https://orcid.org/0000-0003-2315-8081
Figures
Abstract
The built environment plays a key role in promoting active mobility for healthy aging. Encouraging active mobility among older adults, however, can be especially challenging in more rural areas where distances tend to be longer and infrastructure is favoring car traffic. The association of older adults’ perception of attributes of the built environment with cycling and e-biking for transport was investigated. The potential moderating effects of age, sex, and mobility impairments were explored. A cross-sectional survey was conducted in towns and communities of <100,000 inhabitants, including 2,144 participants (mean age: 74; 53% men). Three different cycling outcomes (any cycling/e-biking, frequency (≥3 days/week) and amount (minutes/week)) were analyzed among all cyclists and e-bike users separately, resulting in six dependent variables. The impact of ten environmental attributes on these outcomes was analyzed in separate models. Overall, street connectivity, and the proximity and number of destinations were most important. Only street connectivity and traffic safety were related to minutes cycling or e-biking per week. Cycling amount was higher overall for e-biking but fewer environmental attributes showed an association compared to any cycling, regardless of bicycle type. Traffic safety was not associated with four out of the six cycling outcomes and was inversely associated with minutes cycling or e-biking. Density was not related to any of the variables investigated. Several interaction effects of sex, age, and mobility impairments were found. Further research is needed on environmental attributes influencing e-bike use, the most important types of destinations, and a more differentiated assessment of perceived traffic safety in older adults.
Citation: Brüchert T, Quentin P, Bolte G (2022) The relationship between perceived built environment and cycling or e-biking for transport among older adults–a cross-sectional study. PLoS ONE 17(5): e0267314. https://doi.org/10.1371/journal.pone.0267314
Editor: Yanyong Guo, Southeast University, CHINA
Received: May 1, 2021; Accepted: April 6, 2022; Published: May 3, 2022
Copyright: © 2022 Brüchert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets generated and analyzed during the current study are not publicly available due to data protection rules in Germany. The original data contains re-identifiable information and privacy was ensured to all participants. All participants gave their written informed consent based on the guarantee that the data will be exclusively analyzed at the Institute of Public Health and Nursing Research (IPP), University of Bremen, and that the data will not be made available to third parties. Those researchers who meet the criteria of the legal office of the University of Bremen for access to confidential data may get access to the data at the IPP from the scientific coordinator of the IPP, Heike Mertesacker (hmertesa@uni-bremen.de), on reasonable request.
Funding: The present study, which is part of the AFOOT project, is funded by the German Federal Ministry of Education and Research (BMBF (www.bmbf.de); funding number of University of Bremen (G.B.): 01EL1822B. The funder is neither involved in the study design, collection, analysis and interpretation of data nor the preparation of the manuscript or decision to submit the article for publication.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Worldwide, cycling for transport has come into focus of urban and transport planning as a solution to tackle climate challenges and congestion in cities [1]. The public health sciences have adopted the subject as a means to increase physical activity levels in the population. Physical activity has a wide range of positive health effects including improved cardiorespiratory fitness, reduced cancer risk, and increased wellbeing [2,3]. The WHO recommends 150 minutes of moderate-intensity physical activity per week to realize these benefits [4]. Cycling is classified as a moderately intensive activity [5]. A meta-analysis on cycling found a 10% risk reduction in all-cause mortality when WHO recommendations were met. However, the health benefits were greatest for the first one or two hours of cycling and then decreased [6]. This suggests that while reaching the WHO recommendations for physical activity is important, simply being active compared to not being active at all can have extensive health benefits.
Cycling has also great potential to reduce health inequalities as participation is supposed to be more equally distributed compared to sports and leisure activities, which are more popular in high-income and well-educated population groups [7]. When it is perceived as safe and comfortable, cycling is a cost-effective transport option that is more likely to be taken up by people who are not sufficiently active. In countries with high levels of cycling and safe cycling conditions, such as parts of Germany and the Netherlands, cycling is popular among both genders and across all age groups [8,9]. Cycling is also accessible to disabled people, and may be easier than walking for particular groups (e.g. with a hip problem). However, the design of the built environment needs to consider the needs of people with different levels of experience and ability to support safe cycling [10].
