The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries
Abstract
:1. Introduction
1.1. Literature Review
1.1.1. Active Transport and Subjective Wellbeing
1.1.2. Situation during COVID-19
1.1.3. Condition in Develo** Countries
- (1).
- What associations exist among the various types of active mobility/physical activity and different domains of subjective wellbeing during COVID-19 in the large cities of the MENA region?
- (2).
- What similarities and differences exist among the three patterns resulting from the impact of active lifestyle on the different variables of subjective wellbeing?
2. Materials and Methods
2.1. Case Study
2.2. Data and Variables
2.3. Analysis Methods
3. Results
3.1. Descriptive Statistics
3.2. Model Fit
3.2.1. The Association between Active Lifestyle and Life Satisfaction
3.2.2. The Association between Active Lifestyle and Feeling Energetic
3.2.3. The Association between Active Lifestyle and Peaceful Mind
4. Discussion
5. Strengths and Limitations
6. Conclusions
- First model: perceived separation of the street from the sidewalk by green spaces, perceived facilities attractiveness, population density, and street length.
- Second model: perceived existence of sidewalks, perceived separation of street from the sidewalk by green spaces, perceived distance to shop** malls, population density, and street length.
- Third model: perceived existence of sidewalks, perceived separation of the street from the sidewalk by green spaces, perceived distance to parks, and population density.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Description and Coding |
---|---|
Age | Continuous |
Gender | Female = 1, Male = 0 |
Education | Diploma and Undergraduate = 1, Bachelor = 2, Master = 3, PhD and higher = 4 |
Income | Continuous |
Job | Unemployment = 1, Housewife = 2, Student = 3,Employee = 4 Freelance = 5, Retired = 6 |
P. Distance to Different Services/Land uses | Less than 5 min = 1, 5 to 10 min = 2, 10 to 20 min = 3, 20 to 30 min = 4, More than 30 min = 5 |
Perceptive Walkable Places (1. Existence of sidewalks, 2. Separation of street from sidewalk by green spaces, 3. Existence of shortcut routs) | From very little = 1 to very much = 5 |
Facilities Attractiveness | No existence = 1, Not attractive at all = 2, Not very attractive = 3, Medium = 4, Acceptable Attractiveness = 5, Very attractive = 6 |
Objective Built Environment (Commute distance- Street length, Land use mix, Number of Intersections, Building and Population density) | Continuous |
Mobility Mode Choice (commute & non-commute) | Active (Walking & Cycling) = 1, Other modes (e.g., Private car, Public transport, Metro and …) = 0 |
Frequency of Walking to Parks/Services | Three or more than three times per week = 1, Less than three times per week = 0 |
Subjective Wellbeing (1. Feeling of life satisfaction, 2. Feeling of Being Energetic, 3. Feeling of being relaxed/peaceful) | From Very Low = 1 to Very High= 10 |
Category | Active | Non-Active | Category | More than 3 Times Per Week (Active) | Less than 3 Times a Week (Non-Active) | ||
---|---|---|---|---|---|---|---|
Commute trips | Frequency | 62 | 535 | Walking to parks | Frequency | 107 | 496 |
Percent | 10.3% | 88.7% | Percent | 17.7% | 82.3% | ||
Non-commute trips | Frequency | 147 | 454 | Walking to services | Frequency | 95 | 508 |
Percent | 24.4% | 75.3% | Percent | 15.8% | 84.2% |
Variable/Measure | Estimate | Std. Error | Wald | df | p-Value | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Age | 0.004 | 0.011 | 0.103 | 1 | 0.748 | −0.018 | 0.025 |
Gender | 0.156 | 0.208 | 0.566 | 1 | 0.452 | −0.251 | 0.564 |
Education | −0.098 | 0.152 | 0.412 | 1 | 0.521 | −0.397 | 0.201 |
Job | 0.283 | 0.159 | 3.177 | 1 | 0.075 | −0.028 | 0.593 |
Income | 0.008 | 0.009 | 0.831 | 1 | 0.362 | −0.009 | 0.025 |
Commute distance | −0.087 | 0.079 | 1.219 | 1 | 0.269 | −0.241 | 0.067 |
WalkingPlaces2(Separation of street from sidewalk by green spaces) | 0.149 | 0.082 | 3.326 | 1 | 0.068 | −0.011 | 0.309 |
Facilities attractiveness | 0.191 | 0.082 | 5.469 | 1 | 0.019 | 0.031 | 0.350 |
Population density | −0.153 | 0.086 | 3.148 | 1 | 0.076 | −0.322 | 0.016 |
Street Length | 33.432 | 11.68 | 8.182 | 1 | 0.004 | 10.524 | 56.340 |
Commute trip | 0.480 | 0.246 | 3.789 | 1 | 0.052 | 0.003 | 0.962 |
Non-commute trip | 0.798 | 0.398 | 4.010 | 1 | 0.045 | 0.017 | 1.579 |
