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Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures.
We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometrybased data.
Highresolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wristworn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using opensource packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minutelevel data.
Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participantspecific correlations between activity count and monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cutoff of 1853 using monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published.
The findings from this comparison of accelerometrybased measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitorindependent movement summary (and other measures) and by providing harmonization mapping.
The use of accelerometrybased activity monitors has become increasingly popular in research studies because they provide noninvasive objective measures of physical activity, and with these monitors, physical activity data can be collected continuously for extended periods of time [
Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to know how new summary measures compare with established measures. Harmonizing, or mapping, values of physical activity summaries derived from different algorithms enables knowledge from the thousands of manuscripts that have been published using ActiGraph activity count [
In this study, we aimed to (1) provide simple summaries of associations between pairs of minutelevel measures (ActiGraph activity count and monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, activity intensity) and a guide for the strength of these associations in subgroups defined by demographic information; (2) provide a mapping between any 2 physical activity summary measures considered; (3) derive cutpoints of opensource physical activity measures that correspond to established cutpoints to estimate time spent in different physical activity intensities for activity count.
We conducted a retrospective data analysis study using data collected as part of the National Institute on Aging’s Baltimore Longitudinal Study of Aging (BLSA) from participants who were communitydwelling volunteers free of all major chronic conditions and cognitive and functional impairment at the time of enrollment [
The BLSA study protocol has ongoing approval from the Institutional Review Board (IRB) of the National Institute of Environmental Health Science, National Institutes of Health ("Early Markers of Alzheimer’s Disease [BLSA]", IRB No. 2009074). Informed written consent was obtained from all participants.
Data had been collected with a triaxial accelerometer (ActiGraph GT9X Link; range: ±8
We used 3 raw data quality check flags (
Commonly used minutelevel measures—monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity (
We defined minutelevel data flags that represented whether the device was being worn or not using the
Activity count, monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity data were winsorized [
A separate data set was constructed with imputed data, using a method described in [
The mean daily sums of minutelevel measures were computed for each participant and then aggregated (mean and SD; median and range) across participants.
Pearson correlation coefficients for 4 pairs of measures—activity count and monitorindependent movement summary, activity count and Euclidean norm minus one, activity count and mean amplitude deviation, and activity count and activity intensity—were computed for each participant. For each pair, mean correlations and standard errors were quantified using interceptonly linear regression with participantspecific correlation as the outcome. The effects of demographic characteristics (covariates: age, BMI, and sex) on correlations were estimated using adjusted linear regression with participantspecific correlation as the outcome and α=.05 to determine the statistical significance of coefficients. This procedure was repeated for secondary analyses with a subsample (participants’ age ≤65 years).
To derive the harmonization mapping, relationships were estimated using generalized additive modeling for each pair of measures. The generalized additive models were chosen to allow flexible adaptation to the data rather than imposing a particular functional form of the fit. In each model, the outcome was a minutelevel measure (monitorindependent movement summary, or Euclidean norm minus one, or mean amplitude deviation, or activity intensity), and a smooth term of minutelevel activity count was set as a predictor. For the smooth term, cubic regression splines with a basis dimension equal to 30 were used to allow a flexible relationship between the measure and activity count. Models were estimated with nonparametric smoothing (method:
To assess mapping accuracy in estimating physical activity volume statistics, total activity count (the sum of minutelevel activity count values from a day) was computed for each participant, using activity count data and
To assess whether mapping accuracy depended on participant activity level, MPE values were plotted against the participant's average total activity count.
The utility of the mapping for classifying minutes into various activity intensity classes was assessed. We used activity count cutoffs derived to (1) separate sedentary and active minutes in data collected with a sensor worn on nondominant wrist in older adults [
Minutelevel activity count and
Data from 655 individuals (
The mean participant daily sums (
Study sample (N=655) characteristics.
Characteristic  Value  









Mean (SD)  69.8 (14.2)  


Median (range)  72.0 (22.097.0)  






Mean (SD)  77.4 (17.1)  


Median (range)  76.3 (41.1142.7)  






Mean (SD)  168.0 (9.2)  


Median (range)  167.3 (143.8196.2)  






Mean (SD)  27.3 (5.0)  


Median (range)  26.6 (17.152.5)  






Female count (%)  357 (54.5)  


Male count (%)  298 (45.5)  





White count (%)  445 (67.9)  

Black count (%)  157 (24.0)  


