A cluster analysis of patterns of objectively measured physical activity in Hong Kong
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 meas...
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| Veröffentlicht in: | Public health nutrition 2013-08, Vol.16 (8), p.1436-1444 |
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| creator | Lee, Paul H Yu, Ying-Ying McDowell, Ian Leung, Gabriel M Lam, TH |
| description | 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.
Cross-sectional design.
Objectively measured PA in Hong Kong adults.
Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male).
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 OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity.
Implications are drawn for targeting exercise promotion programmes at the population level. |
| doi_str_mv | 10.1017/S1368980012003631 |
| format | Article |
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Cross-sectional design.
Objectively measured PA in Hong Kong adults.
Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male).
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 OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity.
Implications are drawn for targeting exercise promotion programmes at the population level.</description><identifier>ISSN: 1368-9800</identifier><identifier>EISSN: 1475-2727</identifier><identifier>DOI: 10.1017/S1368980012003631</identifier><identifier>PMID: 22894896</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Accelerometers ; Adolescent ; Adult ; Aged ; Analysis ; Assessment and methodology ; Biological and medical sciences ; Body Composition ; Body Height ; Body Weight ; Chronic Disease ; Cluster Analysis ; Cross-Sectional Studies ; Exercise ; Female ; Hong Kong ; Humans ; Life Style ; Male ; Medical sciences ; Middle Aged ; Motor Activity ; Obesity - metabolism ; Prevalence ; Public health. Hygiene-occupational medicine ; Self Report ; Socioeconomic Factors ; Variables ; Young Adult</subject><ispartof>Public health nutrition, 2013-08, Vol.16 (8), p.1436-1444</ispartof><rights>Copyright © The Authors 2012</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c545t-2fa82cfd590ba14adabeae41122993de0028c66fe659bb3c0a6d4042288979ec3</citedby><cites>FETCH-LOGICAL-c545t-2fa82cfd590ba14adabeae41122993de0028c66fe659bb3c0a6d4042288979ec3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27619686$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22894896$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Paul H</creatorcontrib><creatorcontrib>Yu, Ying-Ying</creatorcontrib><creatorcontrib>McDowell, Ian</creatorcontrib><creatorcontrib>Leung, Gabriel M</creatorcontrib><creatorcontrib>Lam, TH</creatorcontrib><title>A cluster analysis of patterns of objectively measured physical activity in Hong Kong</title><title>Public health nutrition</title><addtitle>Public Health Nutr</addtitle><description>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.
Cross-sectional design.
Objectively measured PA in Hong Kong adults.
Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male).
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 OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity.
Implications are drawn for targeting exercise promotion programmes at the population level.</description><subject>Accelerometers</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Analysis</subject><subject>Assessment and methodology</subject><subject>Biological and medical sciences</subject><subject>Body Composition</subject><subject>Body Height</subject><subject>Body Weight</subject><subject>Chronic Disease</subject><subject>Cluster Analysis</subject><subject>Cross-Sectional Studies</subject><subject>Exercise</subject><subject>Female</subject><subject>Hong Kong</subject><subject>Humans</subject><subject>Life Style</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Motor Activity</subject><subject>Obesity - metabolism</subject><subject>Prevalence</subject><subject>Public health. 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Hygiene-occupational medicine</topic><topic>Self Report</topic><topic>Socioeconomic Factors</topic><topic>Variables</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Paul H</creatorcontrib><creatorcontrib>Yu, Ying-Ying</creatorcontrib><creatorcontrib>McDowell, Ian</creatorcontrib><creatorcontrib>Leung, Gabriel M</creatorcontrib><creatorcontrib>Lam, TH</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Career & Technical Education Database</collection><collection>Nursing & Allied Health Database</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Physical Education Index</collection><jtitle>Public health nutrition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Paul H</au><au>Yu, Ying-Ying</au><au>McDowell, Ian</au><au>Leung, Gabriel M</au><au>Lam, TH</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A cluster analysis of patterns of objectively measured physical activity in Hong Kong</atitle><jtitle>Public health nutrition</jtitle><addtitle>Public Health Nutr</addtitle><date>2013-08-01</date><risdate>2013</risdate><volume>16</volume><issue>8</issue><spage>1436</spage><epage>1444</epage><pages>1436-1444</pages><issn>1368-9800</issn><eissn>1475-2727</eissn><abstract>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.
Cross-sectional design.
Objectively measured PA in Hong Kong adults.
Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male).
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 OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity.
Implications are drawn for targeting exercise promotion programmes at the population level.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><pmid>22894896</pmid><doi>10.1017/S1368980012003631</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Public health nutrition, 2013-08, Vol.16 (8), p.1436-1444 |
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| subjects | Accelerometers Adolescent Adult Aged Analysis Assessment and methodology Biological and medical sciences Body Composition Body Height Body Weight Chronic Disease Cluster Analysis Cross-Sectional Studies Exercise Female Hong Kong Humans Life Style Male Medical sciences Middle Aged Motor Activity Obesity - metabolism Prevalence Public health. Hygiene-occupational medicine Self Report Socioeconomic Factors Variables Young Adult |
| title | A cluster analysis of patterns of objectively measured physical activity in Hong Kong |
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