Background: Air pollution is a major public health concern associated with increased respiratory morbidity and mortality worldwide. It negatively impacts respiratory health, yet limited research based on accurate assessments with multiple sensors exists on its short-term effects. In this study we investigate the short-term association between exposure to multiple air pollutants and lung function in adults during daily mobility. Methods: Data on daily activities of 199 participants of the MobiliSense cohort living in the metropolitan area of Paris were collected between 2018 and 2020. Participants were equipped of two portable ambulatory monitors of air pollutants, a GPS receiver and an accelerometer during their mobility. Exposure to black carbon (BC), nitrogen dioxide (NO₂), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O₃), and particulate matter (PM₂.₅) was recorded continuously. Lung function was assessed using spirometry tests conducted in the morning and evening over three days (N = 2,504), measuring forced expiratory volume in 1 second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC ratio. Mixed-effects linear models were applied to assess the association between pollutant exposure at varying time lags (15 minutes to 6 hours prior to spirometry testing) and lung function outcomes. Results : Multipollutant models showed that increased exposure to BC and PM 2.5 was associated with a reduced lung function. A 1 μg/m 3 increase in BC within 1 or 2 hours prior to testing was associated with a decrease in FEV1 by 0.016 (95% CI -0.024, -0.008) and 0.021 (95% CI -0.034, -0.007) respectively. Similarly, increases in BC exposure over 2 hours to 4 hours were associated with a decrease in the FEV1/FVC ratio. Additionally, PM2.5 exposure 15 or 30 minutes or 1 hour before testing was linked to a 0.60 (95% CI -1.30, -0.03), 0.70 (95% CI -1.39, -0.09) and 0.50 (95% CI -1.10, -0.01) percentage points reduction in the FEV1/FVC ratio. Ozone (O3) was positively associated with FEV1 and FVC. No associations were found for other pollutants or time windows. Conclusion: This study provides evidence that short-term exposure to air pollutants – particularly BC and PM 2.5 – can impair lung function. The findings demonstrate that even brief increases in BC and PM 2.5 during daily mobility are associated with measurable reductions in FEV1 and the FEV1/FVC ratio. By assessing multiple pollutants across short exposure windows (15 minutes to 6 hours), this study strengthens causal inference regarding rapid respiratory effects and underscore the health relevance of transient pollution peaks encountered in urban environments, particularly from traffic emissions. Clinical trial number : not applicable.

Social interactions and daily mobility are two behaviors that mutually transform throughout life. Understanding how they interact is particularly important for elderly people, who are more at risk to be spatially and socially isolated. From data collected among a sample of 225 people aged 60 and over living in the Paris region (France), we explore how their social interactions are embedded into their activity space using the new concept of “sociability space” (the geographical portion of the activity space consisting of places visited with social network member). Sociability places are found to less numerous, less diversified, more spatially dispersed and more decentered from residence than non-sociability places. Regarding traditional social network measures (degree, global clustering, etc.), stronger and more numerous correlations are observed with the structural characteristics of sociability spaces than with non-sociability spaces. Finally, variations in structural characteristics of sociability spaces according to people’s socio-demographic and residential profiles largely differ from those observed in structural characteristics of non-sociability spaces. This empirical study shows that “sociability space” indicators are complementary to social network indicators and provide a more precise picture of geography of social interactions than activity space considered as a whole. Such approach may help to identify particular groups at risk of social isolation, such as people living alone and those with a low level of education, and inspire public policies aiming at both promoting daily mobility and reducing social isolation.

The individual exposure to environmental noise in cities is usually assessed at the residential neighbourhood level with static, year-averaged strategic maps. This representation may underestimate noise exposure, given the mobility of individuals within the city and proximate sources of exposure. Our study employs high-resolution sensor analysis to observe how personal noise exposure differs from modelled noise map metrics, identify socioeconomical and behavioural determinants of exposure, and explore the impact of reallocating certain behaviours to others on daily personal noise exposure (LAeq,24h). Data on daily activities of 259 participants of the MobiliSense cohort living in the metropolitan area of Paris were collected between 2018 and 2020. Participants were equipped of a personal monitor for sound pressure, and of a GPS receiver and an accelerometer. Modes of transport were collected during a mobility survey. Results showed that noise exposure based on personal monitoring during space-time behaviours differed from modelled noise levels at residence. Participants were exposed to values below the recommended critical value for health of 55 dB(A) in urban areas in only 36

