Abstract
OBJECTIVES: While active living interventions focus on modifying urban design and built environment, weather variation, a phenomenon that perennially interacts with these environmental factors, is consistently underexplored. This study’s objective is to develop a methodology to link weather data with existing cross-sectional accelerometry data in capturing weather variation.
METHODS: Saskatoon’s neighbourhoods were classified into grid-pattern, fractured grid-pattern and curvilinear neighbourhoods. Thereafter, 137 Actical accelerometers were used to derive moderate to vigorous physical activity (MVPA) and sedentary behaviour (SB) data from 455 children in 25 sequential one-week cycles between April and June, 2010. This sequential deployment was necessary to overcome the difference in the ratio between the sample size and the number of accelerometers. A data linkage methodology was developed, where each accelerometry cycle was matched with localized (Saskatoonspecific) weather patterns derived from Environment Canada. Statistical analyses were conducted to depict the influence of urban design on MVPA and SB after factoring in localized weather patterns.
RESULTS: Integration of cross-sectional accelerometry with localized weather patterns allowed the capture of weather variation during a single seasonal transition. Overall, during the transition from spring to summer in Saskatoon, MVPA increased and SB decreased during warmer days. After factoring in localized weather, a recurring observation was that children residing in fractured grid-pattern neighbourhoods accumulated significantly lower MVPA and higher SB.
CONCLUSION: The proposed methodology could be utilized to link globally available cross-sectional accelerometry data with place-specific weather data to understand how built and social environmental factors interact with varying weather patterns in influencing active living.
Key Words: Weather, physical activity, sedentary lifestyle
Résumé
OBJECTIFS: Les interventions de promotion de la vie active cherchent surtout à modifier l’aménagement urbain et le milieu bâti, mais les variations météorologiques, un phénomène qui interagit perpétuellement avec ces facteurs environnementaux, sont systématiquement sousexplorées. Notre étude vise à élaborer une méthode pour relier les données météorologiques aux données transversales existantes obtenues par accélérométrie pour capter les variations météorologiques.
MÉTHODE: Nous avons classé les quartiers de Saskatoon en quartiers à agencement quadrillé, en quartiers scindés à agencement quadrillé et en quartiers à agencement curviligne. Par la suite, nous avons utilisé 137 accéléromètres Actical pour recueillir des données sur l’activité physique d’intensité modérée à élevée (APIME) et le comportement sédentaire (CS) auprès de 455 enfants au cours de 25 cycles séquentiels d’une semaine entre avril et juin 2010. Ce déploiement séquentiel était nécessaire pour surmonter la différence de ratio entre la taille de l’échantillon et le nombre d’accéléromètres. Nous avons élaboré une méthode de maillage de données où chaque cycle d’accélérométrie était assorti aux conditions atmosphériques locales (propres à Saskatoon) selon Environnement Canada. Nous avons mené des analyses statistiques pour dépeindre l’influence de l’aménagement urbain sur l’APIME et le CS après la prise en compte des conditions atmosphériques locales.
RÉSULTATS: L’intégration de l’accélérométrie transversale et des conditions atmosphériques locales a permis de saisir les variations météorologiques au cours d’une même transition saisonnière. Globalement, durant la transition du printemps à l’été à Saskatoon, l’APIME a augmenté et le CS a diminué les jours les plus chauds. Après la prise en compte des conditions météorologiques locales, nous avons observé à plusieurs reprises que les enfants vivant dans les quartiers scindés à agencement quadrillé présentaient cumulativement une APIME significativement plus faible et un CS significativement plus élevé.
CONCLUSION: La méthode proposée pourrait servir à relier des données transversales obtenues par accélérométrie disponibles mondialement et des données météorologiques propres à un lieu pour comprendre comment le milieu bâti et les facteurs de l’environnement social interagissent avec diverses conditions atmosphériques pour influencer la vie active.
Mots Clés: temps météorologique, activité physique, mode de vie sédentaire
Footnotes
Conflict of Interest: None to declare.
