Dr Benjamin Chrisinger

Colombia's Recreovía program offers community-based free physical activity (PA) classes in parks. We evaluated built and social environmental factors influencing Recreovía local park environments, and facilitated a consensus-building and advocacy process among community members, policymakers and academic researchers aimed at improving uptake and impact of the Recreovía program. We used a mixed-methods approach, with individual and contextual PA measurements and a resident-enabled participatory approach (the Our Voice citizen science engagement model). Recreovía participants were likely to be women meeting PA recommendations, and highly satisfied with the Recreovía classes. Reported facilitators of the Recreovía included its role in enhancing social and individual well-being through PA classes. Reported barriers to usage were related to park maintenance, cleanliness and safety. The Our Voice process elicited community reflection, empowerment, advocacy and action. Our Voice facilitated the interplay among stakeholders and community members to optimize the Recreovía program as a facilitator of active living, and to make park environments more welcoming.

College and university campuses have long been designed as embodied places of societal values and aspirations, reflecting both academic traditions and heritages alongside social and scientific change and innovation. More pragmatically, these spaces share some commonalities with other living and working environments, and must adapt to changing technological and social norms. Since the 1970's, workplace adaptations included employer-sponsored health promotion programs and facilities. While campus environments such as fitness centers and dining halls have been incorporated into health promotion initiatives, other aspects of human well-being have been neglected. In this paper, we describe an initiative, Contemplation By Design, to incorporate contemplation and mindfulness into the daily lives of all members of the Stanford University community, including students, faculty, staff, and their families, as well as alumni and retirees who live close by. This case study highlights ways that physical planning and programmatic initiatives for contemplative practices have been integrated to deliver generalizable, community-based well-being resources that can be emulated in diverse settings throughout the Stanford University campuses, including the main campus and local satellite campuses. Based on experience drawn from Contemplation By Design, practical recommendations for designing contemplative practice spaces and programs are offered.

<jats:p>Individual well-being is a complex concept that varies among and between individuals and is impacted by individual, interpersonal, community, organizational, policy and environmental factors. This research explored associations between select environmental characteristics measured at the ZIP code level and individual well-being. Participants (n = 3288, mean age = 41.4 years, 71.0% female, 57.9% white) were drawn from a registry of individuals who completed the Stanford WELL for Life Scale (SWLS), a 76-question online survey that asks about 10 domains of well-being: social connectedness, lifestyle and daily practices, physical health, stress and resilience, emotional and mental health, purpose and meaning, sense of self, financial security and satisfaction, spirituality and religiosity, and exploration and creativity. Based on a nationally-representative 2018 study of associations between an independent well-being measure and county-level characteristics, we selected twelve identical or analogous neighborhood (ZIP-code level) indicators to test against the SWLS measure and its ten constituent domains. Data were collected from secondary sources to describe socio-economic (median household income, percent unemployment, percent child poverty), demographic (race/ethnicity), and physical environment (commute by bicycle and public transit), and healthcare (number of healthcare facilities, percent mammogram screenings, percent preventable hospital stays). All continuous neighborhood factors were re-classified into quantile groups. Linear mixed models were fit to assess relationships between each neighborhood measure and each of the ten domains of well-being, as well as the overall SWLS well-being measure, and were adjusted for spatial autocorrelation and individual-level covariates. In models exploring associations between the overall SWLS score and neighborhood characteristics, six of the twelve neighborhood factors exhibited significant differences between quantile groups (p &lt; 0.05). All of the ten SWLS domains had at least one instance of significant (p &lt; 0.05) variation across quantile groups for a neighborhood factor; stress and resilience, emotional and mental health, and financial security had the greatest number of significant associations (6/12 factors), followed by physical health (5/12 factors) and social connectedness (4/12 factors). All but one of the neighborhood factors (number of Federally Qualified Health Centers) showed at least one significant association with a well-being domain. Among the neighborhood factors with the most associations with well-being domains were rate of preventable hospital stays (7/10 domains), percent holding bachelor’s degrees (6/10 domains), and median income and percent with less than high school completion (5/10 domains). These observational insights suggest that neighborhood factors are associated with individuals’ overall self-rated well-being, though variation exists among its constituent domains. Further research that employs such multi-dimensional measures of well-being is needed to determine targets for intervention at the neighborhood level that may improve well-being at both the individual and, ultimately, neighborhood levels.</jats:p>

