Walkability Index for Elderly Health: A Proposal
Abstract
:1. Introduction
2. Methodological Approach
3. Walkability and Elderly Physical Exercise and Health
- <5%—Suitable
- 5% < x < 8%—Acceptable
- >8%—Inappropriate
- (1)
- Age group 60–69 = 51.8 cm—Slow gait; 60.2 cm—Normal gait; 68 cm—Fast gait.
- (2)
- Age group 70–79 = 50.1 cm—Slow gait; 57.9 cm—Normal gait; 66 cm—Fast gait.
4. Walkability Indexes—A Literature Review
5. The Conceptual Design of the WIEH
5.1. First Premise: Definition of Variables and Criteria to Classify the Pedestrian Network
5.2. Second Premise: Integrating Slopes and Stairs
- (1)
- “Suitable slopes” (≤5%): HR of 120 bpm, representing 55% of the recommended HRMA; this fits the moderate activity classification, as the value is within 50–85% of HRMA (Table 3).
- (2)
- “Acceptable slopes” (>5% and ≤8%): It is expected that the HR will always remain under 85% (the maximum recommended percentage of the HRMA), even with this minor increase, from 5% to 8%. Thus, these slopes induce a moderate level of activity. Moreover, as mentioned before, evidence shows that the product of step length and cadence starts decreasing significantly between 15.8% and 21.2% inclines, in both upwards and downwards walking [59]. As our “suitable slopes” remain under these percentages, all the HR averages are expected to match the recommended values. Subsequent studies did not contradict Kawamura’s findings [53,58].
- (3)
- “Steep slopes” (>8%): These slopes are not recommended for elderly walking.
5.3. Calculating WIEH—Walkability Index for Elderly Health
5.4. First Step—Classifying the Pedestrian Network
5.5. Second Step—Integrating Slopes and Stairs
5.6. Third Step—Calculating Age-Friendly Routes
5.7. Fourth Step—Selecting the “Heart-Friendly Route”
- − The route with the greatest number of sections best classified according to the WIEH (green-colored routes), and
- − The route that allows the elderly to walk at a “fast walking speed” during a desirable time period of 30 min.
6. Concluding Remarks and Expected Outcomes of the WIEH
Author Contributions
Funding
Conflicts of Interest
References
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Systematic Areas | Set of Urban Variables | |
---|---|---|
(1) | Urban Tissue | Pedestrian surface quality; sidewalk existence and width; traffic street intersections; existence of stairs; existence of obstacles; land mix use. |
(2) | Urban Scene | Existence of trees/vegetation; existence of urban furniture. |
(3) | Safety | Street lighting quality; diversity of information signs. |
Studies/Authors | Younger-Age Adults | Older-Age Adults | ||
---|---|---|---|---|
Age Groups | Estimated Walking Speed | Age Groups | Estimated Walking Speed | |
[52] | <30 | 1.34 m/s (4.82 km/h) | Over 60 | 1.21 m/s (4.34 km/h) |
30–39 | 1.26 m/s (4.54 km/h) | |||
40–49 | 1.26 m/s (4.54 km/h) | |||
50–59 | 1.23 m/s (4.43 km/h) | |||
[24] | 25–34 | 1.25 m/s (4.5 km/h) | Over 65 | 0.95 m/s (3.42 km/h) |
[25] | – | – | Over 65 | 0.80 m/s (2.88 km/h) |
[23] | – | – | Over 65 | 0.60 m/s to 1.00 m/s |
(2.16 km/h to 3.6 km/h) |
Heart Rate (bpm) | ||
---|---|---|
Moderate Activity | Vigorous Activity | |
Age groups (years) | HR Target | HRMA |
50–85% of HRMA | (220 bpm-age) | |
65 | 78–132 bpm | 155 bpm |
70 | 75–128 bpm | 150 bpm |
75 | 73–123 bpm | 145 bpm |
80 | 70–119 bpm | 140 bpm |
85 | 68–115 bpm | 135 bpm |
90 | 65–111 bpm | 130 bpm |
Slope—% Inclines | Heart Rate/bpm Average |
---|---|
5% slope * (uphill) | 120 bpm |
(0%) (ground flat) | 105 bpm |
−5% (downhill) | 97 bpm |
−10% (downhill) | 96 bpm |
−15% (downhill) | 100 bpm |
−20% (downhill) | 105 bpm |
Female | Male | |||||
---|---|---|---|---|---|---|
Age Group | Slow Gait | Normal Gait | Fast Gait | Slow Gait | Normal Gait | Fast Gait |
60–69 | 47.5 cm | 55.3 cm | 62.5 cm | 56 cm | 65 cm | 73.6 cm |
70–79 | 47.1 cm | 54.2 cm | 60.4 cm | 52.7 cm | 61.5 cm | 71.5 cm |
Domains | Reference | Subject | Results |
---|---|---|---|
Accessibility | [86] | Accessibility to certain facilities, through the construction of two indicators: the percentage of citizens living in the surrounding facilities/services and the percentage of buildings that exist in these areas. Case study: Faro (Portugal) | The results indicate a trend towards an effective urbanism of proximity that can be boosted at the future location of new services. Available indicators also provide an important contribution to municipal management, through the definition of structural pedestrian infrastructures in the city. |
[87] | Review of the quantitative and qualitative aspects relevant for accessibility metrics and empirical studies addressing these aspects in relation to health. No case studies | Studies comparing different types of green space indicators suggest that cumulative opportunity indicators are more consistently and positively related to health than residential proximity ones. In contrast to residential proximity indicators, cumulative opportunity indicators take all the green space within a certain distance into account. | |
