Gender and active travel: a qualitative data synthesis informed by machine learning.

Journal: The international journal of behavioral nutrition and physical activity
PMID: 31864372

Abstract

BACKGROUND: Innovative approaches are required to move beyond individual approaches to behaviour change and develop more appropriate insights for the complex challenge of increasing population levels of activity. Recent research has drawn on social practice theory to describe the recursive and relational character of active living but to date most evidence is limited to small-scale qualitative research studies. To 'upscale' insights from individual contexts, we pooled data from five qualitative studies and used machine learning software to explore gendered patterns in the context of active travel.

Authors

  • Emily Haynes
    European Centre for Environment & Human Health, University of Exeter Medical School, Truro, UK. e.c.haynes@exeter.ac.uk.
  • Judith Green
    School of Population Health & Environmental Sciences, KCL, London, UK.
  • Ruth Garside
    European Centre for Environment and Human Health, University of Exeter Medical School, University of Exeter, Exeter, UK.
  • Michael P Kelly
    Department of Orthopedic Surgery, Washington University, 4921 Parkview Place, A 12, St. Louis, MO 63110, USA.
  • Cornelia Guell
    European Centre for Environment & Human Health, University of Exeter Medical School, Truro, UK.

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