Given the rising number of older adults in many countries and the potential impacts on society and the health care sector the development of age-friendly environments, i.e. environments that support healthy ageing, is a major policy goal globally [11]. The neighborhood built environment, in particular, plays a key role in promoting active mobility for healthy aging [12]. However, this can be especially challenging in more rural areas where distances tend to be longer and car use is high among elderly people [13]. This reliance on car driving can become a problem with age as people may lose the ability to drive or may lack access to a vehicle. In fact, car use is declining with age [9].
Recent systematic reviews have found residential density, street connectivity, land use mix, pedestrian and cycling infrastructure, public transport, aesthetics, and crime and traffic safety to be associated with older adults’ overall physical activity as well as active mobility modes [14–16]. Most of this research, however, has been focusing on correlates of walking and the built environment. To the best of the authors’ knowledge, only three quantitative studies have examined the association between living environment factors and cycling in older adults [15,17]. Moreover, most of the studies–both quantitative and qualitative—on environmental features influencing physical activity were carried out in urban areas and almost exclusively outside of Europe [15,18]. These results are therefore not easily transferable due to different mobility cultures and diverse settlement patterns. Existing studies on infrastructure and active mobility in Germany lack a public health perspective [19]. Furthermore, the increasing popularity of the e-bike might be a promising development for public health that is yet largely unexplored. An e-bike is a bicycle which supports the rider with an electric motor up to a maximum of 250 watts when pedaling. Distances covered by e-bikes are on average four to eight kilometers longer than with conventional bikes [9]. The e-bike is especially popular among the older population [9,20,21]. E-bikes are also often used by people with health problems to maintain or increase personal fitness [22]. However, due to the speed of e-bikes, accidents can be serious. Although studies from China show that older e-bike users have less risky traffic behaviors than younger ones, such as running red lights [23], they face problems in case of balancing at very low speed or while static, mounting and dismounting, or with the ability to maneuver [10]. This emphasizes the importance of safe road conditions that take into account the needs of vulnerable groups.
Overall, little is known about the association of the built environment in less dense urban areas with cycling and e-biking for transport and how this association varies by age, sex, or mobility impairments [15]. As a consequence, the special needs of vulnerable groups, such as the elderly population with limited mobility functions, are not well considered in city and transport planning. Moreover, studies indicate that the perception of certain environmental attributes has a greater effect on cycling [24] and neighborhood satisfaction [25] than objectively measured factors. This is particularly true for perceived safety, which is considered a crucial determinant of cycling behavior [26,27]. As such, measures of interventions need to address peoples’ perception of their environment if they are to successfully promote cycling.
The aim of this study is to investigate the relationship between older adults’ perception of built environment attributes and cycling and e-biking for transport. Furthermore, an explorative approach is taken to analyze potential moderating effects of age, sex and mobility impairments with environmental attributes on cycling behavior.
Material and methods
Study design
Within the project AFOOT—Securing urban mobility of an aging population [28], a cross-sectional postal survey was conducted in the Metropolitan Region of Bremen-Oldenburg in the Northwest of Germany. The survey took place between May and September in 2019. The study was approved by the University of Bremen ethical committee (ethics vote 20181205).
Study population
Study participants are people aged 65 and older. The age limit of 65 years was chosen because it is the usual retirement age and the population group aged 65 years and older is considered as older adults by the Federal Statistical Office in Germany. The study area includes eleven rural districts and two urban municipalities (each with fewer than 100,000 inhabitants) in the state of Lower Saxony. Sampling was carried out by the 117 residents’ registration offices of the included districts and municipalities. A total of 11,000 people were randomly selected. Total response rate was 20.56% (see S1 Fig for details).
Measures
Validated and nationally or internationally established question modules were used in the questionnaire where suitable. The design of the questionnaire (length, font size, contrasts) was adjusted based on experience with the age group. The questionnaire was tested and discussed twice in focus groups with the target population (people aged 65 years and older) to adapt the questions to the German context and to reveal problems with length and complexity of the questionnaire. A specific reliability test of the questionnaire was not performed.