Higgs, C., Badland, H., Simons, K., [88] | Combination of policy-relevant liveability indicators associated with health into a spatial Urban Liveability Index (ULI), examining its association with adult travel behaviours. Case study: Melbourne (Australia) | Urban Liveability Index (ULI) scores were positively associated with active transport behaviour: for each unit increase in the ULI score the estimated adjusted odds ratio for: walking increased by 12%; cycling increased by 10%; public transport increased by 15%; and private vehicle transport decreased by 12%. | |
Urban Design/Walkability | [20] | Review of English-language literature on walkability—from research, practice, and popular discussions. The review highlights potential conflicts between forms of walkability. The term is used to refer to significantly different kinds of phenomena. It clarifies different types of walkability, focusing on the implications of these definitions for urban design and planning. | Significant conclusions derived from a better definition of walkability: (i) walkable environments are not all the same; (ii) biases and assumptions undermine some popular definitions of walkability; (iii) walkable environments for transportation and recreation purposes sometimes overlap, but often do not; (iv) while walkability can be defined in multiple ways, it is broadly considered to be about good design. |
[81,82] | Observation of people in real-life situations, to determine how the built environment impacts social wellness. | Important principles to help guide designers in rethinking the impact of their plans on real life. | |
[21] | Extensive literature review on the contribution of walking to sustainable urban development. | Identification of the walking phenomenon as a key-element of Pedestrian Profile, Pedestrian Activity, and Pedestrian Environment - PLACE. | |
[89] | Comprehensive and objective measurement of the subjective qualities of the urban street environment. | Urban design can explain variation in walking behaviour that urban form cannot. Observational measures are used to validate digital measures, which make it possible to study the relationship between urban design and physical activity. | |
[15] | Verification of whether the methods used in the US to measure the suitability of built environments for walking and cycling can be applied in a European context. Case study: Stuttgart (Germany) | A noticeable relationship between walkability and active transportation was found – the more walkable an area was, the more active residents were. | |
[90] | Definition of a city’s walkability assessment framework capable of highlighting points of strength and weakness in its urban environment. Case study: Milan (Italy) | Design recommendations to make specific evidence-based choices, and to understand what aspects of the urban environment must be improved or implemented to promote a walkable city. | |
[22] | Discrepancies within the use of survey data on pedestrian behaviour; a variety of GIS–derived land use and built environment measures of neighbourhoods; and socioeconomic characteristics obtained from the 2011 National Household Survey. Case study: Montreal (Quebec, Canada). | Some neighbourhoods with higher walking rates are characterized by a lower presence of parking lots and setbacks, and a greater proportion of on-street tree canopy. Linear regressions predicting walking rates confirm these associations, after adjusting for Walk Score and neighbourhood socioeconomic characteristics. | |
[42] | Cross-sectional associations between neighbourhood walkability, crime and physical activity, depending on age and sex of residents. Case study: Hill District and Homewood (Pittsburgh, USA) | Neighbourhood walkability may play a stronger role in Moderate-Vigorous Physical Activity than accessible greenspace or crime in low-income urban communities. Walkability may differentially impact residents depending on their age and sex, which suggests tailoring public health policy design and implementation according to neighbourhood demographics to improve activity for all. | |
[91] | Review of Australian state-level planning policies and standards for public open spaces, including policy-specific spatial measures generated in GIS. Case study: Australia context | Findings support existing literature, indicating that neighbourhoods with greater access to public open spaces (within 400 meters) are associated with higher odds of physical activity. | |