INTRODUCTION:Supplemental Nutrition Assistance Program (SNAP) benefits, which are distributed monthly, help low-income families put food on their tables. Both food spending and caloric intake among recipients decrease over the month following benefit receipt. This pattern, termed the "SNAP-cycle," has serious implications for health and food security of low-income households. To understand better the SNAP-cycle, this study explored (1) differences in diet quality between SNAP and non-SNAP households and (2) the association between the SNAP-cycle and diet quality. METHODS:Multivariate linear regression with SNAP households in the U.S. Department of Agriculture's Food Acquisition and Purchase Survey to evaluate changes in diet quality as time from SNAP distribution increased. Diet quality of food purchases was measured by Healthy Eating Index-2010 total and component scores. Data were collected 2012-2013 and analyzed 2016-2017. RESULTS:Overall dietary quality was low throughout the SNAP-cycle (n=1,377, mean Healthy Eating Index 46.14 of 100). SNAP households had significantly lower Healthy Eating Index scores compared with eligible and ineligible nonparticipants (p<0.05). After controlling for covariates, households in the final 10 days of the benefit cycle had Healthy Eating Index-2010 total scores 2.95 points lower than all other SNAP households (p=0.02). Significant declines in Healthy Eating Index fruit and vegetable scores contributed to worsening diet quality over the SNAP-cycle. CONCLUSIONS:This study provides evidence of low dietary quality throughout the SNAP-cycle with significantly lower Healthy Eating Index scores in the final 10 days of the benefit month. This suggests less healthy purchasing occurs when resources are diminished, but overall that current SNAP levels are insufficient to consistently purchase foods according to dietary guidelines.

BACKGROUND:Identifying elements of one's environment-observable and unobservable-that contribute to chronic stress including the perception of comfort and discomfort associated with different settings, presents many methodological and analytical challenges. However, it also presents an opportunity to engage the public in collecting and analyzing their own geospatial and biometric data to increase community member understanding of their local environments and activate potential environmental improvements. In this first-generation project, we developed a methodology to integrate geospatial technology with biometric sensing within a previously developed, evidence-based "citizen science" protocol, called "Our Voice." Participants used a smartphone/tablet-based application, called the Discovery Tool (DT), to collect photos and audio narratives about elements of the built environment that contributed to or detracted from their well-being. A wrist-worn sensor (Empatica E4) was used to collect time-stamped data, including 3-axis accelerometry, skin temperature, blood volume pressure, heart rate, heartbeat inter-beat interval, and electrodermal activity (EDA). Open-source R packages were employed to automatically organize, clean, geocode, and visualize the biometric data. RESULTS:In total, 14 adults (8 women, 6 men) were successfully recruited to participate in the investigation. Participants recorded 174 images and 124 audio files with the DT. Among captured images with a participant-determined positive or negative rating (n = 131), over half were positive (58.8%, n = 77). Within-participant positive/negative rating ratios were similar, with most participants rating 53.0% of their images as positive (SD 21.4%). Significant spatial clusters of positive and negative photos were identified using the Getis-Ord Gi* local statistic, and significant associations between participant EDA and distance to DT photos, and street and land use characteristics were also observed with linear mixed models. Interactive data maps allowed participants to (1) reflect on data collected during the neighborhood walk, (2) see how EDA levels changed over the course of the walk in relation to objective neighborhood features (using basemap and DT app photos), and (3) compare their data to other participants along the same route. CONCLUSIONS:Participants identified a variety of social and environmental features that contributed to or detracted from their well-being. This initial investigation sets the stage for further research combining qualitative and quantitative data capture and interpretation to identify objective and perceived elements of the built environment influence our embodied experience in different settings. It provides a systematic process for simultaneously collecting multiple kinds of data, and lays a foundation for future statistical and spatial analyses in addition to more in-depth interpretation of how these responses vary within and between individuals.