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Smartwatch-based ecological momentary assessments for occupant wellness
and privacy in buildings
Clayton Miller1,∗, Renee Christensen2, Jin Kai Leong1, Mahmoud Abdelrahman1, Federico
Tartarini3, Matias Quintana1, Andre Matthias Müller4, Mario Frei1
1 College of Design and Engineering, National University of Singapore (NUS), Singapore
2 International Well Building Institute (IWBI), New York, NY, USA
3 Berkeley Education Alliance for Research in Singapore (BEARS), Singapore
4 Saw Swee Hock School of Public Health, National University of Singapore (NUS), National
University Health System, Singapore
*Corresponding email: clayton@nus.edu.sg
SUMMARY
This paper describes the adaptation of an open-source ecological momentary assessment smart-
watch platform with three sets of micro-survey wellness-related questions focused on i)
infectious disease (COVID-19) risk perception, ii) privacy and distraction in an office context,
and iii) triggers of various movement-related behaviors in buildings. This platform was
previously used to collect data for thermal comfort, and this work extends its use to other
domains. Several research participants took part in a proof-of-concept experiment by wearing
a smartwatch to collect their micro-survey question preferences and perception responses for
two of the question sets. Participants were also asked to install an indoor localization app on
their phone to detect where precisely in the building they completed the survey. The experiment
identified occupant information such as the tendencies for the research participants to prefer
privacy in certain spaces and the difference between infectious disease risk perception in
naturally versus mechanically ventilated spaces.
KEYWORDS
Field-based survey, Wearables, Longitudinal data, Wellness, Privacy
1 INTRODUCTION
When rethinking urban planning and building designs, practitioners need to evaluate what
features of the current built environment context contribute to the health and well-being of its
occupants (O’Brien et al. 2020; Altomonte et al. 2020; Stazi et al. 2017). One example of this
challenge is the mitigation of airborne infectious diseases such as COVID-19. Designers or
operators would want to understand, for example, which building features could be intersection
zones that cause congestion or to determine which high-touch building features occupants
perceive as a risk of transmission (Kim and Kang, 2021). Another often overlooked aspect is
understanding what building features relate to the feeling of privacy and distraction. It has been
established that the conventional open plan office space provides many opportunities for people
to feel a lack of privacy in addition to visual and noise-based distractions from those around
them (Kim and de Dear, 2013). Finally, with the emerging emphasis on wellness in the built
environment, buildings are designed with indirect encouragements to nudge occupants to make
decisions that will improve their health (Timm et al. 2018). An example is when staircases are
made more prominent and visually appealing to encourage occupants to use them rather than
elevators or lifts (Potrč et al. 2019). Post-occupancy evaluations can help collect data from
occupants to understand better how they perceive their environment; however, they are limited
in the amount and frequency of data they can collect. Wearable technologies can be used to fill
this gap by capturing feedback at high spatial and temporal resolutions that can improve these
design-related efforts (Li et al., 2018).
The Cozie platform was developed to facilitate repeated right-here-right-now surveys across
extended periods of time using micro-ecological momentary assessments (Jayathissa et al.
2019). The platform proved to be an efficient way to collect subjective feedback as compared
to other post-occupancy feedback methods such as smartphone-based surveys. Subsequent field
studies evaluated the platform’s ability to capture a large number of thermal, aural, and visual
comfort feedback to create personalized comfort models (Jayathissa et al. 2020), the ability to
create personal comfort models using spatial proximity (Abdelrahman et al. 2022) and
understand the thermal comfort transition behavior of occupants between different spaces (Sae-
Zhang et al. 2020). Cozie is open source and can be easily modified to include different sets of
questions beyond thermal comfort. Two versions of Cozie have been developed for both the
Fitbit and Apple Watch platforms. This paper outlines three survey flows for Cozie Fitbit
developed to capture occupant preference information related to the perceived risk of infectious
diseases, privacy and distraction in an office context, and the potential triggers of various
movement-related behaviors in the built environment.
2 METHODS
This paper outlines the deployment of three micro-survey question sets, of which, two are used
in a pilot longitudinal study. These sets include 2-7 multiple-choice questions that are answered
using the Cozie watch-face installed on a Fitbit smartwatch. The surveys have a built-in logic
that determines which follow-up question is presented to a user based on their previous answers.
The following subsections outline the three question flows tested in this study. These surveys
were implemented in the open-source Cozie Fitbit project hosted on a public repository on
GitHub (https://github.com/cozie-app/cozie).
Infectious disease risk perception
The first question set relates to an occupant’s perception that their surroundings impact their
risk of acquiring an infectious disease, as shown in Figure 1. This perception is essential in
evaluating whether there are perceived risks to occupants that could be mitigated by changing
some building features. It also identifies occupant risk perception that may not align with the
science of disease spread. These off-target concerns could then be assuaged through occupant
education. The user first starts from the home screen on the upper left and answers the core
question related to their perception of infection risk at that moment and location. Two follow-
up questions evaluate what aspect of risk is specifically concerning for them. Finally, there is a
question to request the number of people within a 5-m radius.