Chinese count (%)  30 (4.6)  


Hawaiian count (%)  11 (1.7)  

Other nonWhite count (%)  3 (0.5)  

Not reported count (%)  9 (1.4)  









Mean (SD)  5.9 (0.4)  


Median (range)  6.0 (3.0, 7.0)  






Mean (SD)  2.0 (7.8)  


Median (range)  0.0 (0.0, 77.0)  






Mean (SD)  1437.8 (8.0)  


Median (range)  1440.0 (1361.71440.0)  









Good, very good, or excellent count (%)  628 (95.9)  


Fair or poor count (%)  22 (3.4)  


Not reported count (%)  5 (0.8)  






Myocardial infarction, congestive heart failure, ischemic chest pain, vascular procedure, or peripheral artery disease count (%)  55 (8.4)  


Hypertension count (%)  285 (43.5)  


High blood cholesterol count (%)  346 (52.8)  


Stroke or transient ischemic attack count (%)  34 (5.2)  


Pulmonary disease count (%)  74 (11.3)  


Diabetes count (%)  95 (14.5)  


Cancer count (%)  191 (29.2)  


Osteoarthritis count (%)  316 (48.2) 
Mean daily sum values for physical activity measures.
Measure  Value  




Mean (SD)  2,204,169 (600,965)  

Median (range)  2,157,496 (731,9455,071,196)  




Mean (SD)  11,299.7 (2766.0)  

Median (range)  11,195.2 (4252.323,931.5)  




Mean (SD)  47.7 (13.3)  

Median (range)  46.3 (16.1108.1)  




Mean (SD)  30.9 (9.1)  

Median (range)  29.6 (11.875.3)  




Mean (SD)  4157.6 (1068.8)  

Median (range)  4085.5 (1529.79418.6) 
Monitorindependent movement summary was most correlated with activity count (estimated mean 0.988, SE 0.0002), closely followed by activity intensity (estimated mean 0.970, SE 0.0007, mean amplitude deviation (estimated mean 0.913, SE 0.0013), and Euclidean norm minus one (estimated mean 0.867, SE 0.0018) (
The estimated effects of age (with female as the reference level) were not statistically significant in the models for activity count and monitorindependent movement summary (
The results of secondary analysis (Table S1 in
Summary of interceptonly linear regression and adjusted linear regression with outcome defined as participantspecific correlation between activity count and other measures (monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, or activity intensity).
Model and response variable^{a}  Intercept  Age  BMI  Sex^{b}  


Estimate (SE)  Estimate (SE)  Estimate (SE)  Estimate (SE)  








Monitorindependent movement summary  0.988042 
—^{c}  —  —  —  —  —  

Euclidean norm minus one  0.867158 
—  —  —  —  —  —  

Mean amplitude deviation  0.913412 
—  —  —  —  —  —  

Activity intensity  0.969984 
—  —  —  —  —  —  








Monitorindependent movement summary  0.987969 
0.000001 
.97  0.000032 (0.000046)  .48 
–0.001859 (0.000466)  <.001 


Euclidean norm minus one  0.886566 
–0.000532 
<.001 
0.000653 (0.000363)  .07 
–0.000206 (0.003678)  .96 


Mean amplitude deviation  0.892177 
0.000044 
.64  0.000840 (0.000260)  .001 
–0.010410 (0.002632)  <.001 


Activity intensity  0.962364 
0.000063 
.18 
0.000278 (0.000132)  .04 
–0.009576 (0.001340)  <.001 
^{a}Correlation with activity count.
^{b}Female was used as the reference.
^{c}Not included in the model.
For a widely used activity count cutoff 1853 [
Estimated minutelevel mapping. A black solid line shows generalized additive model–fitted values of a measure (monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, activity intensity) given the activity count value. The points represent a subset of the data created by taking every 100th observations from all participant and minutespecific observations; this subset is the same for all 4 plots. AC: activity count; AI: activity intensity; ENMO: Euclidean norm minus one; MAD: mean amplitude deviation; MIMS: monitorindependent movement summary.
Corresponding values of each measure for activity count cutoff values.
Method  Activity count cutoff value  Corresponding value  


