Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health

by | Jun 5, 2018 | Research Publications

Author(s):

  • Chrisinger, Benjamin W.
  • King, Abby C.

Abstract:

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.

Document:

https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0140-1

References:
  1. McEwen BS, Stellar E. Stress and the individual: mechanisms leading to disease. Arch Intern Med. 1993;153:2093–101.Article PubMed CAS  Google Scholar 
  2. Juster R-P, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev. 2010;35:2–16.Article PubMed  Google Scholar 
  3. Lovallo WR. Stress and health: biological and psychological interactions. Thousand Oaks: SAGE Publications; 2015. Google Scholar 
  4. McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act. 2011;8:125.Article PubMed PubMed Central  Google Scholar 
  5. Drewnowski A, Aggarwal A, Tang W, Hurvitz PM, Scully J, Stewart O, et al. Obesity, diet quality, physical activity, and the built environment: the need for behavioral pathways. BMC Public Health. 2016;16:1153.Article PubMed PubMed Central  Google Scholar 
  6. Juarez PD, Matthews-Juarez P, Hood DB, Im W, Levine RS, Kilbourne BJ, et al. The public health exposome: a population-based, exposure science approach to health disparities research. Int J Environ Res Public Health. 2014;11:12866–95.Article PubMed PubMed Central  Google Scholar 
  7. Dowd JB, Simanek AM, Aiello AE. Socio-economic status, cortisol and allostatic load: a review of the literature. Int J Epidemiol. 2009;38:1297–309.Article PubMed PubMed Central  Google Scholar 
  8. Theall KP, Drury SS, Shirtcliff EA. Cumulative neighborhood risk of psychosocial stress and allostatic load in adolescents. Am J Epidemiol. 2012;176:S164–74.Article PubMed PubMed Central  Google Scholar 
  9. Keene DE, Geronimus AT. “Weathering” HOPE VI: the importance of evaluating the population health impact of public housing demolition and displacement. J Urban Health Bull N Y Acad Med. 2011;88:417–35.Article  Google Scholar 
  10. Roe JJ, Aspinall PA, Mavros P, Coyne R. Engaging the brain: the impact of natural versus urban scenes using novel EEG methods in an experimental setting. Environ Sci. 2013;1:93–104. Google Scholar 
  11. Aspinall P, Mavros P, Coyne R, Roe J. The urban brain: analysing outdoor physical activity with mobile EEG. Br J Sports Med 2013;49:272–6.Article PubMed  Google Scholar 
  12. Honold J, Beyer R, Lakes T, van der Meer E. Multiple environmental burdens and neighborhood-related health of city residents. J Environ Psychol. 2012;32:305–17.Article  Google Scholar 
  13. Hammer MS, Swinburn TK, Neitzel RL. Environmental noise pollution in the United States: developing an effective public health response. Environ Health Perspect. 2014;122:115–9.PubMed Article  Google Scholar 
  14. Cohen S, Krantz DS, Evans GW, Stokols D. Community noise, behavior, and health: the Los Angeles noise project. In: Baum A, Singer JE, editors. Adv Environ Psychol [Internet]. Hillsdale, NJ: Erlbaum; 1982. p. 295–317. http://www.psy.cmu.edu/~scohen/LAnoiseproject.pdf. Accessed 23 Jan 2018.
  15. Evans GW, Hygge S, Bullinger M. Chronic noise and psychological stress. Psychol Sci. 1995;6:333–8.Article  Google Scholar 
  16. Paneto GG, de Alvarez CE, Zannin PHT. Relationship between urban noise and the health of users of public spaces—a case study in Vitoria, ES, Brazil. J Build Constr Plan Res. 2017;5:45. Google Scholar 
  17. Stansfeld S, Haines M, Brown B. Noise and health in the urban environment. Rev Environ Health. 2011;15:43–82. Google Scholar 
  18. Paunović K, Jakovljević B, Belojević G. Predictors of noise annoyance in noisy and quiet urban streets. Sci Total Environ. 2009;407:3707–11.Article PubMed CAS  Google Scholar 
  19. Kono S, Sone T. Residents’ response to environmental and neighborhood noise. J Sound Vib. 1988;127:573–81.Article  Google Scholar 
