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Nursing comfort study: are hospitals performing for nurses? IEQ analysis of working conditions inside hospitals

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Nursing Comfort Study: Are Hospitals Performing for
Nurses?
IEQ Analysis of Working Conditions Inside Hospitals

By
Brandt Bradley

Presented to the
FACULTY OF THE
SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In partial fulfillment of the
Requirements of degree
MASTER OF BUILDING SCIENCE

August 2019

i

Acknowledgements
Committee
Before all others, I would like to thank my thesis advisor Joon-Ho Choi of the Master of
Building Science program (MBS). Whenever I was stuck or had a question about my research,
Choi was willing to help. He consistently allowed my paper to be my own work, but gave good
direction whenever he thought it was needed.
I would also like to acknowledge Professor Marc Schiler of the Master of Building Science
program at USC for his role as my second reviewer. I am indebted to his effort to better my
thesis, from topic suggestions to final comments. Marc was there throughout the entire process
and was invaluable toward my completion.
I would also like to acknowledge Professor Kyle Konis of the Master of Building Science
program at USC for being my third committee. I am grateful to his valuable comments through
my writing, from how to display information to inconsistency in my writing.
As my final committee member, I would like acknowledge Brooke Baldwin-Rodriguez for her
assistance in securing the research site and facilitating the research. Also smoothing over the
few minor challenges with some of the floor staff over the course of the study.
MBS Friends
I want to thank the Master of Building Science programs candidates for their continued support
throughout my time at the program.
Notably, I would like to thank Ivan Monsreal for being my desk partner for the duration of the
program. Consistently providing feedback on my ideas and helping me work through the
problems. For the lunches and muffins, we took to discuss the thesis.
In addition, I would like to thank Casey and Erica for their conversations and their ideas about
how to progress my thesis.
Isik Goren, thank you for the consistently being there to provide distractions from thesis.
Without those minor distractions, I may have gone crazy.
For all those in the Master of Building Science I did not mention, thank you for the constant
friendship and discussions.
Finally, I must express my gratitude to my Rachel Kartin for her continuous encouragement and
support throughout the thesis process. In addition to her, continuously helping correct my
grammar and other written mistakes. This accomplishment would not have been possible without
her.
Family
ii

Thank you for putting me through school all this time. Believing in me to succeed even when, I
may not have always known where I was going. For always being there and offering help
whenever you could.
iii

Committee Members

Chair: Joon- Ho Choi
Title: Assistant Professor of Building Science
Affiliation: USC
Email: joonhoch@usc.edu

Second Committee: Marc E. Schiler
Title: Professor of Architecture
Affiliation: USC
Email: marcs@usc.edu

Third Committee Member Kyle Konis
Title: Assistant Professor of Architecture
Affiliation USC
Email: kkonis@usc.edu

Fourth Committee: Brooke Baldwin-Rodriguez
Title: Associate Chief Nursing Officer
Affiliation: USC
Email: Brooke.Baldwin-Rodriguez@med.usc.edu

iv

Abstract

Hospitals are the place where people go to get over an ailment with the expectation of receiving
proper treatment with a quick recovery time. Among all of the staff at the hospital, Nurses are
one of the fundamental workers towards patient recovery. Nurses are involved in all stages of a
patient’s care, from signing the patient in to walking them out the door. Nurses are the front line
of defense for a hospital, ensuring that patients receive proper treatment. Nurses are essential to
patient recovery. Therefore, their comfort should be a major concern as it directly linked to their
productivity. A hospital should respond to their nursing staff’s comfort needs to produce higher
results from its nurses. By minimizing nurse discomfort a hospital can expect a nurse to have
higher productivity values. Nurses are essential to patient recovery. Nurses should not be an
afterthought when it comes to setting up the buildings comfort control systems. It is of the
utmost importance that nurses be given respect inside their space in terms of comfort. Using IEQ
driven data, occupant surveying, and observational information allows us to understand how a
Nurse is considered in their space. The goal of this research is to determine the impacts of IEQ on
hospitals nursing staff. In doing so determine the IEQ satisfaction and workplace productivity of the
users.

The study adopted several data collections methods to produce relevant data. The first data set
came from installing and monitoring sensors around the common work areas of the nursing staff
to collect the IEQ of the space. The second data set came from the nurse comfort surveys.
Lastly the third data set came from architectural observation. Data collection occurred over
several days to ensure that anomalies were minimized in any of the data sets. Each data set
provides additional layers of information about what is occurring inside of the hospital. The
direct monitoring of the IEQ of the space told us the present conditions of the workstations, once
compared to the occupant survey a more complete picture can be made and the problems start to
become clear. After that the architectural observation is used to see what features appear to cause
the most discomfort among the staff. Analyzing and comparing the data sets, makes it possible to
evaluate how comfortable nurses are in their environment. The third step was to produce design
guide recommendations based on the analysis results. In conclusion nurse’s complaints were in
alignment with one another and overall, it was found that IEQ factors were insufficient to some
degree and impacted them negatively. Future research should use a larger data set generated from
several hospitals. To proof the work done in this thesis is able to be used in other hospitals.

KEY WORDS: IEQ, Occupant comfort, Occupant Productivity, Nurse, Data Driven

v

HYPOTHESIS

- Hospitals are not appropriately conditioned to meet the Indoor Environmental Quality (IEQ) of Nurses
who are working the wards.
- Of all of the IEQ factors thermal comfort is the leading factor that Nurse’s care about the most.
- Improving IEQ factors that nurses find unsatisfactory can lead to higher nurse productivity inside of the
hospital….. (future work)

RESEARCH OBJECTIVES
 Investigate IEQ and the impact of IEQ factors inside of a hospital
 Develop an understanding of what Nurses think of their environment, in regards to their comfort
level.
 Study productivity and causes of unproductivity with a focus on nurses
 Establish recommendations towards alleviating nurse discomfort inside of Hospitals, in the form
of design or control guidelines.

Contents
Acknowledgements ........................................................................................................................ i
Committee Members ................................................................................................................... iii
Abstract ......................................................................................................................................... iv
1. Introduction ............................................................................................................................... 1
2. Background and Literature Review ........................................................................................ 5
2.1 IEQ Fundamentals ................................................................................................................ 5
2.1.1 Thermal Quality ............................................................................................................. 7
2.1.2 Air Quality ..................................................................................................................... 8
2.1.3 Relative Humidity ........................................................................................................ 11
2.1.4 Sound Quality .............................................................................................................. 12
2.1.5 Productivity .................................................................................................................. 13
2.1.6 Lighting and Visual Quality......................................................................................... 13
2.2 Sick Building Syndrome (SBS) .......................................................................................... 16
2.3 Nurse Productivity .............................................................................................................. 18
2.3.1 Nurse Retention ........................................................................................................... 19
2.4 Summary of Points .............................................................................................................. 20
3. Methodology ............................................................................................................................ 22
3.1 Methodology Diagram ........................................................................................................ 22
3.2 Data Collection from figure 14 ........................................................................................... 23
3.2.1 On-site Environmental Measurements ......................................................................... 23
3.4.2 User Satisfaction Reporting ......................................................................................... 26
3.4.2 TABS(Technical Attributes Building Systems) Report ............................................... 26
3.4.3 Data Report .................................................................................................................. 27
4 Survey Results .......................................................................................................................... 33
4.1 Collection Outline ............................................................................................................... 33
4.1.1 Timeline of the Data Collection ................................................................................... 33
4.1.2 Response Data from Collected all Surveys .................................................................. 34
Data Metric ........................................................................................................................... 34
Fall ........................................................................................................................................ 34
Spring .................................................................................................................................... 34
Overall- Chart ....................................................................................................................... 34
Overall-Graph ....................................................................................................................... 34
Time Spent at Nursing Station .............................................................................................. 34

Gender Breakdown ............................................................................................................... 34
Age of Occupants .................................................................................................................. 35
Occupation ............................................................................................................................ 35
Education Level .................................................................................................................... 35
4.2.1 Comparison by Seasonal Variation .................................................................................. 38
4.2.2 Comparison for Building Section and Floor .................................................................... 41
1) Comparison by which Building the Responses were Collected in ................................... 41
2) Comparison by which Floor the Responses were Collected in ........................................ 44
4.2.3 Personal Attributes (Age, Occupation, Gender) .............................................................. 46
1) Comparison by Age .......................................................................................................... 46
2) Comparison by Occupation........................................................................................... 50
3) Comparison by Gender ................................................................................................. 53
4.2.4 Comparison by Self-Reported Productivity ..................................................................... 56
4.3 Nurse Decision Tree ........................................................................................................... 57
5 T-hospital: Discussion based on the Data Reported ............................................................. 59
5.1 T-hospital: Lighting evaluation........................................................................................... 61
5.2 T-Hospital: Space Analysis................................................................................................. 63
5.3 WEKA Analysis.................................................................................................................. 63
5.3.1 Analysis from the Survey Alone .................................................................................. 64
5.3.2 Analysis of Productivity based on Building Section and Floor ................................... 66
5.3.3 Analysis of Productivity based on Gender, Age, Occupation, and Education ............ 68
5.3.4 Analysis of Productivity based on Gender and Age .................................................... 70
5.3.5 Analysis of Productivity based on the Measured Data ................................................ 72
5.4 Summary of the Data Analysis ........................................................................................... 74
6. Conclusion of T-hospitals Investigation into IEQ Comfort ............................................... 74
6.1 Proposed Guidelines ........................................................................................................... 77
Issues One ............................................................................................................................. 77
Issue Two .............................................................................................................................. 77
Issue Three ............................................................................................................................ 78
Issue Four .............................................................................................................................. 79
Issue Five .............................................................................................................................. 80
6.2 Limitations of the current methodology ............................................................................. 81
6.3 Future work ......................................................................................................................... 82
7. Bibliography ............................................................................................................................ 84

8. Figure Sheet ............................................................................................................................. 88
9. Appendix .................................................................................................................................. 89
ASHRAE Standards .................................................................................................................. 89
1) Temperature ...................................................................................................................... 90
2) Humidity ........................................................................................................................... 91
3) CO2 ................................................................................................................................... 92
4) Sound levels ...................................................................................................................... 93
Histograms of occupant satisfaction values covering the 7 values ........................................... 94
1) Thermal ............................................................................................................................. 94
2) Space ................................................................................................................................. 95
3) Light.................................................................................................................................. 96
4) Noise ................................................................................................................................. 97
5) Visual ................................................................................................................................ 98
6) Privacy .............................................................................................................................. 99
7) Air ................................................................................................................................... 100
Summary of Histograms ..................................................................................................... 101
Data ............................................................................................................................................. 102
A) Image Processing ............................................................................................................ 102
B) Glare Readings ................................................................................................................ 110
C) e-BOT readout ................................................................................................................ 112
D) Additional Survey responses........................................................................................... 115
E) KECK Floor Plans .......................................................................................................... 121
F) Hand Measurements........................................................................................................ 122
e-BOT measurements.................................................................................................................. 124
T-hospital: Comfort Ranges .................................................................................................... 126

1

1. Introduction

IEQ is one the of features that determines whether a building is successful or not over its
lifetime. IEQ should be a major concern when considering the occupants of the building as
people come with the requirement that they want to be comfortable inside of their space. People
are creatures of comfort, whenever they’re not comfortable they improvise or adapt to make
themselves comfortable. People use their intelligence to make better places where they work,
eat, sleep, play. People will continue to adapt, improvise, and change their environment to ensure
their comfort needs are being met. Rather than leaving that to that in the hands of all users a
building should already be taking into account their needs. As people continue to adapt and
improvise to change our environment to suit their needs.

The building should perform to meet the needs of the people who work in the space to
such a degree that the users are not stopping their work to modify their environment to reach a
desired comfort level. “A building is considered as performing well if the users of the building
appraise it so, and especially when it provides the users with healthy, and comfortable indoor
environment that enhance their productivity and satisfaction.”[1] Occupants determine if the
building is working, not the designers. Regardless of a designer’s good intentions a building can
still fail if the occupants are not satisfied with the building. “In industrialized countries about
90% of the time is spent indoors”[2] People are spending more and more time indoors than in
our past, this is a continuous trend that only gets worse as people age.

Hospitals are the places people go to receive treatment or recover from an ailment.
Hospitals are important to the health of the community as a whole. A hospital is key to keeping
the workers from remaining sick for too long. It is possible to say a hospital is the immune
system of the social body, keeping everything running and operating in a healthy fashion. “They
have traditionally been buildings in which to be cured, shelters in which to suffer and be sick, to
be born, and to die.”[3] Hospitals have been in our culture for a long time and are here to stay.
However not every hospital was built using the best standards with the best conditions possible.
Sometimes the buildings are maintained until the next hospital is built to meet a higher standard
for patient care. Those buildings that are still in use and have not yet met the requirements to be
torn down and rebuilt, can suffer from sick building syndrome (SBS). These buildings are still
used even though they may be the cause of stress on their occupants.

There is a lack of care of Nurses comfort inside of a hospital[4]. Patients are the primary
focus for environmental aspects or controls such as access to the view outside, as this has been
shown to improve recovery times.[5] Nurses are one of most essential people that make or break
a hospital. In most cases nurses, orderlies and nurse’s aides (hereafter referred to in general as
nurses) take care of nearly all minor tasks inside a hospital involving a patient that does not
require a doctor or a surgeon. When nurses are not present, a hospital ceases to function at a
sustainable level in current society. Nurses and nursing as a profession has become the backbone
of the healthcare industry and yet very little if anything is done to respond to a nurses’ IEQ need.
Nurses should not be a second thought when it comes to a building’s occupant comfort level.
Since nurses are instrumental to the success of a hospital, a little more care should be shown to
their comfort. Ensuring that they’re comfortable is a necessary factor to keep them working.
2

The difference between a good and a great hospital is the staff that facilitates the
operation of the building. The current method of bringing interior environmental quality (IEQ) to
acceptable levels is to rely on a set of standards that were determined long ago. No building is
the same and every building faces a unique set of challenges that complicate the standard
approach to conditioning the interior. Rather than relying on standards that don’t work in every
situation, the building should adapt to the occupant to produce better results. Instead of
constantly flooding the interior spaces with set values that do not take account of the actual
occupants, it would be beneficial to study a hospital post occupancy to see what the staff feel is
acceptable. One of the issues with setting a standard and forcing that onto all as comfort is that
nurses, like all people, are unique and unlikely to share identical comfort values. Ideally the
building would respond to the demands of the occupants before they reach a point of
acknowledging the discomfort. Until the time that the building systems correctly respond to the
occupant needs it is imperative to address occupant comfort inside of spaces.

Approaching a hospital with the idea that their staff is not like any other staff would
allow more personalization to the hospital, giving more comfort to their nursing staff. People are
able to adapt to their environment and will find their homeostasis in their environment. That
being said, while they try to reach balance they are distracted and not being productive inside
their work area. The tendency for people to adapt was of great interest to researchers and led to a
field of study around adaptive approaches to comfort. One such study by J.F. Nicol found that
laboratory tests should be retested in the environment before they become comfort standards. In
addition to this, they found that people are inclined to be less comfortable if they have no control
over their space. People desire the ability to modify their space to meet their needs.[6] These two
points are important as current building standards are based on ASHRAE or other similar
standards and often don’t allow for the users to modify their space settings.

This is not saying that the work done in creating the standards is wrong or without
reason, more that the standards are not going far enough. Almost all standards were conducted in
a predefined test bed with controlled conditions that then were repeated until a set condition was
found to be acceptable inside the controlled space. Buildings are not designed like those
controlled test beds, they are unique and as such should be given the opportunity to be modified
to meet their occupants needs. There has been recent work studying how to adjust set points for
different groups of occupants. Some of the recent work done by former MBS students include
Kyeongsuk Lee and Jehyunn Moon. Both of these students investigated the IEQ of office spaces.
Kyeongsuk focused on 2 similar office buildings in Los Angeles area to study the occupant IEQ.
Jehyunn Moon took a broader approach and worked through 9 different buildings of varying
make up and workforce. Both of these researchers found that occupants indeed reacted to their
IEQ to varying degrees and made recommendations on how to improve the office spaces.[7], [8]

IEQ of a space is broadly based on a few key aspects, thermal comfort, air quality,
relative humidity, sound quality, and lighting and visual quality. Each of these aspects affects
each person differently and as such can vary in importance per person. Nurses are people, they’re
impacted by the same IEQ factors that affect their patients and yet the nursing area is entirely
outside the nurse’s control.

3

Thermal comfort is often the first factor that comes to minds of many uncomfortable
workers in general. When surveys are taken of workers in buildings, the primary complaint is
that it’s too cold or it’s too hot. Based on a CareerBuilder survey, 46% of complaints were of this
nature[9]. Although even when the means to resolve a comfort issues arises people may still
choose to not solve their comfort issue, in a recent finding “adaptive opportunities are not always
reflected in actual behaviours”[10].This makes it all more important that the system is able to be
modified as people may not take advantage of other options.

Relative humidity is one of the ways a building condition its. The process of remediating
the air often requires a process of dehumidifying and dehumidifying the air to make the
conditioned. This is part of the set standards that people generally approve of some value so the
system gives that value regardless of condition. Hitomi Tsutsumi found that nurses were able to
notice when the air was too dry or too wet inside a space. In addition it was found that there were
several acceptable ranges depending on the research candidate.[11] Nurses vary and like varied
conditions from one another.

Air quality is important towards determining comfort. Air is the means at which other
factors like temperature or humidity are pushed through a buildings system HVAC (heating,
ventilation, and conditioning). The quality of air is often where problems arise, Sandra Verde
found that “the hospital environment is a dynamic microbial environment, but they also identify
specific factors that may significantly influence the airborne concentration”[12]. Hospitals are
perfect growing beds if not maintained to the highest of standards. If any slip up happens an
infection can spread throughout the hospital before and impact both nurse and patient. It is
imperative that the air quality be maintained to a high standard to ensure it is safe.

Sound quality is key as Nurses are constantly exposed to a variety of on the job sounds. A
study in Hong Kong found that occupants are observant and impacted by sounds.[13]. Sounds in
a hospital can come from the patients, the medical machines, the HVAC, etc. Sound comes from
everything and has to be mitigated carefully to keep it safe. Hill Jn in a recent study, noted
“decreased satisfaction, sleep disturbance, and higher incidence of rehospitalization in patients”

To measure the success of any worker , you look at their productivity. “Generally
productivity is all about speed and accuracy”[14]. That being said, a worker has to be
comfortable in order to reach their individual performance limit. In general, workers have to be
focused when at work in order to perform. Any distraction would result in delays. Recent studies
are finding that productivity can be aided by being comfortable inside a workspace. One of the
signs “Staffs that are not comfortable will take more breaks and loss of concentration in doing
their task.”[14] These minor interruptions can lead to a substantial loss in productivity over time.
These are often no fault of the employee, as the spaces are conditioned to set standards and don’t
fluctuate or give the users ways to modify, only enhancing their discomfort.
For a hospital distraction to lead to nurse errors, these errors may just be a simple failure
to mark something down all the way to grabbing the wrong medicine. There is little room for a
nurse to make an error or be unproductive inside a hospital setting. Job disruptions take a nurse
away from their duties on the floor to their patients. One such disruption is procrastination.
There has been a recent investigation to determine the cause of the disruption. “direct linkage
between procrastination and job stress, also there is a linkage between all dimension of
4

procrastination and job stress”[15]. Malikeh Beheshtifa found that people will take breaks from
work if having been exposed to too much stress.

Nurses are the life blood of a hospital and carry many of the responsibilities that go into
the success of a hospital. Nurses are highly stressed highly motivated people and often times not
considered by outsiders as important. "The past decade has been a turbulent time for US
hospitals and practicing nurses. News media have trumpeted urgent concerns about hospital
understaffing and a growing hospital nurse shortage”[16] This becoming a bigger issue as more
nurses are needed than ever before. One of the leading ideas is that nurses are being overworked
by having too many patients, this gave way to laws being written to limit the patient to nurse
ratio. “Job dissatisfaction among hospital nurses is 4 times greater than the average for all US
workers, and 1 in 5 hospital nurses report that they intend to leave their current jobs within a
year.”[16] This is where the problems begins, with people saying they’ll quit and new people not
coming in to replace the quitting work force. As the problem continues, shortages of capable
nurses are showing up everywhere, which leads to a reduction in the quality of care of the
patients. “Nurse absenteeism may disrupt the working environment, contributing to greater
difficulties in handling workload and affecting employee morale” [17] This is a vicious cycle that
causes great stress on nurses. In addition “Negative work environments, particularly insufficient
staffing, are related to nurse dissatisfaction, absenteeism and turnover; poorer nurse
performance; and worse patient outcomes.” [17]

When the work environment is not promoting worker comfort, it is causing distractions
leading to on the job errors. These errors may be as small as simple reading errors, but can lead
to pretty severe consequences inside of a hospital. “According to the global statistics, everyone
in 300 errors leads to death.”[18] This may not be an issue worth investigating, however “In the
United States, these errors are about 750000 cases, and their mortality rate has been between
44000 to 90000 cases.”[18]. Nurses are people. They make mistakes; their mistakes can cost
people their lives.

