How might we increase physical activity through dog walking?: A comprehensive review of dog walking correlates

Abstract

Background

Physical inactivity and sedentary behaviour are major threats to population health. A considerable proportion of people own dogs, and there is good evidence that dog ownership is associated with higher levels of physical activity. However not all owners walk their dogs regularly. This paper comprehensively reviews the evidence for correlates of dog walking so that effective interventions may be designed to increase the physical activity of dog owners.

Methods

Published findings from 1990–2012 in both the human and veterinary literature were collated and reviewed for evidence of factors associated with objective and self-reported measures of dog walking behaviour, or reported perceptions about dog walking. Study designs included cross-sectional observational, trials and qualitative interviews.

Results

There is good evidence that the strength of the dog-owner relationship, through a sense of obligation to walk the dog, and the perceived support and motivation a dog provides for walking, is strongly associated with increased walking. The perceived exercise requirements of the dog may also be a modifiable point for intervention. In addition, access to suitable walking areas with dog supportive features that fulfil dog needs such as off-leash exercise, and that also encourage human social interaction, may be incentivising.

Conclusion

Current evidence suggests that dog walking may be most effectively encouraged through targeting the dog-owner relationship and by providing dog-supportive physical environments. More research is required to investigate the influence of individual owner and dog factors on ‘intention’ to walk the dog as well as the influence of human social interaction whilst walking a dog. The effects of policy and cultural practices relating to dog ownership and walking should also be investigated. Future studies must be of a higher quality methodological design, including accounting for the effects of confounding between variables, and longitudinal designs and testing of interventions in a controlled design in order to infer causality.