Socioeconomic variables.
Sex was assessed as dichotomous variable (‘Male’, ‘Female’) and age groups were built with respect to quartiles (‘65–69 years’, ‘70–74 years’, ‘75–79 years’, ‘80 years and over’). Information about the relationship status were summarized as marital status with the categories ‘Married’, ‘Single’, and ‘Widowed’ and, additionally, partner status with the categories ‘Having a partner’ and ‘No partner’. Three categories emerged assessing school education: ‘High’ (A-levels, general or subject-specific qualification for a place at university or technical college qualification), ‘Middle’ (Secondary school/vocational secondary school–Polytechnic institute qualification, or class 10 (before 1965: class 8) or other school-leaving qualification (e.g. obtained abroad)) and ‘Low’ (Elementary school or Left school without qualification) [29]. Information about school education and professional education were summarized as education according to the International Standard Classification of Education (ISCED) [30] and classified into three categories: ‘Low’ (ISCED 0–2), ‘Middle’ (ISCED 3–4) and ‘High’ (ISCED 5–6). Income was assessed by asking participants to give information about their monthly net income and their household composition (number and age of household members). Following the guidelines of the OECD [31], the equivalized disposable income was calculated. With respect to the latest poverty line of Lower Saxony [32], three categories were used to classify the level of income: ‘Low’ (< 60% median), ‘Middle’ (≥ 60% median and ≤ median) and ‘High’ (> median). Living situation was collapsed into two categories ‘Own private home’ and ‘Other’ (which included, for example, living with others). Country of birth was analysed as a dichotomous variable (‘Germany’, ‘Other country’). Concerning the area of residence, participants were asked to name their municipality. Based on the classification of the Federal Institute for Research on Building, Urban Affairs and Spatial Development [33] the named municipalities were categorized into four groups: ‘Medium-sized town’ (20,000 - < 100,000 inhabitants), ‘Larger small town’ (10,000 - < 20,000 inhabitants), ‘Small town’ (5,000 - < 10,000 inhabitants or at least basic central function) and ‘Rural community’ (< 5,000 inhabitants).
Health variables.
Participants reported their self-rated health on a 5-point scale [34]. Answers were dichotomized into ‘Very good/good’ or ‘moderate/bad/very bad’. To assess mobility impairments, multiple answers were possible: ‘Yes, I have a walking disability’–‘Yes, I have a visual impairment’–‘Yes, I have (an) other mobility impairment(s)’–‘No, I don’t have mobility impairments’. Answers were dichotomized into ‘None’ versus ‘At least one’. Use of a walking aid was dichotomized as ‘No’ or ‘Yes’ (if any of: Walking stick/ cane, Crutches, Walker, Walking frame, Quad walker, or Wheelchair).
Transport variables.
Car availability assessed access to a car as a driver or passenger. Response categories were ‘Always’, ‘Sometimes’ or ‘Never’. For bicycle ownership participants were asked which of the following applied: ‘conventional bicycle’, ‘e-bike/pedelec’, ‘tricycle’, or ‘none of the three’. Multiple answers were possible.
Dependent variables.
Frequency of cycling for transport was assessed by asking participants to indicate separately how often they usually use each of a conventional bicycle or an e-bike as a means of transport. Response options were: ‘Daily or almost daily’; ‘3–4 days a week’, ‘1–2 days a week’, ‘1–3 days per month’, ‘rarer’, or ‘never’. Respondents were classified into two categories: ‘≥3 days/week’ versus ‘<3 days/week’. To be able to compare any bicycle use with e-biking only, another outcome was defined to include only those people that either owned an e-bike or had no bicycle at all (classified as ‘never’ e-biking for transport). Frequency of e-biking for transport was created accordingly, with two levels: ‘≥3 days/week’ and ‘<3 days/week’.
To assess differences in the relationship between the perceived environment and cycling (regardless of the type of bicycle) as well as e-biking specifically, this procedure was applied to all outcomes (see S2 Fig for more details).
