activity monitor in adolescent females. PLoS One. 2012;7:e7633.

20. Fairclough SJ, Noonan R, Rowlands AV, et al. Wear compliance and activity in children wearing wrist and hip-mounted accelerometers. Med Sci Sports Exerc. 2015;in press.

21. Hänggi JM, Phillips LRS, Rowlands AV. Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. J Sci Med Sport. 2013;16:40–4. doi:10.1016/j.jsams.2012.05.012

22. Hildebrand M, Van Hees VT, Hansen BH, et al. Age-group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;1816–24.

23. Hjorth MF, Chaput JP, Damsgaard CT, et al. Measure of sleep and physical activity by a single accelerometer:

40

Can a waist-worn ActiGraph adequately measure sleep in children? Sleep Biol Rhythms. 2012;10:328–35.

24. Kim Y, Crouter SE, Lee JM, et al. Comparisons of prediction equations for estimating energy expenditure in youth. J Sci Med Sport. 2014;in press.

25. Peterson NE, Sirard JR, Kulbok PA, et al. Validation of accelerometer thresholds and inclinometry for measurement of sedentary behavior in young adult university students. Res Nurs Health. 2015;38:492–8. doi:10.1002/nur.21694

26. Romanzini M, Petroski EL, Ohara D, et al. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2012;1–9.

27. Rowlands AV., Fraysse F, Catt M, et al. Comparability of measured acceleration from accelerometry-based activity monitors. Med Sci Sports Exerc. 2014;47:201–10.

28. Rowlands AV, Rennie K, Kozarski R, et al. Children’s physical activity assessed with wrist- and hip-worn accelerometers. Med Sci Sports Exerc. 2014;2006:2308–16.

29. Santos-Lozano A, Santín-Medeiros F, Cardon G, et al. Actigraph GT3X: Validation and determination of physical activity intensity cut points. Int J Sports Med. 2013;34:975–82.

30. Toftager M, Kristensen PL, Oliver M, et al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10:140

31. Tudor-Locke C, Barreira TV, Schuna JM, et al. Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab. 2014;39:53–7.

32. Tudor-Locke C, Barreira TV, Schuna JM, et al. Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act. 2015;12(1):1–9.

33. Zhu Z, Chen P, Zhuang J. Intensity classification accuracy of accelerometer-measured physical activities in Chinese children and youth. Res Q Exerc Sport. 2013;84:S4–11. doi:10.1080/02701367.2013.850919

34. Zhu Z, Chen P, Zhuang J. Predicting Chinese children and youth’s energy expenditure using ActiGraph accelerometers: a calibration and cross-validation study. Res Q Exerc Sport. 2013;84:S56–63. doi:10.1080/02701367.2013.850989

35. Aadland E, Ylvisåker E. Reliability of the ActiGraph GT3X+ accelerometer in adults under free-living conditions. PLoS One. 2015;10:e0134606. doi:10.1371/journal.pone.0134606

36. Aadland E, Ylvisåker E. Reliability of objectively measured sedentary time and physical activity in adults. PLoS One. 2015;10:e0133296. doi:10.1371/journal.pone.0133296

37. Anastasopoulou P, Tubic M, Schmidt S, et al. Validation and comparison of two methods to assess human energy expenditure during free-living activities. PLoS One. 2014;9:1–7.

38. Barreira TV, Zderic TW, Schuna JM, et al. Free-living activity counts-derived breaks in sedentary time: are they real transitions from sitting to standing? Gait Posture. 2015;15–7.

39. Brønd C, Arvidsson D. Sampling frequency and activity counts Sampling frequency affects the processing of ActiGraph raw acceleration data to activity counts. J Appl Physiol. 2015;in press

40. Cain KL, Conway TL, Adams MA, et al. Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension. Int J Behav Nutr Phys Act. 2013;10:51.

41. Calabró M, Lee JM, Saint-Maurice PF, et al. Validity of physical activity monitors for assessing lower intensity activity in adults. Int J Behav Nutr Phys Act. 2014;11:119.

42. Cellini N, Buman MP, McDevitt EA, et al. Direct comparison of two actigraphy devices with polysomnographically recorded naps in healthy young adults. Chronobiol Int. 2013;30:691–8.

43. Dannecker KL, Sazonova NA, Melanson EL, et al. A comparison of energy expenditure estimation of several physical activity monitors. Med Sci Sports Exerc. 2013;45:2105–12.

44. Donaldson SC, Montoye AHK, Tuttle MS, et al. Variability of objectively measured sedentary behavior. Med Sci Sports Exerc. 2015;in press.

45. Ellis K, Kerr J, Godbole S, et al. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol Meas. 2014;35:2191–203.

41

46. Feito Y, Bassett DR, Thompson DL. Evaluation of activity monitors in controlled and free-living environments. Med Sci Sports Exerc. 2012;44:733–41.

47. Feito Y, Garner HR, Bassett DR. Evaluation of ActiGraph’s low-frequency filter in lab and free-living environments. Med Sci Sports Exerc. 2014;211–7.

48. Gatti AA, Stratford PW, Brenneman EC, et al. GT3X+ accelerometer placement affects the reliability of step-counts measured during running and pedal-revolution counts measured during bicycling. J Sports Sci. 2015;414:1–8. doi:10.1080/02640414.2015.1096018

49. Hickey A, Newham J, Slawinska MM, et al. Estimating cut points: a simple method for new wearables. Maturitas. 2015;83:78–82.