[92] | Examination of associations of policy-derived urban design and empirical measures of POS proximity and density with walking and depression. Case study: Australia context | There are complexities in devising and delivering policies that promote health and wellbeing. However, the findings highlight the importance of identifying and testing spatial measures for public open spaces that are associated with health behaviours and outcomes in different contexts. This type of evidence is required to refine and strengthen implementation science related to (re)designing public open spaces, to better support population health outcomes. | |
[38] | Use of an online participatory mapping method and a novel modelling of individual activity spaces to study the associations between both environmental and individual features and older adults’ walking, in environments where older adults move. Case study: Helsinki Metropolitan Area | Walkway density, residential density, connectivity, and the density of recreational sport places within respondents’ home ranges had an independent effect on older adults’ walking. Residential and public transit stop density affects the motivation of the elderly to walk. Well-connected streets/different destinations may encourage the walking behaviour, even among those who are not very interest in physical activities. Personal goals related to physical activity also had a direct positive effect on walking. Additionally, an indirect effect of gender and of perceived health on walking was found. | |
[44] | Exploration of three hypotheses: (1) trip purpose as an independent correlate of utilitarian walking; (2) associations between environmental attributes surrounding participants’ destinations and walking; (3) association between the distance travelled and walking. Case study: Luxembourg | Trip purposes based on free-time activities – including visits to family and friends, and restaurants and cafés – seem to be less influenced by the barrier effect of distance on walking. | |
[35] | Development of the Walkability City Tool, in response to a need stemming from the lack of compiled, precise, objective information on the walkable network for making strategic urban decisions that affect pedestrian mobility. Case study: Financial District of Panama City | Walkability City Tool examines the studied factors via five topics: Modal Distribution - division of space between the different means of transportation; Urban Grid - characteristics of the sidewalks; Urban Scene - information on the environment around us as we walk; Safety - perception of safety when walking; and Environment - factors that influence walkers. | |
[16] | Investigation of the influence of street greenery and walkability on body mass index. Case study: Cleveland (Ohio, USA) | The study found that associations between body mass index (BMI), Walk Score (WS) and Green View Index (GVI) vary among different age-gender groups. WS has a more significant association with decreased BMI for males over females. GVI has a more significant association with decreased BMI for females than males (in middle-aged and retiree groups). Urban greenery has a stronger correlation with BMI for females rather than males. | |
[33] | Development of an urban built environment evaluation tool, with necessary reliability and validity tests being conducted. Case study: Hangzhou (China) | CUBEST was developed: a reliable and valid instrument that can be used to assess the physical activity-related built environment in Hangzhou, and potentially other cities in China. | |
[34] | Development of an alternative walking index, the Quality of Pedestrian Level of Service (Q-PLOS) method. Case study: Metropolitan Area of Granada (Spain) | The Q-PLOS enabled a more detailed identification of characteristics related to pedestrian mobility, showing that they can be improved through mobility strategies of urban design, such as pedestrian continuity and connectivity of green spaces. | |
Physical Activity and Well-being/Health | [93] | Assessment of both the quantity and quality of street greenery, associating them with the recreational physical activity occurring in green outdoor environments, for 1390 participants in 24 housing units. Case study: Hong Kong (China) | There was a demonstration of the benefit of using Google Street View for health and physical activity studies. The study provides findings to recognize the impacts of environmental factors on residents’ physical activity, hence contributing to targeted intervention strategies for creating activity-friendly urban design. |
[71] | Lessons on how the neighbourhood built environment may affect one aspect – specifically, happiness – of residents’ wellbeing. No case studies | The authors draw lessons from a cross-disciplinary set of studies to reveal how the neighbourhood built environment may affect one aspect of residents’ wellbeing: happiness. Providing residents with access to open, natural, and green spaces may directly increase their happiness. Incorporating design features that allow for social interaction and safety may also promote residents’ happiness. | |