The amount of cycling in minutes per week was measured using the domain specific questions for transport activity from the International Physical Activity Questionnaire long form (IPAQ–LF). The IPAQ–LF has been found to have acceptable test-retest reliability [35]. The IPAQ-LF is designed for adults aged 15–69 years. As participants in this study are 65 years and older and even small amounts of activity may be beneficial for health in this group [6,36], the questions were modified to ask about trips of at least 5 minutes. The IPAQ–LF item used in the analyses distinguish between any cycling for transport and e-biking. The item assessed the number of days during the last week that were spent cycling for ≥5 minutes to get from place to place and the usual minutes spent doing so per day. Minutes per week spent cycling (whether conventional bicycle or e-bike, or both) or e-biking (e-bike use only) were calculated and treated as discrete variables. IPAQ data were truncated at the maximum value of 1260 min/week according to the IPAQ protocol [37].
In addition, dichotomous outcome measures were derived to represent any cycling that lasted for ≥5 minutes during a typical week (whether conventional bicycle or e-bike, or both) or e-biking (e-bike users only) for transport with the categories ‘Yes’ versus ‘No’.
In order to differentiate between bicycling for transport purposes and bicycling for leisure purposes, the questionnaire also addressed this issue, which is, however, not part of this research question.
Independent variables.
Assessment of the environment was largely based on the Neighborhood Environment Walkability Scale (NEWS) questionnaire and scoring protocol [38] with modifications for the German context. The NEWS is the most frequently-used tool to measure perceived neighborhood environmental attributes [39].
Residential density was assessed asking about six types of residences in the participants’ neighborhood: detached single-family residences, townhouses or row houses of 1–3 stories, and apartment houses (1–3 stories, 4–6 stories, 7–12 stories, or more than 13 stories). Participants rated all types on a 5-point scale from ‘All’ to ‘None’. A score was calculated according to the NEWS scoring protocol.
Land use mix was assessed with the question: ‘About how long would it take to get from your home to the nearest facilities for the local supply and services listed below if you walked to them?’. There were four answer options (1–10 minutes, 11–20 minutes, 21–30 minutes, or 31+ minutes) and 11 destinations: small grocery store/supermarket, bakery, café, restaurant, physician, pharmacy, post office, bank/credit union, salon/barber shop, cemetery, recreation center. Land use mix–destinations within 20-minute walk was based on the number of shops accessible in ≤20 minutes. Land use mix–proximity of destinations was measured as the mean of all items. Proximity of bus stop was asked separately using the same question as it has been shown to be a significant determinant of active mobility [15].
To assess walking infrastructure, cycling infrastructure, shared infrastructure (for walking and cycling), street connectivity, aesthetics and traffic safety, participants were asked ‘What does the environment/streetscape in your neighbourhood predominantly look like?’. They rated statements on a 4-point scale from ‘Strongly agree’ to ‘Strongly disagree’ (see S1 File for statements). For all these neighbourhood environmental attributes, subscale scores were calculated as the mean of all items per subscale.
Statistical analysis
Respondents were excluded from this analysis if they were living in an institution (e.g. a care home) (n = 49), had missing data on all outcome variables (n = 32), or used a tricycle (n = 17). The final sample consisted of 2,144 older adults.
Absolute and relative frequencies of the study participants’ characteristics were calculated for the whole sample and stratified by sex. Descriptive statistics and tests of association (Chi-square, Wilcoxon or Kruskal-Wallis test) with each of the outcome variables were conducted for selected variables.
Binary logistic regression was used to identify associations of environmental attributes with the outcome variables ‘any cycling/e-biking’ and ‘frequent cycling/e-biking’ (≥ 3 days/week). Separate models for each of the aforementioned environmental attributes were calculated. Results are presented as Odds Ratios (OR) with corresponding 95% confidence intervals (CI). All environmental variables were treated as continuous variables and recoded so higher scores were expected to be related to more active mobility.
