Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor
(2017) In Medicine and Science in Sports and Exercise 49(11). p.2351-2360- Abstract
PURPOSE: To implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. METHODS: The aggregation method including the frequency band-pass filter was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-hour free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared to counts generated with ActiLife from... (More)
PURPOSE: To implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. METHODS: The aggregation method including the frequency band-pass filter was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-hour free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared to counts generated with ActiLife from ActiGraph GT3X+ data. RESULTS: An optimal band-pass filter was fitted resulting in a root mean squared error (RMSE) of 25.7 counts per 10 second and mean absolute error (MAE) of 15.0 counts per second across the full frequency range. The mechanical evaluation of the proposed aggregation method resulted in an absolute mean (sd) difference of -0.11 (0.97) counts per 10 second across all rotational frequencies compared to the original ActiGraph method. Applying the aggregation method to the 24-hour free-living recordings resulted in an epoch level bias ranging from -16.2 to 0.9 counts per 10 second, a relative difference in the averaged physical activity (counts per minute) ranging from -0.5% to 4.7% with a group mean (sd) of 2.2% (1.7%) and a Cohen’s Kappa of 0.945 indicating almost a perfect agreement in the intensity classification. CONCLUSION: The proposed band-pass filter and aggregation method is highly valid for generating ActiGraph counts from raw acceleration data recorded with alternative devices. It would facilitate comparability between studies using different devices collecting raw acceleration data.
(Less)
- author
- Brønd, Jan Christian ; Andersen, Lars Bo and Arvidsson, Daniel LU
- organization
- publishing date
- 2017-06-09
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Medicine and Science in Sports and Exercise
- volume
- 49
- issue
- 11
- pages
- 2351 - 2360
- publisher
- Lippincott Williams & Wilkins
- external identifiers
-
- wos:000413405400025
- pmid:28604558
- scopus:85020735838
- ISSN
- 0195-9131
- DOI
- 10.1249/MSS.0000000000001344
- language
- English
- LU publication?
- yes
- id
- 7bb545da-ef69-4a66-af1a-dd903e6bb2ac
- date added to LUP
- 2017-08-11 16:01:40
- date last changed
- 2024-04-14 16:06:56
@article{7bb545da-ef69-4a66-af1a-dd903e6bb2ac,
abstract = {{<p>PURPOSE: To implement an aggregation method in Matlab for generating ActiGraph counts from raw acceleration recorded with an alternative accelerometer device and to investigate the validity of the method. METHODS: The aggregation method including the frequency band-pass filter was implemented and optimized based on standardized sinusoidal acceleration signals generated in Matlab and processed in the ActiLife software. Evaluating the validity of the aggregation method was approached using a mechanical setup and with a 24-hour free-living recording using a convenient sample of nine subjects. Counts generated with the aggregation method applied to Axivity AX3 raw acceleration data were compared to counts generated with ActiLife from ActiGraph GT3X+ data. RESULTS: An optimal band-pass filter was fitted resulting in a root mean squared error (RMSE) of 25.7 counts per 10 second and mean absolute error (MAE) of 15.0 counts per second across the full frequency range. The mechanical evaluation of the proposed aggregation method resulted in an absolute mean (sd) difference of -0.11 (0.97) counts per 10 second across all rotational frequencies compared to the original ActiGraph method. Applying the aggregation method to the 24-hour free-living recordings resulted in an epoch level bias ranging from -16.2 to 0.9 counts per 10 second, a relative difference in the averaged physical activity (counts per minute) ranging from -0.5% to 4.7% with a group mean (sd) of 2.2% (1.7%) and a Cohen’s Kappa of 0.945 indicating almost a perfect agreement in the intensity classification. CONCLUSION: The proposed band-pass filter and aggregation method is highly valid for generating ActiGraph counts from raw acceleration data recorded with alternative devices. It would facilitate comparability between studies using different devices collecting raw acceleration data.</p>}},
author = {{Brønd, Jan Christian and Andersen, Lars Bo and Arvidsson, Daniel}},
issn = {{0195-9131}},
language = {{eng}},
month = {{06}},
number = {{11}},
pages = {{2351--2360}},
publisher = {{Lippincott Williams & Wilkins}},
series = {{Medicine and Science in Sports and Exercise}},
title = {{Generating ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor}},
url = {{http://dx.doi.org/10.1249/MSS.0000000000001344}},
doi = {{10.1249/MSS.0000000000001344}},
volume = {{49}},
year = {{2017}},
}