[94] | Visitors’ perceptions and activities in protected areas. Case study: Barcelona (Spain) | The majority of surveyed park visitors reported that physical health was an important motivation for visiting parks; a perceived improvement in their physical health was reported. The most physically-active recreation activities were more practiced by younger people. Nearby residents and visitors reported high levels of perceived physical health, motivation for visiting, and impact of that visit. | |
[95] | In-depth characterization of a neighbourhood’s social and physical environment, in relation to cardiovascular health. Case study: Madrid (Spain) | This experience led to the testing and refining of measurement tools, drawn from epidemiology, geography, sociology, and anthropology, in order to better understand the urban environment in relation to cardiovascular health. |
Systematic Areas | Variables | Weight (%) | Criteria to Classify the Pedestrian Network | |||
---|---|---|---|---|---|---|
Partial Weight | Total Weight | |||||
1. Urban Tissue | Pedestrian surface quality (PSQ) | 8 | 60 | 1 = bad | ||
2 = acceptable | ||||||
3 = good | ||||||
Sidewalks existence and width (SEW) (Average of SE and SW) | Sidewalks existence (SE) | 6 | 12 | 1 = none | ||
1.5 = one side partial or both sides partial | ||||||
2 = one side continuous | ||||||
2.5 = one side continuous and one side partial | ||||||
3 = both sides continuous | ||||||
Sidewalks width (SW) | 6 | 1—SW < 0.90 m | ||||
1.5—0.90 m ≤ SW < 1.20 m | ||||||
2—1.20 m ≤ SW < 1.50 m | ||||||
2.5—1.50 m ≤ SW < 1.80 m | ||||||
3 - SW ≥ 1.80 m | ||||||
Traffic street intersections (TSI) | 12 | 1—>3 | ||||
2—1 or 2 | ||||||
3—no intersections | ||||||
Existence of stairs (EoS) (note: stairs are only considered when there are more than 3 steps) | 8 | 1—>3 stairs | ||||
2—1 or 2 stairs | ||||||
3—no stairs | ||||||
Existence of obstacles (EoO) | 12 | 1 = systematically affects walking | ||||
2 = occasionally affects walking | ||||||
3 = no obstacles | ||||||
Land use mix (LUM) | 8 | 1 = no land use mix | ||||
2 = medium land use mix (at least 2 different uses) | ||||||
3 = high land use mix (3 or more different uses) | ||||||
2. Urban Scene | Existence of trees/vegetation (ETV) | 8 | 16 | 1 = no trees/vegetation | ||
2 = moderate existence of trees/vegetation | ||||||
3 = strong existence of trees/vegetation | ||||||
Existence of urban furniture (EUF) | 8 | 1 = no urban furniture | ||||
2 = moderate existence of urban furniture | ||||||
3 = strong existence of urban furniture | ||||||
3. Safety | Street lighting quality (SLQ) | 12 | 24 | 1 = bad | ||
2 = acceptable | ||||||
3 = good | ||||||
Diversity of information signs (DIS) | 12 | 1 = low | ||||
2 = medium | ||||||
3 = high |
Index Values (isw) | Result |
---|---|
1–1.5 | not suitable |
1.5–2 | less suitable |
2–2.5 | suitable |
2.5–3 | most suitable |
Index Values (is) | Slopes (sl) | Stairs | Result | |
---|---|---|---|---|
1 | Suitable slopes | <5% | No stairs | most recommended |
2 | Suitable slopes | <5% | Stairs/small number of steps | recommended |
3 | Acceptable slopes | 5–8% | Stairs/significant number of steps | less recommended |
4 | Steep slopes | >8% | Stairs/large number of steps | not recommended |
WIEH | Routes Classification | Observations | |
---|---|---|---|
A | ≤0,5 | Not age-friendly | Routes not at all recommended—No quality of pedestrian network and/or existence of steep slopes and/or large number of steps |
B | >0.5 and ≤1 | Less age-friendly | Routes that should be avoided—Unsuitable quality of pedestrian network and/or inexistence of acceptable slopes and/or significant number of steps |
C | >1 and ≤2.5 | Reasonably age-friendly | Routes that can be considered—Acceptable quality of pedestrian network, reasonably suitable slopes and stairs |
D | >2.5 and ≤3 | More age-friendly | Routes that are highly recommended—Good & suitable quality of pedestrian network, with reduced slopes and no stairs |
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Alves, F.; Cruz, S.; Ribeiro, A.; Bastos Silva, A.; Martins, J.; Cunha, I. Walkability Index for Elderly Health: A Proposal. Sustainability 2020, 12, 7360. https://doi.org/10.3390/su12187360
Alves F, Cruz S, Ribeiro A, Bastos Silva A, Martins J, Cunha I. Walkability Index for Elderly Health: A Proposal. Sustainability. 2020; 12(18):7360. https://doi.org/10.3390/su12187360
Chicago/Turabian StyleAlves, Fernando, Sara Cruz, Anabela Ribeiro, Ana Bastos Silva, João Martins, and Inês Cunha. 2020. "Walkability Index for Elderly Health: A Proposal" Sustainability 12, no. 18: 7360. https://doi.org/10.3390/su12187360
APA StyleAlves, F., Cruz, S., Ribeiro, A., Bastos Silva, A., Martins, J., & Cunha, I. (2020). Walkability Index for Elderly Health: A Proposal. Sustainability, 12(18), 7360. https://doi.org/10.3390/su12187360