References
- 1.Arline-Bradley SL. Uneven playing field — Effective strategies to address health inequity through active living research: A commentary to accompany the active living research supplement to annals of behavioral medicine. Ann Behav Med. 2013;45(1):9–10. doi: 10.1007/s12160-012-9447-5. [DOI] [PubMed] [Google Scholar]
- 2.Peel MC, Finlayson BL, McMahon TA. Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11:1633–44. doi: 10.5194/hess-11-1633-2007. [DOI] [Google Scholar]
- 3.Frittis HC, Lough JM. An estimate of average annual temperature variations for North America, 1602 to 1961. Clim Change. 1985;7:203–24. doi: 10.1007/BF00140506. [DOI] [Google Scholar]
- 4.Zhang X, Vincent LA, Hogg WD, Niitsoo A. Temperature and precipitation trends in Canada during the 20th century. Atmos Ocean. 2000;38(3):395–429. doi: 10.1080/07055900.2000.9649654. [DOI] [Google Scholar]
- 5.Merchant AT, Dehghan M, Akhtar-Danesh N. Seasonal variation in leisuretime physical activity among Canadians. Can J Public Health. 2007;98(3):203–8. doi: 10.1007/BF03403713. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Sheridan SC. The redevelopment of a weather-type classification scheme for North America. Int J Climatol. 2002;22:51–68. doi: 10.1002/joc.709. [DOI] [Google Scholar]
- 7.Kolle E, Steene-Johannessen J, Andersen LB, Anderssen SA. Seasonal variation in objectively assessed physical activity among children and adolescents in Norway: A crosssectional study. Int J Behav Nutr Phys Act. 2009;6:36. doi: 10.1186/1479-5868-6-36. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kristensen PL, Korsholm L, Møller NC, Wedderkopp N, Andersen LB, Froberg K. Sources of variation in habitual physical activity of children and adolescents: The European Youth Heart Study. Scand J Med Sci Sports. 2008;18(3):298–308. doi: 10.1111/j.1600-0838.2007.00668.x. [DOI] [PubMed] [Google Scholar]
- 9.McCormack GR, Friedenreich C, Shiell A, Giles-Corti B, Doyle-Baker PK. Sex and age-specific seasonal variations in physical activity among adults. J Epidemiol Commun Health. 2010;64:1010–16. doi: 10.1136/jech.2009.092841. [DOI] [PubMed] [Google Scholar]
- 10.Carson V, John SC, Cutumisu N, Boule N, Edwards J. Seasonal variation in physical activity among preschool children in a northern Canadian city. Res Q Exerc Sport. 2010;81(4):392–99. doi: 10.1080/02701367.2010.10599699. [DOI] [PubMed] [Google Scholar]
- 11.Harrison F, Jones AP, Bentham G v, Sluijs EM, Cassidy A, Griffin SJ. The impact of rainfall and school break time policies on physical activity in 9–10 year old British children: A repeated measures study. Int J Behav Nutr Phys Act. 2011;8:47. doi: 10.1186/1479-5868-8-47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bélanger M, Gray-Donald K, O’Loughlin J, Paradis G, Hanley J. Influence of weather conditions and season on physical activity in adolescents. Ann Epidemiol. 2009;19(3):180–86. doi: 10.1016/j.annepidem.2008.12.008. [DOI] [PubMed] [Google Scholar]
- 13.Colley R, Gorber SC, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Rep. 2010;21(1):63–69. [PubMed] [Google Scholar]
- 14.Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37(11):S523–30. doi: 10.1249/01.mss.0000185658.28284.ba. [DOI] [PubMed] [Google Scholar]
- 15.Esliger DW, Tremblay MS. Technical reliability assessment of three accelerometer models in a mechanical setup. Med Sci Sports Exerc. 2006;38(12):2173–81. doi: 10.1249/01.mss.0000239394.55461.08. [DOI] [PubMed] [Google Scholar]
- 16.Troiano R, Berrigan D, Dodd K, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–88. doi: 10.1249/mss.0b013e31815a51b3. [DOI] [PubMed] [Google Scholar]
- 17.Katzmarzyk PT, Barreira TV, Broyles ST, Champagne CM, Chaput JP, Fogelholm M, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): Design and Methods. BMC Public Health. 2013;13:900. doi: 10.1186/1471-2458-13-900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.City of Saskatoon. Saskatoon Census Metropolitan Area. 2011. [Google Scholar]
- 19.Muhajarine N. Planning for Growth in Saskatoon: Past, Present and Future. 2011. [Google Scholar]
- 20.Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physical activity of Canadian children and youth: Accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Health Rep. 2011;22(1):15–23. [PubMed] [Google Scholar]
- 21.Katapally TR, Muhajarine N. Towards uniform accelerometry analysis: A standardization methodology to minimize measurement bias due to systematic accelerometer wear-time variation. J Sports Sci Med. 2014;13:379–86. [PMC free article] [PubMed] [Google Scholar]
- 22.Rainham DG, Smoyer-Tomic KE, Sheridan SC, Burnett RT. Synoptic weather patterns and modification of the association between air pollution and human mortality. Int J Environ Health Res. 2005;15(5):347–60. doi: 10.1080/09603120500289119. [DOI] [PubMed] [Google Scholar]
- 23.Ding D, Sallis JF, Kerr J, Lee S, Rosenberg DE. Neighborhood environment and physical activity among youth: A review. Am J Prev Med. 2011;41(4):442–55. doi: 10.1016/j.amepre.2011.06.036. [DOI] [PubMed] [Google Scholar]
- 24.Kaushal N, Rhodes RE. The home physical environment and its relationship with physical activity and sedentary behavior: A systematic review. Prev Med. 2014;67:221–37. doi: 10.1016/j.ypmed.2014.07.026. [DOI] [PubMed] [Google Scholar]
- 25.Arline-Bradley SL. Uneven playing field — Effective strategies to address health inequity through active living research: A commentary to accompany the active living research supplement to annals of behavioral medicine. Ann Behav Med. 2013;45(1):9–10. doi: 10.1007/s12160-012-9447-5. [DOI] [PubMed] [Google Scholar]
- 26.Peel MC, Finlayson BL, McMahon TA. Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11:1633–44. doi: 10.5194/hess-11-1633-2007. [DOI] [Google Scholar]