Cortina-Borja, M

Dezateux, C

Geraci, M

Griffiths, LJ

Rich, C

Sera, F

UCL Discovery

Accelerometry Data Processing1Technical report on accelerometry data processing in theMillennium Cohort StudyAuthorsMarco Geraci, Carly Rich, Francesco Sera, Mario Cortina-Borja, Lucy J Griffiths, CarolDezateuxAffiliationCentre for Paediatric Epidemiology and Biostatistics/ MRC Centre of Epidemiology forChild Health, Institute of Child Health, University College London, London WC1N 1EH, UKDate of productionNovember 2012Accelerometry Data Processing2ScopeThis document applies to the preparation of a Standard Operating Procedure (SOP) forprocessing routines and basic statistical analysis of accelerometry data coming from thePhysical Activity Study carried out as part of the UK Millennium Cohort Study (MCS).ObjectiveTo define a collection of routines that can be applied to accelerometry data.BackgroundThe Millennium Cohort Study (MCS) is a UK-wide prospective study of approximately19,000 British children born at the start of the new century. Parents/ guardians of the childrenwere interviewed when the children were aged nine months (first sweep or MCS1), three(MCS2), five (MCS3) and seven years (MCS4). The population was stratified by UK countryand additional stratification by socioeconomic and demographic variables within country wascarried out using data available at the electoral ward level. In particular, families were takenfrom a random sample of electoral wards, the primary sampling unit, disproportionatelystratified to ensure an adequate representation of disadvantaged areas and ethnic minoritygroups. The original cohort comprised 18,819 children. Detailed information was obtained ondemographic, social, and health factors relating to the children, and the children’s siblingsand parents through interviews of the main respondents and their partners in the home.At aged seven years, accelerometers were introduced into the study to measure the levels andpatterns of physical activity (PA) and sedentary behaviour (SB) in the MCS cohort children.All children participating in the MCS4 interviews (14,043) were invited to participate inwearing an accelerometer. Children were asked to wear an accelerometer for sevenconsecutive days (including two weekend days). Accelerometers were programmed tomeasure data using a short sampling period (15 second epochs), and were set to record bothcounts and steps. Accelerometry data were collected between May 2008 and August 2009and comprised 13,219 consenting (94.1%) children.The MCS4 accelerometer study also included a sub-study that investigated seasonal variationin levels and patterns of children’s PA and SB. Children who wore their accelerometer duringwinter 2008/09 (November 2008 - January 2009) and returned their accelerometer for theAccelerometry Data Processing3main physical activity data collection were invited to participate in a seasonal study. Theywere asked to wear their accelerometer on four further occasions using the samemeasurement protocol, so that a measurement was made in each season throughout a singlecalendar year. The MCS4 seasonal accelerometer study took place between November 2008and October 2009. Accelerometer measurements were also obtained in a further winterseason between December 2009 and January 2010 to investigate any variation in levels andpatterns of children’s PA and SB between winter 2008/09 and winter 2009/10.Data Processing ProcedureRaw data coming from accelerometers must undergo a series of operations in order to be usedin subsequent statistical analyses. Three stages of increasing level of data refining wereidentified:1) Stage: pre-processing. Input: level-0 data (raw data). Criteria: standard. Output:level-1 data.a. Data cleaning and integrity. Detection and marking of aberrant values (all 5digit values; all one value; values equal to 32,767 which indicates voltagesignal saturation within the piezosensor – Esliger et al, 2005; file that does notreturn to baseline – Sherar et al, 2011); negative values; other errors in datarecording.b. Data and metadata consistency. Correspondence between accelerometermetadata and study-defined parameters – e.g., start time and date, epoch,mode.c. Storage.2) Stage: processing. Input: level-1 data. Criteria: recommended. Output: level-2 data.a. Time stamping. Date and time attribution to values.b. Labelling. Classification of wear and non-wear time status on epoch-by-epochbasis using number of consecutive zero-counts.c. Truncation. Exclusion of low and high end days..d. Storage.3) Stage: post-processing. Input: level-2 data. Criteria: recommended. Output: level-3data.a. Censoring. Detection and marking of valid day and minimum number of validdays per child.Accelerometry