Generating ActiGraph counts from raw acceleration recorded by an alternative monitor.
Open accessAbstract
PURPOSE: This study aimed 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-h 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 with counts generated with ActiLife from ActiGraph GT3X+ data. RESULTS: An optimal band-pass filter was fitted resulting in a root-mean-square error of 25.7 counts per 10 s and mean absolute error 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 s across all rotational frequencies compared with the original ActiGraph method. Applying the aggregation method to the 24-h free-living recordings resulted in an epoch level bias ranging from -16.2 to 0.9 counts per 10 s, 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.
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Wearable cameras that take regular photographs offer a suitable alternative but have not yet been used in sleep studies.
“Wearable cameras that take regular photographs offer a suitable alternative25,26 but have not yet been used in sleep studies.”
From Screen Use at Bedtime and Sleep Duration and Quality Among Youths (2024) · cites this paper as [25]
Several observational studies using wearable image cameras to describe screen time did not investigate associations with sleep outcomes.
“Furthermore, although several observational studies used wearable image cameras to describe screen time,25,26,39,40 they did not investigate associations with sleep outcomes.”
From Screen Use at Bedtime and Sleep Duration and Quality Among Youths (2024) · cites this paper as [25]
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Self-citation
- Authors
- BRØND, J. C., ANDERSEN, L. B., & ARVIDSSON, D.
- Affiliations
- University of Southern Denmark (DK)
- Western Norway University of Applied Sciences (NO)
- Lund University (SE)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2017-11
- Volume
- 49
- Issue
- 11
- Pages
- 2351-2360
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2621707983
- Cited by
- 166
- References
- 26
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 34 · 2023: 22 · 2022: 26 · 2021: 27 · 2020: 12 · 2019: 14 · 2018: 6
- FWCI
- 6.67
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc)
- Lund University Publications (Lund University) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, other-oa)
- Funders
- Vetenskapsrådet
- Syddansk Universitet
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd ((COFAS‐2, 2013‐2716))
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- bandpass filtering, physical activity monitoring, ActiGraph GT3X+, physical activity intensity classification
- MeSH terms
- Acceleration, Female, Humans, Male, Software, Reproducibility of Results, Exercise, Young Adult, Actigraphy
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