Linear regression models were used to examine associations of environmental attributes with the two outcomes ‘cycling amount’ and ‘e-biking amount’. A log-linear model was fitted to these variables as the empirical distributions in minutes/week were positively skewed. To account for overdispersion, a negative binomial regression with canonical log link function was performed. Results are presented as the exponential of L’Beta with corresponding 95% confidence intervals. It can be interpreted as the proportional increase or decrease in minutes per week cycling/e-biking associated with changes in the environmental attributes.
Multivariable logistic and linear regression analyzes were computed to analyze the association of environmental attributes with all six outcomes of active mobility behavior adjusted for age, sex, education (ISCED), income, partner status, self-rated health, area of residence and car ownership, as these variables were positively associated with one or more outcome variables in the previous tests of association (p-value <0.05).
Separate models were run to estimate interaction effects of environmental attributes with sex, age and mobility impairments. As this approach is exploratory, a probability level of 0.1 was set to not miss any weak but possible interaction effects. All environmental attributes with an interaction effect significant at a 0.1 probability level with any active mobility outcome in crude models are presented as stratified results. Adjusted models were run with the same covariates as in the main analysis, except for the moderator of mobility impairments. Here, self-rated health was excluded as covariate as it was correlated with mobility impairments.
All analyzes were carried out using SAS statistical software package version 9.4 (SAS Institute, Cary, NC, USA).
Results
Study population characteristics
Fifty-four percent of the whole sample was between 65–74 years old and 53% were men (see Table 1). Nearly the whole sample had a middle/high educational level (ISCED, 92%), lived in their own private home (97%), sometimes or always had access to a car (97%) and were of German origin (97%). The majority of the study participants (84%) owned some type of bicycle.
More men than women had a high educational level according to ISCED and had more often access to a car or bicycle/e-bike. More women than men were widowed or had no partner.
Frequency of cycling or e-biking for transport
Nearly two thirds of the study population engaged in any cycling for transport (65%, n = 1362) and one third in any e-biking (36%, n = 418) (see S2 Fig). The median time spent cycling and e-biking for transport was 120 minutes per week (n = 1231) and 150 minutes per week (n = 373), respectively. In this study, 42% (n = 881) of participants cycled three or more days a week for transport and 31% (n = 221) used an e-bike three or more days a week for this purpose.
Socio-demographic characteristics and cycling
Engaging in any cycling/e-biking and cycling/e-biking frequency decreased with age and prevalence was higher in people with middle/high education (ISCED) (see Table 2). Any cycling/e-biking and frequent cycling/e-biking were associated with being male, perceiving a good/very good financial situation, being married, having a partner, having a good/very good health status, not using a walking aid, not having mobility impairments and always having a car available. Area of residence was only associated with any e-biking and frequent e-biking with higher amounts in larger small towns and small towns.
Participants with good/very good self-rated health cycled more minutes per week, while participants with at least one mobility impairment had higher median minutes per week e-biking. Participants with no access to a car cycled/e-bicycled up to 180 more minutes per week for transport in the median than participants who sometimes or always had access to a car. It should be noted, however, that the group of people without access to a car was quite small. Overall, the median of minutes per week e-biking was always higher than in the group with any type of bicycle.
Perceived environmental attributes and cycling
Most of the investigated environmental characteristics were associated with active mobility using any type of bicycle (see Table 3).
Street connectivity was positively associated with all of the six cycling outcomes in adjusted models. Each unit increase in perceived street connectivity was associated with a 35–55% higher chance of active mobility (any or frequent cycling/e-biking) and 14–26% more minutes per week cycling/e-biking for transport.
Proximity of destinations showed the strongest positive association with any cycling and frequent cycling compared to the other environmental attributes: each unit increase in proximity raised the chance to engage in any cycling by 48% and in cycling ≥3 times per week by 62%. Moreover, each additional destination within 20 minutes walking distance increased the likelihood of cycling/e-biking and frequent cycling/e-biking by 7–11% and cycling minutes per week by 2%. Proximity of a bus stop was positively associated with any cycling and frequency of cycling for transport but not with any e-biking or frequent e-biking.