Therefore, this study is focused on identifying the IEQ impacts on the nursing staff. The
outcome of will be given as evidence-based design strategies for healthcare facility designs. With
the new developed guidelines, hospitals should become more comfortable to nurses. The guides
should help in reducing the potential causes for nursing error, while promoting a more working
conducive atmosphere.

5

2. Background and Literature Review

2.1 IEQ Fundamentals
Indoor Environmental Quality (IEQ) is one metric that determines the success of a
building. One reason for this is that IEQ has been linked to comfort, health, and productivity of
occupants. The CDC refers to IEQ as “the quality of a building’s environment in relation to the
health and wellbeing of those who occupy space within it” (CDC). The government has
acknowledged that IEQ does impact workers. This, along with private organizations like
ASHREA is making acceptable standards part of the new building codes. When a building does
not reach acceptable standards, it is possible for workers to become sick.

The masters of building science (MBS) program have attempted and completed several
post-occupancy evaluations (POE). In more recent years a study conducted by Jehyunn Moon
found that “significant relationships existing between IEQ satisfaction and human factors”[19].
Although the focus was aimed at an office environment the goal was focused on the satisfaction
of occupants. Inside their work they were able to make the connection between environmental
satisfaction and other IEQ variables being affected by human characteristics such as age or
gender. The end result of their data collection were a set of recommendations on how to remedy
IEQ issues through building systems control. One of the down sides to this test case was the
limitation of age groups inside the occupants, as such the data is always skewed toward the
majority making the minority opinion nearly worthless. In addition, instead of focusing the
efforts to analyze a single space the researcher broadly reviewed nine offices that shared similar
compositions but may be for different uses.

One of the benefits of this POE was the sheer size of data the researcher collected in a
short time, with just over 400 measurements. Through the use of ordinal logistic regression,
two-sample t-test, and a decision tree, the various IEQ results started to show a pattern. The
regression model utilized “95% being adopted as a statistically significant threshold in this
6

study”[19]. This allowed the data to be more valid as it was considered true. In the below figure
are the results of the collected surveys showing occupant evaluations of the IEQ.
Figure 1- Rose chart of survey questions

In another master’s thesis produced by the MBS group, the focus was investigating
modern office buildings using POE evaluation. The strength of this thesis compared to the earlier
one was the purposeful selection of site. Rather than taking any office and comparing the results
broadly. Kyeongsuk Lee instead used two very similar offices in downtown Los Angeles to
compare data. One of the early findings was “While actual IEQ conditions were almost the same,
the user groups reported different environmental satisfaction levels” [8]. However the study
found that users tended to be satisfied with the given level of several of the IEQ factors. The
most common trend was for human factors to have an impact on “user's environmental
perceptions, especially regarding lighting quality”[8]. One of the weaknesses of this work was
that there is no account for the office condition present, but yet there is a great deal of thought
involving the office. In the usage of instrumentation this thesis adapted the e-BOT to measure the
individual workstations IEQ. This helps solidify the results by showing very select regions of the
office as compared to others.

To interpret the results of all the surveys collected, a one-way analysis of variance
(ANOVA) and other statistical tools were used to organize the data and pull out the pattern from
the data. By organizing the data into clusters, the data begins to make sense and a pattern
7

emerged of “occupants were moderately consistent with the under 5 years time frame and the
more than 30 h”[8]

The benefit of these works is their focus on the office setting, both generate findings
through a physical data collection process. Both utilize an array of sensors to test the IEQ of the
workspaces, surveys to measure the occupant feeling toward the space, and finally, to some
degree take account of the building as a representable feature.
2.1.1 Thermal Quality

Ismail Budaiwi investigated how to fix thermal comfort problems in occupied structures.
In their initial findings, it was stated that “Creating suitable thermal conditions for satisfying
human desires for thermal-comfort has been recognized to be an essential requirement of the
indoor environment”. [20] It is vital for a space to be created that takes into account people’s
comfort. When people are pushed outside their comfort, they will begin to look for ways to
become comfortable again, often through the use of mechanical systems such as HVAC[21].
Throughout the history of humankind there has shown that when faced with a problem about
comfort people found a solution to their individual problem. When faced with discomfort in it
has been seen that “occupants remain comfortable by using localized heating or cooling to
maintain individual comfort using a personalized conditioning system” [10] Current methods to
determining comfort of a space are too limited. Budaiwi found that the ASHRAE and Fanger
[22]comfort model rely on a stable condition inside the space to begin any type of analysis. This
idea is flawed, as a real space changes and varies across the space with very few spots actually
being the set value. Many studies have shown that air velocity plays a role in determining the
comfort of occupants[23]. In addition Fanger in an earlier paper believes that time “No
significant difference was observed between ambient temperatures preferred by subjects in the
morning and in the evening.”[24],. One such study was conducted by the Berkley Lab indoor
environment group found that “Indoor temperature is one of the fundamental characteristics of
the indoor environment. It can be controlled with a degree of accuracy dependent on the building
and its HVAC system.”[25]. Although the current methods to analyze a space are limited the
goal still remains of “Achieving thermal-comfort is usually paramount in buildings involving
people occupancy”[20]. Thermal comfort problems inside of a building are best addressed as soon
as possible. But, they're often hard to identify immediately. A proposed solution to the issue is a
systematic method to identify the thermal comfort issue and resolve it.

Budaiwi made great progress in validating the importance of thermal comfort, however,
IEQ is more than just thermal comfort. For a building to be successful a full analysis of a space
needs to be done to resolve any IEQ issues. Where thermal may be the most apparent it is not the
only possible issue, or may not be the easiest to fix.

Madhavi Indraganti studied the impact of using adaptative thermal comfort models rather
than set points to solve thermal comfort. The study included over 100 subjects to see where
moderate temperatures were acceptable. “Thermal comfort indoors is very important to the
designer as poor comfort leads to high energy consumption and affects the users' health
adversely”[26] One of the benefits to this type of research is the goal is to reduce energy while
meeting occupant needs.
8

Figure 2: Comfort based on Thermal. [26]
Based on these analyses of indoor temperatures and thermal sensations across the term of
the study, occupants find being around 30 degrees Celsius or warmer they would be comfortable.
The value of 0 is considered neutral comfort, with + being warmer and – being cooler. When the
occupants were not thermally comfortable it was noticed that cooling systems were in use and
resulted in higher energy demand of the space.

Spaces can be designed to aid the occupant in thriving inside unfavorable conditions
“…strong emphasis on the design, application, and the use of controls.”[26] are required to make
a space usable by the occupant .When a space is designed to meet the needs of the occupant,
occupants in turn don’t require additional comfort, reducing the distractions in the space.
Comfort does not need to be expensive nor does it need to be systematically applied to a space.
People are constantly adapting to their space and if put into a space that is not satisfactory, they
will use their tools to fix their comfort problem. In the case of India, users turn to ac in the
summer months. These AC’s are neither efficient nor solutions to the issue, the building was not
designed to accommodate the users and this is their response because their home is not meeting
their need for thermal comfort. The users have taken it upon themselves to find a way to meet
their comfort level through the personal modification of the space. The space should of started
meeting the comfort value for the occupants rather than the occupants having to make changes.
In the modern office building occupants are plugged into the central system.

2.1.2 Air Quality

Sandra Cabo Verde set out to research the impact of poor air quality on hospital acquired
infections. In their study they sought to “confirm that the hospital environment is a dynamic
microbial environment, but they also identify specific factors that may significantly influence the
airborne concentration”[12] Hospitals are at risk of microbe growths taking off in the air
handling system of a hospital. To validate their hypothesis air was collected in three hospitals
emergency services rooms to see if there was a cause for alarm.
9

Study of Bacteria inside ventaliation [12]

From their investigation it was found that some rooms inside of a hospital had “exceeded
conformity criteria for bacteria defined in national legislation”[12]. It was also noted that
bacteria were found in higher concentrations during winter than summer, this finding has been
noticed over several other investigations.

Additionally, the study also found that “fungi were found to be significantly higher than
outdoors, suggesting fungal contamination sources from”[12] This finding leads to the idea that
hospitals’ air handling systems are inefficient at scrubbing the air clean.

Gunaratne investigated IAQ based on the
staff activities associated to a hospital’s
theatre. The test location was a hospital in
Sri Lanka, inside the operating room. The
main purpose of this study was
investigating the gas concentrations and
their long-term impacts on the occupants.
A side study was to see if the current air
handling system was sufficient enough to
vent the space of gas build up.
ng of a surgery

The operating room in the figure
on the side that was used in the test of the
study: The space did not hold a constant
population density; however, it was noted
that the occupant density fell throughout
a surgery. The equipment inside the space
was considered top of the line or new,
however, the space was considered old.
The investigation took measurements
over several days to accumulate data
across different surgeries. The room was operated as normal, in order to understand how
the staff felt about the space a survey was issued.
Figure 3- Operating room[27]
10

The survey results showed that the occupants were indeed suffering from some type of
discomfort. The majority reported headaches and lethargy. In line with other sick building
syndromes noticeable amounts of the building’s occupants complained about some type of
irritation often times throat irritation. Overall the symptoms created a link between IAQ and
SBS. The build up of volatile organic compounds (VOCs) inside the space was due to the
process of administering sedative drugs or preparing patients for surgery. Some of the
compounds believed to be an issue were the bactericide, germicide, and antiseptic drugs such as
ether were used prior to surgery and off gassed during the surgery. It was noticed that although
the medical staff did not display any immediate issues to this, the researchers had trouble
breathing. [28] The isssues began appearing after some time had passed for the surgery team. It
was theorized that they had become acoustomed to the elevated VOC’s that at times could reach
50x normal values. [28]

With more emphasis being given to healthy buildings to combat poor IAQ. New
buildings seeking to meet LEED standards are put through a series of tests, one of which requires
a building to flush its occupied spaces fully with fresh air to reduce the impact of VOC’s.
“Path 1. Before occupancy
Install new filtration media and perform a building flush-out by supplying a total air
volume of 14,000 cubic feet of outdoor air per square foot (4 267 140 liters of outdoor air per
square meter) of gross floor area while maintaining an internal temperature of at least 60°F
(15°C) and no higher than 80°F (27°C) and relative humidity no higher than 60%.”[29]
Even after a building has received a flush out, it is still required by LEED standards to be
monitored. This ensures the building that there is no buildup of hazardous chemicals in the
air.[30] If any chemical is found in above the allowed amount, the issue must be solved and the
entire test must be repeated.
Figure 4: Exposure Levels and Discomfort[28]
11

Figure 5- Sampling point for Maximum VOC Concentration[31]
“Buildings and spaces with good indoor environmental quality protect the health and
comfort of building occupants” [31] LEED has built itself on the promotion of the occupants
well-being above the cost of doing things. In addition, LEED buildings are required to be well
documented and studied to ensure quality is being met inside their building. LEED
acknowledges that the goal has to constantly change and improve as such their standards are
updating and producing better change through incremental updates.
2.1.3 Relative Humidity

Hitomi Tsutsumi ran experiments with the focus on Humidity to understand the impact of
humidity on the productivity and comfort of humans in office conditions.[11] Through the use of
a two chamber environment (figure 6) allowing subjects to pass from the warm humid condition
into the natural chamber. Users reported that “While the humidity sensation vote in Chamber 1
was around ?2, ‘‘humid’’, subjects reported their humidity sensation vote near 0, ‘‘neutral’’, just
after entering Chamber 2.”[11]. Showing that users were aware of the change in humidity in the
two spaces. Subjects also showed varying results to humidity change with 70% humidity
showing more tiredness in their workspace.
12

Figure 6-Plan of climate chambers.

In Li Lan’s study a relationship was “..suggest that the optimum performance is achieved
when people feel slightly cool..”[32]. An easy way to achieve this slight cool factor inside a
space is through the control of the relative humidity (RH). In a paper by Jorn Toftum claims that
“…humidity affects comfort are not completely known”[33]

Figure 7-Percentage of Persons Dissatisfied

2.1.4 Sound Quality

Sound is an important part of the work experience of the common worker. Sounds are
generated in all modern buildings in some fashion.. The sounds found inside of the building can
come from a few areas which are not always inside the building. HVAC and other building
features generate sound during their operation, which is then bounced around inside of the
building. In addition, the occupants generate sound from their activities inside the work area.
13

Another sound that shown to have some impact on workers is the sound from outside, from cars
to the weather.

The results of a study by CM MAK suggest that the most annoying sources of sound
inside a workplace came from “conversation, ringing phones and machines”[13]. Although the
sounds were the most common issues among workers, further investigation showed that not all
workers found the same sound annoying. When broken down into two types, high and low
productivity worker sets, the lower productivity workers reacted stronger to background noise
and human activity, and exterior environment sounds. Sound affects all workers to some degree
whether high or low performer . Of all the IEQ factors, sound rates fairly high to modern office
workers. By reducing the clutter of distracting sounds it may be possible to enhance worker
experience in their space.

2.1.5 Productivity

The measure of productivity differs between jobs as each workplace requires different
levels of acceptance. “diversity of the service industries has meant that each field has
developed its own productivity measures”[34]. In a general sense productivity is “complex
concept that involves economic, quality, and effectiveness elements as well as political and
social values.”[35] Work is a struggle between production and cost from a company point of a
view. Workers are pressed to produce better results and often forgo their own health to ensure
the results are better. One such problem arises from this thinking is coming to work sick.
Presenteeism, “to designate the phenomenon of people, despite complaints and ill health
that should prompt rest and absence from work” [36] Making this issue more apparent is the fact
people are ignoring the issue of being sick. In addition “occupational groups that experience high
sickness presenteeism also experience high sickness absenteeism”[36]. Workers then are faced
with a dilemma of diminished productivity while working sick or taking time off to recover
effectively to get back to work. To make this even worse it was seen that when workers are not
given time to de-stress from work or where it is impossible to be absent from work then the
workers only get sicker.

2.1.6 Lighting and Visual Quality

Joon-Ho Choi saw ample studies on IEQ on office environments and instead opted to
“ investigates how indoor environments with lighting during the day affect patients’ average
length of stay (ALOS) in a hospital”[37]. With the goal of producing tangible results that would
promote change towards healthier patient rooms. Through investigation Choi found that “natural
window views with daylight are recognized as a significant factor in increasing indoor
environmental quality” [37], with a good view and proper lighting patients would have better
health outcomes and higher productivity. In the figure (9) below is the standard test room in the
investigation.
14

Figure 8- Floor plan of the selected hospital (left) and a typical patient room in the hospital.

The method that Choi used to analyze each room was a detailed physical investigation of
the occupant’s space. This includes on site measurements of a single occupant room looking at
the “The physical environments, including geometry, indoor room surface reflectance, and
transparency of blinds and glazing were investigated." [37] Including measuring the light coming
from the exterior into the patient space. In addition, simulation software was used to understand
the conditions of the space. In the end of these data collection and simulations Choi found the
relationship of “morning light has a more positive effect than light in the afternoon does, and
provides physiological benefits for humans.”[37] In figure 10 below, there were noticeable
reductions in the length of stay across the varying wards.
15

Figure 9- Comparison between SE and NW rooms
In addition to natural light promoting faster recoveries in hospitals it is also related to how
people perform in their work environment. A study by A Borisuit found “An optimal indoor
environment can increase comfort, productivity, health and well-being in office workers”[38] In
addition just having access to daylight improved the mood and was generally more desirable than
artificial light. One of the benefits of natural light is it “modulates many non-visual functions
such as the biological clock that drives our approximate 24-hour (circadian) rhythms of alertness,
core body temperature, hormonal secretion “modulates many non-visual functions such as the
biological clock that drives our approximate 24-hour (circadian) rhythms of alertness, core body
temperature, hormonal secretion”[38]. Through this investigation it was seen that people would
value the access to natural light rather than having electric light. “higher visual acceptance scores
under DL than EL conditions, despite the lack of a direct outside view”[38]
16

2.2 Sick Building Syndrome (SBS)
Redlich noticed a problem that occurred where various symptoms or ailments are being
found in non-industrial buildings. The problem was linked to the exposure to extremely low
levels of foul elements in the air inside of buildings. The problem with SBS is it is hard to
diagnose off the bat, as not everyone is affected the same way or may not be affected at all. To
make matters worse, some buildings were built with volatile gases that take a while before
exposure starts to affect occupants. Symptoms vary among users and can be life threatening but
often lead to unpleasant working environment and eventually to a productivity loss.

Figure 10- Typical Sick Building Syndrome symptoms[39]
“A typical setting for SBS is a new or newly remodeled building with some type of
heating, ventilation, and air conditioning (HVAC) system, although older buildings that have
dirty carpets and no effective HVAC systems are also commonly affected. Appearances can be
deceptive. Architecturally attractive buildings can have serious indoor air problems, especially
when new or recently renovated (see figure 10). Non-industrial environments cannot be assumed
to be clean and free of significant exposures based on appearances.” [39]
Even buildings built to code can suffer over the long term; no building is free of the risk
of SBS problems. There are several suspected factors that are thought to cause SBS symptoms,
but the running theory is a cluster of small issues compounded over time, creating a toxic
working environment for occupants.

Some of the main factors thought to be the causes come from the HVAC system of a
building. There are many ways to have interior air pollution build up, from renovation to
cleaning products gasses can build up inside a space not properly ventilated. The physical
products of a building can store chemicals used in production and off-gas over time. The
(Environmental Protection Agency) EPA has found that office equipment such as copiers and
17

printers, glues and adhesives, all the way to building materials and furnishings can off gas toxic
VOC’s into a space[40]. If this happens in a poorly ventilated space it can cause an issue.

SBS is hard to asses in a single case, often it requires several people to become ill before
a pattern is noticed to link the sickness to a building. To help ensure a building is not causing
sickness, monitoring the air quality and the HVAC are key. Often the case in new buildings the
best solution is time and high-volume ventilation to allow the material to off-gas. Most SBS
experienced by the person can be recovered from with time away from the space, rarely does the
condition get to the extreme.

William Fisk investigated the relationship of sick building syndrome when compared to
ventilation from the HVAC. A primary finding was that “providing more outdoor air ventilation
will reduce prevalence rates of SBS symptoms”[41], this leads to requiring more outdoor air
inside a space. This study is one of the first to make a systematic analysis of sbs and ventilation
rates.

Figure 11 Ventilation rate and illness
Martinac Höppe conducted a study on indoor climate and found that “air-handling
systems are commonly used for achieving comfortable indoor climates, their use has also been
linked to a variety of problems, some of which have received attention within the context
of ”sick building syndrome””[2]. The interior conditioning of the space is conducted by the
HVAC system and yet the system is linked to being the cause of SBS. The interior climate is
determined by a IEQ parameters such as air quality, humidity, etc. Man has consistently
developed ways to move from outside to inside, it has reached such a degree that 90% of a post
18

industrialized humans time can be expected to be indoors. People have come to expect comfort
to an accepted condition of working with anything other than comfortable being undesirable.

