Abstract
The ubiquity of mobile sensing and smartphone capabilities offer a significant opportunity to obtain real-world sensor data and momentary mental wellbeing fused at the point of exposure. In this paper, we present the design, implementation and evaluation and user experiences of Urban Wellbeing; a cross-platform mobile application, which aids in quantifying the relationship of the environment, behaviour and mental wellbeing. Urban wellbeing integrates: (i) real-time environmental sensor data in the form of Air Quality Index, (ii) momentary mental wellbeing assessment in the form of emojis, (iii) image and the type of environment and (iv) noise levels in decibels. We report early findings from trials conducted based on the design of Urban Wellbeing to promote engagement. Our preliminary results of Urban Wellbeing, tested with both iOS and Android smartphones demonstrate that it can be effective as a personal environmental and wellbeing sensing application and engaging for users.
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References
Ecological momentary assessment - gov.uk. https://www.gov.uk/guidance/ecological-momentary-assessment
Air, C., Plan, S.: Clean air strategy plan (2011)
Bakolis, I., et al.: Urban mind: using smartphone technologies to investigate the impact of nature on mental well-being in real time. BioScience 68, 134–145 (2018). https://doi.org/10.1093/biosci/bix149, https://academic.oup.com/bioscience/article/68/2/134/4791430
Beute, F., de Kort, Y.A.: The natural context of wellbeing: ecological momentary assessment of the influence of nature and daylight on affect and stress for individuals with depression levels varying from none to clinical. Health Place 49, 7–18 (2018). https://doi.org/10.1016/J.HEALTHPLACE.2017.11.005
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Therapy Exp. Psychiatry 25, 49–59 (1994). https://doi.org/10.1016/0005-7916(94)90063-9
Cummins, R.A., Ps, F.A.S.: Personal Wellbeing Index-Adult (PWI-A) (English) 5th Edition The International Wellbeing Group MANUAL 2013 Personal Wellbeing Index-Adult (2013)
Donaire-Gonzalez, D., et al.: Expoapp: an integrated system to assess multiple personal environmental exposures. Environ. Int. 126, 494–503 (2019). https://doi.org/10.1016/j.envint.2019.02.054
Food, for Environment, R.A.D.: Automatic urban and rural network (aurn)- defra, uk (2018). https://uk-air.defra.gov.uk/networks/network-info?view=aurn
Geetha, A., Ramya, P.S., Sravani, C., Ramesh, M.: Real time air quality index from various locations. Int. J. Recent Technol. Eng. (IJRTE) 9, 368–372 (2020). https://doi.org/10.35940/ijrte.b3493.079220
Haddad, H., de Nazelle, A.: The role of personal air pollution sensors and smartphone technology in changing travel behaviour. J. Transp. Health 11, 230–243 (2018). https://doi.org/10.1016/j.jth.2018.08.001
Johnson, T., Kanjo, E.: Sensor fusion and the city: visualisation and aggregation of environmental & wellbeing data. IEEE (2021). https://ieeexplore.ieee.org/document/9562852
Johnson, T., Kanjo, E.: Episodes of change: emotion change in semantic trajectories of multimodal sensor data. IEEE (2023)
Johnson, T., Kanjo, E.: Urban wellbeing: a portable sensing approach to unravel the link between environment and mental wellbeing (2023). https://doi.org/10.1109/LSENS.2017.0000000, http://www.michaelshell.org/contact.html
Johnson, T., Kanjo, E., Woodward, K.: Digitalexposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning, January 2021
Kanjo, E.: NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping. Mob. Netw. Appl. 15, 562–574 (2010). https://doi.org/10.1007/S11036-009-0217-Y
Kanjo, E., Younis, E.M., Sherkat, N.: Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach. Inf. Fusion 40, 18–31 (2018). https://doi.org/10.1016/j.inffus.2017.05.005
Laville, S.: Air pollution a cause in girl’s death, coroner rules in landmark case—london—the guardian (2020)
Lee, B.J., Kim, B., Lee, K.: Air pollution exposure and cardiovascular disease. Toxicol. Res. 30, 71–75 (2014). https://doi.org/10.5487/TR.2014.30.2.71
Maitre, L., et al.: Human early life exposome (helix) study: a European population-based exposome cohort, September 2018. https://doi.org/10.1136/bmjopen-2017-021311
Nieuwenhuijsen, M.J., Donaire-Gonzalez, D., Foraster, M., Martinez, D., Cisneros, A.: Using personal sensors to assess the exposome and acute health effects. Int. J. Environ. Res. Public Health 11, 7805–7819 (2014). https://doi.org/10.3390/ijerph110807805
Perkbox: The 2018 UK workplace stress survey—perkbox (2018)
Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008). https://doi.org/10.1146/ANNUREV.CLINPSY.3.022806.091415, https://pubmed.ncbi.nlm.nih.gov/18509902/
Asimina, S., et al.: Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environ. Monit. Assess. 190(3), 1–12 (2018). https://doi.org/10.1007/s10661-018-6537-2
de Vries, L.P., Baselmans, B.M.L., Bartels, M.: Smartphone-based ecological momentary assessment of well-being: a systematic review and recommendations for future studies. J. Happiness Stud. 22(5), 2361–2408 (2020). https://doi.org/10.1007/s10902-020-00324-7
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Johnson, T., Kanjo, E. (2023). Designing an Interactive Mobile Assessment Tool to Quantify Impact of the Environment on Wellbeing. In: Salvendy, G., Wei, J. (eds) Design, Operation and Evaluation of Mobile Communications . HCII 2023. Lecture Notes in Computer Science, vol 14052. Springer, Cham. https://doi.org/10.1007/978-3-031-35921-7_20
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