Walking infrastructure was positively associated with any cycling and cycling frequency. Cycling infrastructure and shared infrastructure were positively associated with any cycling/e-biking and frequent cycling but not with frequent e-biking after adjustment. There was no association for minutes cycling or e-biking with land use mix, infrastructure and proximity of a bus stop.
Aesthetics showed positive associations with engaging in any cycling and frequent cycling/e-biking but not with cycling/e-biking amount.
Traffic safety was not associated with any cycling/e-biking and cycling/e-biking frequency in adjusted models, and was in fact inversely associated with cycling and e-biking amount; each unit increase in the perception of traffic safety meant a 13% reduction in cycling and 20% reduction in e-biking minutes per week in the adjusted models.
No association between residential density and any of the six cycling outcomes could be observed.
Interaction effects
In stratified analysis some of the observed associations between the built environment attributes and cycling outcomes were stronger or only apparent in some groups. Among men, cycling amount was positively associated with proximity of destinations and number of close destinations, but not among women (see Table 4 in S1 Table). Among women, there were inverse associations for proximity of a bus stop with cycling amount and for traffic safety with cycling and e-biking amount.
For the younger age group (70–74 years), cycling infrastructure was positively associated with e-biking amount. The older age group (75–79 years) showed a positive association between aesthetics and e-biking amount. In contrast to the younger age groups and the main analysis (Table 3), traffic safety among 75-79-year-olds in the sample was positively associated with cycling frequency, with a 52% higher chance of frequent cycling with each unit increase in traffic safety. The association among the oldest age group pointed in the same direction but was not significant.
An inverse association for traffic safety with cycling amount could be observed among people with at least one mobility impairment. This group was also 2.4 times more likely to cycle with their e-bike frequently with each unit increase in street connectivity. In contrast, the other environmental attributes were only associated with cycling among people without mobility impairments: each unit increase in aesthetics was positively associated with any cycling, shared infrastructure with any cycling/e-biking, proximity of a bus stop with any e-biking, and walking infrastructure with e-biking amount.
Discussion
Our study is one of the first to investigate the association between environmental attributes and cycling/e-biking for transport in older adults. Overall, the estimates point to the expected direction of a positive relationship between a cycling-friendly built environment and cycling for transport. The most important environmental attributes were street connectivity and land use mix. Only street connectivity led to an increase of the amount of cycling and e-biking. For e-biking, fewer environmental attributes showed an association compared to cycling of any kind. Unexpectedly, traffic safety was not associated with four out of the six cycling outcomes and was even inversely associated with minutes spent cycling or e-biking for transport. Density was not associated with any of the outcome variables investigated. Explorative analysis indicated that especially mobility impairments, but also sex and age might play a role as effect modifier.
The prevalence of frequent cycling (≥3 times/week) was three times higher in our study sample than in the general population of this age cohort in Germany [9]. However, the pattern that men cycle slightly more frequently than women and highly educated groups more than those with middle or low education is the same in the general population [9]. A German transport survey has shown that most e-bike owners in Germany are older people living in medium-sized and small-town or rural areas, which might explain the high percentage of e-bike owners in our study. The median number of minutes e-biking was higher in our study sample compared to cycling of any type, which is in line with previous investigations [9,40]. Most interestingly, e-biking amount was significantly higher among those with at least one mobility impairment. This confirms results from other studies which suggest that e-biking enables people with decreasing physical health to continue cycling [22].
In our main analysis, better neighborhood street connectivity showed the strongest association with cycling. Street connectivity indicates short distances between intersections and therefore probably reachable destinations, which coincides with the second most important factor in this study, ’proximity of destinations’. Other studies have concluded that street connectivity is a facilitator rather than an essential determinant of cycling. They confirm our finding that proximity to destinations visited daily is a consistent correlate of both older adults’ total active mobility behavior [15] and cycling [27]. The same has been found for adults’ total active mobility [41] and cycling [42]. However, we found no association between proximity of destinations and an increased number of minutes cycled per week. This is in line with findings among older adults from Belgium [43]. One can assume that the closer the destinations are, the fewer minutes people would cycle. The fact that the estimators in our study nevertheless point in a positive direction indicates that people who perceive accessible destinations in their residential environment tend to cycle for more minutes because they visit them more frequently. This supports our result concerning the number of destinations within a short distance, specifically that more minutes per week are spent cycling when there are more shops nearby. In the case of proximity to a bus stop, e-biking alone was not associated, although cycling generally showed a positive association. Previous research suggests that people with e-bikes might be worried to lock their e-bikes at the bus stop because of fear of theft [22,44].