2.3 Nurse Productivity
Naziatul Syima Mahbob investigated the correlation between IEQ and productivity. One
of the key findings was that productivity is impacted by the interior environment.
“Human performance and working environment is related to each other and have a major
impact on the work efficiency and production output”[14] Things such as being sick are able to
be correlated to a building in the form of SBS. Simply being sick results in overall lowered
productivity efficiency. Even healthy staff can suffer from lowered productivity when their
environment isn’t meeting their needs. “Staffs that are not comfortable will take more breaks and
loss of concentration in doing their task.”[14] These micro breaks can add up to some significant
time wasted a term for this has been applied of presenteeism .When employees work they
become stressed, simply existing causes stress as “…stress is inevitable..”[15] Most if not all
workplaces cause stress on the worker. Nursing is no exception, there are constant stress factors
for nurses to deal with. From patients to other staff the hospital is far from a stress-free
environment. Most of the time stress is caused by “…fear of failure, errors and changes can be
due to unrealistic experiences…”[15] Nurses are always faced with stress factors that directly
impact their productivity.
“Nurses reported more positive job experiences and fewer concerns with care quality, and
patients had significantly lower risks of death and failure to rescue in hospitals with better care
environments.”[42] One of the indicators of good productivity are patients. In short when
patients are doing good nurses are doing good. The result of the investigation found that “Care
environment elements must be optimized alongside nurse staffing and education to achieve high
quality of care.” [42]. The working environment is key to the productivity of nurses. The survey
was issued to over 10000 nurses and 200000 patients and found “…the likelihood of patients
dying within 30 days of admission was 14% lower in hospitals with better care environments
than in hospitals with poor care environments”[42] Nurse’s performed better in better
environments. In the following figure(4) it is shown that the better care environments in addition
to lower work loads lead to lower patient mortality inside of hospitals.
19

Figure 12- Nurse Care Environment[42]
2.3.1 Nurse Retention

There is a growing discontent among nursing staff inside of hospitals, leading to a
nursing shortage nationwide. Both nurses and physicians agree that insufficient nursing skills are
making it nearly impossible to provide quality care. The majority of the blame is thought to be
due to the overdemanding workloads placed on nurses. In order to combat this, laws have been
passed to attempt to resolve the issue. Patient loads are the main factor of what the discussion has
been about for a while. In California the two major predictive values prior to turnover of nursing
staff are job dissatisfaction and burnout.[16]
20

In an attempt to determine the impact of changing nurse workload down to 6 or less
patients, a study was conducted across hundreds of hospitals. In the emotional aspect of job
satisfaction, nurses were impacted when patients died while on the job. Patient Mortality and
failure to rescue was seen to weigh heavily on nurses and has shown that lowering patient deaths
leads to higher registered nursing attrition levels.

Figure 13: Failure of Nurse's to save a Patient, Satisfaction of Nurses[16]
The idea that workers in general eventually tire of their job has been called “Burnout
syndrome is a psychological response that occurs when chronic job stress leads to emotional
exhaustion and cynicism”[43] When people burn out they quit, this happens in every industry,
people as a whole on occasion change to other jobs. However, this is always an issue no matter
the industry, when skilled workers leave issues production gaps arise.

There has always been an issue of nurse retention in a survey handed out to a thousand
hospitals showed that “ 47% reported that recruiting OR nurses was difficult or very
difficult”[20] while in “California it was found 60% had difficulty recruiting OR nurses”[20].
While this was an issue back in 1986, hospitals began to focus on nurse retention to ensure that
they were keeping qualified staff in hospitals. To understand burnout syndrome a British team
wanted to see how likely nurses were to quit. The issue of nurses quitting is still ongoing and has
been noticed in England where 1400 nurses who responded to a survey where fifteen percent
said they would quit and of those fifteen percent, 79% actually quit before the next
year.[44].This research was conducted in 2001 on several local hospitals “nurses who report
overall dissatisfaction with their jobs have a 65% higher probability of intending to quit”[44].
This stands to reason that if you think you’re going to quit, you likely will quit. A key take away
is that people are still quitting after being trained to be nurses.
2.4 Summary of Points
In summation, the desire to understand how users feel inside their space has been the
topic of many research papers. In India researchers like Ismail Budaiwi had made it the body of
their work to develop an understanding of how people. Collections of researchers have even
developed preliminary codes to help designers to understand people’s comforts inside of
ASHRAE standards. Mathematical equations are used in the prediction of occupant comfort,
such as Fanger’s comfort model. Even with all that has been done in these past years, it is still
not a complete picture. As the built environment continues to change and adapt around the
21

world, more and more attention needs to be given to the occupant who resides inside of the
building.
The world is not full of just new buildings or buildings yet created, there are buildings in
use currently that have been built in the past prior to the preliminary understanding of comfort
was being written. These old buildings are still usable and often still in use and likely to continue
being used into the future. Yet, these buildings are problematic as their infrastructure needs
updates or modifications to bring it up to code.
Due to the aging infrastructure and poor building designs, occupants can become ill due
to a building. The term for this is Sick Building Syndrome (SBS) while hard to see prior to a
building being opened for use, is still an issue. Redlich found that buildings that were not used as
manufacturing are showing signs of SBS. To go further it was found that buildings take time to
off gas their chemicals used in making the manufactured goods used in the building. A common
element that requires time to off gas are the glues used in setting carpets, furniture, etc.
ASHRAE has set standards that are the minimum code for building ventilation rates to help
ensure a building doesn’t contain gas. William Fisk found that by mixing more outside air into the
recycled air reduced the risk of SBS air quality issues. Martinac Höppe discerned that HVAC is one
of the elements impacting the SBS rate in a building.
Nurse Productivity has been discussed to being directly impacted by the indoor
environment. Naziatul Syima Mahbob investigated the relationship between IEQ and
productivity as a general finding. In their investigation found that workers were taking breaks
when they become uncomfortable in their workspace. These issues become severe when working
culture promotes working as much as possible, often means even when sick. One of the larger
findings made by T. Job and M. Beheshtifar was that stress directly impacts a workers level of
productivity. L. H. Aiken found that nurses self reported higher productivity when they’re having
postivie experiences or less stress in their work environment. In a side of that, the productivity
problem is also caused by unskilled nurses. Nursing is a challenging profession and is a
physically and emotionally tasking job to take on. L. H. Aiken made note of the nationwide
shortage and specifically in California of qualified nurses. Nurses are often suffering from
burnout syndrome which leads to them having job dissatisfaction. M. A. Shields and M. Ward
found that once a nurse reports of job dissatisfaction they are likely to quit their job before the
next year. This leads to a major problem for all hospitals of not being able to retain or hire nurses
I. M. Budaiwi discovered.

22

3. Methodology

3.1 Methodology Diagram

Figure 14- Method for Data Collection

23

3.2 Data Collection from figure 14
One of the measuring devices that was used is an instrument cart. Connected to the cart
are various sensors that measure temperature, light, air quality, a camera, and a data acquisition
device. The cart was placed in a front of each work area for 5 mins to collect the various data on
a timed interval. The data sets were stored on the personal digital assistant (PDA) and averaged.
Concurrently, hand held sensors were used to capture various light readings from the desk and
computer area, as well as record the various IEQ values at that desk location. The data was
stored and analyzed later.

The ideal data contains numerous participants to allow for data metrics. Optimally the
data set should contain male and female participants of working age to analyze the differences in
gender. To aid the understanding of the occupant data, information will be collected about the
workstations of the occupants and the occupants. In order to limit bias in the data collection,
various desks in the vicinity will be used, rather than a cluster of desks in a single corner. By
documenting the various attributes of the work station, a more concise understanding of how
each desk is impacted by the various environmental factors will have been able to be determined.
In addition, the desk arrangement will also have been reviewed as pertains to orientation (North,
East, South, West) in relation to building layout.

3.2.1 On-site Environmental Measurements

To collect valid data a series of sensors were used and, in some cases, left in the
workstations at the hospital. The sensor that was left was a HOBO MX1102 CO2 logger seen in
the below figure. This sensor collects several environmental data points to be investigated.
Collected data includes CO
2
, humidity, temperature, and dewpoint. Included in the references are
the technical specifications of the module.

Figure 15 HOBO MX1102 CO2 logger[45]
24

The E-Bot was used in the collection process. The E-Bot has three thermal sensors at half
a meter, one meter, and one point eight meters off the ground, these offsets place the sensors
with in the breathing zone as defined by the American Society of Heating Refrigeration and Air-
Conditioning Engineer known as ASHRAE. Following ASHRAE 62.1 “the region within an
occupied space between planes 3 and 72 in. (75 and 1800 mm) above the floor and more than 2 ft
(600 mm) from the walls or fixed air-conditioning equipment.”[46]. By keeping the collection
sensors within the ASHRAE’s range the data collected can be compared based on the standards.
In addition to the thermal sensors the E-Bot contains a CO
2
sensor one meter off the ground. The
sensor is situated above the chair height. The reason the sensor is located at this height is to
determine if the carbon dioxide build up is present exceeding acceptable levels determined by
ASHRAE. In the same area is an acoustic sensor. The location is near the middle of the chair
space to collect data that is coming from all directions around the occupant’s seat.
The above sensors are placed where the occupant exists the standard seat height is between
thirty-five and fifty-two centimeters off the ground as seen in the figure below.[47]

Figure 16 Comparison of dimensions of prototype chair with published recommendations[47]

25

Figure 17 E-Bot

26

3.4.2 User Satisfaction Reporting

A modified occupant survey questionnaire COPE(Cost-Effective Open-Plan
Environment) was used to understand how an occupant feels in their space. The questionnaires
operated using a 7-point scale to report a satisfaction level for the different IEQ components with
the scores meaning: 1—very dissatisfied, 2—dissatisfied, 3—slightly dissatisfied, 4—neutral,
5—slightly satisfied, 6—satisfied, and 7—very satisfied. These questions form the framework to
better understand the occupants view of their environment. Rather than giving the occupant a
binary response of are they comfortable, the survey was aimed at gauging the degree at which
they are comfortable. The questions helped the occupant in assessing their space to narrow down
which features they believed had caused the most discomfort to them in their space. The
questions are kept in a qualitative style rather than assessing exact level of discomfort towards
one or the other IEQ factors. In doing the survey this way, the occupants are able to approximate
their environment to their individual standard. The survey took the different IEQ factors and
organized them in a way that did not give one factor more significance. By removing any
hierarchy to the questionnaire, the users are not guided to the theoretical results. Users instead
arrive at a conclusion derived from their own thoughts in regards to their work space.
The survey contains 35 questions that relate to the IEQ feeling of the space, gauging the
occupant satisfaction across the varying factors. Among the questions in the survey the below
questions are examples of the level of questioning the nurse.
-15 Your ability to alter physical conditions in your work area
-18 Quality of lighting in your work area
-30 How many sick or leave of absence days do you use a month?

3.4.2 TABS(Technical Attributes Building Systems) Report

In understand the issue of nurse comfort, acknowledging the physical space in which the
work was being conducted was required. By rationalizing the collected sensor and occupant data
with the building report, a larger picture can be seen.

The hospital as a whole is a combination of many different features that have direct
impact on each other to varying degrees. While the report does not break down the inner
workings of each component, the report does address the components. Through understanding
the building systems(components) the issues identified surveying can be addressed. Taking this
approach ensures that the environment is part of the analysis in which the occupant data is
collected in.

In addition, the work space was also documented for its physical characteristics,
including workspace and site conditions, to link the user comfort factor to the environmental
measurements obtained. The recorded attributes of the space include such properties that
described the enclosure, interior, mechanical, lighting, network, and building amenities that
could influence thermal, visual, acoustic, and spatial satisfaction and environmental
27

measurements. This data was recorded in the TABS report, which was used as understanding of
present conditions.

3.4.3 Data Report

Analyses were performed after grouping the collected data by deliberately into large
enough samples. The data groups were done by season, desk location, and gender. This study
used various statistical analyses including a two-sample t-test, one-way analysis of variance, and
logistic regression analysis. The varied analyses were used to equate occupant input, technical
input, and environmental input to generate expected results. Each analysis maintained a 95%
significance level to examine the relationship between the collected elements. The analyses were
focused on the various data sets that had a large enough sample size to be relevant.

After collecting the raw data, it has to be organized. Due to the three sources of data, each
has a different method of which the data is organized in. The survey is inputted into a excel
spreadsheet and then a preliminary data cleanup is done. After the cleanup was finished any
invalid responses were noted and based on the percent incomplete may be thrown out. For a
survey to be considered valid, 80% of the questions had to be filled out.

The environmental data collected by the hand sensors and the hobo device are also
inputted into an excel document. This is to validate the sensors and hobo were collecting similar
results. This step ensures that the data collected by the hobo is accurate and shows the
environment of the space over time. The hobo device was left in the common work area of the
work space and was left for up to five days to determine the HVAC

A) What type of data is collected?
There are three data sets that were collected:
The primary collection were the occupant surveys. These were handed out to help
evaluate the space from a user’s perspective. Each survey contains 36 questions about IEQ. For
the majority of the survey there was a simple very unsatisfactory or very satisfactory on a 7-point
bar. This allowed the occupant to evaluate their feelings in a more accurate way than simple
asking if they were comfortable or not.
In addition to the survey IEQ data was collected via handheld sensors and the e-BOT.
The handheld sensors were used to evaluate the individual desks. Desks were randomly selected,
to get an average of the workstation. To confirm that the readings were accurate the e-BOT was
left in the same locations to measure more accurately the readings. The e-BOT took
measurements every few seconds for five minutes then averaged the value. When these two
values are compared the result shows that the two sets of equipment were correctly reading the
space.
28

The following sensors were used
Thermal heat gun Windspeed tester Humidity sensor Light sensor

Finally, a TABS report was generated about the hospital overall to see if there are any
specific conditions unique to the facility. This report covered the entire building based on user
feedback. Each section breaks a different part of the building down
B) Where is the data collected?
The data collection occurred on the floors 3,4,5,6,7,8,9 in both the gold and cardinal
sections of the hospital. The goal was to collect as many possible data sets from as many unique
conditions. The floors were not identical and presented different conditions. The staff was
stressed by different
Nursing wards were the only places where data collection occurred. The data represents
only the values present at the time of collection inside the workstation. This does not include
patient rooms. Although nurses are working inside the patient’s rooms, that room is developed
for patients care not nurse care. Also, private offices were not included into the data set, as only
a few hospital staff ever use a private office. 3.5 Procedure for data collection

C) From whom was the data being collected?
The three data collection process will only collect the most basic of data of an individual.
See figure 19 below, the most invasive questions in the survey are surface level questions. No
private information was needed to be shared between the researcher and the nursing staff.

Figure 18: Sample Survey Questions
In addition to the staff questionnaires, a simple IEQ survey was given at their workstation
to see if there are any problem area arose. The survey was also left at the workstations for
additional periods of time to allow for more nurses to opt into the survey when the researcher
was not present. This bolsters the data set by having additional responses. While the researcher
was present, the IEQ cart was set up to record the onsite measurements of the individual desks in
the station
The IEQ cart is set up to take measurement over the course of five minutes. Once the cart
was placed into position a running the sensors was done through (National Labs sensor
29

software). This allowed for the data to be simultaneously collected via multiple instruments. The
instruments on the cart included thermal heat gun, windspeed tester, humidity sensor, and a light
sensor.

How often is the data being collected?
Questionnaires were given to each workstation, in order to obtain a larger data set the
questionnaire was given out repeatedly over the course of the research. The surveys were broken
into two research periods, with the first occurring between 8/15/18- 9/22/18, and the second
occurring between 1/16/19- 2/11/19. In spreading out the collection period and giving a seasonal
break, the data set is able to be doubled and provide useful seasonal comparisons.
The sensors used to collect data
Using various IEQ sensors, various data sets were collected using the cart. The primary
sensor used in the collection process was an e-BOT, which allowed for specific desk
measurements to be collected. The primary values that the e-bot was developed to collect are
relative humidity (RH), carbon-dioxide (CO2), particulate matters (PM) and total volatile
organic compounds (TVOC)[7] The e-BOT was left at each measured desk area for five minutes
to run the sensors on 5-second intervals to aggregate the spots IEQ data. The e-BOT was to
remain in the general work area of the hospital worker, and at best to take the space of where a
chair would normally sit. By replacing the chair with the e-BOT the IEQ data is aligned more to
the occupant. Three thermal sensors were located at 1.1m, 0.6m, and 0.1m from the ground level.
Based on ASHRAE 62.1 the breathing zone is between three and six feet off the ground and at
least 2 feet away from any wall or ac units. In addition, the “industry standard for desks is 29
inches tall, or 73.5 cm”[48]. This places the thermal sensors between the ground, the chair, the
desk, and the top of the breathing zone. Several hand sensors were also used to collect the air,
noise, temperature, and light of the work desk. These values are used to compare results and
ensure that measurements are accurate.
In addition to the e-bot’s data collection, several hand-held sensors were used to collect
air quality, noise quality, temperature quality, and light quality of the space.

30

The procedure to collect information

Experiment set up and data collection process
Step 1: Collect tools
- Configure and Calibrate the tools
o Ensure values are in acceptable ranges
- Test case
o Do a short case study to ensure tools readout appropriately
- Review survey
Step 2: Install tools on site
- Place remote sensors in workstation
- Leave sensors to collect data for desired amount of time
Step 3: Issue survey and collect real time data
- Involve nurse’s participation in the survey
o While nurses are away utilizing their desk to collect sensor cart data
- Be detailed in the step by step process of the sensor collection
Step 4: Collect the tools
- Retrieve the remaining sensors from the floors
- Collect data from the sensors
Step 5: Repeat step 2-4 at a later date to see if any patterns emerge
Step 6: Analyze the collected data.

1) Why real people?
The problem discussed in the body of the thesis is that hospitals are not appropriately
conditioned to meet the IEQ of Nurse’s working there. To obtain valid results the utilization of
real subjects was required from the very beginning. Research began in determining the process to
best collect data from human candidates.
This study was approved by the USC IRB, and the approval number is UP-18-00574. This allowed for
research to be collected from the workstations and its users.
31

2) The Individual set up
In reviewing the hospital nurses, the survey was developed to account for their varying
comfort conditions. In the review of the hospital’s staff, the thought was to enlist nearly 100% of
the staff in the research over two seasons. The aim was to receive 150 survey responses per
season. This would give the research 300 total data sets.
In addition, the survey was set up to directly confront the nurse about their space. The
questions cover issues about thermal comfort, RH, Air quality, Lighting, and sound. These
directed IEQ questions allowed the nurse to evaluate their space as specifically as possible. The
majority of questions were on a weighted scaled of very unsatisfactory to very satisfactory as
seen in figure 20 below. In addition, there were specific questions about the nurse’s personal
qualities seen in figure 21 below. Questions of this nature allowed for the sorting of participants
data into clusters to see what patterns emerged.
The survey was designed to have 36 questions and be able to completed within
approximately five to ten minutes. Once a survey is completed it is returned to the researcher.
The survey was optional for the occupants to take, as such it was not expected to receive a 100%
response of nurses. Additionally, it is expected to see some delays based on the patient load
present.

Figure 19: Survey Response Scale

Figure 20: Personal Survey Questions

The survey should be distributed first or as soon as able and then the researcher should
head to test the physical sensors.
32

The first round of sensors involved the hobo devices which helped predict the internal
cycle of HVAC system. Hand sensors should be used around the workstation to collect instant on
site data that can be used to evaluate the feedback from the survey.
3) The Workstation
In order to compare the nurse survey time was spent to investigate the individual work
stations in the hospital.
Not every seated desk area was required to be sampled in each workstation to understand
the space. Each floor supported different styles workstations with varying amounts of nursing
staff present at any given time. The selection of workstation was determined by the suspected
population to be present. Workstations across the hospital varied anywhere from three to twenty-
two workstations. In order to collect the most possible amounts of data focus was given to the
larger workstations of at least five workstations. In each workstation however the IEQ cart was
not placed at each individual desk, rather it was placed one or two spaces apart, to collect varied
data throughout the space. Figure 22 demonstrates the spacing of possible IEQ collection points.

33

4 Survey Results
4.1 Collection Outline
Phase One: Surveys were distributed in the fall between the hours of 10 am and 10
pm. Overall the amount of returned surveys was not at the desired results. The expectation was to
have at least 150 completed surveys during this phase. Phase one resulted in a total of 79
responses. In addition to the survey, the IEQ cart was left at several desks to collect varied IEQ
data sets. Overall the cart collected 21 measurements across three surveyed floors. These results
are in the appendix. Hobo devices were also left to record the temperature and humidity of the
spaces. One intended purpose was that by leaving the hobo it would be possible to detect the
hospital’s own internal conditions and compare them on a floor by floor basis. Conditions were
measured once per floor for a duration between 48 and 120 hours.

Phase Two: Surveying increased greatly in this phase, due to the lack of data generated
in phase one, surveys were handed out at an increased rate. Of the desired 150 surveys, a total of
226 surveys were collected over the two-week period. This increase in collection was due to an
increase in man power for research. A small research team was used to facilitate the additional
data collection. The increased man power also resulted in 78 e-Bot measurements. Between the
two phases, collection goals were met, as 304 survey responses were collected, 99 e-bot
measurements were taken, and 96 hand measurements were collected. Hobos were used for only
phase one to establish that there was a facility management plan in effect. With the first round of
collection finished, the researcher altered the method of collection for the second round of
collection in the spring. The collection process occurred on several floors at a time during each
measurement cycle.