Studies often investigate the long-term impact of social contacts on mental health in older adults, neglecting momentary effects. This research, grounded in the consideration of daily activity, explores how time-varying social contacts associate with momentary depressive symptoms among 216 older adults in the Île-de-France region. Employing a geographically-explicit ecological momentary assessment approach (GEMA), we collected participants' depressive symptoms, mobility locations, and social contacts data via smartphone surveys, GPS receivers, and mobility survey over 7 days. Bayesian mixed models with random effects at individual and daily levels, considering time autocorrelation, were employed. Participants engaging with social contacts exhibited lower depression not only immediately but also in the following hours. Interestingly, a longer duration of time spent with social contacts did not lead to a sharper decrease in depression levels. Notably, larger decreases were observed when the number of social contacts increased from one to two, especially with friends or family members.

BACKGROUND:Social networks are known to protect against depressive symptoms in older adults. However, most research relies on retrospective self-reported depression measures and cross-sectional data, which may introduce bias. Ecological momentary assessment with longitudinal data overcomes these limitations by repeatedly measuring the subject's experience in the present moment. This study examined how social network characteristics relate to momentary depressive symptoms and their daily fluctuations in older adults. METHODS:We analysed data from 216 older adults in Paris, France, using the Healthy Aging and Networks in Cities and Promoting Mental Well-Being and Healthy Aging in Cities studies. Social network characteristics included network size and frequency of in-person and digital interactions per week. Depressive symptomatology was assessed using a daily smartphone survey of the Center for Epidemiological Studies-Depression over a week. Linear mixed-effect models estimated associations between social network characteristics and momentary depressive symptoms, while multivariable linear models examined relationships with daily symptom fluctuations. RESULTS:Network size and frequency of contact from digital communications per week were not associated with fewer depressive symptoms; however, there was suggestion that having more in-person contact was related to fewer depressive symptoms (exp(β) = 0.90, 95% CI 0.82 to 1.00). Moreover, having a larger social network (exp(β) = 0.91, 95% CI 0.85 to 0.98) and more in-person contacts (exp(β) = 0.96, 95% CI 0.93 to 0.98) were associated with less fluctuations in daily depressive symptoms, but no association for the frequency of contact from digital communications was observed. CONCLUSION:Findings from this study suggest that larger social networks and more in-person contact may promote more stable and better mental health among older adults.

Past epidemiological studies using fixed-site outdoor air pollution measurements as a proxy for participants’ exposure might have suffered from exposure misclassification.In the MobiliSense study, personal exposures to ozone (O3), nitrogen dioxide (NO2), and particles with aerodynamic diameters below 2.5 µm (PM2.5) were monitored with a personal air quality monitor. All the spatial location points collected with a personal GPS receiver and mobility survey were used to retrieve background hourly concentrations of air pollutants from the nearest Airparif monitoring station. We modeled 851343 minute-level observations from 246 participants.Visited places including the residence contributed the majority of the minute-level observations, 93.0%, followed by active transport (3.4%), and the rest were from on-road and rail transport, 2.4% and 1.1%, respectively. Comparison of personal exposures and station-measured concentrations for each individual indicated low Spearman correlations for NO2 (median across participants: 0.23), O3 (median: 0.21), and PM2.5 (median: 0.27), with varying levels of correlation by microenvironments. Generally, a large degree of individual variability in the correlation between personal and fixed-site measurements was found for all air pollutants. Results from mixed-effect models indicated that personal exposure was very weakly explained by station-measured concentrations (R2 < 0.07) for all air pollutants. The fit of the model was relatively high for O3 in the active transport microenvironment (R2: 0.25) and for PM2.5 in active transport (R2: 0.16) and in the separated rail transport microenvironment (R2: 0.20). Model fit slightly increased with decreasing distance between participants’ location and the nearest monitoring station.Our results demonstrate a relatively low correlation between personal exposure and station-measured air pollutants, confirming that station-measured concentrations as proxies of personal exposures can lead to exposure misclassification. However, distance and the type of microenvironment are shown to affect the extent of misclassification.