Figure 1. Infectious disease risk perception micro-survey flow overview. The arrows indicate
the flow of questions with a black arrow indicating a single path and a blue arrow indicating
multiple paths based on the option selected.
Privacy and distraction perception
The next question set, seen in Figure 2, focuses on capturing whether occupants feel like they
need more privacy and whether they find their surroundings distracting. The survey begins with
a question about whether someone is in a group or alone and what category of activity they are
undertaking at that point in time. These foundational questions establish the context for the
subsequent ones about distractions. A person working alone has different focus, sound, and
visual privacy needs than a group of people. The following questions focus on whether the
person feels as if they need more privacy, and if so, what are the specific concerns and what are
they worried about in terms of others around them. These follow-up questions dive into whether
the person needs privacy because they worry about others seeing and/or hearing their
appearance, work, or behavior.
Triggers related to movement in buildings
The last set of questions, shown in Figure 3, captures the effectiveness of nudging occupants to
be more active in buildings, i.e., the use of stairways instead of elevators/lifts and adjustable
height workstations. These activities are design-driven, and evaluation of users’ actual behavior
in buildings with these features informs about the adoption and need for education, signage,
and placement. This flow begins with a question asking whether the occupant has used the stairs
or lift in the building in the last one hour and, based on the response, seeks to determine the
reason for that decision. The flow then moves to a question about if and how the occupant is
using an adjustable-height workstation. This question set is included for schematic purposes,
but data from this flow are not included in this paper.
Figure 2. Privacy, distraction, and surroundings impact micro-survey question flow overview.
Figure 3. Evaluation of movement-related decisions micro-survey question flow overview.
Deployment in a university building context
The proof-of-concept test deployment for this paper included six participants who were asked
to wear a Fitbit smartwatch and use the Cozie application. Only data for the first two question
sets are included in this study. The watch generated a vibration-based prompt for feedback
every 1, 2, or 3 hours between 9 am and 9 pm. Each question set was expected to take between
3-9 seconds to complete, and only one set was deployed at a time. Participants were able to
provide feedback outside of the prompts, provided that two consecutive responses were more
than 15 minutes apart. They were required to install an indoor localization app that uses
Bluetooth signals from beacons to track their movements within the case study buildings. The
methodology for the first two question sets was approved by the NUS Institutional Review
Board (IRB) (NUS-IRB-2020-135), and participants were incentivized with an SG$50 voucher.
3 RESULTS AND DISCUSSION
The data collection for this experiment resulted in subjective feedback from six participants
over the span of 30 days. Figure 4 illustrates aggregations of this feedback from the Privacy
and Distraction question flows. For this data set, most of the activity of the test subjects was
solitary, with a strong emphasis on the occupant focusing. Less than half the time, there were
distractions nearby, with the most significant reason being noise. Despite the distraction, the
subjects felt as if they needed more privacy only 6% of the time, and the primary source of that
feeling was requiring more protection from people seeing their work. Figure 5 shows the
aggregated results and Infectious Disease Risk Perception question flow. For this data set,
participants felt for a slim majority of the time that there was an increase of risk of infection,
with ventilation being the most prominent concern followed by surfaces and then people
density.
Figure 4. Aggregated data from the pilot deployment of the Privacy and Distraction flow
Figure 5. Aggregated data from the pilot deployment of the Infection Risk Perception flow
4 CONCLUSION
This paper outlines the development of micro-ecological momentary assessments on a
smartwatch to characterize occupant preference in wellness and privacy topics that have been
previously unexplored. An experiment was conducted that collected spatially and temporally
diverse preference data from real-world occupants of a case study building. The results gave
insight into whether privacy and distraction were issues and how much perceived risk of
infectious diseases was present. In future work, larger case study deployments across different
buildings with a diversity of design objectives could be used to compare design features in the
context of these three wellness and satisfaction objectives. In addition, there are opportunities
to further analyze which spatial regions are better or worse for the objective of each question
flow and why. The anonymized data set and example code for this paper can be found in a
GitHub repository (https://github.com/buds-lab/ema-for-occupant-wellness-and-privacy). The
Cozie platform is available for both Fitbit smartwatches (https://cozie.app/) and Apple Watches
(https://www.cozie-apple.app/) and is designed to be an open-source, community-driven effort
for others to collaborate.
ACKNOWLEDGEMENT
The author team would like to thank several student researchers who contributed to the data
collection process for this publication including Charis Boey Shand Yin, Chua Yun Xuan, Ang
Si Hui Pearlyn, and Tan Jing En Charlene. This research was funded by the Singapore Ministry
of Education (MOE) through the Tier 1 Grants: Ecological Momentary Assessment (EMA) for
Built Environment Research (A-0008301-00-00) and The Internet-of-Buildings (IoB) Platform
– Visual Analytics for AI Technologies towards a Well and Green Built Environment (A-
0008305-00-00). The Republic of Singapore’s National Research Foundation (NRF) through
the SinBerBEST program provided partial support for manpower for this project.
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