4.1.1 Timeline of the Data Collection

Figure 21: Phases of Data Collection

34

4.1.2 Response Data from Collected all Surveys

Data Metric Fall Spring Overall- Chart Overall-Graph
Time Spent at
Nursing
Station
 20% or
less
 40% or
less
 60% or
less
 80% or
less
 80% or
more

Amount Percent
7 10%
26 35%
18 24%
14 19%
9 12%
74 100%

Amount Percent
37 17%
76 36%
49 23%
20 9%
31 15%
213 100%

Amount Overall
44 15%
102 36%
67 23%
34 12%
40 14%
287 100%

Gender
Breakdown
 Male
 Female
 Prefer
Not to
say

Amount Percent
25 33%
50 67%
0 0%
75 100%

Amount Percent
44 19%
179 79%
5 2%
228 100%

Amount Overall
69 23%
229 76%
5 2%
303 100%

44, 15%
102, 36%
67, 23%
34, 12%
40, 14%
69, 23%
229, 75%
5,
2%
35

Age of Occupants
 18-29
 30-39
 40-49
 50-59
 60-69
 70-79

Amount Percent
18 24%
27 36%
18 24%
7 9%
4 5%
0 0%
74 100%

Amount Percent
45 21%
76 35%
63 29%
24 11%
11 5%
0 0%
219 100%

Amount Percent
63 22%
103 35%
81 28%
31 11%
15 5%
0 0%
293 100%

Occupation
 Nurse
 Technical
Staff
 Physician
 Administra
tive
Amount Percent
56 76%
8 11%
8 11%
2 3%
74 100%

Amount Percent
169 75%
41 18%
11 5%
4 2%
225 100%

Amount Percent
225 75%
49 16%
19 6%
6 2%
299 100%

Education Level
 High
School
 Some
University
 Bachelor’s
Degree
 Graduate
Degree
Amount Percent
1 1%
7 9%
54 72%
13 17%
75 100%

Amount Percent
19 8%
28 12%
144 64%
34 15%
225 100%

Amount Percent
20 7%
35 12%
198 66%
47 16%
300 100%

Table 1: Survey Collection Results
63, 21%
103, 35%
81, 28%
31, 11%
15, 5% 0, 0%
225, 75%
49, 17%
19, 6%
6, 2%
20,
7%
35, 11%
198, 66%
47, 16%
36

The surveys indicate that nurses were likely to respond to a questionnaire of this style.
During the fall season, the staff usually reported to have spent 40% or less of their worktime
inside the workstation. Whereas the vast majority fell somewhere between 80%-40% or less time
at their workstations. In the spring the pattern is similar with the largest percentage of the work
staff working 40% or less of their time in the workstation. However, the spring shows that the
staff is fairly spread out across how long they spend in the station, with the lowest being 80% or
less at 9.4% and the highest being 40% or less with 35.7%. Interestingly in the fall ~9.5% of
work staff felt they worked less than 20% at their workstation, but in the spring this value almost
doubled to 17.4%. There was no clear cause for this drastic change in behavior.

Time spent at
workstation
Fall Spring
20% or less 9.5% 17.4%
40% or less 35.1% 35.7%
60% or less 24.3% 23.0%
80% or less 18.9% 9.4%
80% or more 12.2% 14.5%

The ratio of males to females in the population shows a clear discrepancy during both
survey phases. Although the value does shift to show more women than men in the spring, this
may be due to the volume of data collected in the spring (229) vs fall (76). More data is needed
to confirm this pattern. It would appear that on average the majority of the survey respondents
are female. This is likely due to US cultural influence, which promotes nursing as a female
profession. Occupation wise, the majority of the reviewed staff were nurses in both seasons the
largest group of responses came from nurses. This appears to suggest that the majority of a
hospital’s healthcare force is of the nursing profession or that nurses were the most likely to
respond as they had the time to respond.

Gender Fall Spring
Male 33.3% 19.2%
Female 67.7% 78.6%
Prefer Not to Say 0.0% 02.2%

0%
10%
20%
30%
40%
20% or
less
40% or
less
60% or
less
80% or
less
80% or
more
Fall Spring
0%
20%
40%
60%
80%
100%
Male Female Prefer Not to say
Fall Spring
37

Age Range
Fall Spring
18-29
24% 21%
30-39
36% 35%
40-49
24% 29%
50-59
9% 11%
60-69
5% 5%
70-79
0% 0%

Occupation
Fall Spring
Nurse
76% 75%
Technical Staff
11% 18%
Physician
11% 5%
Administrative
3% 2%

The range of ages is fairly constant despite the change in seasons, the majority of survey
respondents fall between the ages of 18-49, with the most common age being between 30-39.
The probable cause for this age dynamic is hospital work; such as nursing, technical staff,
physician, administrative; as a profession has a lot of on the job training and it can take years to
become proficient. The fact remains that the majority of healthcare professionals in this study
are under the age of 50. Based on the education of the faculty, the results show that the majority
of the hospital staff does have at least a bachelor’s education.
Education
Level Fall Spring
High School 1% 8%
Some
University 9% 12%
Bachelor’s
Degree 72% 64%
Graduate
Degree 17% 15%

0%
5%
10%
15%
20%
25%
30%
35%
40%
18-29 30-39 40-49 50-59 60-69 70-79
Fall Spring
0%
10%
20%
30%
40%
50%
60%
70%
80%
Nurse Technical Staff Physician Administrative
Fall Spring
0%
10%
20%
30%
40%
50%
60%
70%
80%
High School Some
University
Bachelor’s
Degree
Graduate
Degree
Fall Spring
38

4.2.1 Comparison by Seasonal Variation
Although seasons in LA are mild, they in fact do have a fair amount of variation to them.
From sun exposure to temperature and humidity, each season provides different conditions that
affect occupants. The T-HOSPITAL hospital has to account for the varying seasons to best meet
the needs of hospital users and hospital staff.

Table 2: Question 1-20 based on Season

Season
Q5 Q4 Q3 Q2 Q1
Spring Fall Spring Fall Spring Fall Spring Fall Spring Fall
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Season
Season
Q10 Q9 Q8 Q7 Q6
Spring Fall Spring Fall Spring Fall Spring Fall Spring Fall
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Season
Season
Q15 Q14 Q13 Q12 Q11
Spring Fall Spring Fall Spring Fall Spring Fall Spring Fall
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Season
Season
Q20 Q19 Q18 Q17 Q16
Spring Fall Spring Fall Spring Fall Spring Fall Spring Fall
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Season
-3
-2
-1
0
1
2
3
Fall Spring *Significant P-value
39

*The highlighted questions in Table 3 demonstrate where there was a large difference
between the fall and the spring seasonal collection. Conducting the survey in both seasons
allowed for the occupant surveys to take account of seasonal variations. The limited significance
of the question does not mean there were not issues, it rather means that the issues did not
drastically change with the season. Q15(Space Satisfaction) was an example of this from the
survey, although the results showed dissatisfaction there was little to no change between the
seasons. In addition although the questions were too similar to one another, they did show a
pattern of becoming progressively worse as the season changed aside from Q2(Air Satisfaction)
and Q15(Space Satisfaction). This would reason that change of seasons is having an impact on
the workers. A study conducted by Falagas found that there were seasonal variations in mortality
rates and was able to associate those deaths to hot and cold weather.[49]. In addition,
architecturally speaking in the study, there were limited opportunities for the staff to have access
to the outside view and the IEQ conditions hardly changed throughout the year. Something more
must be happening that was not accounted for in this study and needs to be further reviewed.

Average Value Fall Spring Delta Δ
Percent Change
P-value
Q1 Light Satisfaction 1.307 1.159 0.148
2.11%
0.450
Q2 Air Satisfaction 0.270 0.491 -0.220
3.14%
0.929
Q3 Thermal Satisfaction -0.013 0.537 -0.551
7.87%
0.113
Q4 Visual Satisfaction 0.240 0.042 0.198
2.83%
0.274
Q5 Privacy Satisfaction -0.280 -0.603 0.323
4.61%
0.196
Q6 Privacy Satisfaction -0.067 -0.444 0.377
5.39%
0.071
Q7 Noise Satisfaction -0.293 -0.728 0.434
6.20%
0.072
Q8 Space Satisfaction 0.107 -0.469 0.576
8.23%
0.037*
Q9 Noise Satisfaction -0.013 -0.364 0.351
5.01%
0.045*
Q10 Light Satisfaction 1.053 0.853 0.200
2.86%
0.179
Q11 Light Satisfaction 0.720 0.593 0.127
1.81%
0.529
Q12 Light Satisfaction 0.716 0.564 0.152
2.17%
0.435
Q13 Light Satisfaction 0.878 0.709 0.169
2.41%
0.163
Q14 Air Satisfaction 0.149 0.123 0.025
0.36%
0.301
Q15 Space Satisfaction -0.243 -0.224 -0.019
0.27%
0.586
Q16 Visual Satisfaction -0.616 -0.976 0.360
5.14%
0.025*
Q17 Privacy Satisfaction 0.787 0.042 0.745
10.64%
0.000*
Q18 Light Satisfaction 1.000 0.714 0.286
4.09%
0.111
Q19 Privacy Satisfaction -0.360 -0.657 0.297
4.24%
0.205
Q20 Space Satisfaction 0.042 -0.024 0.065
0.93%
0.360
Table 3: Average and P-value for Season
40

These questions shed some light onto the impact the season has on the hospitals staff. Q8
(Space Satisfaction), asks the respondent if their workspace is adequate for their work. This
question was not expected to be affected by the change of season. The spatial conditions
remained constant throughout the study, which is why the results are interesting. One reason that
a change could have occurred is if the demand for space increased from fall to spring, caused by
an increase in patients. Another reason this could have occurred is that the hospital took on
additional staff and the already limited space was made even smaller. Q9 (Noise Satisfaction),
asks if the background noise not caused by speech was satisfactory. Seasonal variation was
expected to occur here, as the climate control system are expected to operate differently
throughout the year. When the systems are being used to a greater degree, they would generate
an increasing amount of noise. Q16 (Visual Satisfaction), asks if the view from where the worker
sits was acceptable. One of the interesting issues of hospital designs is there are almost no views
of the public from a nursing station. Therefore, seasonal change should again not have impacted
the result and yet for some reason those who responded in the spring felt that there was a more
noticeable lack of available views. This drastic difference should not have occurred as the spaces
did not change over the investigation. One reason the staff may have had a lower degree of
satisfaction is the patient load increased, reducing the opportunities for views. Additionally, the
staff’s need for views may have changed, once the weather outside changed. Q17 (Privacy
Satisfaction), asks if the space between the staff was acceptable. Reasons that this drastic p-value
difference occurred are fairly straight forward, there was an increase in working staff in the
spring whereas the fall saw less busy times. From the current study, there is no way to prove this
actually occurred and is a guess based off the survey. Further investigation is needed to
understand the impact of seasons on occupation satisfaction.

Season
Q17 Q16 Q9 Q8
Spring Fall Spring Fall Spring Fall Spring Fall
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Sesaonal Issues Q8, Q9, Q16, Q17
41

4.2.2 Comparison for Building Section and Floor
1) Comparison by which Building the Responses were Collected in

T-Hospital hospital is a combination of two buildings that were built at different times.
Although it was recently renovated, the conditions are still fairly different per building section.
Another factor to compare are the floors. Not every floor is generic or identically laid out, each
floor has some unique condition such as an intensive care unit.

Table 4: Questions 1-20 Based on Building Section

assignment
Q5 Q4 Q3 Q2 Q1
G C G C G C G C G C
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Building Section
assignment
Q10 Q9 Q8 Q7 Q6
G C G C G C G C G C
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Building section
assignment
Q15 Q14 Q13 Q12 Q11
G C G C G C G C G C
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Building Section
assignment
Q20 Q19 Q18 Q17 Q16
G C G C G C G C G C
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Building Section
42

The T-HOSPITAL hospital that was used for the studies collection was comprised of two
different buildings that were attached together. The Cardinal being the original hospital from
1980 and the Gold being the expansion in the 2000’s. The differences in each building
contributed to the satisfaction or dissatisfaction of the workforce there. The results of Table 5
show that across all the questions, the occupants found the Gold building to be a better work
environment. Even if the Gold building is better, it was still found that staff there were
dissatisfied. The highlighted questions in Table 5 were the issues that were found to be
statistically significant (P-Value >0.05). Each Of those statistically significant questions showed
that the Cardinal section reporting lower satisfaction levels. This would reason that the Cardinal
building is the worse of the two buildings.
Building Section
Average Value Gold Cardinal Delta Δ
Percent Change
P-Value
Q1 Light Satisfaction 1.268 0.860 0.408
5.83%
0.069
Q2 Air Satisfaction 0.479 0.220 0.259
3.70%
0.193
Q3 Thermal Satisfaction 0.423 0.260 0.163
2.33%
0.331
Q4 Visual Satisfaction 0.227 -0.551 0.778
11.11%
0.004*
Q5 Privacy Satisfaction -0.410 -1.040 0.630
9.00%
0.037
Q6 Privacy Satisfaction -0.238 -0.860 0.622
8.89%
0.016
Q7 Noise Satisfaction -0.538 -0.980 0.442
6.31%
0.093
Q8 Space Satisfaction -0.143 -1.160 1.017
14.53%
0.002*
Q9 Noise Satisfaction -0.192 -0.660 0.468
6.69%
0.056
Q10 Light Satisfaction 1.030 0.306 0.723
10.33%
0.005*
Q11 Light Satisfaction 0.745 0.060 0.685
9.79%
0.014*
Q12 Light Satisfaction 0.703 0.122 0.581
8.30%
0.049*
Q13 Light Satisfaction 0.764 0.700 0.064
0.91%
0.594
Q14 Air Satisfaction 0.157 0.000 0.157
2.24%
0.284
Q15 Space Satisfaction -0.202 -0.360 0.158
2.26%
0.298
Q16 Visual Satisfaction -0.868 -0.960 0.092
1.31%
0.185
Q17 Privacy Satisfaction 0.423 -0.660 1.083
15.47%
0.000*
Q18 Light Satisfaction 0.933 0.082 0.851
12.16%
0.000*
Q19 Privacy Satisfaction -0.525 -0.840 0.315
4.50%
0.179
Q20 Space Satisfaction 0.043 -0.240 0.283
4.04%
0.253
Table 5: Building Variation Results
43

Figure 22: Building Question
The highlighted questions in Table 5 are the values that had significant differences
between the Cardinal and Gold sections of T-HOSPITAL. Q4 (Visual Satisfaction),
demonstrates this very simply, as it allowed the user to evaluate the aesthetic appearance of their
nursing station; the variation means there are clear differences presented in the Cardinal and
Gold. This was due to the fact that Cardinal is the original hospital with a few updates and Gold
is the new addition. Q8 (Space Satisfaction) again demonstrates the old and new issues inside of
T-HOSPITAL, by asking users to evaluate the size of the workspace allotted in the workstation.
Cardinal again reported a lower satisfaction, this happened because the spaces were developed at
different times and this difference is affecting the users. Q10 (Light Satisfaction) determines if
the light provided for computer work is satisfactory, this should not have been as much of an
issue since both buildings are very similar in terms of conditioning. 11 (Light Satisfaction), Q12
(Light Satisfaction) and Q18 (Light Satisfaction) were set up to ask users to account for the
amount of reflected light inside their workstations that affected them or their computers. This is
due to the differences in lighting placement, whereas that was not a considered factor of review it
seems likely that the arrangement is different and is causing this issue to become noticed.
Privacy was a large concern among the occupants and it appears that is dependent upon which
building the staff were in. Cardinal, at times, were a full point lower in satisfaction than Gold.
This issue is hard to pin down as the staff workloads were not well accounted for in this survey,
but it would reason that there were more staff per workstation in Cardinal than Gold. These
questions begin to show the effect of merging two differently built buildings into one, as there
are clear differences being noticed by the staff that contribute to the success of either building.

-1.500
-1.000
-0.500
0.000
0.500
1.000
1.500
Q1 Light Satisfaction
Q2 Air Satisfaction
Q3 Thermal…
Q4 Visual Satisfaction
Q5 Privacy Satisfaction
Q6 Privacy Satisfaction
Q7 Noise Satisfaction
Q8 Space Satisfaction
Q9 Noise Satisfaction
Q10 Light Satisfaction
Q11 Light Satisfaction
Q12 Light Satisfaction
Q13 Light Satisfaction
Q14 Air Satisfaction
Q15 Space Satisfaction
Q16 Visual Satisfaction
Q17 Privacy…
Q18 Light Satisfaction
Q19 Privacy…
Q20 Space Satisfaction
Gold Cardinal
44

2) Comparison by which Floor the Responses were Collected in

Comprised of several stories, each floor of T-Hospital shares many similar conditions,
and yet the floor may be impacting the perception of those conditions.

Table 6: Questions 1-20 Based on Floor Number

Floor
Q5 Q4 Q3 Q2 Q1
9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Floor
Floor
Q10 Q9 Q8 Q7 Q6
9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Floor
Floor
Q15 Q14 Q13 Q12 Q11
9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Floor
Floor
Q20 Q19 Q18 Q17 Q16
9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5 9 8 7 6 5
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Floor
45

T-HOSPITAL is a multi-story healthcare complex. In this study floors 5, 6, 7, 8, 9 were
used for investigation. By surveying the hospital’s staff on each of the floors, the idea was to
check for noticeable differences on a per floor basis. Table 7 highlights the results from Table 6.
Both of these results take each floor and compare their survey responses together. When looking
at Table 6 there are distinct patterns forming. One such pattern is that floors 5, 7, and 9 are lower
and floors 6 and 8 are higher. This is interesting as the floors should be receiving nearly
identical conditioning and yet there are differences. Of the questions highlighted in Table 7, they
almost all deal with how the user interacts with their space outside of Q1 (Light Satisfaction) and
Q12 (Light Satisfaction). When looking at the statistically significant questions (P-Values >.05)
from Table 7, almost half of the questions register as significant. Those questions that were not
found to be significant all dealt with some broad IEQ determination such as thermal, air, or
lighting. This was likely caused by the spaces being fairly equally treated but the user base being
impacted by one of the other issues such as privacy concern. Table reveals that there are issues
that are not unique to one floor, rather that each floor is handling the issue differently and is
succeeding differently. For instance, Q5 (Privacy Satisfaction) shows that although all of the
responses were unsatisfactory, they were not all to the same degree. This is a repeated
occurrence when this data is examined by floors. If T-HOSPITAL was equally unsatisfactory on
each respective floor, then the entire hospital would suffer equally. As it stands some of the
floors are better received by the workforce in areas of lighting, air, visual, and space satisfaction.
Floor
Average Value 9 8 7 6 5 P-Value
Q1 Light Satisfaction 1.536 1.444 1.064 1.089 1.123 0.566
Q2 Air Satisfaction -0.036 0.906 0.085 0.982 0.263 0.011
Q3 Thermal Satisfaction 0.000 0.667 0.383 0.482 0.263 0.412
Q4 Visual Satisfaction 0.464 0.759 -0.183 0.429 -0.607 0.000*
Q5 Privacy Satisfaction -0.286 -0.093 -0.830 -0.214 -0.825 0.005*
Q6 Privacy Satisfaction -0.250 0.000 -0.660 -0.089 -0.456 0.038
Q7 Noise Satisfaction -0.714 -0.130 -0.766 -0.571 -0.821 0.170
Q8 Space Satisfaction 0.464 0.226 -0.713 -0.018 -0.860 0.000*
Q9 Noise Satisfaction -0.071 0.204 -0.532 -0.125 -0.544 0.007*
Q10 Light Satisfaction 0.750 1.352 0.670 1.089 0.754 0.076
Q11 Light Satisfaction 0.250 1.074 0.277 1.071 0.526 0.008*
Q12 Light Satisfaction 0.185 1.057 0.376 1.000 0.368 0.008*
Q13 Light Satisfaction 0.630 1.074 0.585 1.109 0.439 0.064
Q14 Air Satisfaction -0.643 0.698 0.043 0.491 -0.232 0.001*
Q15 Space Satisfaction -0.143 0.111 -0.628 0.250 -0.411 0.003*
Q16 Visual Satisfaction -1.154 -0.074 -0.978 -0.893 -1.368 0.003*
Q17 Privacy Satisfaction 0.821 0.722 0.223 0.071 -0.333 0.002*
Q18 Light Satisfaction 1.036 1.093 0.628 0.836 0.596 0.355
Q19 Privacy Satisfaction -1.036 0.093 -0.914 -0.375 -0.649 0.001*
Q20 Space Satisfaction -0.074 0.585 -0.228 -0.018 -0.158 0.010*
Table 7: Floor Variation Results
46

4.2.3 Personal Attributes (Age, Occupation, Gender)
1) Comparison by Age

A condition that constantly changes with time for people and often age impacts a
person’s perception of what is comfortable.

