A Scoping Review of How Income Affects Accessing Local Green Space to Engage in Outdoor Physical Activity to Improve Well-Being: Implications for Post-COVID-19
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Theme 1: Environment and Well-Being
3.2. Theme 2: Physical Activity and Income/Socioeconomic Status
3.3. The Relationship between Income and Exercise in Green and Blue Spaces during the COVID-19 Pandemic Lockdown
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Author and Date | Country of Data Collection | Methodology | Percentage of Females and Males | Age Range | Type of Physical Activity | Main Finding Related to Income/Socioeconomic Status | |
---|---|---|---|---|---|---|---|
1. | Ahuja et al., 2018 | USA | Community based participatory research (CBPR) | 56% Females 44% Males | 57–60 years old | Sedentary time rather than physical activity per se. | Low socioeconomic status was correlated with more sedentary behaviour. |
2. | Boone-Heinonen et al., 2010 | USA | Longitudinal study (waves I and III of National data). | 51% Females 49% Males | 14.9–15.7 years in Wave I/and 21.3–21.9 years in Wave III | Pay facility availability and public facility availability. | Men in built environments do more moderate vigorous physical activity because of the availability of pay facilities. |
3. | Cerin et al., 2009 | Australia | A stratified two-stage sampling design | 60% Females 40% Males | 20–65 years | Walking for transport | People with low socioeconomic status tend to walk less for transport reasons. |
4. | Cleland et al., 2010 | Australia | Survey | 100% Female 0% Male | 18–45 years | Leisure Time Physical Activity (LTPA) | Older women in deprived areas tend to do less Leisure Time Physical Activity. |
5. | Cohen-Cline et al., 2015 | USA | Twin study | 68% Females 32% Males | Not stated | General physical activity | This study supports the hypothesis that greater access to residential green space is associated with less depression. |
6. | Haughton et al., 2006 | USA | Survey | 67% Females 33% Males | 18–65 years | Current physical activity level | Income may influence physical activity but there is no clear link. Physical environmental factors were associated with walking, moderate-intensity activity, and vigorous-intensity activity; however, the relationships varied by form of activity. |
7. | Kerr et al., 2014 | USA | Survey | 100% Females 0% Males | 50–79 years | Walking | Age friendly elements are needed to get more elderly people walking. The study indicated that more intense activity is likely to occur in recreational settings that may be free from traffic (or other hazards). |
8. | Kim and Yang, 2017 | Korea | Survey | 49% Females 51% Males | 20–70 years | Neighbourhood walking | Surrounding areas need to be a certain way to encourage people to walk in their neighbourhood. It is necessary to contemplate various urban design techniques (including trees for shelter) that can encourage people to walk for leisure. |
9. | Lee, 2007 | USA | Telephone survey | 54% Females 46% Males | Adults | Physical activities including recreational physical activities and transportation physical activities. | This study found that having high health risk was associate with less physical activity for both recreation and transportation purposes, while being economically challenged was associated with more physical activity for transportation purposes. |
10. | Maas et al., 2008 | The Netherlands | Interviews | 54% Females 46% Males | 12 to over 65 years | Physical activity sports and walking for commuting purposes. | Those who live in more green environments do not necessarily walk more for commuting purposes. People with more green space in their living environment do not walk more often for commuting purposes and do not walk for commuting purposes for a longer period. |
11. | Mytton et al, 2012 | England, UK | Cross-sectional observational study. | 56% Females 44% Males | 16 years and above | Physical activity levels. | The findings would appear to accord with the national study of all-cause and cardiovascular mortality. This study found that those living in the greenest quintile of England (using the generalised land use database) had lower adjusted mortality. They suggested that this was most likely due to greater physical activity. However, the study could not explain the higher odds of being obese or overweight among those living in the greenest quintile of England found in a similar study. |
12. | Niedermeier et al., 2017 | Austria | Cross-sectional study | 47% Females 53% Males | 18 years and above | Mountain physical activity | The prevalence of mental health issues is lower in Austrians engaging in mountain physical activity |
