Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity

Chastin, S.F.M.; Granat, M.H.

doi:10.1016/j.gaitpost.2009.09.002

Cited by 323Physical Activity and HealthContext-Aware Activity Recognition SystemsErgonomics and Musculoskeletal Disorders

Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity

Chastin, S.F.M., Granat, M.H.·2010·Gait & Posture

DOI 10.1016/j.gaitpost.2009.09.002

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Abstract

The purpose of this study was to develop and test a generic technique to robustly quantify the pattern of sedentary behaviour from objective records. The technique was applied to four groups of subjects: a healthy group with an active occupation (N=54), a healthy group with a sedentary occupation (N=53), a group of subjects with chronic low back pain (N=5) and a group of subjects with chronic fatigue syndrome (N=14). This study presents the first evidence that bouts of sedentary activity are power law distributed. Results showed that there was no significant difference in total sedentary time between the groups, however, the patterns of accumulation of sedentary time were significantly different for the groups. Sedentary groups accumulated their total sedentary time from a small number of longer sedentary bouts. Active groups tended to break their sedentary time into a greater number of shorter bouts. This suggests that the power law exponent alpha and the GINI index G, used to describe the pattern of accumulation of sedentary time, could be used to evaluate and quantify sedentary behaviour.

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The accelerometer data were classified every minute of the day as MVPA, SB or LIPA depending on a threshold; this method is less accurate than postural allocation at distinguishing between SB and LIPA, so these two components might not be entirely accurate and different thresholds might lead to different results.
“The accelerometer data were classified every minute of the day as MVPA, SB or LIPA depending on a threshold. This method is less accurate than postural allocation at distinguishing between SB and LIPA [57], therefore it is possible that these two components of the composition might not be entirely accurate and that different thresholds might lead to different results.”
From Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach (2015) · cites this paper as [57]

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Methods for objective measure, quantification and analysis of sedentary behaviour and physical activity
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Authors: Chastin, S. F. M., & Granat, M. H.
Affiliations: Glasgow Caledonian University (GB)
Corresponding author: Chastin, S.F.M.
ORCID: Chastin, S.F.M.
Granat, M.H.
Venue: Gait & Posture
Publisher: Elsevier BV
Published: 2010-01
Volume: 31
Issue: 1
Pages: 82-86
ISSN: 0966-6362 (ISSN-L)
DOI: 10.1016/j.gaitpost.2009.09.002
OpenAlex: W2031292431
Cited by: 323
References: 33
Citations by year: 2026: 15 · 2025: 11 · 2024: 22 · 2023: 17 · 2022: 22 · 2021: 20 · 2020: 28 · 2019: 20 · 2018: 26 · 2017: 22 · 2016: 30 · 2015: 23 · 2014: 19 · 2013: 18 · 2012: 14
FWCI: 6.41
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Gait & Posture (publishedVersion)
PubMed (publishedVersion)
ResearchOnline (Glasgow Caledonian University) (submittedVersion)
Topics: Health Sciences › Medicine › Physiology › Physical Activity and Health
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Social Sciences › Psychology › Social Psychology › Ergonomics and Musculoskeletal Disorders
Keywords: sedentary behavior, physical inactivity, sedentary bouts, power-law distribution, Gini coefficient, chronic low back pain, chronic fatigue syndrome
MeSH terms: Adult, Analysis of Variance, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Occupations, Posture, Fatigue Syndrome, Chronic, Low Back Pain, Sedentary Behavior

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Methods for objective measure, quantification and analysis of sedentary behaviour and inactivity

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