Table 8: Questions 1-20 Based on Age

Age
Q5
Q4
Q3
Q2
Q1
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Age
Age
Q10
Q9
Q8
Q7
Q6
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Age
Age
Q15
Q14
Q13
Q12
Q11
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Age
Age
Q20
Q19
Q18
Q17
Q16
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
60-69
50-59
40-49
30-39
18-29
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Age
47

-2.000
-1.500
-1.000
-0.500
0.000
0.500
1.000
1.500
2.000
Q1 Light Satisfaction
Q2 Air Satisfaction
Q3 Thermal…
Q4 Visual Satisfaction
Q5 Privacy Satisfaction
Q6 Privacy Satisfaction
Q7 Noise Satisfaction
Q8 Space Satisfaction
Q9 Noise Satisfaction
Q10 Light Satisfaction
Q11 Light Satisfaction
Q12 Light Satisfaction
Q13 Light Satisfaction
Q14 Air Satisfaction
Q15 Space Satisfaction
Q16 Visual Satisfaction
Q17 Privacy…
Q18 Light Satisfaction
Q19 Privacy…
Q20 Space Satisfaction
18-29 30-39 40-49 50-59 60-69
48

Age presented a pattern where the age of the respondent had an impact on the overall
response. In this case, the young responders found the spaces more satisfactory, and the older
responders found the spaces less satisfactory. Of the 20 questions, only 15 and 17 showed the
older staff having a high satisfaction. This dissonance between the pattern and the actual results
is likely caused by the occupants’ working conditions. One reason is that the older occupants are
in more managerial positions and have more recognition and respect in their space and do not
feel crowded by other staff or have more or less desire to leave behind changes.
In reviewing the highlighted values from Table 10, the big issues between the age groups are
noise and space, but for the most part there do not seem to be any significant differences based
on the current age group breakdown. Q7 (Noise Satisfaction) and Q9 (Noise Satisfaction) had
asked the user to evaluate the level of noise present at their workstation. Q7 (Noise Satisfaction)
asked about conversations and Q9 (Noise Satisfaction) about system sounds like HVAC. The
measurements did not show any noticeable sound differences between the spaces, so users must
have been attuned to hearing the mechanical systems running. Question 7 deals with noise and
privacy, as can be shown by Q5 (Privacy Satisfaction), Q6 (Privacy Satisfaction), and 19
(Privacy Satisfaction) privacy and conversational noise is handled differently in each age group.
One reason this may occur is because as the occupants get older they’re more inclined to do the
work without talking whereas younger workers need conversations to be productive. Lastly Q8
(Space Satisfaction) showed that the size of the personal workspace was different based on age.
One reason for this is that as the staffers age, they are given more authority or respect in their
Age
Average Value 18-29 30-39 40-49 50-59 60-69 P-value
Q1 Light Satisfaction 1.532 1.071 1.049 1.167 1.533 0.484
Q2 Air Satisfaction 0.803 0.367 0.247 0.267 0.533 0.450
Q3 Thermal Satisfaction 0.581 0.347 0.432 0.100 0.200 0.644
Q4 Visual Satisfaction 0.452 -0.112 0.088 -0.033 0.071 0.898
Q5 Privacy Satisfaction -0.032 -0.755 -0.543 -0.433 -1.133 0.144
Q6 Privacy Satisfaction 0.161 -0.571 -0.457 -0.233 -0.600 0.165
Q7 Noise Satisfaction 0.016 -0.643 -0.877 -0.700 -1.600 0.002*
Q8 Space Satisfaction 0.210 -0.459 -0.444 -0.931 0.400 0.046*
Q9 Noise Satisfaction 0.355 -0.388 -0.444 -0.833 -0.267 0.020*
Q10 Light Satisfaction 1.452 0.800 0.815 0.467 0.467 0.079
Q11 Light Satisfaction 1.242 0.582 0.407 0.233 0.133 0.080
Q12 Light Satisfaction 1.210 0.515 0.423 0.233 0.067 0.122
Q13 Light Satisfaction 1.242 0.526 0.800 0.500 0.267 0.064
Q14 Air Satisfaction 0.565 0.153 -0.076 0.071 -0.600 0.238
Q15 Space Satisfaction 0.082 -0.092 -0.506 -0.667 0.133 0.214
Q16 Visual Satisfaction -1.033 -0.887 -0.763 -0.833 -0.933 0.988
Q17 Privacy Satisfaction 0.452 0.133 0.123 -0.100 0.933 0.257
Q18 Light Satisfaction 0.887 0.745 0.788 0.567 0.933 0.816
Q19 Privacy Satisfaction -0.098 -0.643 -0.840 -0.600 -0.800 0.058
Q20 Space Satisfaction 0.230 -0.021 0.113 -0.724 -0.133 0.115
Table 9 Age Variation Results
49

workspace and have more room to work whereas younger staffers are given the remaining space
to share.
50

2) Comparison by Occupation
One’s occupation may be a factor that is modifying respondents’ acceptance of their
space. Again, if a job is not an issue, there should be little to no difference in results. The survey
given allowed for the occupant to select one of the following job categories: Nurse, Physician,
Technical Staff, Administration. However, out of 305 surveys there were only six administration
responses. For this section the administrator role was removed from the data. In addition, some
of the staff did not register all of the responses and the surveys were removed from the
calculation. From the remaining data set 225 nurses were found to be the majority. From the
investigation, every space that was surveyed utilized the open office concept. Architectural
elements like walls were limited in favor of clear sightlines from the nursing station to each of
the patient rooms. In doing so the hospitals staff are also able to traverse the area with limited
effort.

Table 10: Questions 1-20 Based on Occupation
Q29
Q5
Q4
Q3
Q2
Q1
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on occupation
Occupation
Q10
Q9
Q8
Q7
Q6
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Occupation
Occupation
Q15
Q14
Q13
Q12
Q11
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Occupation
Occupation
Q20
Q19
Q18
Q17
Q16
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
Technical Staff
Physician
Nurse
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Occupation
51

-2.000
-1.000
0.000
1.000
2.000
3.000
Technical Nurse Physican Admin
52

Average Value Technical Nurse Physician P-Value
Q1 Light Satisfaction 1.020 1.186 1.526 0.455
Q2 Air Satisfaction 0.531 0.308 1.368 0.041*
Q3 Thermal Satisfaction 0.429 0.330 0.842 0.261
Q4 Visual Satisfaction -0.063 0.023 1.263 0.002*
Q5 Privacy Satisfaction -0.469 -0.605 0.105 0.151
Q6 Privacy Satisfaction -0.122 -0.465 0.211 0.168
Q7 Noise Satisfaction -0.592 -0.702 0.105 0.082
Q8 Space Satisfaction -0.163 -0.472 0.632 0.058
Q9 Noise Satisfaction -0.224 -0.386 0.526 0.073
Q10 Light Satisfaction 0.583 0.892 1.421 0.581
Q11 Light Satisfaction 0.224 0.609 1.632 0.075
Q12 Light Satisfaction 0.444 0.540 1.526 0.051*
Q13 Light Satisfaction 0.563 0.710 1.579 0.083
Q14 Air Satisfaction -0.022 0.089 0.947 0.051*
Q15 Space Satisfaction -0.042 -0.381 0.737 0.027*
Q16 Visual Satisfaction -0.938 -0.915 -0.368 0.333
Q17 Privacy Satisfaction 0.224 0.167 0.895 0.226
Q18 Light Satisfaction 0.938 0.721 1.053 0.967
Q19 Privacy Satisfaction -0.245 -0.799 0.632 0.003*
Q20 Space Satisfaction 0.319 -0.160 0.684 0.057
Table 11: Occupation Variation Results
It was proposed that based off the type of work being done by the workforce there would
be varying degrees of comfort. Although, the focus is on promoting nurse occupant comfort, the
other participants are still part of the hospital’s care staff. The various job roles have differing
responsibilities and those impact the level to which they use the space. Physicians come and go
to check on the various patients under their care, but never stay in a space all that long. Whereas
technical staff are assigned to a workstation and remain; there throughout their shift, only leaving
for breaks or emergencies. Nurses are similar to the technical staff, but they also go in and out of
patients’ rooms, which are not inside the work area. To this point, physicians have the highest
satisfaction across the survey with nurses and technical staff having lower satisfaction scores.
Based on the occupation of the respondents, the statistically significant questions (P-value>0.05)
are highlighted in Table 13. Q2 (Air Satisfaction) and Q14 (Air Satisfaction) deal with air quality
of the space, for whatever reason there is a noticeable trend where air quality is easier to accept
based on the staffers time in the space. Physicians are only ever present in the space when
they’re checking on their patients whereas nurses remain ever constant. Q4 (Visual Satisfaction)
dealt with space in the workstation, again the physicians do not have issues with a space since
they themselves do not use it. One thing to note is that the survey responses showed that some of
the nurses felt that they had no agency in their space and the physicians would come in and take
over, even using the breakrooms. Questions 14 (Air Satisfaction), 15 (Space Satisfaction), and 19
(Privacy Satisfaction) all show very similar patterns, where the majority of those who use the
space find it to be unsatisfactory when compared to the responses of physicians. One scenario
that is likely to cause this is that the physician comes and goes as needed, while the rest of the
staff adjust around them, creating a space where nurses and technical staff are always trying to
retain space for their own work while never being able to claim any one section for it.
53

3) Comparison by Gender

Men and women do not share the same comfort values, this has been shown in past
research. An investigation by Choi found that " significant differences are found in the thermal
satisfaction comparison between genders”[50]. The question becomes, what factors do occupants
feel are not gender equal in their space? Are the open floor plan nursing stations meeting the
needs of everyone?

Table 12: Questions 1-20 Based on gender

Gender
Q5 Q4 Q3 Q2 Q1
Male Female Male Female Male Female Male Female Male Female
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Gender
Gender
Q10 Q9 Q8 Q7 Q6
Male Female Male Female Male Female Male Female Male Female
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Gender
Gender
Q15 Q14 Q13 Q12 Q11
Male Female Male Female Male Female Male Female Male Female
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-15 Based on Gender
Gender
Q20 Q19 Q18 Q17 Q16
Male Female Male Female Male Female Male Female Male Female
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 based on Gender
54

-1.5
-1
-0.5
0
0.5
1
1.5
2
* Q1 Light Satisfaction
* Q2 Air Satisfaction
* Q3 Thermal Satisfaction
Q4 Visual Satisfaction
* Q5 Privacy Satisfaction
* Q6 Privacy Satisfaction
Q7 Noise Satisfaction
Q8 Space Satisfaction
Q9 Noise Satisfaction
Q10 Light Satisfaction
* Q11 Light Satisfaction
* Q12 Light Satisfaction
Q13 Light Satisfaction
* Q14 Air Satisfaction
Q15 Space Satisfaction
* Q16 Visual Satisfaction
* Q17 Privacy Satisfaction
Q18 Light Satisfaction
* Q19 Privacy Satisfaction
Q20 Space Satisfaction
Female Male *Significant P-values
55

The highlighted questions in Table 15 demonstrate where there were differences between
the females and males. In addition to male and female the survey also allowed respondents to
choose N/A as 2 people opted to do. Those results greatly skewed the data and as such they were
removed from the data set to ensure accuracy. The results demonstrated a simple pattern that
men were notably more comfortable in their space than women, regardless of the factor. The
highlighted questions in Table 16 were found to be the statistically significant questions (P-
value>0.05). In addition, there were no questions where men were more unsatisfied and
significant.

Architectural features did not seem to be a gender specific issue among the work staff,
rather the genders differed more about how the space felt and how well the lighting performed.

Gender
Average Value Female Male Delta Δ
Percent Change
P-Value
Q1 Light Satisfaction 1.082 1.603 0.521
7.44%
0.015*
Q2 Air Satisfaction 0.247 1.075 0.828
11.83%
0.002*
Q3 Thermal Satisfaction 0.219 0.941 0.722
10.31%
0.016*
Q4 Visual Satisfaction 0.005 0.426 0.422
6.03%
0.157
Q5 Privacy Satisfaction -0.607 -0.265 0.343
4.90%
0.045*
Q6 Privacy Satisfaction -0.452 -0.044 0.408
5.83%
0.050*
Q7 Noise Satisfaction -0.688 -0.397 0.291
4.16%
0.253
Q8 Space Satisfaction -0.390 -0.118 0.272
3.89%
0.370
Q9 Noise Satisfaction -0.379 0.059 0.438
6.26%
0.074
Q10 Light Satisfaction 0.820 1.239 0.419
5.99%
0.245
Q11 Light Satisfaction 0.466 1.206 0.740
10.57%
0.011*
Q12 Light Satisfaction 0.456 1.132 0.677
9.67%
0.011*
Q13 Light Satisfaction 0.668 1.088 0.420
6.00%
0.107
Q14 Air Satisfaction -0.009 0.559 0.568
8.11%
0.046*
Q15 Space Satisfaction -0.344 0.118 0.462
6.60%
0.257
Q16 Visual Satisfaction -1.060 -0.358 0.702
10.03%
0.020*
Q17 Privacy Satisfaction 0.160 0.485 0.325
4.64%
0.041*
Q18 Light Satisfaction 0.716 1.044 0.329
4.70%
0.154
Q19 Privacy Satisfaction -0.799 0.134 0.933
13.33%
0.001*
Q20 Space Satisfaction -0.107 0.324 0.431
6.16%
0.069
Table 13: Gender Variation Result
56

4.2.4 Comparison by Self-Reported Productivity
Productivity is an overall evaluation of a person’s performance, whereas the survey
allowed the occupant to self-evaluate their own productivity when compared to others. This
allows for investigation into what role IEQ has on productivity. More data is needed to
understand the impact IEQ has on productivity. From the investigation it appears that lighting
issues are the most pressing issue inside the T-hospital.
** Lower reported productivity coupled with Lower IEQ scores. Is the result I am expecting.

-----Step 1---- -----Step 2---- -----Step 3----- -----Step 4-----
Stepwise Regression Coef P Coef P Coef P Coef P
Constant 1.1965 1.1591 1.092 1.1081
Q18 Light Satisfaction 0.1778 0.001 0.1181 0.053 0.1376 0.026 0.1146 0.071
Q13 Light Satisfaction

0.1107 0.063 0.1372 0.026 0.115 0.068
Q8 Space Satisfaction

-0.0868 0.091 -0.1173 0.034
Q4 Visual Satisfaction

0.0915 0.138

S

1.2291

1.22245

1.21744

1.21417
R-sq

4.90%

6.34%

7.52%

8.42%
R-sq(adj)

4.48%

5.52%

6.29%

6.79%
R-sq(pred)

2.73%

3.23%

3.65%

3.77%
Mallows’ Cp -1.17 -2.57 -3.35 -3.49

Table 14: Questions 1-20 Based on Self-Reported Productivity
Productivity
Q5 Q4 Q3 Q2 Q1
3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q1-5 Based on Self Reported Productivity
Productivity
Q10 Q9 Q8 Q7 Q6
3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q6-10 Based on Self Reported Productivity
Productivity
Q15 Q14 Q13 Q12 Q11
3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q11-Q15 Based on Self Reported Productivity
Productivity
Q20 Q19 Q18 Q17 Q16
3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2 3 2 1 0 -1 -2
3
2
1
0
-1
-2
-3
Data
95% CI for the Mean
Individual standard deviations are used to calculate the intervals.
Q16-20 Based on Self Reported Productivity
57

4.3 Nurse Decision Tree

Figure 23: Nurse Decision Tree
58

When trying to determine the important factors impacted by gender, Weka was used to
predict the pattern. At first glance it appears that women have more issues than men in this tree.
However, that may be due to a lack of data input from men. With only 69 male responses their
information can only be analyzed so far before nothing is left to discern. For a proper male
analysis to be done, more data is needed to enlarge the data set to aid the prediction

When looking at the larger data pool of women, Weka begins to show how the issues
relate and impact one another. Women, if satisfied, found Noise to be prime issue with privacy
being next. This was interesting as privacy and noise can at times be paired together,
conversations in open office plans carry throughout the space, so if there are no noise issues then
privacy will be the next factor for consideration. Light becomes the next factor as long as the
prior considerations meet satisfaction. The impact of light has been applied to an occupants
wellbeing inside a space, from the findings women care more about their wellbeing and thus
require better light. Air was the final question found in the relationship chain. The overall air
quality of the hospital was within the ASHRAE standards. However, it goes in line with women
care about their wellbeing, the air could be improved for the facility. The 5
th
deciding factor was
the amount of time the women spent at the work station. Often times the work being done,
requires movement in and out of the workstation. This may be impacting the overall satisfaction
of the healthcare workers.
59

5 T-hospital: Discussion based on the
Data Reported

Throughout the collection period, several
measurements were taken into account to help
rationalize the occupant’s satisfaction inside their
space. The survey was a wonderful tool to
understand how the occupants perceived their
space. However, the survey only served to identify
issues the staff felt were problems. To verify any of
the survey results, an additional layer of
information was required. In this case physical
measurements were taken on the various floors of
the T-hospital and additional site photographs were
taken for digital measurements; the information
was then processed to help validate the survey
issues. The data collection occurred on floor 5-9
inside of T-hospital. The results of the collection
were 304 surveys, 99 e-BOT measurements, 96
hand measurements, and 74 sets of photographs.
The findings were compared to current comfort
standards to justify any issues found in the survey.
There were issues found across every aspect thanks
to the survey. However, only the significant issues
(P-value <.05) were tallied up in Table 1. To better
understand the issues, it was necessary to analyze
the survey along with the measured data set.

Table 15: Summary of P-Values from Survey
Result section Questions P-value
Seasonal
Q8 Space Satisfaction
Q9 Noise Satisfaction
Q16 Visual Satisfaction
Q17 Privacy Satisfaction
0.037
0.045
0.025
0.000
Building
Q4 Visual Satisfaction
Q8 Space Satisfaction
Q10 Light Satisfaction
Q11 Light Satisfaction
Q12 Light Satisfaction
Q17 Privacy Satisfaction
Q18 Light Satisfaction
0.004
0.002
0.005
0.014
0.049
0.000
0.000
Floor
Q4 Visual Satisfaction
Q5 Privacy Satisfaction
Q8 Space Satisfaction
Q9 Noise Satisfaction
Q11 Light Satisfaction
Q12 Light Satisfaction
Q14 Air Satisfaction
Q15 Space Satisfaction
Q16 Visual Satisfaction
Q17 Privacy Satisfaction
Q19 Privacy Satisfaction
Q20 Space Satisfaction
0.000
0.005
0.000
0.007
0.008
0.008
0.001
0.003
0.003
0.002
0.001
0.010
Age
Q7 Noise Satisfaction
Q8 Space Satisfaction
Q9 Noise Satisfaction
0.002
0.046
0.020
Occupation
Q2 Air Satisfaction
Q4 Visual Satisfaction
Q12 Light Satisfaction
Q14 Air Satisfaction
Q15 Space Satisfaction
Q19 Privacy Satisfaction
0.041
0.002
0.051
0.051
0.027
0.003
Gender
Q1 Light Satisfaction
Q2 Air Satisfaction
Q3 Thermal Satisfaction
Q5 Privacy Satisfaction
Q6 Privacy Satisfaction
Q11 Light Satisfaction
Q12 Light Satisfaction
Q14 Air Satisfaction
Q16 Visual Satisfaction
Q17 Privacy Satisfaction
Q19 Privacy Satisfaction
0.015
0.002
0.016
0.045
0.050
0.011
0.011
0.046
0.020
0.041
0.001
60

Issue
Reported Season
Building
Section Floor Age Occupation Gender
Total
Reported
Thermal - - - - - 1 1
Space - - 3 1 1 - 5
Light - 4 2 - 1 3 10
Noise - - 1 2 - - 3
Visual 1 1 2 - 1 1 6
Privacy - 1 3 - 1 4 9
Air - - 1 - 2 2 5
Table 16: Summary of Survey findings

61

5.1 T-hospital: Lighting evaluation
One of the IEQ factors more closely investigated was the lighting levels inside the space.
The goal was to find glare issues caused by exterior light conditions; however, the majority of
the nursing stations were located in the core of the building mass. The process was adapted to see
if the spaces were being over or under illuminated via the internal lighting systems.
# Image 1 Image 2 Image 3 Image 4 Glare Reading
1

2

Figure 24 Glare Analysis: Additional Measurements in Appendix A
# Floor Lum. Min. Lum.Max Lum.Avg Root Mean Square illuminance Solid Angle Source Threshold UGR
1 7 2.13 1957.88 37.31 100.75 124.14 6.283 27 14.2
2 7 3.31 6703.78 47.47 137.61 144.84 6.283 30 15.5
Avg 6.72 3.36 5812.45 44.49 130.82 126.85 6.283 62.65 13.1
Figure 25 Illuminance and UGR: Additional Measurements in Appendix
The glare analysis was consistent across the floors with only a few high UGR areas and
the results were therefore acceptable. For further glare examples review appendices, A and B. No
natural light or directed light sources were seen impacting the screens. The spaces were all
illuminated via overhead lamps and there were no individual desk light sources in the spaces.
Overall, the spaces consist of varying conditions but are for the most part are very similar in
terms of lighting levels. Key factors in this claim are the unified glare rating (UGR) values, used
in determining the comfort level of glare. Any
UGR value over 19 would border between
comfort and discomfort[51]. However, out of
the 72 photographic measurements only 5
processed values that passed a UGR 19, the
majority of the data fell into the comfort range.
The findings from the photographic measurements can be found in Table 4 and Table 5.
In fact, there were more imperceptible glare readings than uncomfortable readings.
UGR
Discomfort Glare
Criterion
Count
10 Imperceptible
13
13 Just perceptible
20
16 Perceptible
28
19 Just acceptable
6
22 Unacceptable
5
25 Just uncomfortable
0
28 Uncomfortable
0
Total
72
Table 17: CIE Illumination standards
Floor
Measurements taken UGR
Avg.
8 18 13.75
7 31 14.44
6 11 10.93
5 14 11.01
Table 18: UGR average across the Floor
62

Overall, the sections of the hospital that were measured generally held acceptable
conditions. Basing the next assumption on the collected data, it would be reasonable to assume
that for the most part the hospital is showing acceptable UGR levels for the majority of spaces.
The average UGR for the spaces can be seen in Table 5, floors seven and eight were found to
have just over the perceptible glare level threshold. While floors five and six were showing just
over imperceptible glare reading.
The surveys reported “unsatisfied” scores in terms of lighting conditions, but the
measurements fall within the acceptable range of the International Commission on Illumination
(CIE) standards. In conducting a UGR analysis of the T-hospitals floors, it was expected to find a
glare issue. However, there were only five unacceptable findings from the UGR calculations.
Those five readings were taken on differing floors and were not seen replicated on that floor
again. These five issues appear to be outliers caused by very local conditions that are not
standard across the hospital.