Walking to green spaces | 0.620 | 0.310 | 3.996 | 1 | 0.046 | 0.012 | 1.228 |
Walking to services | 0.931 | 0.313 | 8.845 | 1 | 0.003 | 0.317 | 1.544 |
Model Summary | −2 Log likelihood | Chi-Square | df | p-value | Nagelkerke R2 | ||
1433.000 | 50.986 | 14 | <0.0001 | 0.245 | |||
Goodness of fit | Omnibus Test | ||||||
Value | df | Value/df | Likelihood Ratio X2 | df | p-value | ||
Deviance | 1515.624 | 3109 | 0.487 | 52,988 | 14 | <0.0001 | |
Pearson Chi-Square | 3059.308 | 3109 | 0.984 | ||||
Log Likelihood | −757.812 |
Variable/Measure | Estimate | Std. Error | Wald | df | p-Value | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Age | −0.021 | 0.011 | 3.574 | 1 | 0.059 | −0.042 | 0.001 |
Gender | −0.259 | 0.207 | 1.555 | 1 | 0.212 | −0.665 | 0.148 |
Education | −0.185 | 0.150 | 1.522 | 1 | 0.217 | −0.479 | 0.109 |
Job | 0.246 | 0.158 | 2.430 | 1 | 0.119 | −0.063 | 0.556 |
Income | 0.047 | 0.082 | 0.326 | 1 | 0.568 | −0.207 | 0.114 |
Commute distance | 0.000 | 0.009 | 0.001 | 1 | 0.979 | −0.017 | 0.017 |
WalkingPlaces1 (Existence of sidewalks) | 0.188 | 0.082 | 5.307 | 1 | 0.021 | 0.028 | 0.348 |
WalkingPlaces2 (Separation of street from sidewalk by green spaces) | 0.149 | 0.082 | 3.326 | 1 | 0.068 | −0.011 | 0.309 |
p. Distance to shop** malls | −0.325 | 0.093 | 12.274 | 1 | 0.000 | −0.508 | −0.143 |
Population density | −0.153 | 0.086 | 3.148 | 1 | 0.076 | −0.322 | 0.016 |
Street Length | 33.432 | 11.68 | 8.182 | 1 | 0.004 | 10.524 | 56.340 |
Commute trip | 1.348 | 0.401 | 11.317 | 1 | 0.001 | 0.563 | 2.133 |
Non-commute trip | 0.681 | 0.249 | 7.501 | 1 | 0.006 | 0.194 | 1.168 |
Walking to green spaces | 0.813 | 0.308 | 6.962 | 1 | 0.008 | 0.209 | 1.417 |
Walking to services | 1.119 | 0.315 | 12.656 | 1 | 0.000 | 0.503 | 1.736 |
Model Summary | −2 Log likelihood | Chi-Square | df | p-value | Nagelkerke R2 | ||
1422.909 | 76.294 | 15 | <0.0001 | 0.208 | |||
Goodness of fit | Omnibus Test | ||||||
Value | df | Value/df | Likelihood Ratio X2 | df | p-value | ||
Deviance | 1424.469 | 2901 | 0.491 | 44.392 | 15 | <0.0001 | |
Pearson Chi-Square | 1390.606 | 2901 | 0.994 | ||||
Log Likelihood | −712.235 |
Variable/Measure | Estimate | Std. Error | Wald | df | p-Value | 95% Confidence Interval | |
---|---|---|---|---|---|---|---|
Lower Bound | Upper Bound | ||||||
Age | −0.020 | 0.011 | 3.390 | 1 | 0.066 | −0.042 | 0.001 |
Gender | −0.283 | 0.203 | 1.942 | 1 | 0.163 | −0.682 | 0.115 |
Education | −0.153 | 0.148 | 1.057 | 1 | 0.304 | −0.444 | 0.138 |
Job | 0.183 | 0.159 | 1.333 | 1 | 0.248 | −0.128 | 0.495 |
Income | 0.085 | 0.081 | 1.087 | 1 | 0.297 | 0.074 | 0.244 |
Commute distance | −0.004 | 0.009 | .191 | 1 | 0.662 | −0.021 | 0.013 |
WalkingPlaces1 (Existence of sidewalks) | 0.215 | 0.090 | 5.735 | 1 | 0.017 | 0.039 | 0.391 |
WalkingPlaces2 (Separation of street from sidewalk by green spaces) | 0.183 | 0.082 | 5.003 | 1 | 0.025 | 0.023 | 0.343 |
p. Distance to park | −0.285 | 0.091 | 9.822 | 1 | 0.002 | −0.464 | −0.107 |
Population density | −0.156 | 0.083 | 3.575 | 1 | 0.059 | −0.318 | 0.006 |
Commute trip | 0.971 | 0.399 | 5.923 | 1 | 0.015 | 0.189 | 1.752 |
Non-commute trip | 0.649 | 0.247 | 6.891 | 1 | 0.009 | 0.164 | 1.133 |
Walking to green spaces | 0.765 | 0.304 | 6.348 | 1 | 0.012 | 0.170 | 1.360 |
Walking to services | 1.050 | 0.315 | 11.133 | 1 | 0.001 | 0.433 | 1.666 |
Model Summary | −2 Log likelihood | Chi-Square | df | p-value | Nagelkerke R2 | ||
1449.137 | 65.846 | 14 | <0.0001 | 0.180 | |||
Goodness of fit | Omnibus Test | ||||||
Value | df | Value/df | Likelihood Ratio X2 | df | p-value | ||
Deviance | 1437.015 | 2947 | 0.488 | 53.054 | 14 | <0.0001 | |
Pearson Chi-Square | 2944.898 | 2947 | 0.999 | ||||
Log Likelihood | −718.507 |
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Ranjbarnia, B.; Kamelifar, M.J.; Masoumi, H. The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries. Healthcare 2022, 10, 1603. https://doi.org/10.3390/healthcare10091603
Ranjbarnia B, Kamelifar MJ, Masoumi H. The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries. Healthcare. 2022; 10(9):1603. https://doi.org/10.3390/healthcare10091603
Chicago/Turabian StyleRanjbarnia, Behzad, Mohammad Javad Kamelifar, and Houshmand Masoumi. 2022. "The Association between Active Mobility and Subjective Wellbeing during COVID-19 in MENA Countries" Healthcare 10, no. 9: 1603. https://doi.org/10.3390/healthcare10091603