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Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study
by
Keadle, Sarah Kozey
, Freedson, Patty S
, Lee, I-Min
, Shiroma, Eric J
in
Accelerometers
/ Accelerometry - instrumentation
/ Accelerometry - methods
/ Adult
/ Aged
/ Algorithms
/ Analysis
/ Automatic Data Processing - instrumentation
/ Automatic Data Processing - methods
/ Biostatistics
/ Biostatistics and methods
/ Body Mass Index
/ Cohort Studies
/ Data processing
/ Environmental Health
/ Epidemiologic Measurements
/ Epidemiology
/ Exercise - physiology
/ Female
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Middle Aged
/ Monitoring, Ambulatory - instrumentation
/ Physical Examination
/ Public Health
/ Research Article
/ Sample Size
/ Studies
/ Time Factors
/ Vaccine
/ Womens health
2014
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Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study
by
Keadle, Sarah Kozey
, Freedson, Patty S
, Lee, I-Min
, Shiroma, Eric J
in
Accelerometers
/ Accelerometry - instrumentation
/ Accelerometry - methods
/ Adult
/ Aged
/ Algorithms
/ Analysis
/ Automatic Data Processing - instrumentation
/ Automatic Data Processing - methods
/ Biostatistics
/ Biostatistics and methods
/ Body Mass Index
/ Cohort Studies
/ Data processing
/ Environmental Health
/ Epidemiologic Measurements
/ Epidemiology
/ Exercise - physiology
/ Female
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Middle Aged
/ Monitoring, Ambulatory - instrumentation
/ Physical Examination
/ Public Health
/ Research Article
/ Sample Size
/ Studies
/ Time Factors
/ Vaccine
/ Womens health
2014
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Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study
by
Keadle, Sarah Kozey
, Freedson, Patty S
, Lee, I-Min
, Shiroma, Eric J
in
Accelerometers
/ Accelerometry - instrumentation
/ Accelerometry - methods
/ Adult
/ Aged
/ Algorithms
/ Analysis
/ Automatic Data Processing - instrumentation
/ Automatic Data Processing - methods
/ Biostatistics
/ Biostatistics and methods
/ Body Mass Index
/ Cohort Studies
/ Data processing
/ Environmental Health
/ Epidemiologic Measurements
/ Epidemiology
/ Exercise - physiology
/ Female
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Middle Aged
/ Monitoring, Ambulatory - instrumentation
/ Physical Examination
/ Public Health
/ Research Article
/ Sample Size
/ Studies
/ Time Factors
/ Vaccine
/ Womens health
2014
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Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study
Journal Article
Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study
2014
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Overview
Background
Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output.
Methods
Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points.
Results
Using algorithms alone resulted in \"mail-days\" incorrectly identified as \"wear-days\" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points.
Conclusions
Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V
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