  20. Swan M. Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. J Sens Actuator Netw. 2012;1:217–53.Article  Google Scholar 
  21. Whooley M, Ploderer B, Gray K. On the integration of self-tracking data amongst quantified self members. In: Proceedings of the 28th international BCS human computer interaction conference HCI 2014-Sand Sea Sky-Holiday HCI. BCS; 2014. p. 151–60.
  22. Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture. 2014;40:11–9.Article PubMed  Google Scholar 
  23. Barrett MA, Humblet O, Hiatt RA, Adler NE. Big data and disease prevention: from quantified self to quantified communities. Big Data. 2013;1:168–75.Article PubMed  Google Scholar 
  24. Althoff T, Sosič R, Hicks JL, King AC, Delp SL, Leskovec J. Large-scale physical activity data reveal worldwide activity inequality. Nature. 2017;547:336.Article PubMed PubMed Central CAS  Google Scholar 
  25. Carlson JA, Jankowska MM, Meseck K, Godbole S, Natarajan L, Raab F, et al. Validity of PALMS GPS scoring of active and passive travel compared to SenseCam. Med Sci Sports Exerc. 2015;47:662–7.Article PubMed PubMed Central  Google Scholar 
  26. Ellis K, Godbole S, Kerr J, Lanckriet G. Multi-sensor physical activity recognition in free-living. In: Proceedings of ACM international conference ubiquitous computing; 2014. P. 431–40.
  27. UCSD-PALMS-Project—home [Internet]. https://ucsd-palms-project.wikispaces.com/. Accessed 24 Apr 2018.
  28. Resch B. People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. In: Krisp J, editors. Progress in location-based services. Berlin: Springer; 2013. p. 391–406. Google Scholar 
  29. Zeile P, Resch B, Exner JP, Sagl G. Urban emotions: benefits and risks in using human sensory assessment for the extraction of contextual emotion information in urban planning. In: Geertman S, Ferreira Jr. J, Goodspeed R, Stillwell J, editors. Planning support systems and smart cities. Cham: Springer; 2015. p. 209–25 Google Scholar 
  30. Zeile P, Resch B, Loidl M, Petutschnig A, Dörrzapf L. Urban emotions and cycling experience—enriching traffic planning for cyclists with human sensor data. GI_Forum 2016. 2016;1:204–16. Google Scholar 
  31. Healey JA, Picard RW. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst. 2005;6:156–66.Article  Google Scholar 
  32. King AC, Winter SJ, Sheats JL, Rosas LG, Buman MP, Salvo D, et al. Leveraging citizen science and information technology for population physical activity promotion. Transl J Am Coll Sports Med. 2016;1:30–44.PubMed PubMed Central  Google Scholar 
  33. Buman MP, Winter SJ, Sheats JL, Hekler EB, Otten JJ, Grieco LA, et al. The Stanford Healthy Neighborhood Discovery Tool: a computerized tool to assess active living environments. Am J Prev Med. 2013;44:e41–7.Article PubMed PubMed Central  Google Scholar 
  34. Wang CC, Cash JL, Powers LS. Who knows the streets as well as the homeless? Promoting personal and community action through photovoice. Health Promot Pract. 2000;1:81–9.Article  Google Scholar 
  35. Belon AP, Nieuwendyk LM, Vallianatos H, Nykiforuk CIJ. Perceived community environmental influences on eating behaviors: a photovoice analysis. Soc Sci Med. 1982;2016(171):18–29. Google Scholar 
  36. Winter SJ, Rosas LG, Romero PP, Sheats JL, Buman MP, Baker C, et al. Using citizen scientists to gather, analyze, and disseminate information about neighborhood features that affect active living. J Immigr Minor Health. 2015;18(5):1126–38.Article  Google Scholar 
  37. Buman MP, Bertmann F, Hekler EB, Winter SJ, Sheats JL, King AC, et al. A qualitative study of shopper experiences at an urban farmers’ market using the Stanford Healthy Neighborhood Discovery Tool. Public Health Nutr. 2015;18:994–1000.Article PubMed  Google Scholar 
  38. Sheats JL, Winter SJ, Romero PP, King AC. FEAST (Food Environment Assessment using the Stanford Tool): development of a mobile application to crowdsource resident interactions with the food environment. Ann Behav Med. 2014;47:(abstract).