Introduction The residential environment is hypothesized to influence sleep quality within urban settings. Factors associated with the residential environment include air and noise pollution, area socioeconomic status, green and blue spaces, and other neighborhood features. This study seeks to quantify the association of selected environmental factors with sleep quality in the daily lives of 211 older adults residing in the Paris metropolitan area with sensor-based methods. Methods Participants’ sleep and physical activity were monitored over a 7-day period using 2 accelerometers. Ecological momentary assessment (EMA) surveys were administered 4 times a day to assess depressive and anxiety symptoms. Environmental factors surrounding participants’ residential addresses, including noise and air pollution, walkability, green and blue space availability, median income, and population density, were computed using geoprocessing methods. Hierarchical mixed models with a random intercept at the individual level were fitted to estimate the adjusted association between residential environmental factors and sleep outcomes [total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO)]. Potential effect modification of or mediation by physical activity and depression and anxiety levels were explored in the analyses. Results We observed an effect size of 1.4 more minutes of sleep for each increase of one thousand euro in neighborhood median income (Confidence Intervals: 0.35, 2.45). The average adjusted difference in total sleep time between the 10th and 90th percentiles of neighborhood median income was 23.6 minutes of sleep. Other environmental factors and depression and anxiety levels did not exhibit correlations with sleep outcomes. Conclusions The results reveal a positive association between median income at the residential level and TST, while no associations were identified for SE and WASO. In conclusion, these findings underscore the impact of neighborhood socioeconomic status on total sleep time within the context of urban living, highlighting the need for further research.

Several urban design characteristics may influence walking behaviour and the cognitive ability to navigate urban environments. Route complexity and the presence of certain urban design features may influence specific cognitive functions, such as spatial memory by stimulating the hippocampus to acquire place knowledge. In turn, cognitive abilities likely influence individuals' spatial navigation. The complexity of cities may pose challenges to those experiencing cognitive decline due to ageing and emerging dementia. However, evidence on the moderating role of cognitive abilities in the association between environmental factors and walking behaviours is still lacking. The present study aimed to explore the extent to which cognitive abilities (visuospatial working memory, selective attention, cognitive inhibition and cognitive flexibility) moderate the associations between characteristics of the urban environment and walking behaviours, including engagement in walking and the spatial complexity of walking trips. We analysed mobility behaviour in 324 adults aged >= 50 years living in Melbourne, Australia from the iMAP (international Mind, Activities and urban Places) project. Global Positioning System mobility and diary data over 7 days were used to quantify the complexity of the mobility patterns and exposure context of individuals (both the residential neighbourhood environment and other activity spaces). Participants' cognitive abilities were assessed through cognitive tests before the mobility survey. Multilevel (covariate-adjusted) regression models with random intercepts at the neighbourhood and participant levels were used to estimate the associations of environmental variables with walking behaviour at the day level, as well as the moderating role of cognitive abilities. Our study suggests that individuals with low cognitive abilities are more influenced by environmental conditions, whether these conditions are favourable or not to walking. Residential environments with a wide range of services favoured engagement in walking at least 15 min per day in people with low cognitive flexibility. Urban obstacles, such as tall buildings, compound the challenges of individuals with low cognitive flexibility by decreasing the odds of walking at least 15 min per day. Disordered urban environments (with irregular street orientation) and conflicting information (highly connected, dense environments), led to the selection of more convoluted and less connected routes in people with low cognitive flexibility and low visual spatial ability. These results provide new insights into the interactions between urban environments and individual cognitive status on walking behaviours and suggest that cognitive abilities should be considered in policies promoting environmental condition that favour walking activities.

Renewable Energy Communities (RECs) are deemed to be capable of providing several benefits to their members, as well as to the area in where such associations operate. In this sense they could be considered as capable of expanding the Capability set of the inhabitants of a city to improve their individual and collective well-being. In this contribution, the Urban Capability approach is applied to the EU-introduced concept of RECs to evaluate the preference of different socio-demographic groups in translating RECs from capabilities to functionings. The proposed methodology aims to learn the preference model of individuals depending on their socio-demographic characteristics and geographical location, and to determine the correspondence between preference towards the performance of possible alternative RECs configurations across the city, and the performance thresholds set by inhabitants towards participation. The contribution also presents the questionnaire designed to operationalize the methodology and collect the necessary structured data to build a Machine Learning model able to learn individual preferences. Such structured data will also be used to enrich statistical data from census surveys to "spatialize" preferences related to socio-demographic characteristics of the population. Such types of data could have implications for policy makers in terms of allocation of resources and the evaluation of different levers to better support a wider and fairer participation of inhabitants to energy transition, while, at the same time, removing the barriers to achieve higher level of individual and collective well-being.