63

5.2 T-Hospital: Space Analysis
T-hospital is divided between two buildings that were merged to create the hospital
facility. The investigated floors however were all intensive care unit (ICU). The gold building
was the newer building and was built as to expand the current hospital. To aid this expansion, the
gold had additional floors built into the development. This was the reason for the lack of cardinal
floors on floors 8 and 9. Every floor was run as an ICU, however each ICU varied slightly in
terms of layout or what type or patients they handled. In the appendix are the plans of each of the
floors used throughout the investigation.

Floor 9 Floor 8 Floor 7 Floor 6 Floor 5
Gold ICU ICU & Telemetry ICU Telemetry ICU & Telemetry
Cardinal

ICU & Telemetry Telemetry ICU& Stepdown

5.3 WEKA Analysis
The initial IEQ survey was comprised of twenty questions across various topics. With the
amount of data collected, the chance of each survey question yielding a result reduced greatly.
To clarify the information into a useable form, the questions were reduced to their uniqueness
and importance. While all of the data that was collected is important, there were several lighting-
based questions, and not all of them were needed. For example, question 13 “Amount of direct
glare from daylight” there was little to no natural light inside the space, so this question’s
importance was limited. Generalized questions like Q2 Air satisfaction and Q3 Thermal
Satisfaction were retained as they were directly related to the IEQ of the space. Spatial questions
Q15 and Q20 were both dropped even though they were unique. They may be worth
investigating in the future but for now, they were not included into the analysis. In the Figure 3
was the determination of which questions would be used in the WEKA analysis. WEKA allowed
for a more in-depth analysis through the use of decision trees.

Survey Question Important Survey Question Important
Q1 Light Satisfaction  Q11 Light Satisfaction 
Q2 Air Satisfaction  Q12 Light Satisfaction 
Q3 Thermal Satisfaction  Q13 Light Satisfaction 
Q4 Aesthetic Satisfaction  Q14 Air Satisfaction 
Q5 Privacy Satisfaction  Q15 Space Satisfaction 
Q6 Visual Privacy
Satisfaction 
Q16 View Satisfaction

Q7 Noise Satisfaction  Q17 Privacy Satisfaction 
Q8 Space Satisfaction  Q18 Light Satisfaction 
Q9 Noise (Mech.)
Satisfaction 
Q19 Distraction
Satisfaction 
Q10 Light Satisfaction  Q20 Space Satisfaction 

Figure 26: Survey Question Selection
64

5.3.1 Analysis from the Survey Alone

Figure 27: Survey

To better understand the results of the survey, an analysis had to be made between the
occupants and their reported productivity. This was done to understand the importance of the
selected factors from within the survey. The first factor that impacts productivity was Q2 air
satisfaction, this was an unexpected finding. This was an odd relation because T-hospital was
measured and aligned to the IEQ standards of ASHRAE. There may be something else going on
that was not measured from the initial study. It is possible the staff is relating air satisfaction to
other factors and are just unable to differentiate those issues from their air satisfaction. For
example, the air may be too humid and this is affecting the perception of the air. Either way this
does help correlate that IEQ and more specifically indoor air quality (IAQ) is something that is
directly impacting the productivity of the staff. Continuing down the branches to rows two and
three is a subtle link of aesthetics and visual privacy, this would mean that the physical space of
the hospital is not meeting the needs of the staff. T-hospital was a hospital. It met the
requirements, but it appears that for those that work there, it was not meeting their personal
needs for the space. These were the primary determining factors from the survey; the majority of
65

the data was categorized under them. However, they were not the only issues found through the
use of WEKA.

The minor impacting factors of Figure 4 from the survey were found on rows 4 and
below. Row 4 is where the data begins to become more challenging to discern, since the splits
now rather than ending begin to branch into other issues. In this case, whether Privacy was found
to be satisfactory or not determines if there was an issue with the view or with the light of the
workstation. Due to the fact that all workstation locations were in the middle of the floor plate,
the view for the hospital staff only consisted of patient rooms. Before addressing the view
satisfaction, light satisfaction was a found to be the larger issue from the prior chapter. Setting
aside the view issue, as the view consisted of patient rooms and the interior of the building and
looking at light satisfaction. Light was an issue inside the space, but in what regard was the light
found to be unpleasant is unknown. From the Minitab and Excel comparisons, Q18 was
significantly more different between the two buildings. This means that the building has some
impact on how the respondents feel about IEQ of their spaces. Coming back to the issue about
view satisfaction, even in the prior section, view was always found to be have an unsatisfied
response. So, it is not surprising that view was found here. However, the link between view and
its branch of Q9 Noise (Mech.) Satisfaction was not well understood. However, When linking
this minor factor to the overall air issue, there may be something going on inside the HVAC that
is causing issues for the staff. By following the minor factors to their ends, the data has been
clustered down to the last groups. The two ends are both thermal satisfactions. Although the data
is not impressive with only a handful of results in either tail, it does match the prior comparisons.
Where no matter how the data was broken down, thermal satisfaction was never found to be a
major issue, outside of gender.
66

5.3.2 Analysis of Productivity based on Building Section and Floor

Figure 28: Productivity as compared by survey and building information

67

The building factors that were collected via the survey were building section and floor
number. While building section clearly defines the building into two parts and is able to
aggregate the data set into a large pool, attempting to do the same with the floor section only
becomes a source of problems as each floor provides another source of potential data branching.
It was decided that to improve the results of this section, removing the floor and retaining only
the building section would yield the better results.

To understand productivity, it was necessary to first understand if the floor and building
section had any impact on the user’s productivity. Figure 5 is the decision tree of that
information. Right off the bat, the primary issue is still air satisfaction. This means that from the
data provided regardless of the building there is some type of air issue going on in the work
stations. The second row is where building section begins to matter. From the prior findings
through the comparisons, it is known that gold and cardinal buildings are not viewed from the
staff’s perspective as equal. However, the issues that correlate to that difference seem to be
space, visual, and distraction. A further investigation, is needed to understand the differences
between the cardinal and gold buildings of T-hospital. From this finding alone, it can be
confirmed that Gold is indeed a more satisfactory space than cardinal.

From the previous analysis of the survey alone, the primary three factors when responded
to negatively remained unchanged. However, the minor branches changed to being distraction
and light but still ending with thermal. Distraction satisfaction is a clear issue, as the tail ends
with 99. However, this would indicate that staff are being distracted by each other and their
productivity is suffering. Throughout the investigation there were no visible issues present
between the staff, there may be something else happening. One factor that might be helpful to
understanding this issue, is the additional feedback. This may become a useful factor to
understanding this very human issue. But for now it is saved for future analysis. The finishing
element to the tree is thermal satisfaction. The fact that thermal appears again, although the tree
changed appears to mean that thermal satisfaction is an issue. There must be a thermal issue
going on inside the hospital, since thermal was not found to be a specific issue under building
section but it still was found to be a general issue.

68

5.3.3 Analysis of Productivity based on Gender, Age, Occupation, and Education

Figure 29 Productivity as compared Gender, Age, Occupation, and Education
69

By combining all the human factors taken into account it was the hope to see which held
more weight. One issue about attempting to add so many decisive factors into the tree is the data
breakdown occurs rapidly leaving little to no significance to be found in the branches. In Figure
6 the tree looks very impressive but there is very little connection occurring as almost all the
ends result in sub twenty results. The two ends that go above twenty are occupational and
education and they’re dead ended afterwards. To make a clearer understanding possible,
reducing the sheer volume of variables is required. In this case. Since it is already known that the
majority of staff reported a bachelors for education and nursing as occupation, these two
variables were removed. In addition, age was broken down into 4 categories which only aided in
breaking the information down without giving it any importance. The ages were merged into just
two groups to give more meaning to the range. To resolve this issue would require more data to
be collected to bolster the variables.
70

5.3.4 Analysis of Productivity based on Gender and Age

Figure 30: Productivity as compared with Gender and Age

71

The reason for redoing the previous analysis with the removal of education and
occupation was to understand the clearer factors of age and gender. From the Minitab and Excel
findings there are clear distinctions between the genders and ages, however it was unclear
whether there was a relation between any of the factors. From the start, air is still the primary
factor that has the most impact on productivity. This means regardless of age or gender IAQ
matters to the overall productivity of the workforce. The second row is when a human factor
makes an appearance, and it is age. This means age has more importance between age and
gender for the productivity. An interesting finding is that the two age groups don’t have the
same issue. In this case the 18-39 group found distractions to be an issue, whereas 40+ found
aesthetics to be. The thought is the younger workers are more concerned with the job and find
distractions from other to be more of an issue than other things. Where as the 40+ group finds the
space they work in to be the most defining issue, regardless of other issues. Following the 40+
group down again in row 4, they suffer from thermal issues. This is the first time a large data set
has appeared in the thermal category from the prior analyses. The older occupants in the hospital
are not satisfied with their thermal conditions.

When following the aesthetic branch of the tree, the first divide occurs between age and
gender. After the primary factors that have been seen across the study to be the issues, gender
and age begin to have an impact. There is an interesting relationship here as well, when gender
was the third issue age was not seen until the fifth issue. While is gender was the issue age was
the next issue presented. If they were the same distance apart then the human factor would have
been interchangeable, but since they’re not there is a distinction to be made. Meaning that both
gender and age should be considered important factors when determining occupation
satisfaction.

Aesthetics have been found to be an issue numerous number of times, but the question to
whom has not be able to be answered until now. The left side of the tree has the 40+ group, while
the right side has women ages 18-39. While it is shown that “aesthetic” exists under other
categories, these were the examples where human factors connected to aesthetics directly. T-
hospital is not equally meeting the visual satisfaction of the works staff, which was found inside
the previous chapter, but not clearly understood. While this may not be impressive, it does mean
that the spaces are not equally treating the staff and there could be other issues that may appear
in a larger data series.

72

5.3.5 Analysis of Productivity based on the Measured Data

Figure 31: Productivity as compared to the Measured IEQ

When taking into account all the physical measurements taken across T-hospital
buildings some of the elements were found to not be as important as initially thought. The
measurements like dust which were collected via the e-BOT were never investigated. While the
thermal measurements at .2m and .6m were useful overall there was not a large enough thermal
change to prove useful. UGR, was suspected to be unsatisfactory to the hospital, but it was found
to exist well within the acceptable range based on building standards and code. For a list of
factors found to be important refer to Figure 9.

73

Measurement Importance Measurement Importance
Floor Temp  CO
Temp .6M

Dust
Temp 1.2m

Acoustic 
Temp 1.6m  Carbon 
Temp
Radiant

Decibels(sound) 
RH  UGR
TVOC

Figure 32: Physical Measurement Usage
A minor issue arose when trying to pin e-BOT measurements to a certain survey, that was
the survey started in one spot and was returned from another. The idea that there was going to be
a way to assign a single desk to each occupant was removed. Instead, to make the best use of the
e-BOT’s data, it was aggregated into zones rather than individual stations. In addition, the survey
collection far out-paced the overall e-BOT collection by a factor of over three to one. This
allowed the data to be relevant to where the occupant worked rather than the single desk space
that may or may not be used consistently. Figure 8 is the result of that data aggregation. Again,
the primary two factors are air and aesthetics, but this time carbon becomes a factor of
importance. Of all the measured data collected dealing with T-hospitals IAQ carbon dioxide,
WEKA assigned it as most important when compared to others. Although this is an unexpected
finding, as the carbon dioxide found to be on the site was considered acceptable to ASHRAE
standards, it is entirely possible that the IAQ is within standards and found to be unsatisfactory.
This idea comes from a previous finding made by Gunaratne, where the over exposure to off
gassing surgical cleaners had delayed reactions on the staff upwards of 72 hours post
exposure[27]. The hospital staff has to maintain an extremely clean environment, which may be
leading to over exposure of cleaning agents. However, this would need rigorous study to
understand the issue. The staff may be exposed to off gassing of other elements other than CO2.
Since the was found to be the most impactful factor among the measured elements at T-hospital
it would be worth a more stringent investigation to occur with the IAQ as the focus.

74

5.4 Summary of the Data Analysis
The survey and the data were paramount in better understanding the issues surrounding
T-hospital. However, more data was needed to better understand some of the issues. At times
there was not enough data to fully analyze the human elements into a useable form, while at
times simplifying some of the factors allowed it to be processed. In going forward, more data
both survey and measurements are needed to understand the patterns better.

From the analysis of the data, there a few issues that are affecting productivity. IAQ of T-
hospital is having the most pronounced impact on the staff’s productivity. However, it is unclear
as to why that is the case. Across the board T-hospital was found to be within the ASHRAE and
other codes for occupant comfort, and still the IAQ was found to be an issue. There were no
extreme data sets that would have construed the analysis. It may very well mean, that the
standards are not meeting the current occupant’s satisfaction for IAQ. The standards may not be
right for T-hospital, where as they may for work for another. Each building is unique from those
whom work there to the climate outside and likely there is not a one size fits all solution to IEQ.
It could also mean T-hospital is an outlier and the standards are working as intended, but only a
further study into other hospitals would be able to confirm this. In addition to the IAQ issue, the
aesthetics of T-hospital were found to be impacting productivity. This is a very human factor, as
what is pleasing to the eye is subjective. It does how mean, that the current aesthetics are not
visually pleasing to the staff. There not be a simple way to fix the two above issues, but a further
study should be conducted to further understand them and their impact.

Additionally, minor issues kept seeing a repeat, such as thermal comfort. Although there
was only one thermal issue recognized through the P-value analysis, the issue kept reappearing
through a WEKA analysis. Thermal comfort of T-hospital should be given another review, there
something happening thermally through the survey but the data did not find through this review.
All that is left from the analysis is the question about how can the occupants be thermally
unsatisfied if they’re measured data puts them inside the comfort zone.
Figure 33: IEQ Cart Locations: Eighth Floor of
KECK
75

6. Conclusion of T-hospitals Investigation into IEQ Comfort

To understand the T-hospital, a post occupancy evaluation (POE) needed to occur. The
measurements aided in determining the validity of the hypothesis. The goal was to determine if
T-hospital was found satisfactory to the staff and if the IEQ of the spaces was within building
codes. To that end, a hands-on method was adopted to collect the data for the study. Over the
course of several weeks in both the fall and spring seasons, data was collected through the use of
various tools. In doing so, we sampled 303 surveys, 99 sets hand measurements, 90 e-BOT
measurements, and 72 photosets.

The next step was to analyze the data found in order to understand the spaces. In order to
make sense of the various sources of information, the data had to be compared together. Through
the survey, several categorical factors were given to help understand the differences between
issues with a focus on age, gender, building location, floor, etc. Each element provided different
information about what was considered important to the occupants. From the findings the
primary issues of T-hospital are below.
1) Air satisfaction
2) Aesthetics
3) Thermal Satisfaction

Hypothesis: Thermal Satisfaction, Air satisfaction most important IEQ factors.
The initial hypothesis, was to determine if thermal satisfaction was the most important IEQ
factor inside of T-hospital. This idea comes from previous research in past POE projects. The
current findings found air satisfaction to be more important to staff satisfaction over thermal
satisfaction inside of T-hospital. In order to prove the hypothesis T-hospital’s IEQ had to be
measured and compared to the nurse satisfaction. The purpose of this was to determine whether
or not the building was meeting acceptable IEQ standards and understand how occupants felt
about their space. From the onsite measurements it was found that T-hospital was within IEQ
code, however the occupants survey showed that they were still dissatisfied with their IEQ. This
would make the current IEQ standards ineffective as the staff was uncomfortable inside the
workstation.

Therefore, the initial purpose was to determine if T-hospital was not meeting the
acceptable IEQ standards was found to be false and that would result in staff dissatisfaction. The
building indeed was built to code and the measured data supported that. However, the survey
showed that the staff was not comfortable inside their workstations, making the current standards
ineffective at meeting the needs of the current staff. In addition, the secondary hypothesis was
aimed at determining if thermal satisfaction was the primary issue within T-hospital. This idea
comes from previous research in past POE projects. The main issue inside of T-hospital was
found to be air quality, not thermal satisfaction.

By taking measure of the IEQ elements inside of T-hospital and comparing them to IEQ
standards, allowed for the understanding that the current building IEQ codes maybe insufficient
in satisfying the occupant IEQ. This research may help reshape the IEQ standards or even
promote the occupant integration into the IEQ setting on a large scale. On the small scale this
information would promote hospitals change their IEQ to meet the staff’s needs, rather than
76

blanket coating spaces in a set standard. The following section provides guidelines to help
relieve the issues identified in the previous chapters.
77

6.1 Proposed Guidelines
Based on the results of the data collection it was found that air satisfaction and aesthetics
had the largest impacts to the staff. In addition, thermal comfort was found to be a minor issue.
Based on the findings, it is recommended that focus be on the following issues in ranked order:
1) Air satisfaction: Air flow, air conditions
2) Aesthetics: views in and around the workstation
3) Thermal comfort: spaces were too cold or too hot for individual staff members.
4) Noise: background noise at the nursing station from mechanical systems
5) Distractions: lack of privacy between people, visual or acoustic.
Figure 34: Found Issues via Investigation
Issues One

The largest issue found in the study was air satisfaction, however it was not determined
what exactly is the issue with the air. From measurements, the air conditions were within the
ASHRAE standards for both humidity and temperature. However, it was found that the air speed
was limited throughout the space with a 0.00- 0.01 m/s air velocity. These measurements would
indicate limited air flow in and around the workstation. A simple solution to increase air flow
would be to add additional fans inside the work spaces, to help increase the air flow around the
space. This however doesn’t change the actual quality of the overall air, and a full HVAC
investigation may be needed to see if the ventilation rate can be improved.
Clean our vents please!
So much dust. Allergies act up at work
Air Quality is horrible, widow access would be relaxing, enjoy open floor plan.
Air quality needs to be better now I notice more. Dry nose/coughing and dry throat of me
and my colleagues.
Move workstation around. Air quality is filled with debris.
Figure 35: Additional feedback: Air issues
Issue Two

The aesthetics of t-hospital were found to be an issue responsible for lowered
productivity. From what was noticed at T-hospital the issue boils down to the lack of an outside
view, because the majority of views outside are located in patient rooms. This requires staff to
decide if they want to see the outside, they have to interact with the patient’s space. To resolve
this would require modifying the floor layout to allow some natural views outside for the staff.
This would be a problem at T-hospital as it was designed to put patient recovery first it appears,
and any staff solution for staff issues cannot impact patients. Instead, rather than changing the
layout, adding other visual interactions to occur inside the workstations may prove useful. Based
on the findings of Miller DB “staff was most aware of periodic changes in decor and considered
change as "very important"”[52]. Introducing some type of visual stimulus would help the staff
enjoy their space more, this can be similar to nursing homes or children’s hospitals where they’re
78

painting walls or installing other visual elements to the wall, some examples are located in Table
1
Visually Pleasing Room[53] T-HOSPITAL Currently
Designed Surfaces[54]

Creative Wall[55]
Table 19: Comparison and Suggestions
Issue Three

Thermal comfort was found to be a minor factor when analyzing with WEKA. While the
factor may not sway the productivity much, it was still a known issue in other investigations.
Before any suggestion can be made, it is required to understand if the staff is too cold or too hot.
With an apparent air issue inside T-hospital, there may also be a thermal issue as air is the means
with which heat is exchanged inside the space. Simply increasing the air flow into the space and
or modifying the humidity to better regulate the thermal satisfaction may solve the issue. By
returning to the space and resurveying the occupant comfort would determine if the issue was
resolved.