  39. Chrisinger BW, Ramos A, Shaykis F, Martinez T, Banchoff AW, Winter SJ, King AC. Leveraging citizen science for healthier food environments: a pilot study to evaluate corner stores in Camden, New Jersey. Front Public Health. 2018;6:89.Article PubMed PubMed Central  Google Scholar 
  40. Szeto I. Our voice discovery tool [Internet]. Irvin Szeto; 2017. https://play.google.com/store/apps/details?id=edu.stanford.ourvoice.discoverytool&hl=en. Accessed 2 June 2018.
  41. Discovery tool our voice on the app store [Internet]. App store. https://itunes.apple.com/us/app/discovery-tool-our-voice/id1171935766?mt=8. Accessed 7 Jan 2018.
  42. Galaxy Tab E Lite 7.0ʺ 8 GB (Wi-Fi) Tablets—SM-T113NDWAXAR | Samsung US [Internet]. Samsung Electron. Am. http://www.samsung.com/us/mobile/tablets/all-other-tablets/samsung-galaxy-tab-e-lite-7-0-8gb-wi-fi-white-sm-t113ndwaxar/. Accessed 7 Jan 2018.
  43. Feinerer I, Hornik K, Artifex Software, Inc. tm: text mining package [Internet]. 2017. https://cran.r-project.org/web/packages/tm/index.html. Accessed 2 June 2018.
  44. Lang D, Chien G. wordcloud2: create word cloud by “htmlwidget” [Internet]. 2018. https://cran.r-project.org/web/packages/wordcloud2/index.html. Accessed 2 June 2018.
  45. Cheng J, Karambelkar B, Xie Y, Wickham H, Russell K, Johnson K, et al. Leaflet: create interactive web maps with the JavaScript “Leaflet” Library [Internet]. 2017. https://cran.r-project.org/web/packages/leaflet/index.html. Accessed 8 Jan 2018.
  46. Leaflet for R—introduction [Internet]. https://rstudio.github.io/leaflet/. Accessed 15 Mar 2018.
  47. Leaflet—an open-source JavaScript library for interactive maps [Internet]. http://leafletjs.com/. Accessed 15 Mar 2018.
  48. Wand M, updates) BR (R port and. KernSmooth: functions for kernel smoothing supporting Wand & Jones (1995) [Internet]. 2015. https://cran.r-project.org/web/packages/KernSmooth/index.html. Accessed 2 June 2018.
  49. Ruginski I. Visualizing interactive topographic maps using kernel density in leaflet [Internet]. 2017. http://www.ianruginski.com/visualizingtopographicmaps_tutorial.html. Accessed 25 Jan 2018.
  50. ESRI. ArcMap 10.5.1. Redlands, CA: ESRI; 2017. Google Scholar 
  51. San Francisco Basemap Street Centerlines | DataSF | City and County of San Francisco [Internet]. https://data.sfgov.org/Geographic-Locations-and-Boundaries/San-Francisco-Basemap-Street-Centerlines/7hfy-8sz8/about. Accessed 2 May 2018.
  52. Land Use | DataSF | City and County of San Francisco [Internet]. San Franc. Data. https://data.sfgov.org/Housing-and-Buildings/Land-Use/us3s-fp9q. Accessed 2 May 2018.
  53. Getis A, Ord JK. The analysis of spatial association by use of distance statistics. Geogr Anal. 1992;24:189–206.Article  Google Scholar 
  54. Ord JK, Getis A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal. 1995;27:286–306.Article  Google Scholar 
  55. How Hot Spot Analysis (Getis-Ord Gi*) works—ArcGIS Pro | ArcGIS Desktop [Internet]. http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm. Accessed 27 Apr 2018.
  56. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. ArXiv14065823 Stat [Internet]. 2014; http://arxiv.org/abs/1406.5823. Accessed 2 June 2018.
  57. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw [Internet]; 067. https://ideas.repec.org/a/jss/jstsof/v067i01.html. Accessed 1 Feb 2018.
  58. Taylor S, Jaques N, Chen W, Fedor S, Sano A, Picard R. Automatic identification of artifacts in electrodermal activity data. In: EMBC. 2015.
  59. EDA Explorer [Internet]. http://eda-explorer.media.mit.edu/. Accessed 10 Jan 2018.
  60. Cushing CC, Walters RW, Hoffman L. Aggregated N-of-1 randomized controlled trials: modern data analytics applied to a clinically valid method of intervention effectiveness. J Pediatr Psychol. 2014;39:138–50.Article PubMed  Google Scholar