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BioMed Research International
Volume 2017 (2017), Article ID 9069730, 11 pages
https://doi.org/10.1155/2017/9069730
Research Article

Individual, Social, and Environmental Correlates of Active Transportation Patterns in French Women

1CRNH-Rhône-Alpes, CARMEN, INSERM U1060, Université de Lyon 1, INRA U1235, Lyon, France
2Luxembourg Institute of Socio-Economic Research (LISER), Esch-sur-Alzette, Luxembourg
3Laboratoire, Image, Ville et Environnement, Université de Strasbourg, Strasbourg, France
4Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Centre de Recherche en Epidémiologie et Biostatistiques, INSERM U1153, INRA U1125, Cnam, COMUE Sorbonne Paris Cité, Université Paris 13, Bobigny, France
5Department of Nutrition, Pitié-Salpêtrière Hospital (AP-HP), Institute of Cardiometabolism and Nutrition (ICAN), Université Pierre et Marie Curie-Paris 6, Paris, France
6Department of Geography, Lab-Urba, Université Paris-Est, Créteil, France
7Department of Public Health, Hôpital Avicenne (AP-HP), Bobigny, France
8Department of Geography, UMR 7533, LADYSS, Université Paris 8, Saint-Denis, France

Correspondence should be addressed to Julie-Anne Nazare; rf.1noyl-vinu@erazan.enna-eiluj

Received 20 January 2017; Revised 4 May 2017; Accepted 23 May 2017; Published 22 June 2017

Academic Editor: Jung-Min Lee

Copyright © 2017 Camille Perchoux et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The objectives were (1) to define physical activity (PA) and sedentary behaviors (SB) patterns in daily life contexts (work, leisure, and transportation) in French working women from NutriNet-Santé web-cohort and (2) to identify pattern(s) of active transportation and their individual, social, and environmental correlates. 23,432 participants completed two questionnaires to evaluate PA and SB in daily life contexts and individual representations of residential neighborhood and transportation modes. Hierarchical cluster analysis was performed which identified 6 distinct movement behavior patterns: (i) active occupation, high sedentary leisure, (ii) sedentary occupation, low leisure, (iii) sedentary transportation, (iv) sedentary occupation and leisure, (v) active transportation, and (vi) active leisure. Multinomial logistic regressions were performed to identify correlates of the “active transportation” cluster. The perceived environmental characteristics positively associated with “active transportation” included “high availability of destinations around home,” “presence of bicycle paths,” and “low traffic.” A “positive image of walking/cycling,” the “individual feeling of being physically active,” and a “high use of active transport modes by relatives/friends” were positively related to “active transportation,” identified as a unique pattern regarding individual and environmental correlates. Identification of PA and SB context-specific patterns will help to understand movement behaviors’ complexity and to design interventions to promote active transportation in specific subgroups.