The survey feedback seems to indicate that this issue was apparent as shown in Figure 10.
Our temperature can be unpredictable. During winter season, the nurses’ station is very cold.
Noise level can be very high esp. during daytime when a lot of staff are in the unit.
Figure 36: Additional feedback: Thermal issues

79

Issue Four

Sound was found to be another minor factor for the staff. The measurements for sound
were found to be fairly regular with only peaks during announcements over intercoms. It is likely
that because the staff are packed into tiny workstations, they’re more directly overhearing each
other. One simple solution would be to modify the space by adding partitions to help divide up
the area. However, this is not advised as it would impact nurse access to patients. Additionally,
the HVAC may be causing a sound issue as well. Mechanical systems and cleaning machines are
fairly loud as shown by Figure 11. The overabundance of sounds, while not outside of safety
regulations, was still an issue to the staff. One method of reducing the amount of sound in the
workspace is to build better walls. Instead of having hard surfaced linear walls, breaking the wall
panels into softer material which absorbs sound waves rather than reflecting them and or by
breaking the linear halls into curves to redirect sound away from people. In addition, adding
more sound absorbing panels or Helmholz cavities into the wall would help break down the
sound reverberations. These are just some examples as to combat the sound issue inside of T-
hospital.
Noise level is high especially in the mornings, floor buffer (Zamboni looking cart) usually on
at 7-8 am, some pt’s still sleeping and c/o loud noise. The floor uses fluorescent lighting
fixtures, makes my eyes-tired midday on. Do not like fluorescent lighting.
Figure 37: Nurse sound Issue
80

T-HOSPITAL hard surfaces Sound Dampening Panels[56]

Curved Wall Design[57]
Design Panel[58]
Table 20: Sound Dampening Ideas

Issue Five

Personal privacy was limited, at best, inside of T-hospital. This lack of privacy leads to
distractions for staff, visitors, and patients and is likely caused by a lack of partitioning walls or a
way to mitigate sight lines. However, it is ill advised to put up partition walls inside a hospital to
limit visibility as this could cause a patient to go unheard and suffer. It is recommended that a
breakroom for the staff be provided where they can get out of sight of everyone and be able to
de-stress rather than be to be distracted by another person. While breakrooms already exist inside
the hospital, the additional survey feedback seemed to indicate that staff do not get to de-stress
inside.
Nursing station is too small and the nurses refuse to use the WOW in the patient’s rooms.
Auxiliary staff use the units nurses station as a hangout/meeting area. They stand over us to
talk shop while we are trying to answer phones and call lights. Its really rude.
Our job is getting harder throughout the years. Mostly we are so overwhelmed that do not
think about air pollution or view from windows. We get distracted continuously and do not
have privacy at all. Not even when eating lunch.
Figure 38:Additional feedback: Privacy issues

81

6.2 Limitations of the current methodology
In spite of significant findings and discussion in the research, there are limitations that
were hard to overcome. The method to collect data does in fact work, however it does not collect
enough data. One of the downsides to optional surveys is they’re in fact not required to be
completed. Often, the hospital staff openly declined to take the survey. One of the major verbal
complaints about the survey was the shear length. In a future rendition of this method a shorter
and more concise survey may prove to provide higher feedback results. Nurse’s often would
roam the workstation not using a single desk for very long; as such the survey is unable to relate
to a single desk. One of the questions asks the staff to self-compare their productivity to that of
hospital workers, this question became problematic without a way to compare perceived vs
actual productivity. The use of the survey however was productive as the response rate was
around 80%. This was fairly high for a survey this and is a benefit of passing the survey out by
hand. However, if this survey was to be distributed to a larger audience moving the survey to a
mobile device or email-based application would be required. This may increase the total data
collected, but may lower the overall return rate. In addition, it would allow for the data to be auto
inputted which becomes a tedious effort with more data sets. There are benefits and risk to
digitizing the survey, however it would be more effective at larger scales.

One of the problems was that the survey was meant to align the worker to the workspace
and then be able to evaluate each workspace inside the workstation. Since the hospital staff were
very mobile and often changed desks repeatedly during shifts, the survey therefore was unable to
be used to understand an individual workspace, but rather only informs of the overall conditions.
Somehow it is necessary to either generalize the survey to be about the workstation or be able to
pin down the worker to a workspace.

Hand measurements are great to confirm the results by the e-BOT but again since the
occupant survey isn’t being used on a per desk situation, these findings don’t tell us how a single
occupant feels, but rather generalizes the space. In addition, taking hand measurements is a little
redundant as the spaces began having similar values to that of the e-BOT measurements. The
hand tools worked well due to their speed and ease of use, however they’re incapable of taking
long term measurements, and were giving similar results as the e-bot and hobos. Removal of
hand tools would not result in less data, it would redundant data.
Changing from hand measurements to long term sensors would be more beneficial to
study. The study utilized very limited amounts of HOBO devices to measure the space. These
sensors were moved and shuffled about around the space, rather than remaining in one spot for
the duration of the study. Overall the hobos were used to determine if a HVAC pattern existed,
which could be confirmed by facility management. In addition, the current study was very brief
and may not be a good representation of the space. To better understand the space, long-term
measurement are needed to see how vast the climate changes inside the space over the course of
a year rather than a few days a season. In During the collection phase, a rainstorm occurred that
resulted in raised humidity in the interior environment. Understanding these large-scale
environmental factors could prove important to maximizing the data’s impact.

The current method of data collection occurs through the use of manual measurements.
While data collection does occur, there are limits to how much data can be collected by one
82

individual. While the use of hand measurements allows for understanding of the intricacies of the
workstation, it does require a lot of time and effort to accurately collect data. At best this slows
down the collection process and at worst limits the overall data amount. Either a team is needed
or automation is needed to increase the collection of data. While a team would increase the
amount of data, it may also result in changes to how the data was collected. Issues from how they
operate the sensor to how they record the data could all be minor differences that impact the
overall study. While automating the sensors would streamline the data collection, it may not
allow for the precision findings to occur.

In theory, by adopting more and more installed sensors, rather than hand held would
allow the current methodology would be able to collect far more data. This would allow for
longer study times to occur, instead of a few days it could be months’ worth of data. Improving
the data collection method would provide benefit towards higher understanding of the site.
6.3 Future work
To ensure the information collected in T-hospitals investigation is valid and useful the
following points are recommended.

1) Further Study Needed

It would be incorrect to say that this one year of data collection is the end-all state of T-
HOSPITAL. To validate the results of this test it has to be repeated and shown to have similar
results. If the results change it should be determined why they changed. Possible reasons may be
staffing changes, policy changes, or other unknown factors.

2) Broader Study Needed

A larger and more thorough investigation needs to be conducted. From what has been
seen, T-HOSPITAL hospital has areas that nurses find to be issues and may benefit from direct
intervention of their space. This however is only one hospital of the 6,210 hospitals[59] across
the United States, let alone the world. Each hospital is not a mirror of T-HOSPITAL so the
possibility of having unique conditions is highly likely. These differing factors may produce
different needs to the staff of T-HOSPITAL. More hospitals in general need to be investigated
and reviewed to have a more specific recommendations rather than a generalization. There is no
one solution to the growing demand for nursing occupational comfort inside a hospital. Rather,
each situation needs to be addressed and solutions need to be designed based on the individual
hospital. Evaluating the comfort increase per design modification should be done to make the
most impact for the least investment. This would allow for a hospital to prioritize the most
impactful factors and focus its renovation spending to produce the best outcome. If these
hypotheses hold true across other hospitals in other regions, then that would lead to the belief
that even more hospitals are fundamentally in need of study.

3) Other Building Typologies and Work Staff

83

The limitations of this work are that it was too focused on hospital staff, primarily nurses,
and thus these results do not yet apply to those outside of this workforce. However, by
incorporating more work types and building types, this style of work may be applicable to more
situations outside of the hospital. Hospital nurses are not the only work force that are faced with
inequality in their working environment. Diagnosing and determining how to resolve all IEQ
conflicts should occur in other working structures. In addition to studying differing working
buildings, focus should be given to different work types as well.

4) Determining Validity of Guidelines

From the onset of the investigation, the purpose was to determine the IEQ issue and put
forth some solution to attempt to fix the issue. To understand the impact of the proposed
guidelines, a study into how they impacted the staff would need to occur. It is suspected that if
the guidelines were implemented effectively then the staffing population would see an increase
in their comfort level. This would need to be tested to ensure that the comfort of the occupant did
increase. But with an increase in overall comfort it is expected that the productivity would also
increase. To confirm this, resurveying the population would be needed to see if they’re responses
had improved in the areas that changes had occurred in.

84

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88

8. Figure Sheet
Figure 1- Rose chart of survey questions ........................................................................................ 6
Figure 2: Comfort based on Thermal. [26] ..................................................................................... 8
Figure 3- Operating room ............................................................................................................... 9
Figure 4: Exposure Levels and Discomfort .................................................................................. 10
Figure 5- Sampling point for Maximum VOC Concentration ...................................................... 11
Figure 6-Plan of climate chambers. .............................................................................................. 12
Figure 7-Percentage of Persons Dissatisfied................................................................................. 12
Figure 8- Floor plan of the selected hospital (left) and a typical patient room in the hospital. .... 14
Figure 9- Comparison between SE and NW rooms ...................................................................... 15
Figure 10- Typical Sick Building Syndrome symptoms .............................................................. 16
Figure 11 Ventilation rate and illness ........................................................................................... 17
Figure 12- Nurse Care Environment[41] ...................................................................................... 19
Figure 13: Failure of Nurse's to save a Patient, Satisfaction of Nurses[16] ................................. 20
Figure 14- Method for Data Collection ........................................................................................ 22
Figure 15 HOBO MX1102 CO2 logger[44] ................................................................................. 23
Figure 16 Comparison of dimensions of prototype chair with published recommendations[46] 24
Figure 17 E-Bot............................................................................................................................. 25
Figure 18: Sample Survey Questions ............................................................................................ 28
Figure 19: Survey Response Scale ................................................................................................ 31
Figure 20: Personal Survey Questions .......................................................................................... 31
Figure 1: Phases of Data Collection.............................................................................................. 33
Figure 2: Building Question.......................................................................................................... 43
Figure 3: Nurse Decision Tree ...................................................................................................... 57
Figure 1 Glare Analysis: Additional Measurements in Appendix A ............................................ 61
Figure 2 Illuminance and UGR: Additional Measurements in Appendix .................................... 61
Figure 3: Survey Question Selection ............................................................................................ 63
Figure 4: Survey ............................................................................................................................ 64
Figure 5: Productivity as compared by survey and building information .................................... 66
Figure 6 Productivity as compared Gender, Age, Occupation, and Education ............................ 68
Figure 7: Productivity as compared with Gender and Age ........................................................... 70
Figure 8: Productivity as compared to the Measured IEQ ............................................................ 72
Figure 9: Physical Measurement Usage ........................................................................................ 73
Figure 21: IEQ Cart Locations: Eighth Floor of KECK ............................................................... 74
Figure 1: Found Issues via Investigation ...................................................................................... 77
Figure 2: Additional feedback: Air issues .................................................................................... 77
Figure 11: Additional feedback: Thermal issues .......................................................................... 78
Figure 12: Nurse sound Issue ........................................................................................................ 79
Figure 17:Additional feedback: Privacy issues............................................................................ 80
Figure 10: Humidity Comfort Range .......................................................................................... 125
Figure 11: Lighting Comfort Levels ........................................................................................... 126
Figure 12: Thermal comfort Ranges based on ASHRAE ........................................................... 126

89

9. Appendix
ASHRAE Standards
Categories Standard Guidelines Sources
Thermal Quality
Temperature
Cooling Season
(0.5 clo)
76-82 °F (RH 30%)
ASHRAE 55 (2017)
74-78 °F (RH 60%)
Overall (U.S.) 74-82 °F
(23.3-27.8 °C
Heating Season
(1.0 clo)
69-78 °F (RH 30%)
68-75 °F (RH 60%)
Overall (U.S.) 68-78 °F
(20.0-25.6 °C)
Floor Surface Temperature
66.2- 84.2 °F
(19-29 °C)
Radiant Temperature Asymmetry
Warm Ceiling: <9.0 °F
ASHRAE 55 (2017) Cool Wall: < 18.0 °F
Vertical Air Temperature
<5.4 °F ASHRAE 55 (2017)
Relative Humidity
<65% ASHRAE 62 (2017)
30-60% ASHRAE 62 (2017)
Air Speed
< 40 ft/min ASHRAE 55 (2017)
< 50 ft/min CCOHS (2017)
Indoor Air Quality
Carbon Dioxide
700 ppm above outdoor CO2 level or 1000 ppm ASHRAE 62 (2017)
<800 ppm EPA (IAQ spec.)
Carbon Monoxide <9 ppm EPA (IAQ spec.)
Total Volatile Organic Compounds <200 ug/m³ above outdoor TVOC concentration
EPA
Particulates PM 2.5 : 1 < 1,554,278 #/CF or 20 ug/m³
Aircuity PM 10: < 17,204 #/CF or 40 ug/m³
Total Particulates: 20 ug/m³ EPA (IAQ spec.)
Lighting Quality
Illuminance
300 to 500 Lux (Horizontal)
IESNA- RP-1-04(2017) 50 lux (vertical work surface, eg CRT monitor)
Unified Glare Rating
< 19 CIE
Luminance Ratio 3.1 or 1.3 IESNA- RP-1-04(2017)
Acoustic Component
Room Criteria
< 40 (Open-plan offices)
ASHRAE 55 (2017) < 35 (Private Offices)
Quality Assessment index < 5 dB ASHRAE 55 (2017)

90

1) Temperature

From the findings
1- Nurse’s work stations are fairly constant in thermal conditioning between 21.5°C and
25°C
2- Outside weather phenomenon have little to no impact on internal conditioning.
3- High probability of centralized facility management operating with a set value range
for interior temperature
In comparison to the ASHRAE standards the work spaces may actually be to cold for the
hospital staff. With the temperature variations between 21.5 and 25, the hospital does fall below
the ASHRAE 23.3-27.8 °C guidelines, making the spaces fairly cool. This may be to the benefit
of the staff however, as they’re constantly moving about, the cooler air may facilitate their work
better.
0
5
10
15
20
25
30
D
a
y
1
D
a
y
2
D
a
y
3
D
a
y
4
D
a
y
5
Floor 9 Floor 8 Floor 7 G
Floor 7 C Floor 6 G Floor 6 C
91

2) Humidity

Findings
1- Floors are not seeing the same results. Humidity varies widely but still comes back to
a similar spot for a good duration.
2- The elevations may be caused from outside sources, as the sensors were left for a long
duration in the hospital no observation as to what happened was recorded.
ASHRAE believes that humidity between under 65%, is conducive and the EPA believes that
between 30-60%is the correct range for humidity to limit mold growth. The hospital
measurements from the hobos indicate that humidity is between 8-65% humidity, this fall within
the ASHRAE but not EPA standard.
0
10
20
30
40
50
60
70
D
a
y
1
D
a
y
2
D
a
y
3
D
a
y
4
D
a
y
5
Humidity Value
Floor 9 Floor 8 Floor 7 G Floor 7 C Floor 6 G Floor 6 C
92

3) CO2

Findings
1- Fairly stable readings for CO2.
2- They don’t all start at the same value, although they all started at the same time. There is
variation occurring on each floor for CO2 build up.
3- CO2 is all within the acceptable range of building code
ASHRAE has published that CO2 should be under 1000 ppm to be considered acceptable for
building occupants. T-HOSPITAL has a range of 200-650ppm well under the value ASHRAE
found to be acceptable.
0
100
200
300
400
500
600
700
D
a
y
1
D
a
y
2
D
a
y
3
D
a
y
4
D
a
y
5
Carbon Dioxide Level
Floor 9 Floor 8 Floor 7 G Floor 7 C Floor 6 G Floor 6 C
93

4) Sound levels

Findings
1- Fairly consistent sound measurements occurred
2- The peaks were noted as being caused by the intercom/announcements being made to
the hospital and they are not the norm of the spaces. The peaks occurring anywhere
from 80-92 decibels, as long as they’re infrequent it is satisfactory.
3- Average reading for the space was still 48 decibels is acceptable and safe for
occupants.
ASHRAE standards indicate that under 50 decibels is considered acceptable for an open floor
plan work environment. This is meets standards, but could be improved to enhance satisfaction.

0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
0 20 40 60 80 100 120
Decibals
Measurement
94

Histograms of occupant satisfaction values covering the 7 values
1) Thermal

Findings
1- The majority of the respondents were neutral or positive about the thermal
conditioning of the space.
2- The average is 0.33 making the result just above 0 overall.
3- Occupants seem to okay with the current thermal conditions across the board
Unsatisfied (-3,-2,-1) Neutral (0) Satisfied (1,2,3)
76, 26% 90 31% 123, 43%

95

2) Space

Findings
1- The majority of people do not find the space allotted to them as acceptable
2- The average is -0.40 making the result just below 0 overall.
3- Space seems to be a topic that the respondents were not satisfied with.

Unsatisfied (-3,-2,-1) Neutral(0) Satisfied (1,2,3)
132, 46% 61, 21% 95, 33%

96

3) Light

Findings
1- People are very comfortable with the light levels
2- The average is 0.56 making the result above 0 overall.
3- Light be a localized issue, where the space just doesn’t have enough light in one area

Unsatisfied (-3,-2,-1) Neutral(0) Satisfied (1,2,3)
76, 26% 90, 31% 123, 43%

97

4) Noise

1- The majority of people do not find the space allotted to them as acceptable
2- The average is -0.39 making the result just below 0 overall.
3- Noise seems to be a topic that the respondents were not satisfied with.

Unsatisfied (-3,-2,-1) Neutral (0) Satisfied (1,2,3)
117, 40% 92, 32% 80, 28%

98

5) Visual

Findings
1- The majority of people do not find the visual stimulus allotted to them as acceptable
2- The average is -0.39 making the result just below 0 overall.
3- Visual stimulus seems to be a topic that the respondents were not satisfied with.

Unsatisfied (-3,-2,-1) Neutral(0) Satisfied (1,2,3)
86, 30% 97, 34% 104, 36%

99

6) Privacy

Findings
1- The majority of people do not find the Privacy allotted to them as acceptable
2- The average score was -0.49 across the board
3- Privacy seems to be a topic that the respondents were not satisfied with.

Unsatisfied (-3, -2,-1) Neutral (0) Satisfied (1,2,3)
124, 43% 86, 30% 79, 27%

100

7) Air

Findings
1- The majority of people do find the air quality satisfactory
2- The average score was 0.33 across the board
3- Air quality seems to be a topic that the respondents were satisfied with.

Unsatisfied (-3, -2,-1) Neutral (0) Satisfied (1,2,3)
85, 30% 66, 23% 137, 48%

101

Summary of Histograms
Thermal issues were not really present inside of the hospital, the unsatisfied responses may be
very localized cases inside of the hospital. ASHRAE standards find 23.3-27.8(cooling) and 20-
25.6(heating) as acceptable, based on the hobo readings, hand measurements, and e-BOT, the
hospital falls within those comfort ranges.
Space issues were found, it would appear that occupants are not satisfied with the spatial
conditions of T-HOSPITAL.
Light issues were very localized, almost all the responses have the occupants satisfied by the
current light levels. ASHRAE standards for light are between 13-19 UGR, from the UGR
analysis in the appendix the vast majority of the space is within that space and in more instances
under 13 than over 19 UGR.
Noise was found to be an issue from the response. It is however hard to pin down exactly what
the cause for this was. CIE standards show that as long as the sound is under 50 decibels it is
acceptable. The average recorded decibel was 48.8 with a few ping spikes due to
announcements. Noise issues are more likely to be caused from the lack of privacy.
Visual issues were noticed from the responses.
Privacy was the biggest issue among the issues. It appears that Privacy is more dissatisfying to
the occupants than the over chilled air.
Air quality was not seen as an issue based on the responses. Based on ASHRAE standards for
treated air, the T-HOSPITALs air was well within the margins based on hobo sensors.

