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Cited by 126Physical Activity and HealthContext-Aware Activity Recognition SystemsObesity, Physical Activity, Diet

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Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand

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Aittasalo, Minna, Vähä-Ypyä, Henri, Vasankari, Tommi, Husu, Pauliina, Jussila, Anne-Mari, Sievänen, Harri·2015·BMC Sports Science, Medicine and Rehabilitation
DOI 10.1186/s13102-015-0010-0PMC PMC4527117
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Abstract

BACKGROUND: Using raw acceleration data to assess the intensity of physical activity enables direct comparisons between studies using different accelerometer brands. Mean amplitude deviation (MAD in mg) calculated from resultant tri-axial raw acceleration signal was recently shown to perform best in classifying the intensity of physical activity in adults irrespective of the accelerometer brand. This study compared MAD values and cut-points between two different accelerometers in adolescents. METHODS: Twenty voluntary participants (10 girls and 10 boys) of average age of 14 wore two accelerometers (Actigraph GTX3, Pensacola FL, USA and Hookie AM13, Espoo, Finland) and heart rate monitors (M61, Polar Electro Oy, Kempele, Finland) while completing ten 2-min patterns of typical activities ranging from sedentary behaviour to light, moderate and vigorous-intensity locomotion. Bland-Altman method examined the agreement of MAD values between the accelerometers. Correlation coefficient between individual heart rates and MAD values indicated the validity of pattern-based intensity classification. Generalized ordinal logistic regression determined the intensity-specific MAD cut-points for both accelerometers. RESULTS: MAD values varied from 3 mg (lying supine) to 1609 mg (running). Hookie gave higher values than Actigraph in accelerations exceeding 700 mg. The correlation coefficient between MAD values and heart rates was 0.96 for Hookie and 0.97 for Actigraph. Respectively, the MAD cut-points were 29 and 27 (light), 338 and 330 (moderate), and 604 and 558 (vigorous). CONCLUSIONS: MAD values and cut-points of Hookie and Actigraph showed excellent agreement. Analysing raw accelerometer data with MAD values may enable the comparison of accelerometer results between different studies also in adolescents.

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Fig. 1 An example of the minute-by-minute comparison of mean absolute deviation (MAD, mg) calculated from the Hookie and Actigraph acceleration data of a single participant across all 10 activities
Fig. 1 An example of the minute-by-minute comparison of mean absolute deviation (MAD, mg) calculated from the Hookie and Actigraph acceleration data of a single participant across all 10 activitiesCC BY · PubMed Central
Fig. 2 Difference in the mean absolute deviation (MAD, mg) calculated from Hookie and Actigraph acceleration data in all 10 activities plotted against the mean of MAD values in the same activities. Dashed lines show 95 % limits of agreement
Fig. 2 Difference in the mean absolute deviation (MAD, mg) calculated from Hookie and Actigraph acceleration data in all 10 activities plotted against the mean of MAD values in the same activities. Dashed lines show 95 % limits of agreementCC BY · PubMed Central
Fig. 3 Individual mean absolute deviations (MAD, mg) calculated from Hookie (left panel) and Actigraph (right panel) acceleration data plotted against the mean heart rate within five activities from slow walking to running
Fig. 3 Individual mean absolute deviations (MAD, mg) calculated from Hookie (left panel) and Actigraph (right panel) acceleration data plotted against the mean heart rate within five activities from slow walking to runningCC BY · PubMed Central

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Metadata

Authors
Aittasalo, M., Vähä-Ypyä, H., Vasankari, T., Husu, P., Jussila, A.-M., & Sievänen, H.
Affiliations
Urho Kaleva Kekkonen Institute (FI)
Corresponding author
Aittasalo, Minna
ORCID
Venue
BMC Sports Science, Medicine and Rehabilitation
Publisher
Springer Science and Business Media LLC
Published
2015-08-07
Volume
7
Issue
1
Pages
18-18
ISSN
2052-1847 (ISSN-L)
DOI
10.1186/s13102-015-0010-0
PMC
PMC4527117
OpenAlex
W1689929051
Cited by
126
References
21
Citations by year
2026: 4 · 2025: 8 · 2024: 13 · 2023: 14 · 2022: 20 · 2021: 21 · 2020: 8 · 2019: 11 · 2018: 11 · 2017: 8 · 2016: 7 · 2015: 1
FWCI
6.62
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Sports Science Medicine and Rehabilitation (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Tekes
  • Pirkanmaan Sairaanhoitopiiri
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
Keywords
physical activity intensity classification, adolescents, ActiGraph, heart rate monitors, cut points, sedentary behavior, high-intensity exercise

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