The positive relationship between cycling infrastructure (defined as a cycling path, walking path or shared paths) and any cycling and frequent cycling is in line with evidence on cycling behavior in adults [27,45,46]. In a recent study of older adults in Belgium, the authors found that the type of cycle path (i.e. separated vs. no cycling infrastructure) was the most important attribute among all participants and among those with the highest levels of mobility impairments and lowest levels of cycling [17]. Improving the availability and quality of walking and cycling facilities may therefore benefit those older adults that are most in need of cycling infrastructure. However, we found a positive relationship between walking or shared infrastructure and cycling for transport. It is important, however, that infrastructure to encourage cycling does not inadvertently discourage walking. Cycling on the footpath, for example, is not practical as this can produce conflict and safety issues with walkers who also need safe and convenient space for active mobility [18]. In comparison, frequent e-biking showed a weak association with cycling and shared infrastructure, and no association with walking infrastructure. In fact, e-bike users in a qualitative study reported they feel more safe cycling on the street with other vehicles that are driving at a similar speed [22].
The negative association of active modes of transport with traffic safety has also been reported for total minutes of walking in adults in a study comparing 17 countries [42], for daily cycling for transport in Belgian older females [47], and for total active mobility in an older population from South Africa [48]. The lack of an association between traffic safety and the other cycling outcomes investigated in our study is consistent with a study examining Brazilian older adults’ total active mobility behavior [49]. This result might be explained by the cross-sectional nature of this study in terms of reported perception of environmental attributes, which suggests that people who cycle frequently are more aware of traffic safety problems. Qualitative studies with older adults point to the importance of traffic safety when walking [13,18]. Although we found no association in the overall sample, we can confirm a relationship for the older age groups (75+) in our study who cycled/e-bicycled more frequently with increased traffic safety, which further highlights the importance of managing safety concerns for age-friendly environments.
Previous results on the relationship between aesthetics and active mobility have been mixed, which may be explained by different measurement approaches [27,50,51]. In contrast to another study [27], we found no interaction between neighborhood aesthetics and sex. Concerning e-biking, our results showed that aesthetics were associated with frequent e-biking but not with any e-biking, which suggests that an aesthetically pleasing environment might support more frequent e-bicycle use but does not influence uptake. Furthermore, our results show an interaction effect with younger age (<75 years) on more minutes of e-biking. One may conclude, that at some point–given experience and routine with age–cycling in aesthetically pleasing environments might become less important for time spent cycling per week in older age while traffic safety issues come into focus.
No association between density and any of the active mobility outcomes could be observed in our study. Results of studies using the same instrument are mixed [27,42]. We believe this to be an artefact of the density measure we used. This instrument was developed and validated in urban areas with more diverse housing [38]. Our study area is characterized by many single-family residences and row houses of 1–3 stories. Therefore, residential density in our study might be quite homogenous. In future studies, a valid assessment of residential density applicable for more rural areas should be considered.
Limitations
This study is not without limitations. The cross-sectional nature of this study does not allow conclusions to be drawn about the causality of the associations. Furthermore, older adults tend to overreport their physical activity using the IPAQ questionnaire [52], which has to be considered. However, we assume that this potential misclassification is independent of the built environment. Also, questions on the perceived environment used in this study might be less relevant in extremely rural areas, where no sidewalks at all are present and street trees, lighting, benches and litter bins are less common. This might have led to missing values or misclassification and limiting the extent to which unfavorable environments could be characterized. Lastly, compared to the general population of the age group 65 years and older in the state of Lower Saxony, there were more men in our sample (53% vs. 43%) more people were married (77% vs. 60%) [53,54] and the proportion of very good/good self-rated health was higher (59% vs. 47%) [55].