Data Processing4b. Classification of valid data. Definition of sedentary behaviour and of light,moderate, and vigorous activity. Definition of behaviourally implausiblevalues.c. Collapsing data. Creation of ready-to-use datasets.d. Storage.Accelerometry Data Processing5MCS Accelerometry DataMain survey12,625 accelerometers were sent for the main survey (Winter 2008/09, hereafter S1) of which10,034 were returned. Individual raw accelerometer files were stored as “.dat” files (ASCII).Seasonal surveysOverall, a total of 705 out of 1289 (55.0%) invited parents gave consent for their child toparticipate in the MCS4 seasonal accelerometer study (spring, summer, autumn 2009 andwinter 2009/10, hereafter S2, S3, S4, S5 respectively). Individual raw accelerometer fileswere stored as “.dat” files (ASCII).ResultsIn this section we report the results of the application of data processing procedure to theMCS accelerometry data.Stage 1: Pre-processingThis stage is regarded as basic. The criteria should be considered as standard.a) Data cleaning and integrityThere were 11 files with 237 negative counts in S1. In one file there were 211 counts equal to-32,768. Overall, negative counts were equal to -32,768 (234), -32,161 (1), -21582 (1), -9,049(1). Files with negative measurements for counts had also negative measurements for steps.Aberrant values were in files with all zero counts. Files with zero counts had also zero steps.Variables defined: file identifier; acceleration count values; step values; data error coding(codes: 0 – no error, 1 – aberrant value, 2 – negative value, 3 – other errors).Accelerometry Data Processing6Table 1 (Stage 1.a). Data errors report: Number of files (number of countmeasurements) by survey sweep.S1 S2 S3 S4 S5Total processed files 8,968* 587 554 507** 435Aberrant values 31(2,962,154)2 (85,748) 2 (110,888) 2 (206,220) 1 (59,358)Negative values 11 (237) 1 (1) 1 (1) - -Other errors - - - - -*This includes 4 MCS singletons each with two different ‘dat’ files. If 1 of the 2 files (either with laterstarting date or with no data in it) is excluded, the number of remaining files is 8,964.**One file was corrupted and not-readable.b) Data and metadata consistencyDates were expected to be in ranges 20/05/2008 to 18/05/2009 (S1), 31/03/2009 to23/10/2009 (S2, S3, S4), 12/01/2010 to 25/01/2010 (S5) and in d/m/y format. Mode wasexpected to be set to 1 (counts and steps). All files had epoch set to 15 seconds.Variables defined: file identifier; total number of measurements; epoch; start date and time;time zone (GMT, BST); coded survey sweep (S1 - S5); accelerometer mode; dummy variablefor marking odd number of measurements when mode set to 1 (codes: 0 – false, 1 – true).Table 2 (Stage 1.b). Metadata consistency report: Number of files by survey sweep.Number of files S1 S2 S3 S4 S5Date as expected 8,985 590 556 508 428Date in m/d/y format 22 - - - -Outside date range 2 - - 1 9Mode 0 23 - - 18 -Odd total number ofvalues- - - - -c) StorageStage 1 output files were stored as “.Rdata” files (R format).Stage 2: ProcessingThis stage is regarded as intermediate. The criteria are strongly recommended.a) Time stampingVariables defined: time and date for each measurement.Accelerometry Data Processing7b) LabellingNon-wear time defined as any time period of consecutive zero-counts for a minimum of 20minutes.Variables defined: dummy variable to identify wear time (codes: 1 – non-wear, 2 – wear).Table 3 (Stage 2.a). Wear time report: Number of epochs and proportion of mean weartime per child by survey sweep.S1 S2 S3 S4 S5Total number of epochs 854,078,139 48,053,824 57,772,538 63,126,298 39,913,381Wear 148,923,106 12,084,905 11,344,388 10,343,553 9,106,509Non-wear 705,155,033 35,968,919 46,428,150 52,782,745 30,806,872Mean wear time (as % oftotal time) per child22.2% 28.3% 21.9% 17.2% 24.6%c) TruncationThe threshold was set to ≥ 150 minutes per day1. To determine the first and the last day ofwear, total wear time (TWT) for each day was assessed. The first day with TWT exceedingthe threshold was set as start date. The last day with TWT above the threshold was set as enddate.Variables defined: dummy variable to identify start date (codes: 0 – false, 1 – true); dummyvariable to identify end date (codes: 0 – false, 1 – true); dummy variable to identify truncateddays (codes: 0 – false, 1 – true).d) StorageStage 2 output files were stored as “.Rdata” files (R format).1 A study (Sera et al, 2011. Unpublished Technical Report) was performed to discriminate between days whenthe accelerometer was worn and days when it was likely not worn (e.g., during postal delivery) on the basis ofthe minimum number of ‘valid’ hours per day. Initially, a ‘manual’ screening of a sample