A cluster analysis of patterns of objectively measured physical activity in Hong Kong
A cluster analysis of patterns of objectively measured physical activity in Hong Kong
Objective The health benefits of exercise are clear. In targeting interventions it would be valuable to know whether characteristic patterns of physical activity (PA) are associated with particular population subgroups. The present study used cluster analysis to identify characteristic hourly PA patterns measured by accelerometer. Design Cross-sectional design. Setting Objectively measured PA in Hong Kong adults. Subjects Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44·2 years, 45·9 % male). Results Two clusters were identified, one more active than the other. The 'active cluster' (n 480) was characterized by a routine PA pattern on weekdays and a more active and varied pattern on weekends; the other, the 'less active cluster' (n 1234), by a consistently low PA pattern on both weekdays and weekends with little variation from day to day. Demographic, lifestyle, PA level and health characteristics of the two clusters were compared. They differed in age, sex, smoking, income and level of PA required at work. The odds of having any chronic health conditions was lower for the active group (adjusted OR = 0·62, 95 % CI 0·46, 0·84) but the two groups did not differ in terms of specific chronic health conditions or obesity. Conclusions Implications are drawn for targeting exercise promotion programmes at the population level.
Body composition, Chronic disease, Exercise, Motor activity, Sedentary lifestyle
1436-1444
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Yu, Ying Ying
3ab8a0fe-8340-4f54-8a0b-14dbb402c7fd
McDowell, Ian
d45606ed-356e-4ab0-9611-a106f332959e
Leung, Gabriel M.
05520107-4b1b-4adf-a291-20f4d8941219
Lam, T. H.
342e044c-2bbc-413c-b3fb-ad2c399b5fb7
August 2013
Lee, Paul H.
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Yu, Ying Ying
3ab8a0fe-8340-4f54-8a0b-14dbb402c7fd
McDowell, Ian
d45606ed-356e-4ab0-9611-a106f332959e
Leung, Gabriel M.
05520107-4b1b-4adf-a291-20f4d8941219
Lam, T. H.
342e044c-2bbc-413c-b3fb-ad2c399b5fb7
Lee, Paul H., Yu, Ying Ying, McDowell, Ian, Leung, Gabriel M. and Lam, T. H.
(2013)
A cluster analysis of patterns of objectively measured physical activity in Hong Kong.
Public Health Nutrition, 16 (8), .
(doi:10.1017/S1368980012003631).
Abstract
Objective The health benefits of exercise are clear. In targeting interventions it would be valuable to know whether characteristic patterns of physical activity (PA) are associated with particular population subgroups. The present study used cluster analysis to identify characteristic hourly PA patterns measured by accelerometer. Design Cross-sectional design. Setting Objectively measured PA in Hong Kong adults. Subjects Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44·2 years, 45·9 % male). Results Two clusters were identified, one more active than the other. The 'active cluster' (n 480) was characterized by a routine PA pattern on weekdays and a more active and varied pattern on weekends; the other, the 'less active cluster' (n 1234), by a consistently low PA pattern on both weekdays and weekends with little variation from day to day. Demographic, lifestyle, PA level and health characteristics of the two clusters were compared. They differed in age, sex, smoking, income and level of PA required at work. The odds of having any chronic health conditions was lower for the active group (adjusted OR = 0·62, 95 % CI 0·46, 0·84) but the two groups did not differ in terms of specific chronic health conditions or obesity. Conclusions Implications are drawn for targeting exercise promotion programmes at the population level.
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Published date: August 2013
Keywords:
Body composition, Chronic disease, Exercise, Motor activity, Sedentary lifestyle
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Local EPrints ID: 480694
URI: http://eprints.soton.ac.uk/id/eprint/480694
ISSN: 1368-9800
PURE UUID: 77ff694a-30ad-4b7d-b250-41e8e02594e4
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Date deposited: 08 Aug 2023 16:52
Last modified: 17 Mar 2024 04:17
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Contributors
Author:
Paul H. Lee
Author:
Ying Ying Yu
Author:
Ian McDowell
Author:
Gabriel M. Leung
Author:
T. H. Lam
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