Monitorindependent movement summary  Euclidean norm minus one  Mean amplitude deviation  Activity intensity 
Separate sedentary and active in older adults [ 
1853  10.558  0.022  0.039  3.620 
Separate sedentary and light activity in young to older adults [ 
2860  15.047  0.033  0.057  5.273 
Separate light and moderatetovigorous activity in young to older adults [ 
3940  19.614  0.046  0.078  7.025 
In the task of estimating total activity count, MAPE values were lowest for monitorindependent movement summary (mean 2.5, SD 2.4), followed by activity intensity (mean 6.3, SD 5.1), mean amplitude deviation (mean 11.3, SD 8.4), and Euclidean norm minus one (mean 14.3, SD 10.3). MPE values were similar for monitorindependent movement summary (mean 0.2, SD 3.2), activity intensity (mean 0.3, SD 7.6), mean amplitude deviation (mean –0.3, SD 13.3), and Euclidean norm minus one (mean 4.6, SD 16.1). The findings for median absolute percentage error and median percentage error were similar to those for MAPE and MPE, respectively (Table S2 in
Based on visual inspection, there was larger variability in MPE values among participants with smaller mean total activity count values, but there was no apparent tendency for lower or higher MPE values based on participants’ average total activity counts (Figure S1 in
In the task of predicting whether the activity count for a given minute was above a certain cutoff, for the cutoff equal 1853, participantspecific classification accuracy (Table S3 in
The
Smoothed 24hour median activity counts per minute for each age group: <60 years (green), 6067 years (red), 6874 years (blue), and ≥75 years (orange). Semitransparent thick colored lines represent results obtained with activity count; they are the same for all 4 plots. Solid thin colored lines represent results obtained with values mapped into activity count from monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, or activity intensity. AC: activity count; AI: activity intensity; ENMO: Euclidean norm minus one; MAD: mean amplitude deviation; MIMS: monitorindependent movement summary.
Correlations between activity count and the other raw data summary metrics were all large (mean
To the best of our knowledge, the correlation between activity count and monitorindependent movement summary in continuous data collected in the freeliving environment has not been previously explored. The activity count measure had the highest mean participantspecific correlation with monitorindependent movement summary (mean
Harmonization mapping can be particularly useful to translate commonly used cutoff values of physical activity intensity levels from activity count into measures implemented in opensource software. For the tasks of predicting sedentary minutes for an activity count cutoff of 1853 [
To the best of our knowledge, we are the first to provide freely available R software (SummarizedActigraphy R package) with a unified interface for computation of the 4 opensource measures from raw accelerometry data, with complicated mathematical formulas distilled into a readerfriendly text (
First, the data were from a sample that consisted of predominantly middleaged to older adults (
Second, physical activity measures were computed using raw accelerometry data collected at a frequency of 80 Hz. While this frequency matches that of physical activity data from NHANES 20112014 [
Third, data had been collected with sensors worn on the nondominant wrist only. While we expect the results to be generalizable to data from sensors worn on the dominant wrist, we presume that correlations and mapping would not be applicable to chest or hipworn sensors, because physical activity volume statistics (eg, total activity count) calculated from raw data collected by these devices are expected to be substantially lower than when measured at wrist.
Fourth, harmonization mapping was estimated using generalized additive modeling, which does not offer an easy, closedform formula of the transformation. While a closedform formula could be obtained using polynomial regression models, the choice of generalized additive models allowed for thorough estimation of a relationship between activity count and other measures in a more flexible way.
Finally, our results may be conditional upon the data preprocessing methods used; however, we believe that the steps we performed are commonly done [
Activity count was highly correlated with monitorindependent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. Mapping provides a way to harmonize accelerometry data sets with different summary measures; however, further research is warranted to test the validity of mapping with data collected at a different frequency or from different body locations.
Raw accelerometry data quality control.
Opensource summary measures of raw accelerometry data.
Results.
Baltimore Longitudinal Study on Aging
mean absolute percentage error
mean percentage error
National Health and Nutrition Examination Survey
This work was funded by National Institute on Aging (grant U01AG057545) and a Johns Hopkins University Catalyst Award.
Data used in this manuscript were collected as part of the National Institute on Aging’s Baltimore Longitudinal Study of Aging. A project repository with all data preprocessing and analysis code is publicly available on GitHub (muschellij2/blsa_mims).
MK implemented the final version of the analyses and took the lead in finalizing the manuscript. JM coinitiated the work, wrote the first draft of this manuscript and the analyses, and authored the
CMC is a consultant for Bayer and Johnson and Johnson. Both these consulting contracts have been disclosed through the Johns Hopkins University Edisclose system. The current manuscript is not related to or influenced by any of these contracts.