13. | Prince et al., 2011 | Canada | Cohort study | 54% Females 46% Males | 18–65 years | Indoor recreation facilities, outdoor–winter, outdoor–summer, park area, bike/walking path, green space (k2) per 1000 people. | Socioeconomic status may have more of an effect on female physical activity level than male physical activity level. The results of this study suggest that in Ottawa, Canada variation in PA and overweight/obesity levels can be attributed to the neighbourhood of residence. Findings suggest that neighbourhood-level interventions that support physical activity and healthy weight control may need to be gender tailored. Furthermore, the recreation environment may play less of a role in physical activity levels, specifically higher intensity physical activity, than access to amenities in the food environment, a possible indicator of mixed land use. The social environment, specifically neighbourhood-level sense of belonging, voting participation and SES may play more important roles in male outcomes, while individual-level SES may be more important for females. |
14. | Shin et al., 2011 | USA | Cross-sectional study | 100% Females 0% Males | 55–84 years | Physical activities for older adults and duration | Although the total amount of greenery and the amount of street greenery were found to be important factors that increase older African American women’s physical activity, it is difficult to identify which type of greenery (e.g., trees, shrubs, or grass) has an essential impact on their physical activity. |
15. | Tomita et al., 2017 | South Africa | Survey | 100% Females 0% Males | 20 to over 35 years | Exposure to green space | The findings suggest that, in South Africa, African individuals belonging to the middle-income group and residing in a green living environment had a reduced risk of incident depressive symptoms. Within the context of South Africa, with its long history of racially determined income disparity and land tenure inequality, the findings highlight the importance of green space for its apparent protective effects against onset of depression. |
Appendix B
Author and Date | Statistical Tests Conducted | Proportions of Missing/Incorrect Data Reported | Response Rates Reported | Inclusion/Exclusion Criteria Explicitly Stated | |
---|---|---|---|---|---|
1. | Ahuja et al., 2018 | Student’s t test and Factor Analysis | No missing data reported | No response rate reported | No |
2. | Boone-Heinonen et al., 2010 | Regression analysis | Yes | No response rate reported | Yes |
3. | Cerin et al., 2009 | Product coefficient test performed using bootstrapping re-sampling techniques | No missing data reported | 12% response rate | No |
4. | Cleland et al., 2010 | Chi-square, ANOVA and Regression analyses | Yes | 54% response rate | No |
5. | Cohen-Cline et al., 2015 | Multilevel random intercept model | No | No response rate reported | Yes |
6. | Haughton et al., 2006 | Structural equation modelling (SEM) using LISREL for Windows, version 8.52 | Yes | No response rate reported | Yes |
7. | Kerr et al., 2014 | Linear regression models | No | No response rate reported | No |
8. | Kim and Yang, 2017 | One-way ANOVA and regression models | No | 24% response rate | No |
9. | Lee, 2007 | ANOVA and Pearson correlation analysis | No | 34% response rate | Yes |
10. | Maas et al., 2008 | Multilevel logistic regression analysis | No | No response rate explicitly stated, but 4899 answered all vital questions. | No |
11. | Mytton et al, 2012 | Logistic regression was used to test for an association between physical activity and green space (in quintiles) | No | No response rate reported | Yes |
12. | Niedermeier et al., 2017 | Multiple linear regression analyses | Yes | No response rate reported | No |
13. | Prince et al., 2011 | Chi-square, binary logistic regression models, a six–step modelling strategy | Yes | The response rates for the survey years are as follows: 60% (2003); 59% (2004); 64% (2005); 66% (2006); and 59% (2007). | No |
14. | Shin et al., 2011 | Correlational analyses and Chi-square | Yes | 34% response rate | Yes |
15. | Tomita et al., 2017 | Logistic regression models | No | No response rate reported | Yes |
References
- Word Health Organization. A European Framework to Promote Physical Activity for Health; WHO: Copenhagen, Denmark, 2007; pp. 1–39. [Google Scholar]
- NICE. Physical Activity and the Environment NICE Guideline [NG90]. 2018. Available online: www.nice.org/guidance/ng90/resources/physical-acivity-and-the-environment-pdf-1837448441797 (accessed on 16 September 2020).