of files to identifystart and end time points was conducted. Then, the output produced by automatic procedures for severalcutoffs was compared to the results of manual detection. This produced an optimal cut off equal to 2.5 hoursper day.Accelerometry Data Processing8Table 4 (Stage 2.b). Truncation report: Number of days by survey sweep.S1 S2 S3 S4 S5Total number of days 154,452 8,707 10,406 11,317 7,212Truncated days 45,105 2,304 4,403 4,305 2,474Percentage total 29.2% 26.5% 42.3% 38.0% 34.3%Stage 3: Post-processingThis stage is regarded as advanced. The criteria are recommended.a) CensoringValid days were defined as days with TWT ≥ 360 minutes of wear time, excluding time for measurements classified as errors in Stage 1. Valid files were defined as accelerometer fileswith at least 2 valid days (Rich et al, 2012a).Variables defined: dummy variable to identify valid days (codes: 0 – false, 1 – true); dummyvariable to identify valid files (codes: 0 – false, 1 – true);.b) Classification of valid dataAccelerometer count thresholds as detailed in Pulsford et al (2011) were applied to definesedentary behaviour (< 100/4 counts/15 seconds) and light (≤ 2240/4), moderate (≤ 3840/4) (PA type 2), and vigorous activity (> 3840/4). The threshold for extremely high counts wasset to ≥ 11,715/4 counts per 15 seconds (Rich et al, 2012b). c) Collapsing dataData were collapsed into smaller and ready-to-use datasets to be used in statistical analyses.Data were collapsed (i.e., aggregated) by identifier, date and hour. The following logicalexpressions were used: exclusion of errors, exclusion of extreme values, exclusion of non-wear time, exclusion of truncated days (i.e., before start date and after end date). Files withnon-valid data were reported with NAs.Variables defined: file identifier; MCS identifier and child number; dummy variable toidentify files to be excluded (see note Table 1); dummy variable to identify empty files(codes: 0 – false, 1 – true); time stamp; time zone, day of the week; weekday; dummyvariable to identify weekend days (codes: 0 – false, 1 – true); month, calendar season basedon actual equinoxes and solstices; season as defined in Mattocks et al, 2007; survey sweep;Accelerometry Data Processing9total wear time (minutes) per day for counts classified at different PA intensities; total weartime (minutes) per day including extreme counts; dummy variable to identify valid days(codes: 0 – false, 1 – true); time (minutes) spent in sedentary behaviour; time (minutes) spentin light activity; time (minutes) spent in moderate activity; time (minutes) spent in vigorousactivity; time (minutes) spent at PA intensity classified as extremely high; non-wear time(minutes); total counts per day; total steps per day; total counts per day divided by number ofdays; total counts per day divided by total wear time per day; total steps per day divided bytotal wear time per day; multiple birth status; MCS survey weights adjusted for missing data– country-specific; MCS survey weights adjusted for missing data – all of UK.Further details on MCS sampling weights and adjustment for non-response/ non-complianceare given in Plewis (2007) and Geraci et al (2012).d) StorageStage 3 output files were stored as “.Rdata” files (R format).Data and softwareThe present document describes the data processing procedures used in the MCS physicalactivity study and is integrating part of the technical documentation. The raw and processeddata, together with additional accompanying documentation, will be available in the UK DataArchive starting late 2012. The processing software is publicly available (Geraci, 2012).Accelerometry Data Processing10HOW TO ACKNOWLEDGE THIS DOCUMENTGeraci M, Rich C, Sera F, Cortina-Borja M, Griffiths LJ, and Dezateux C (2012). Technical report onaccelerometry data processing in the Millennium Cohort Study. London: University College London.Available at http://discovery.ucl.ac.uk/1361699.APPENDIX A – ACTIGRAPH GT1M TECHNICAL CHARACTERISTICSName: Actigraph GT1M (Actigraph, Pensacola, Florida)Axes: bi-axialDimensions: L 3.8 X W 3.7 X H 1.8 cmWeight: 24 gSolid state sensor: micro-electro-mechanical systems (MEMS)Dynamic range: 0.05-2.5 gFrequency range: 0.25-2.5 HzCurrent firmware: v7.5.0MEMS undergoes a precise batch manufacturing process to ensure high repeatability. A filteris implemented within the software GT1M to remove unit-to-unit variability. Accelerometervendor manufactures the device to ensure the initial tolerance specification on sensitivityvaries by ± 10% (Silva et al, 2010).APPENDIX B – GLOSSARYCount (acceleration count). “Count” is an enduring term. Prior to solid-state analog-to-digital converter commercial availability, activity monitors utilized