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Exploring patterns of accelerometry-assessed physical activity in elderly people

Sandra Ortlieb12, André Dias234, Lukas Gorzelniak2, Dennis Nowak56, Stefan Karrasch178, Annette Peters9, Klaus A Kuhn2, Alexander Horsch1023, Holger Schulz111* and KORA Study Group

Author Affiliations

1 Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

2 Institute of Medical Statistics and Epidemiology, TUM, Munich, Germany

3 Computer Science Department, University of Tromsø, Tromsø, Norway

4 Tromsø Telemedicine Laboratory, Norwegian Center for Integrated Care and Telemedicine, Tromsø, Norway

5 Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital of Munich (LMU), Munich, Germany

6 Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research, Munich, Germany

7 Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig-Maximilians-Universität, Munich, Germany

8 Institute of General Practice, University Hospital Klinikum rechts der Isar, Technische Universität München, Munich, Germany

9 Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

10 Department of Clinical Medicine, University of Tromsø, Tromsø, Norway

11 Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany

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International Journal of Behavioral Nutrition and Physical Activity 2014, 11:28  doi:10.1186/1479-5868-11-28

Published: 28 February 2014

Additional files

Additional file 1:

Methods.

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Additional file 2: Table S1:

Comparison of cut-points: Freedson vs. Copeland, by activity group. Median (5%/95%). Table S2. Pairwise comparisons of characteristics and clinical parameters, by activity group. Table S3. Pairwise comparisons of PA variables, by activity group. Table S4. Pairwise comparisons of PA variables, by age group and BMI group.

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Additional file 3:

Illustration of physical activity counts, bouts and Lorenz-curves for two subjects with different GINI-Indices. Figure S1A-C – subject I: Visualization of physical activity (1 day) of a subject with a relatively high GINI-Index for moderate to vigorous physical activity (GMVPA=0.72). A) Activity counts of a day provided in 4 segments: The two lines (at 100 and 1952 counts) reflect the cut-points for light activity and moderate to vigorous physical activity (MVPA). Values ≤ 100 correspond to sedentary PA, values between 100-1951 to light PA and ≥ 1952 to MVPA. B) Corresponding bouts of the day provided in 4 segments: A bout is defined as consecutive minutes spent in a specific intensity level, i.e. sedentary, light or MVPA, without an interruption. The intensity levels are provided for 1-minute epochs ( 1-minute). C) Lorenz-Curves: The GINI-index (G) corresponds to the area between the curve and the line of perfect equality (G = 0), marked by a solid line. The figure shows a GMVPA = 0.72, which means that mainly few long bouts are responsible for the activity pattern. Figure S2A-C – subject II: Visualization of physical activity (1 day) of a subject with a relatively low GINI-Index for moderate to vigorous physical activity (GMVPA=0.10). A) Activity counts of a day provided in 4 segments: The two lines (at 100 and 1952 counts) reflect the cut-points for light activity and moderate to vigorous physical activity (MVPA). Values ≤ 100 correspond to sedentary PA, values between 100-1951 to light PA and ≥ 1952 to MVPA. B) Corresponding bouts of the day provided in 4 segments: A bout is defined as consecutive minutes spent in a specific intensity level, i.e. sedentary, light or MVPA, without an interruption. The intensity levels are provided for 1-minute epochs ( 1-minute). C) Lorenz-Curves: The GINI-index (G) corresponds to the area between the curve and the line of perfect equality (G = 0), marked by a solid line. The figure shows a GMVPA=0.10, which means that mainly short bouts of similar length contribute to the activity pattern.

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