- Davies, H. The Well-being of Future Generations (Wales) Act 2015. Environ. Law Rev. 2016, 18, 41–56. [Google Scholar] [CrossRef]
- Welsh Government. Prosperity for All: The national Strategy; Welsh Government: Wales, UK, 2017. [Google Scholar]
- Parry, S.; Straker, L. The Contribution of Office Work to Sedentary Behaviour Associated Risk; BMC Public Health: London, UK, 2013. [Google Scholar]
- Mental Health Foundation. Managing Mental Health Problems in the Workplace; Mental Health Foundation: London, UK, 2006; pp. 1–2. [Google Scholar]
- International Classification of Diseases-10 (ICD-10). 2020. Available online: https://www.who.int/classifications/classification-of-diseases (accessed on 16 September 2020).
- Public Health England. Interventions to Prevent Burnout in High Risk Individuals: Evidence Review; Public Health: London, UK, 2016.
- Longo, A.; Hutchinson, W.G.; Hunter, R.F.; Tully, M.A.; Kee, F. Demand response to improved walking infrastructure: A study into the economics of walking and health behaviour change. Soc. Sci. Med. 2015, 143, 107–116. [Google Scholar] [CrossRef][Green Version]
- Lynch, M.; Spencer, L.H.; Edwards, R.T. A Systematic Review Exploring the Economic Valuation of Accessing and Using Green and Blue Spaces to Improve Public Health. Int. J. Environ. Res. Public Health 2020, 17, 4142. [Google Scholar] [CrossRef]
- Freeman, S.; Eykelbosh, A. COVID-19 and Outdoor Safety: Considerations for Use of Outdoor Recreational Spaces. 2020. Available online: https://ncceh.ca/documents/guide/covid-19-and-outdoor-safety-considerations-use-outdoor-recreational-spaces (accessed on 16 September 2020).
- Mayhew, K.; Anand, P. COVID-19 and The UK Labour Market. Oxford Rev. Econ. Policy 2020, 1–11. [Google Scholar] [CrossRef]
- Muscogiuri, G.; Pugliese, G.; Barrea, L.; Savastano, S.; Colao, A. Obesity: The “Achilles heel” for COVID-19? Metabolism 2020, 108, 8–10. [Google Scholar] [CrossRef] [PubMed]
- The Lancet Diabetes & Endocrinology. Obesity and COVID-19: Blame isn’t a strategy. Lancet Diabetes Endocrinol. 2020, 8, 731. [Google Scholar] [CrossRef]
- Booth, F.W.; Roberts, C.K.; Thyfault, J.P.; Ruegsegger, G.N.; Toedebusch, R.G. Role of inactivity in chronic diseases: Evolutionary insight and pathophysiological mechanisms. Physiol. Rev. 2017, 97, 1351–1402. [Google Scholar] [CrossRef] [PubMed]
- Blundell, R.; Costa Dias, M.; Joyce, R.; Xu, X. COVID-19 and Inequalities. Fisc. Stud. 2020, 41, 291–319. [Google Scholar] [CrossRef] [PubMed]
- Lynch, M.; Ezeofor, V.; Spencer, L.H.; Tudor Edwards, R. Economic and modelling techniques used to value the health benefits of engaging in physical activity in green and blue spaces: A systematic review. Lancet 2018, 392, S55. [Google Scholar] [CrossRef]
- Braveman, P. What are health disparities and health equity? we need to be clear. Public Health Rep. 2014, 129, 5–8. [Google Scholar] [CrossRef][Green Version]
- Bornmann, L.; Mutz, R. Growth Rates of Modern Science: A Bibliometric Analysis Based on the Number of Publications and Cited References. J. Assoc. Inf. Sci. Technol. 2015. [Google Scholar] [CrossRef][Green Version]
- Singer, M.; Bulled, N.; Ostrach, B.; Mendenhall, E. Syndemics and the biosocial conception of health. Lancet 2017, 389, 941–950. [Google Scholar] [CrossRef]
- Singh, P.S. Economic Impact of an Eye Clinic Liaison Officer (ECLO) on Health, Social Care and Welfare Budgets: A Case Study. 2013. Available online: https://studylib.net/doc/6795431/-eclo--on-health-and-social-care-budgets--a-case-study (accessed on 16 September 2020).