either the 'thresholdcrossing' or 'cycle count' activity measurement. The threshold crossing technique involvedincrementing a “count” each time the magnitude of acceleration (activity) exceeded a giventhreshold. The cycle count approach produced a “count” when enough force was applied tomove a mechanical lever through a full cycle (up and down). The latter of these twoapproaches is very similar in nature to the modern day pedometer measurement technique.ActiGraph's original activity monitor, the 7164 model, utilized a mechanical lever capable ofmeasuring the change in acceleration with respect to time (g/sec, where g is gravity or 9.806Accelerometry Data Processing11m/s2). To suppress unwanted motion and enhance human activity, the acceleration signal waspassed through an analog band-pass filter, the output of which yields a dynamic range of4.26g/sec (+/-2.13g/sec) at 0.75Hz (center frequency of the filter). Using a sample rate of 10samples-per-second, this filtered signal was then digitized into 256 distinct levels by an 8-bitsolid-state analog-to-digital converter, producing 4.26g/sec per 256 levels or 0.01664g/sec/count (each level is considered 1 count). When each filtered sample is multiplied by thesample window of 0.1sec, a resolution of 0.001664g/count is achieved (Source:http://support.theactigraph.com/faq/counts).Epoch. On ActiGraph devices, “epochs” are whole-second numbers (e.g., 1s, 5s, 60s, 120s,etc.). For each epoch interval, data samples taken from the accelerometer inside the device ata rate of 30Hz are first filtered then accumulated before being stored in memory. Forexample, a device set to collect 60 second epoch data will store 30 × 60 = 1800 accumulatedsamples for each enabled axis on the device every 60 seconds (Source:http://support.theactigraph.com/faq/data-stored-actigraph-device).Mode. Devices can be set to store data in raw mode by selecting a sample frequency (12Hzor 30Hz) rather than a whole-second number in the “Epoch” dropdown during theinitialization process. When raw mode is set, devices store raw (non-filtered/accumulated)accelerometry data (in units of gravity – G’s) directly into memory at the selected rate (12Hzor 30Hz). When raw mode is enabled, non-accelerometer channels (steps, inclinometer, andlux) are disabled (Source: http://support.theactigraph.com/faq/data-stored-actigraph-device).Accelerometry Data Processing12REFERENCESEsliger DW, Copeland JL, Barnes JD, and Tremblay MS (2005). Standardizing andoptimizing the use of accelerometer data for free-living physical activity monitoring. Journalof Physical Activity and Health 2(3): 366-383.Everitt BS and Skrondal A (2010). The Cambridge Dictionary of Statistics. Cambridge:University Press Cambridge.Geraci, M. (2012), Pawacc: Physical Activity with Accelerometers. Available at http://r-forge.r-project.org/projects/pawacc.Geraci M, Sera F, Griffiths LJ, Cortina-Borja M, Rich C, Pouliou T, and Dezateux C (2012).Missing data in accelerometry-based surveys on physical activity: Adjusting for non-responseand non-compliance in the Millennium Cohort Study (in preparation).Mattocks C, Leary S, Ness A, Deere K, Saunders J, Kirkby J, et al (2007). Intraindividualvariation of objectively measured physical activity in children. Medicine and science insports and exercise 39(4): 622-9.Plewis I (2007). Non-response in a birth cohort study: the case of the Millennium CohortStudy. International Journal of Social Research Methodology 10(5): 325-334.Pulsford RM, Cortina-Borja M, Rich C, Kinnafick F-E, Dezateux C, and Griffiths LJ (2011).Actigraph accelerometer-defined boundaries for sedentary behaviour and physical activityintensities in 7 year old children. PLoS ONE 6(8): e21822. doi:10.1371/journal.pone.0021822Rich C, Geraci M, Griffiths LJ, Sera F, Dezateux C, and Cortina-Borja M (2012a). Qualitycontrol methods in accelerometer data processing: Defining minimum wear time (underreview).Rich C, Geraci M, Griffiths LJ, Sera F, Dezateux C, and Cortina-Borja M (2012b). Qualitycontrol methods in accelerometer data processing: identifying extreme counts (under review).Sherar L, Griew P, Esliger DW, Cooper A, Ekelund U, Judge K, and Riddoch C (2011).International children's accelerometry database (ICAD): Design and methods. BMC PublicHealth 11(1): 485-497Silva P, Esliger D, and Welk G (2010). Technical reliability assessment of the ActigraphGT1M Accelerometer. Measurement in Physical Education and Exercise Science 14: 79-91.

Technical report on accelerometry data processing in the Millennium Cohort Study

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Technical report on accelerometry data processing in the Millennium Cohort Study

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