Social networks play a crucial role in promoting healthy aging, yet the intricate mechanisms connecting social capital to health present a complex challenge. Additionally, the majority of social network analysis studies focusing on older adults typically concentrate on the participants' individual relationships, often overlooking the interconnections between these relationships. In this study, we went further than current ego-centered network studies by determining global social network metrics and the structure of relationships among older adult participants of the RECORD Cohort using the Veritas-Social questionnaire. The aim of this study is to identify key dimensions of social networks of older adults, and to evaluate how these dimensions relate to depressive symptoms, life satisfaction, and well-being. Using Principal Component Analyses (PCA), we identified four social network dimensions with psychological meanings. Dimension 1 (homophily) was positively linked with perceived accessibility to services in one’s residential neighborhood but this same dimension was negatively linked with the level of study (i.e., Bachelor, Master, PhD, etc.). Dimension 2 (social integration) and Dimension 3 (social support) were only linked to the number of people living (being in the same residence) with ego (i.e. the interviewed participant). Dimension 4 was linked with perceived accessibility to local services. Finally, and rather surprisingly, we found that none of the four network dimensions, even the degree, were linked to the three health status metrics.

The urban environment plays an important role for the mental health of residents. Researchers mainly focus on residential neighbourhoods as exposure context, leaving aside the effects of non-residential environments. In order to consider the daily experience of urban spaces, a people-based approach focused on mobility paths is needed. Applying this approach, (1) this study investigated whether individuals' momentary mental well-being is related to the exposure to micro-urban spaces along the daily mobility paths within the two previous hours; (2) it explored whether these associations differ when environmental exposures are defined considering all location points or only outdoor location points; and (3) it examined the associations between the types of activity and mobility and momentary depressive symptomatology. Using a geographically-explicit ecological momentary assessment approach (GEMA), momentary depressive symptomatology of 216 older adults living in the Ile-deFrance region was assessed using smartphone surveys, while participants were tracked with a GPS receiver and an accelerometer for seven days. Exposure to multiple elements of the streetscape was computed within a street network buffer of 25 m of each GPS point over the two hours prior to the questionnaire. Mobility and activity type were documented from a GPS-based mobility survey. We estimated Bayesian generalized mixed effect models with random effects at the individual and day levels and took into account time autocorrelation. We also estimated fixed effects. A better momentary mental wellbeing was observed when residents performed leisure activities or were involved in active mobility and when they were exposed to walkable areas (pedestrian dedicated paths, open spaces, parks and green areas), water elements, and commerce, leisure and cultural attractors over the previous two hours. These relationships were stronger when exposures were defined based only on outdoor location points rather than all location points, and when we considered within-individual differences compared to between-individual differences.

Data, supplementary material and scripts for the paper "Structure and drivers of social networks and their links with health in older adults" Social network is an important factor in promoting healthy aging. However, the mechanisms linking social capital to health are complex. Moreover, most of the social network analysis studies on older adults consider only participants’ relationships and not how these relationships are themselves connected. In this study, we went further than current ego-centered network studies by determining global social network metrics and the structure of relationships among older adult participants of the RECORD Cohort using the Veritas-Social questionnaire. The aim of this study is to identify key dimensions of social networks of older adults, and to evaluate how these dimensions relate to depressive symptoms, life satisfaction, and well-being. Using Principal Component Analyses (PCA), we identified four social network dimensions with psychological meanings. Dimension 1 (homophily) was positively linked with perceived accessibility to services in one’s residential neighborhood but negatively linked with the level of study. Dimension 2 (social integration) as Dimension 3 (social support) was only linked to the number of people living with ego. Dimension 4 was linked with perceived accessibility to local services. Finally, and rather surprisingly, we found that none of the four network dimensions, even the degree, was linked to the three health status metrics.

Life expectancy emphasizes the importance of adapting to and adopting new measures that will benefit healthy aging. A promising lead is the potential benefits of pets for older adults in their homes. This study aimed to test the influence of socio-demographic and environmental factors on the presence of dogs and cats around older adults. The direct and indirect effects of pets, using sociality and mobility factors, were tested on health variables. The social cohesion of a community and type of dwelling appeared to be related to the presence of pets. In addition, the results showed mixed effects of pets, namely a positive influence on mental health via the social network of older adults, and a negative influence in the form creation of a certain stress. In general, the effects of pets on the lives of older adults constitute an important research framework to pursue in the context of healthy aging.