Implications
As proximity of destinations seems to be one of the most important correlates of cycling, it is important to preserve facilities and services. This is especially important in smaller communities, where even investments in better cycling infrastructure are unlikely to offset the negative effect that longer distances can have on cycling. Proximity of destinations was more important for cycling in older adults than a variety of shops and services in our study. Future studies should investigate which kind of shops and services are most important to ensure they are in a reachable distance in order to reduce car dependency and enable elderly people to live autonomously. This could help communities with a small population and low purchasing power to prioritize on preserving or locate certain services.
Providing cycling facilities seems desirable to increase bicycle use among older adults. Although previous studies have mostly been conducted in larger cities [46,56], this study has confirmed the positive effect of cycling and shared infrastructure as well as walking infrastructure on cycling and cycling frequency for smaller towns. However, knowledge about the status-quo of infrastructure in more differentiated town sizes and its impacts on cycling levels is still limited. Even more important than cycling infrastructure was street connectivity. Although the overall street network is hard to change, shortcuts for cyclists might be included in transport and settlement planning to increase street connectivity. Furthermore, the study results suggest that aesthetically pleasing environments, for example those with trees, gardens and attractive buildings, may favor cycling.
Traffic safety has been reported in qualitative studies to be a crucial determinant of active mobility [8,18], and measures improving safety have been found to be effective in encouraging cycling in intervention studies [40]. In this study, however, this association was only found within older age groups. We suspect this is a result of the entanglement of perception of traffic safety with rising cycling frequency and experience in our data, and recommend that future studies investigate how to assess perceived traffic safety quantitatively and in greater detail to better understand this issue.
Conclusions
This study emphasizes opportunities to promote cycling through shaping aspects of the environment, especially through maximizing the proximity to destinations, street connectivity, and cycling infrastructure. This is actually in line with the recently adopted national strategy for the promotion of cycling in Germany. The National Cycling Plan 3.0 aims to double the kilometers travelled by bike or e-bike by the year 2030 and to make cycling both safer and more attractive. A key objective is to create a seamless network of safe, intuitive and convenient cycle paths and streets that enable all age groups to cycle more frequently. The importance of accessible destinations is less in the foreground. But the need for an integrated approach to city and transport planning and a promotion of a “city of short distances” is acknowledged [57].
Further research is needed to identify beneficial factors for e-biking. In our study, people over the age of 65 and with mobility impairments used an e-bike, which may support them to stay active and mobile in their neighborhood and even cover longer distances. However, awareness of e-bikes among all road users has to be promoted if e-bike users are supposed to cycle on the street. Street space in smaller communities or historic centers is limited and it is not always possible to set up a separated cycle path that is wide enough to allow for cyclists travelling at different speeds. For public health, the current findings underline the importance of investigating not only the amount of physical activity, but also any engagement in active mobility regardless of the quantity. Any cycling at all can make all the difference for independent living in old age. The coronavirus pandemic since 2020 is a direct reminder that health is a public good worth protecting. Not only individual pre-existing conditions, but also the living conditions of older people in the community influence how older people can cope with health burdens. Healthy living environments are an important resource that can be shaped by the municipalities.
Supporting information
S2 Fig. Creation and definition of outcomes and respective reference categories.
https://doi.org/10.1371/journal.pone.0267314.s002
(DOCX)
S1 File. Statements on the assessed environmental attributes (based on the NEWS questionnaire).
https://doi.org/10.1371/journal.pone.0267314.s004
(DOCX)
Acknowledgments
We thank Birgit Reineke for data management and support in coding and Maddie White for checking on grammar and language. We are also grateful for our colleagues from TU Dortmund University, Sabine Baumgart and Karsten Zimmermann, who provided insights and expertise on urban and municipal planning that greatly assisted the research. Last but not least, we would like to thank all the participants who took the time to complete the questionnaire and thus made this research possible.
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