- Ahuja, C.; Ayers, C.; Hartz, J.; Adu-Brimpong, J.; Thomas, S.; Mitchell, V.; Peters-Lawrence, M.; Sampson, D.; Brooks, A.T.; Wallen, G.; et al. Examining relationships between perceptions and objective assessments of neighborhood environment and sedentary time: Data from the Washington, D.C. Cardiovascular Health and Needs Assessment. Prev. Med. Rep. 2018, 9, 42–48. [Google Scholar] [CrossRef] [PubMed]
- Boone-Heinonen, J.; Guilkey, D.K.; Evenson, K.R.; Gordon-Larsen, P. Residential self-selection bias in the estimation of built environment effects on physical activity between adolescence and young adulthood. Int. J. Behav. Nutr. Phys. Act. 2010, 7, 1–11. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Cerin, E.; Leslie, E.; Owen, N. Explaining socio-economic status differences in walking for transport: An ecological analysis of individual, social and environmental factors. Soc. Sci. Med. 2009, 68, 1013–1020. [Google Scholar] [CrossRef] [PubMed]
- Haughton, L.; Wyrwich, K.W.; Brownson, R.C.; Clark, E.M.; Kreuter, M.W. Individual, social environmental, and physical environmental influences on physical activity among black and white adults: A structural equation analysis. Ann. Behav. Med. 2006, 31, 36–44. [Google Scholar] [CrossRef] [PubMed]
- Kerr, J.; Norman, G.J.; Millstein, R.; A Adams, M.; Morgan, C.; Langer, R.D.; Allison, M. Neighborhood Environment and Physical Activity among Older Women: Findings from the San Diego Cohort of the Women’s Health Initiative. J. Phys. Act. Health 2014, 11, 1070–1077. [Google Scholar] [CrossRef]
- Kim, H.; Yang, S. Neighborhood walking and social capital: The correlation between walking experience and individual perception of social capital. Sustainability 2017, 9, 680. [Google Scholar] [CrossRef][Green Version]
- Niedermeier, M.; Hartl, A.; Kopp, M. Prevalence of mental health problems and factors associated with psychological distress in mountain exercisers: A cross-sectional study in Austria. Front. Psychol. 2017, 8, 1–8. [Google Scholar] [CrossRef][Green Version]
- Prince, S.A.; Kristjansson, E.; Russell, K.; Billette, J.-M.; Sawada, M.; Ali, A.; Tremblay, M.S.; Prud’Homme, D. A Multilevel Analysis of Neighbourhood Built and Social Environments and Adult Self-Reported Physical Activity and Body Mass Index in Ottawa, Canada. Int. J. Environ. Res. Public Health 2011, 8, 3953–3978. [Google Scholar] [CrossRef]
- Shin, W.H.; Kweon, B.S.; Shin, W.J. The distance effects of environmental variables on older African American women’s physical activity in Texas. Landsc. Urban Plan. 2011, 103, 217–229. [Google Scholar] [CrossRef]
- Tomita, A.; Vandormael, A.M.; Cuadros, D.; Di Minin, E.; Heikinheimo, V.; Tanser, F.; Slotow, R.; Burns, J.K. Green environment and incident depression in South Africa: A geospatial analysis and mental health implications in a resource-limited setting. Lancet Planet. Health 2017, 1, e152–e162. [Google Scholar] [CrossRef]
- Cleland, V.; Ball, K.; Hume, C.; Timperio, A.; King, A.C.; Crawford, D. Individual, social and environmental correlates of physical activity among women living in socioeconomically disadvantaged neighbourhoods. Soc. Sci. Med. 2010, 70, 2011–2018. [Google Scholar] [CrossRef] [PubMed]