50. Huberty J, Ehlers DK, Kurka J, et al. Feasibility of three wearable sensors for 24 hour monitoring in middle-aged women. BMC Women’s Health. 2015;15:55.

51. Korshøj M, Skotte JH, Christiansen CS, et al. Validity of the Acti4 software using ActiGraph GT3X+ accelerometer for recording of arm and upper body inclination in simulated work tasks. Ergonomics. 2014;57:247–53.

52. Kozey-Keadle S, Libertine A, Lyden K, et al. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43:1561–7.

53. Lee JA, Williams SM, Brown DD, et al. Concurrent validation of the ActiGraph GT3X+, Polar Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based and free-living settings. J Sports Sci. 2014;33:991–1000. doi:10.1080/02640414.2014.981848

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

55. Lyden K, Kozey Keadle SL, Staudenmayer JW, et al. Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc. 2012;44:2243–52.

56. Ozemek C, Kirschner MM, Wilkerson BS, et al. Intermonitor reliability of the GT3X+ accelerometer at hip, wrist and ankle sites during activities of daily living. Physiol Meas. 2014;35:129–38.

57. Peeters G, van Gellecum Y, Ryde G, et al. Is the pain of activity log-books worth the gain in precision when distinguishing wear and non-wear time for tri-axial accelerometers? J Sci Med Sport. 2013;16:515–9. doi:10.1016/j.jsams.2012.12.002

58. Ried-Larsen M, Brønd JC, Brage S, et al. Mechanical and free living comparisons of four generations of the ActiGraph activity monitor. Int J Behav Nutr Phys Act. 2012;9:1–10. doi:10.1186/1479-5868-9-113

59. Rosenberger ME, Buman MP, Haskell WL, et al. 24 hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2015;in press.

60. Ryde GC, Gilson ND, Suppini A, et al. Validation of a novel, objective measure of occupational sitting. J Occup Health. 2012;54:383–6.

61. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14:411–6.

62. Schneller M, Pedersen M, Gupta N, et al. Validation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settings. Sensors. 2015;15:6133–51.

63. Slater JA, Botsis T, Walsh J, et al. Assessing sleep using hip and wrist actigraphy. Sleep Biol Rhythms. 2015;13(2):172–8. doi:10.1111/sbr.12103

64. Staudenmayer J, He S, Hickey A, et al. Methods to estimate aspects of physical activity and sedentary behavior from high frequency wrist accelerometer measurements. J Appl Physiol. 2015; doi:10.1152/japplphysiol.00026.2015

65. Stec MJ, Rawson ES. Estimation of resistance exercise energy expenditure using triaxial accelerometry. J Strength Cond Res. 2012;26:1413–22.

66. Steeves JA, Bowles HR, Mcclain JJ, et al. Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion. Med Sci Sports Exerc. 2015;47:952–9.

67. Tudor-Locke C, Barreira TV, Schuna JM. Comparison of step outputs for waist and wrist accelerometer attachment sites. Med Sci Sports Exerc. 2014;47(4):839–4.

68. Vähä-Ypyä H, Vasankari T, Husu P, et al. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging. 2014;64–70.

69. Van Nassau F, Chau JY, Lakerveld J, et al. Validity and responsiveness of four measures of occupational sitting

42

and standing. Int J Behav Nutr Phys Act. 2015;12:144.

70. Zinkhan M, Berger K, Hense S, et al. Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography. Sleep Med. 2014;15:1107–14. doi:10.1016/j.sleep.2014.04.015

71. Aguilar-Farias N, Brown WJ, Peeters GM. ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport. 2014;17:293-6. doi:10.1016/j.jsams.2013.07.002

72. Barreira TV, Brouillette RM, Foil HC, et al. Comparison of older adults steps/day using NL-1000 pedometer and two GTX+ accelerometer filters. J Aging Phys Act. 2012;21:402–16.

73. Choi L, Ward SC, Schnelle JF, et al. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44:2009–16.

74. Hall KS, Howe CA, Rana SR, et al. METs and accelerometry of walking in older adults: standard versus measured energy cost. Med Sci Sports Exerc. 2013;45:574–82.

75. Keadle SK, Shiroma EJ, Freedson PS, et al. Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study. BMC Public Health. 2014;14(1):1210.

76. Webber SC, Magill SM, Schafer JL, et al. GT3X+ accelerometer, yamax pedometer, and SC-StepMX pedometer step count accuracy in community-dwelling older adults. J Aging Phys Act. 2014;22:334–41.

77. Wanner M, Martin BW, Meier F, et al. Effects of filter choice in GT3X accelerometer assessments of free-living activity. Med Sci Sports Exerc. 2013;45:170–7.

78. Zisko N, Carlsen T, Salvesen Ø, et al. New relative intensity ambulatory accelerometer thresholds for elderly men and women: the Generation 100 study. BMC Geriatr. 2015;15:97.

43

Date post:	15-May-2018
Category:	Documents
Upload:	hoangtuyen
View:	213 times
Download:	0 times

static-content.springer.com10.1007... · Web viewThis study concludes that combining acc. data from...

Documents