Documented relationships between black carbon (BC) exposure and blood pressure (BP) have been inconsistent. Very few studies measured both BC exposure and ambulatory BP across the multiple daily environments visited in the general population, and none adjusted for personal noise exposure, a major confounder. Our study addresses these gaps by considering 245 adults living in the Grand Paris region. Personal exposure to BC was monitored for 2 days using AE51 microaethalometers. Ambulatory BP was measured every 30 min after waking up using Arteriograph 24 monitors (n = 6772). Mixed effect models with a random intercept at the individual level and time-autocorrelation structure adjusted for personal noise exposure were used to evaluate the associations between BC exposure (averaged from 5 min to 1 h before each BP measurement) and BP. To increase the robustness of findings, we eliminated confounding by unmeasured time-invariant personal variables, by modelling the associations with fixed-effect models. All models were adjusted for potential confounders and short-term time trends. Results from mixed models show that a 1-μg/m3 increase in 5-minute averaged BC exposure was associated with an increase of 0.57 mmHg in ambulatory systolic blood pressure (SBP) (95 % CI: 0.30, 0.83) and with an increase of 0.36 mmHg in diastolic blood pressure (DBP) (95 % CI: 0.14, 0.58). The slope of the exposure-response relationship gradually decreased for both SBP and DBP with the increase in the averaging period of BC exposure from 5 min to 1 h preceding each BP measurement. Findings from the fixed-effect models were consistent with these results. There was no effect modification by noise in the associations, across all exposure windows. We found evidence of a relationship between BC exposure and acute increase in ambulatory SBP and DBP after adjustment for personal noise exposure, with potential implications for the development of adverse cardiovascular outcomes.

Evaluating environmental resources such as urban and territorial opportunities means understanding their value. Monetary values are not always suitable to synthesize the opportunity that such resources give to the development of individuals in urban space. We propose a spatial multi-criteria analysis method oriented to analyse values that individuals give to different urban and territorial opportunities within a capability approach framework. We analyse visitors’ behaviour in space and we learn their urban capabilities considering the values they give to environmental resources and assets. In order to learn such values we use a multi-attribute value measurement method. Furthermore, we define groups of people with similar values of urban opportunities and with similar relevant urban capabilities. This consents to design more legitimated policies aimed to expand the set of relevant capabilities for a given population target. The method is experimented and illustrated with a case study of urban planning for the city of Alghero.

Urban peripherality is a multidimensional phenomenon, requiring operational tools for analysis and policy design. In this paper, we explore if and how the concept of walkability can be employed as an indicator of peripherality. For this purpose, we employ the capability-wise walkability score (CAWS) to assess neighbourhoods of two case study cities to classify them into four classes (periphery, semi-periphery, semi-core, core). In comparing neighbourhoods on both walkability and a set of neighbourhood-level socioeconomic variables, we argue that walkability should be incorporated as part of a comprehensive framework for the analysis of processes of peripherilisation, since walkability should be seen as one relevant factor of urban capabilities, and hence the lack thereof fits into the definition of urban periphery.

The analysis of urban walkability has been extensively explored in the last decades. Despite this growing attention, there is a lack of studies attentive on how citizens' values, individual abilities and urban environment favour or hinder the propensity to walk. Hence, there is a need to explore how preferences and values of citizens vary in space in order to design walkability policies able to improve the capability set of citizens. In this perspective, the design of spatial decision tools aimed to plann public policies for the development of walkable cities needs further investigation. We propose a Multiple Criteria Decision Analysis (MCDA) method aimed to elaborate walkability decision maps for different groups of citizens that reflect their capability to walk in the urban environment. We tested the method in the city of Alghero (Italy). First, we analysed walkability under a normative model named CAWS; then we made a survey with 358 participants in order to study the driving values that influence their choice to walk and finalised to build an evaluation model attentive to individual differences. Cluster analysis was employed to group citizens into 11 groups based on their sociodemographic characteristics and preferences on spatial criteria of walkability. Finally, by integrating GIS with MCDA we built a set of decision maps representative of the walkability of the 11 groups of citizens. Results highlight the importance of citizens' values for policy design, allow the interpersonal comparison among individuals and group preferences and give new suggestions for the formulation of walkability oriented urban policies. Moreover, the results confirm the usability of the general method as a decision support tool supporting the design of urban policies.

We present a survey of operational methods for walkability analysis and evaluation, which we hold show promise as decision-support tools for sustainability-oriented planning and urban design. An initial overview of the literature revealed a subdivision of walkability studies into three main lines of research: transport and land use, urban health, and livable cities. A further selection of articles from the Scopus and Web of Science databases focused on scientific papers that deal with walkability evaluation methods and their suitability as planning and decision-support tools. This led to the definition of a taxonomy to systematize and compare the methods with regard to factors of walkability, scale of analysis, attention on profiling, aggregation methods, spatialization and sources of data used for calibration and validation. The proposed systematization aspires to offer to non-specialist but competent urban analysts a guide and an orienteering, to help them integrate walkability analysis and evaluation into their research and practice.