- Cohen-Cline, H.; Turkheimer, E.; Duncan, G.E. Access to green space, physical activity and mental health: A twin study. J. Epidemiol. Community Health 2015, 40, 1291–1296. [Google Scholar] [CrossRef] [PubMed]
- Lee, C. Environment and active living: The roles of health risk and economic factors. Am. J. Health Promot. 2007, 21, 293–304. [Google Scholar] [CrossRef] [PubMed]
- Maas, J.; Verheij, R.A.; Spreeuwenberg, P.; Groenewegen, P.P. Physical activity as a possible mechanism behind the relationship between green space and health: A multilevel analysis. BMC Public Health 2008, 8, 1–13. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Mytton, O.T.; Townsend, N.; Rutter, H.; Foster, C. Green space and physical activity: An observational study using Health Survey for England data. Health Place 2012, 18, 1034–1041. [Google Scholar] [CrossRef][Green Version]
- Assemi, B.; Zahnow, R.; Zapata-Diomedi, B.; Hickman, M.; Corcoran, J. Transport-related walking among young adults: When and why? BMC Public Health 2020, 20, 1–13. [Google Scholar] [CrossRef][Green Version]
- National Trust. National Trust Website FAQs. 2020. Available online: https://www.nationaltrust.org.uk/features/reopening-and-coronavirus-faqs.
- Dahmann, N.; Wolch, J.; Joassart-Marcelli, P.; Reynolds, K.; Jerrett, M. The active city? Disparities in provision of urban public recreation resources. Health Place 2010, 16, 431–445. [Google Scholar] [CrossRef]
- Heynen, N.; Kaika, M.; Swyngedouw, E. Politicizing the production of urban natures. Nat. Cities Urban Polit. Ecol. Polit. Urban Metab. 2006, 1–20. [Google Scholar] [CrossRef]
- Heynen, N.; Perkins, H.A.; Roy, P. The Impact of Political Economy on Race and Ethnicity in Producing Environmental Inequality in Milwaukee. Urban Aff. Rev. 2006, 42, 3–25. [Google Scholar] [CrossRef]
- Moore, S.A.; Faulkner, G.; Rhodes, R.E.; Brussoni, M.; Chulak-Bozzer, T.; Ferguson, L.J.; Mitra, R.; O’Reilly, N.; Spence, J.C.; Vanderloo, L.M.; et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: A national survey. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Munn, Z.; Peters, M.D.J.; Stern, C.; Tufanaru, C.; McArthur, A.; Aromataris, E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med. Res. Methodol. 2018, 18, 143. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Spencer, L.H.; Lynch, M.; Lawrence, C.L.; Edwards, R.T. A Scoping Review of How Income Affects Accessing Local Green Space to Engage in Outdoor Physical Activity to Improve Well-Being: Implications for Post-COVID-19. Int. J. Environ. Res. Public Health 2020, 17, 9313. https://doi.org/10.3390/ijerph17249313
Spencer LH, Lynch M, Lawrence CL, Edwards RT. A Scoping Review of How Income Affects Accessing Local Green Space to Engage in Outdoor Physical Activity to Improve Well-Being: Implications for Post-COVID-19. International Journal of Environmental Research and Public Health. 2020; 17(24):9313. https://doi.org/10.3390/ijerph17249313
Chicago/Turabian StyleSpencer, Llinos Haf, Mary Lynch, Catherine L. Lawrence, and Rhiannon Tudor Edwards. 2020. "A Scoping Review of How Income Affects Accessing Local Green Space to Engage in Outdoor Physical Activity to Improve Well-Being: Implications for Post-COVID-19" International Journal of Environmental Research and Public Health 17, no. 24: 9313. https://doi.org/10.3390/ijerph17249313