Patterns of health behaviour associated with active travel: a compositional data analysis
Abstract Background Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how pe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-03-01
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| Series: | International Journal of Behavioral Nutrition and Physical Activity |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12966-018-0662-8 |
| _version_ | 1818525116381790208 |
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| author | Louise Foley Dorothea Dumuid Andrew J. Atkin Timothy Olds David Ogilvie |
| author_facet | Louise Foley Dorothea Dumuid Andrew J. Atkin Timothy Olds David Ogilvie |
| author_sort | Louise Foley |
| collection | DOAJ |
| description | Abstract Background Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. Methods We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. Results 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. Conclusions Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel. |
| first_indexed | 2024-12-11T06:05:26Z |
| format | Article |
| id | doaj.art-ad78a0ee7f7c4007b56ca2ed6d2ace39 |
| institution | Directory Open Access Journal |
| issn | 1479-5868 |
| language | English |
| last_indexed | 2024-12-11T06:05:26Z |
| publishDate | 2018-03-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Behavioral Nutrition and Physical Activity |
| spelling | doaj.art-ad78a0ee7f7c4007b56ca2ed6d2ace392022-12-22T01:18:18ZengBMCInternational Journal of Behavioral Nutrition and Physical Activity1479-58682018-03-0115111210.1186/s12966-018-0662-8Patterns of health behaviour associated with active travel: a compositional data analysisLouise Foley0Dorothea Dumuid1Andrew J. Atkin2Timothy Olds3David Ogilvie4MRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of CambridgeSchool of Health Sciences, University of South AustraliaSchool of Health Sciences, Faculty of Medicine and Health Sciences, University of East AngliaSchool of Health Sciences, University of South AustraliaMRC Epidemiology Unit & UKCRC Centre for Diet and Activity Research (CEDAR), School of Clinical Medicine, University of CambridgeAbstract Background Active travel (walking or cycling for transport) is associated with favourable health outcomes in adults. However, little is known about the concurrent patterns of health behaviour associated with active travel. We used compositional data analysis to explore differences in how people doing some active travel used their time compared to those doing no active travel, incorporating physical activity, sedentary behaviour and sleep. Methods We analysed cross-sectional data from the 2014/15 United Kingdom Harmonised European Time Use Survey. Participants recorded two diary days of activity, and we randomly selected one day from participants aged 16 years or over. Activities were categorised into six mutually exclusive sets, accounting for the entire 24 h: (1) sleep; (2) leisure moderate to vigorous physical activity (MVPA); (3) leisure sedentary screen time; (4) non-discretionary time (work, study, chores and caring duties); (5) travel and (6) other. This mixture of activities was defined as a time-use composition. A binary variable was created indicating whether participants reported any active travel on their selected diary day. We used compositional multivariate analysis of variance (MANOVA) to test whether mean time-use composition differed between individuals reporting some active travel and those reporting no active travel, adjusted for covariates. We then used adjusted linear regression models and bootstrap confidence intervals to identify which of the six activity sets differed between groups. Results 6143 participants (mean age 48 years; 53% female) provided a valid diary day. There was a statistically significant difference in time-use composition between those reporting some active travel and those reporting no active travel. Those undertaking active travel reported a relatively greater amount of time in leisure MVPA and travel, and a relatively lower amount of time in leisure sedentary screen time and sleep. Conclusions Compared to those not undertaking active travel, those who did active travel reported 11 min more in leisure MVPA and 18 min less in screen time per day, and reported lower sleep. From a health perspective, higher MVPA and lower screen time is favourable, but the pattern of sleep is more complex. Overall, active travel was associated with a broadly health-promoting composition of time across multiple behavioural domains, which supports the public health case for active travel.http://link.springer.com/article/10.1186/s12966-018-0662-8Active travelWalkingBicyclingPhysical activitySedentary behaviourScreen time |
| spellingShingle | Louise Foley Dorothea Dumuid Andrew J. Atkin Timothy Olds David Ogilvie Patterns of health behaviour associated with active travel: a compositional data analysis International Journal of Behavioral Nutrition and Physical Activity Active travel Walking Bicycling Physical activity Sedentary behaviour Screen time |
| title | Patterns of health behaviour associated with active travel: a compositional data analysis |
| title_full | Patterns of health behaviour associated with active travel: a compositional data analysis |
| title_fullStr | Patterns of health behaviour associated with active travel: a compositional data analysis |
| title_full_unstemmed | Patterns of health behaviour associated with active travel: a compositional data analysis |
| title_short | Patterns of health behaviour associated with active travel: a compositional data analysis |
| title_sort | patterns of health behaviour associated with active travel a compositional data analysis |
| topic | Active travel Walking Bicycling Physical activity Sedentary behaviour Screen time |
| url | http://link.springer.com/article/10.1186/s12966-018-0662-8 |
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