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Daily mobility has been shown to contribute to the wellbeing of older adults, as it promotes healthy and independent living. However, very little is known about how the complex relationships between locations, geographic environments and daily mobility relate to wellbeing. In the current paper, we rely on the concept of ‘motility’– defined as potential mobility– and the concept of ‘movement’– defined as actual mobility– to take a step forwards in disentangling the relationship between mobility and wellbeing. We further examine how both motility and movement relate to two complementary definitions of wellbeing: hedonic wellbeing as a measurement of happiness, and eudaimonic wellbeing as the actualisation of an individual’s human potential. To investigate this relationship, we draw up a conceptual framework stressing pathways linking mobility to wellbeing, which we empirically test using structural equation modelling on a stratified sample of 470 older adults. We first quantitatively confirm that motility is defined by access, competences, appropriation and attitudes to modes of transportation. We then observe that motility has direct effects on eudaimonic wellbeing and, to a lesser extent, on hedonic wellbeing. Part of the motility effects on wellbeing are mediated by movement. Separating mobility into motility and movement stresses the independent and complementary role that potential and realised mobility play in shaping older adults’ wellbeing.

BackgroundGiven the challenges of aging populations, calls have been issued for more sustainable urban re-development and implementation of local solutions to address global environmental and healthy aging issues. However, few studies have considered older adults’ daily mobility to better understand how local built and social environments may contribute to healthy aging. Meanwhile, wearable sensors and interactive map-based applications offer novel means for gathering information on people’s mobility, levels of physical activity, or social network structure. Combining such data with classical questionnaires on well-being, physical activity, perceived environments and qualitative assessment of experience of places opens new opportunities to assess the complex interplay between individuals and environments. In line with current gaps and novel analytical capabilities, this research proposes an international research agenda to collect and analyse detailed data on daily mobility, social networks and health outcomes among older adults using interactive web-based questionnaires and wearable sensors.Methods/DesignOur study resorts to a battery of innovative data collection methods including use of a novel multisensor device for collection of location and physical activity, interactive map-based questionnaires on regular destinations and social networks, and qualitative assessment of experience of places. This rich data will allow advanced quantitative and qualitative analyses in the aim to disentangle the complex people-environment interactions linking urban local contexts to healthy aging, with a focus on active living, social networks and participation, and well-being.DiscussionThis project will generate evidence about what characteristics of urban environments relate to active mobility, social participation, and well-being, three important dimensions of healthy aging. It also sets the basis for an international research agenda on built environment and healthy aging based on a shared and comprehensive data collection protocol.

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An evidential neural network (ENN) for predicting individual travel mode is presented. This model can be used to support management decision making and to build predictions under uncertainty related to changes in people's behavior, the economic context, or the environment and policy. The presented model uses individuals’ characteristics, transportation mode specifications, and data related to places of work and residence. The data set analyzed was taken from a survey conducted in 2007 and contains information on the daily mobility (e.g., from home to work) of individuals who either lived or worked in Luxembourg. Individual characteristics were extracted to relate daily mobility (journeys between home and work, in particular) to the characteristics of working individuals. Information about public transportation specification and some geographical particularities of residential areas and workplaces were used. Rates of successful prediction obtained by the ENN and several alternative approaches were compared by cross-validation. The results showed that the ENN was superior to the studied alternatives.