Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics

Alex Rowlands, Stuart J. Fairclough, Tom Yates, Charlotte L. Edwardson, Melanie J. Davies, Fehmidah Munir, Kamlesh Khunti, Victoria H. Stiles

Research output: Contribution to journalArticle

Abstract

Purpose: The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sex-specific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) %fat (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile ‘norms’ showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sex- specific norms that would be beneficial to researchers.
Original languageEnglish
JournalMedicine and Science in Sports and Exercise
Early online date12 Jul 2019
Publication statusPublished - 1 Oct 2019

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Exercise
Health
Type 2 Diabetes Mellitus
Fats
Bone and Bones
Wrist
Software
Regression Analysis
Research Personnel
Population
Datasets

Keywords

  • GENEActiv
  • ActiGraph
  • Axivity
  • wrist-worn
  • GGIR
  • intensity gradient

Cite this

Rowlands, A., Fairclough, S. J., Yates, T., Edwardson, C. L., Davies, M. J., Munir, F., ... Stiles, V. H. (2019). Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics. Medicine and Science in Sports and Exercise.
Rowlands, Alex ; Fairclough, Stuart J. ; Yates, Tom ; Edwardson, Charlotte L. ; Davies, Melanie J. ; Munir, Fehmidah ; Khunti, Kamlesh ; Stiles, Victoria H. / Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics. In: Medicine and Science in Sports and Exercise. 2019.
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abstract = "Purpose: The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sex-specific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) {\%}fat (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/{\%}fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile ‘norms’ showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sex- specific norms that would be beneficial to researchers.",
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Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics. / Rowlands, Alex; Fairclough, Stuart J.; Yates, Tom; Edwardson, Charlotte L.; Davies, Melanie J.; Munir, Fehmidah; Khunti, Kamlesh; Stiles, Victoria H.

In: Medicine and Science in Sports and Exercise, 01.10.2019.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics

AU - Rowlands, Alex

AU - Fairclough, Stuart J.

AU - Yates, Tom

AU - Edwardson, Charlotte L.

AU - Davies, Melanie J.

AU - Munir, Fehmidah

AU - Khunti, Kamlesh

AU - Stiles, Victoria H.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Purpose: The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sex-specific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) %fat (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile ‘norms’ showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sex- specific norms that would be beneficial to researchers.

AB - Purpose: The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sex-specific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) %fat (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile ‘norms’ showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sex- specific norms that would be beneficial to researchers.

KW - GENEActiv

KW - ActiGraph

KW - Axivity

KW - wrist-worn

KW - GGIR

KW - intensity gradient

M3 - Article

JO - Medicine and Science in Sports and Exercise

JF - Medicine and Science in Sports and Exercise

SN - 0195-9131

ER -