102

Data
A) Image Processing
# Image 1 Image 2 Image 3 Image 4 Glare Reading
1

2

3

4

5

6

7

8

103

9

10

11

12

13

14

15

16

17

104

18

19

20

21

22

23

24

25

26

105

27

28

29

30

31

32

33

34

35

106

36

37

38

39

40

41

42

43

44

107

45

46

47

48

49

50

51

52

53

108

54

55

56

57

58

59

60

61

62

109

63

64

65

66

67

68

69

70

71

110

72

73

74

B) Glare Readings
# Floor
Lum.
Min. Lum.Max Lum.Avg
Root Mean
Square illuminance
Solid
Angle
Source
Threshold UGR
1 7 2.13 1957.88 37.31 100.75 124.14 6.283 27 14.2
2 7 3.31 6703.78 47.47 137.61 144.84 6.283 30 15.5
3 7 2.1 2158.75 36.7 95.28 124.16 6.28 30 13.6
4 7 2.09 2602.4 32.12 74.01 112.39 6.28 29 15
5 7 2.08 6305.35 42.58 114.56 152.09 6.28 35 14.3
6 7 2.08 2723.75 38.57 98.84 135.99 6.28 31 14.9
7 7 2.08 2900.96 51.52 140.36 142.88 6.28 30 14.8
8 8 2.06 1636.11 30.72 86 107.16 6.28 542 10.2
9 8 2.1 2537.78 36.05 84.63 127.46 6.28 31 14.4
10 8 2.05 2071.69 35.31 95.02 109.44 6.28 28 12.3
11 8 2.05 2071.69 35.31 95.02 109.44 6.28 28 12.3
12 8 2.14 2203.29 36.96 74.04 128.92 6.28 30 14
13 8 1.58 1717.07 26.22 23.4 94.67 6.283 54 10.9
14 8 1.69 2794.42 71.04 232.46 184.22 6.283 44 13.1
15 7 3.35 4409.06 55.71 146.16 165.03 6.283 656 3.5
16 7 3.33 4407.31 53.13 168.76 142.21 6.283 33 13.5
17 7 3.33 4451.94 61.5 160.5 173.29 6.283 49 11.3
18 7 3.33 3837.95 52.1 175.75 127.37 6.283 31 12.8
19 7 3.33 3792.12 57.7 171.89 144.69 6.283 42 10.5
20 6 3.33 3947.29 51.79 155.41 126.13 6.283 30 13.3
21 6 3.34 3499.84 51.66 175.39 141.48 6.283 42 9.5
22 6 3.33 4108.8 48.65 146.87 133.56 6.283 468 4.7
23 6 3.33 3499.84 36.95 113.56 116.35 6.283 40 14.1
24 6 3.33 3062.06 46.07 162.8 100.93 6.283 39 8.3
111

25 7 1.69 4575.28 72.24 288.42 181.17 6.283 29 20.3
26 7 1.69 5211.17 76.55 308.3 185.69 6.283 26 21.3
27 7 1.69 4789.58 72.81 285.71 189.6 6.283 27 20.8
28 7 1.66 5794.1 64.94 268.47 179.44 6.283 30 18.5
29 7 1.59 6829.44 59.62 229.64 169.68 6.283 112 9
30 7 1.66 4593.3 67.99 275.62 173.45 6.283 27 21.3
31 7 1.69 5800.51 77.35 283.56 192.25 6.283 48 14.4
32 7 1.67 6747.82 69.36 319.15 180.16 6.283 28 18.5
33 7 1.58 1149.57 27.36 32.3 91.04 6.283 24 13.9
34 7 1.63 3141.73 36.62 100.92 96.31 6.283 26 10.9
35 7 1.64 1525.63 29.42 37.08 99.19 6.283 27 12.5
36 7 1.69 2169.03 38.02 95.2 137.28 6.283 32 14.4
37 7 1.69 2258.37 45.9 117.47 162.74 6.283 48 11
38 8 1.57 5648.86 24.43 125.26 61.11 6.283 20 10.4
39 8 1.69 2099.51 36.21 104.63 124.62 6.283 27 14.8
40 8 1.62 2398.54 35.24 97.7 118.42 6.283 32 15
41 8 1.6 2160.04 35.99 86.51 116.97 6.283 30 15
42 8 1.67 2722.02 28.23 72.33 101.82 6.283 25 16
43 8 6.55 3898.39 42.22 133.53 143.86 6.283 30 14.9
44 8 6.54 1949.3 40.08 117.21 131.71 6.283 29 14.5
45 8 6.53 2398.86 44.41 140.63 157.62 6.283 31 16.7
46 8 1.69 2535.25 40 119.95 145.94 6.283 31 16.3
47 8 3.33 4108.8 38.21 102.08 134.2 6.283 31 15.3
48 8 3.35 5497.19 48.03 165.09 147.98 6.283 42 11.4
49 7 3.36 4192.86 34.46 106.89 116.7 6.283 29 13.9
50 5 5.65 11471.86 46.72 143.43 119.64 6.283 37 11.6
51 5 5.63 13706.36 68.08 235.76 131.95 6.283 504 8.2
52 5 5.63 13303 41.82 138.59 109.7 6.283 52 12.1
53 5 5.62 13071.34 20.65 26.96 53.25 6.283 26 9.6
54 5 2.98 4830.56 38.61 31.13 134.96 6.283 54 13.4
55 5 5.63 12565.05 25.6 37.43 60.41 6.283 24 11
56 5 2.98 4830.56 38.61 31.13 134.96 6.283 54 13.9
57 5 5.66 9815.6 29.96 24.45 88.81 6.283 26 14
58 7 2.9 5237.32 27.95 59.68 64.67 6.283 25 14
59 7 2.94 5343.75 34.9 42.82 100.68 6.283 42 10
60 7 5.63 14145.98 35.02 70.38 96.01 6.283 29 14.7
61 7 5.64 14145.98 52.39 158.27 137.98 6.283 34 16.8
62 7 5.65 11245.31 46.26 180.47 178.92 6.283 42 17.4
63 6 5.63 15580.27 38.31 141.8 82.32 6.283 28 11.6
64 6 5.65 12665.13 47.01 144.73 120.84 6.283 37 11.7
65 6 5.62 12537.59 58.34 218.84 196.88 6.283 22 21.1
66 6 2.91 3871.84 50.95 77.04 97.19 6.283 71 5.7
67 6 2.9 4230.31 62.07 139.94 146.99 6.283 69 9.8
112

68 6 5.66 12051.96 21.99 25.67 65.71 6.283 23 10.4
69 5 5.62 5079.9 40.01 32.81 93.48 6.283 38 12.9
70 5 5.62 11934.12 55.27 212.07 108.7 6.283 40 8
71 5 5.68 11050.6 49.47 180.03 118.69 6.283 38 12.4
72 5 5.62 11839.82 37.93 130.88 101.49 6.283 28 8.9
73 5 5.62 10809.73 24.75 38.24 59.9 6.283 25 8.8
74 5 5.62 11160.8 40.78 115.23 75.23 6.283 28 9.4
Avg 6.72 3.36 5812.45 44.49 130.82 126.85 6.283 62.65 13.1

C) e-BOT readout
Sort Order Floor Floor Temp Temp .6M Temp 1.2m Temp 1.6m Temp Radiant RH TVOC CO Dust Acoustic C02 Season
1 9 18.49 18.85 19.04 NaN NaN NaN 1.18 0.04 -293.09 -0.01 573.89 Spring
2 9 21.58 22.08 22.10 NaN NaN NaN 1.18 0.02 -78.40 NaN NaN Spring
3 9 21.93 22.21 22.44 NaN NaN NaN 1.18 0.02 -31.66 6.25 657.91 Spring
4 9 21.81 22.04 22.05 NaN NaN NaN 1.18 0.02 -40.31 6.24 682.70 Spring
5 9 22.84 23.22 22.80 NaN NaN NaN 1.18 0.02 -35.12 6.24 650.19 Spring
6 9 23.10 23.28 23.36 NaN NaN NaN 1.18 0.02 -29.93 6.24 660.49 Spring
7 9 23.46 23.64 22.96 NaN NaN NaN 1.18 0.02 -28.19 6.24 659.49 Spring
8 9 22.82 23.12 22.66 NaN NaN NaN 1.18 0.02 -23.00 6.24 690.18 Spring
9 8 NaN NaN NaN NaN NaN NaN 1.18 0.02 -33.39 6.24 657.99 Spring
10 8 NaN NaN NaN NaN NaN NaN 1.18 0.02 -28.19 6.24 665.71 Spring
11 8 NaN NaN NaN NaN NaN NaN 1.18 0.02 -24.73 6.25 665.98 Spring
12 8 NaN NaN NaN NaN NaN NaN 1.18 0.02 -21.27 6.24 670.65 Spring
13 8 NaN NaN NaN 23.38 24.10 44.96 1.18 0.02 -26.46 6.24 685.23 Spring
14 8 NaN NaN NaN 21.80 22.71 46.93 1.18 0.02 -47.24 6.25 658.21 Spring
15 8 NaN NaN NaN 23.31 23.89 44.01 1.18 0.02 -21.27 6.24 658.23 Spring
16 7 NaN NaN NaN 23.54 24.04 43.36 1.18 0.02 -23.00 6.24 653.34 Spring
17 7 NaN NaN NaN 23.45 24.34 43.18 1.18 0.02 -28.19 6.24 661.06 Spring
18 7 NaN NaN NaN 23.22 24.43 41.90 1.18 0.02 -21.27 6.25 658.35 Spring
19 7 NaN NaN NaN 22.28 22.97 46.09 1.20 0.02 -28.19 6.24 685.51 Spring
20 7 NaN NaN NaN 66.35 23.03 46.16 1.18 0.02 -24.73 6.24 675.78 Spring
21 7 NaN NaN NaN 22.50 23.07 46.14 1.18 0.02 -24.73 6.24 658.19 Spring
22 7 NaN NaN NaN 22.11 23.07 46.73 1.18 0.02 -24.73 6.24 658.17 Spring
23 7 NaN NaN NaN 22.28 23.11 47.22 1.18 0.02 -33.39 6.25 673.22 Spring
24 7 NaN NaN NaN 22.58 23.24 46.84 1.18 0.02 -36.85 6.25 707.30 Spring
25 7 22.35 22.39 22.31 22.54 22.74 45.46 1.18 0.02 -50.70 6.24 721.25 Spring
26 7 22.45 22.56 22.48 22.74 23.12 45.15 1.18 0.02 -50.70 6.24 759.75 Spring
27 7 22.98 23.30 23.27 23.71 23.73 43.46 1.18 0.02 -55.90 6.24 796.60 Spring
28 7 22.66 22.67 22.68 22.94 23.55 45.41 1.18 0.01 -40.31 6.24 699.69 Spring
29 7 22.49 22.62 22.68 23.04 23.68 45.22 1.18 0.02 -45.51 6.24 675.37 Spring
30 7 22.93 22.97 22.99 23.38 23.82 44.71 1.18 0.02 -57.63 6.24 680.56 Spring
113

31 7 23.06 23.44 23.19 23.46 24.04 43.58 1.18 0.01 -43.78 6.24 675.34 Spring
32 6 22.69 22.76 22.57 22.66 23.29 44.77 1.18 0.02 -28.19 6.24 663.41 Spring
33 6 23.36 22.72 22.70 23.05 23.61 45.38 1.18 0.01 -28.19 6.25 690.30 Spring
34 6 22.66 22.59 22.53 22.86 23.48 45.57 1.18 0.01 -24.73 6.24 677.99 Spring
35 6 21.35 21.40 21.40 21.65 22.41 46.88 1.18 0.01 -31.66 6.24 748.68 Spring
36 6 22.52 22.67 22.80 23.07 23.65 44.43 1.18 0.01 -28.19 6.24 739.39 Spring
37 6 23.08 23.10 23.10 23.54 23.95 43.71 1.18 0.01 -48.97 6.25 796.94 Spring
38 6 22.73 22.84 22.95 23.10 23.61 45.02 1.18 0.02 -47.24 6.25 829.68 Spring
39 6 21.32 21.41 21.57 22.05 22.97 47.98 1.18 0.01 -14.34 6.24 663.37 Spring
40 7 22.78 22.79 22.79 23.05 23.40 19.06 1.18 0.16 -81.87 6.24 677.97 Spring
41 7 22.99 22.99 22.80 22.95 23.26 19.13 1.18 0.26 -42.05 6.24 714.20 Spring
42 7 22.37 22.36 22.26 22.22 23.12 19.21 1.18 0.26 -45.51 6.24 675.42 Spring
43 7 22.67 22.79 22.67 22.73 23.53 19.43 1.18 0.26 -59.36 6.25 680.35 Spring
44 7 22.83 23.02 22.92 23.20 23.78 19.26 1.18 0.26 -57.63 6.25 679.60 Spring
45 8 22.96 23.06 22.68 22.71 23.47 19.08 1.18 0.26 -28.19 6.25 685.28 Spring
46 8 22.68 22.70 22.75 22.95 23.82 21.09 1.18 0.31 -48.97 6.25 804.07 Spring
47 8 22.71 22.77 22.94 23.09 23.87 21.37 1.18 0.31 -45.51 6.25 823.76 Spring
48 8 22.68 22.75 22.72 23.12 23.66 21.51 1.18 0.31 -48.97 6.25 852.20 Spring
49 7 22.55 22.61 22.57 22.66 23.31 32.38 1.20 0.09 -14.34 6.23 637.21 Spring
50 7 21.78 21.58 21.91 22.45 22.88 33.71 1.18 0.14 -14.34 6.24 624.65 Spring
51 7 22.42 22.50 22.43 22.81 23.38 33.61 1.18 0.14 -23.00 6.24 639.53 Spring
52 7 22.70 22.93 22.77 23.39 23.55 33.14 1.18 0.04 -33.39 6.24 689.79 Spring
53 7 22.24 22.28 22.45 22.83 23.31 33.34 1.18 0.04 -33.39 6.24 687.22 Spring
54 7 21.80 21.83 21.68 22.06 22.63 34.52 1.18 0.04 -48.97 6.24 689.90 Spring
55 7 22.10 22.25 22.13 22.50 23.09 34.77 1.20 0.04 -42.05 6.25 684.56 Spring
56 7 23.14 23.45 23.28 23.87 23.85 32.98 1.18 0.05 -43.78 6.24 717.68 Spring
57 7 23.33 23.49 23.39 23.70 23.93 32.54 1.18 0.05 -38.58 6.24 723.68 Spring
58 7 23.23 23.41 23.35 23.76 23.93 32.62 1.18 0.06 -40.31 6.25 711.65 Spring
59 NaN NaN NaN NaN NaN NaN 0.00 0.00 24.35 NaN NaN Spring
60 5 23.20 23.31 23.17 23.48 24.15 32.59 0.00 0.00 24.35 6.09 731.52 Spring
61 5 22.23 22.30 22.27 22.65 23.11 33.48 1.18 0.91 -28.19 6.24 677.68 Spring
62 5 22.03 21.99 21.89 22.25 22.74 33.80 1.18 0.35 -24.73 6.24 680.29 Spring
63 5 22.21 22.26 22.16 22.54 22.89 34.08 1.18 0.40 -42.05 6.25 684.96 Spring
64 5 22.46 22.63 22.91 24.02 23.63 33.10 1.18 0.40 -29.93 6.24 672.31 Spring
65 5 22.62 22.83 22.81 23.97 23.48 33.12 1.18 0.26 -29.93 6.25 685.07 Spring
66 5 22.77 22.90 22.88 23.63 23.47 32.96 1.18 0.31 -29.93 6.25 690.06 Spring
67 5 22.74 22.94 22.91 23.41 23.68 33.66 1.18 0.35 -81.87 6.25 748.23 Spring
68 5 22.37 22.55 22.52 23.11 23.17 33.86 0.00 0.00 24.35 6.24 767.71 Spring
69 5 22.10 22.24 22.21 22.85 22.97 34.45 1.18 0.31 -76.67 6.24 773.77 Spring
70 NaN NaN NaN NaN NaN NaN 0.00 0.00 24.35 NaN NaN Spring
71 NaN NaN NaN NaN NaN NaN 0.00 0.00 24.35 NaN NaN Spring
72 6 22.52 22.52 22.45 22.73 23.29 31.99 1.18 0.19 -10.88 6.24 632.42 Spring
73 6 22.27 22.27 22.20 22.20 22.98 31.97 1.18 0.23 -16.08 6.24 632.39 Spring
114

74 6 22.09 22.11 21.92 22.14 22.72 32.31 1.18 0.31 -33.39 6.25 650.26 Spring
75 6 22.14 22.17 22.11 22.68 23.09 33.08 1.18 0.26 -26.46 6.25 650.23 Spring
76 6 22.48 22.47 22.40 22.82 23.28 32.14 1.18 0.46 -16.08 6.25 632.35 Spring
77 6 22.48 22.42 22.27 22.73 23.34 32.05 1.18 0.40 -45.51 6.24 670.74 Spring
78 9 24.24 24.16 23.96 24.14 24.48 46.22 0.00 0.00 24.35 64.61 619.40 Fall
79 9 24.21 24.13 23.91 24.05 24.41 46.12 1.18 1.63 -7.42 54.53 625.61 Fall
80 9 21.64 21.75 22.29 22.58 22.91 48.21 1.18 0.23 -159.78 0.30 645.58 Fall
81 9 21.97 21.97 22.04 22.34 22.85 48.94 1.18 0.23 -149.39 0.29 686.66 Fall
82 9 22.01 22.24 22.32 22.38 23.15 48.23 1.18 0.23 -147.66 0.29 674.92 Fall
83 9 22.29 22.60 22.66 22.94 23.55 46.56 1.18 0.23 -139.00 0.29 665.02 Fall
84 9 22.76 22.79 22.60 22.78 23.47 47.59 1.18 0.23 -149.39 0.29 689.70 Fall
85 9 22.68 22.74 22.66 22.72 23.58 48.49 1.18 0.23 -139.00 0.29 721.07 Fall
86 9 22.93 22.89 22.75 22.93 23.70 48.02 1.18 0.23 -140.73 0.29 721.04 Fall
87 9 22.12 22.07 22.23 22.55 23.21 48.17 1.18 0.23 -135.54 0.29 736.53 Fall
88 8 21.77 21.73 21.74 22.00 22.47 49.94 1.18 0.26 -139.00 52.05 744.61 Fall
89 8 20.80 20.78 20.97 21.00 21.96 50.89 1.18 0.26 -130.34 50.89 699.50 Fall
90 8 21.56 21.57 21.47 21.64 22.22 51.06 1.18 0.23 -145.93 56.10 742.82 Fall
91 8 21.58 21.49 21.37 21.48 22.13 50.80 1.18 0.23 -132.08 53.95 743.45 Fall
92 8 21.51 21.41 21.40 21.60 22.38 50.84 1.18 0.26 -135.54 57.49 743.13 Fall
93 7 22.82 22.73 22.58 22.68 23.25 49.17 1.18 0.26 -132.08 63.91 760.55 Fall
94 7 22.82 22.94 22.53 22.64 23.12 48.46 1.18 0.26 -140.73 54.14 748.59 Fall
95 7 22.75 22.84 22.66 22.62 23.45 48.41 1.18 0.26 -149.39 54.20 743.61 Fall
96 7 23.04 23.09 22.91 22.97 23.68 47.83 1.18 0.26 -152.85 58.27 777.89 Fall
97 7 22.69 23.01 22.97 23.15 23.74 46.63 1.18 0.26 -154.58 54.14 773.88 Fall
98 7 22.79 23.01 22.91 23.23 23.84 46.61 1.18 0.31 -163.24 48.12 784.87 Fall
99 7 22.66 22.80 22.73 23.24 23.78 46.95 1.18 0.26 -164.97 49.98 777.43 Fall

115

D) Additional Survey responses

Season Floor Issue Commentary
1
Fall 5 E Noise, Privacy
Noise level during change of shift is a bit high.
Privacy during reprieve is an issue especially
when visitors are passing by during that time.
2
Fall 5 E Light
At night lights dim to promote rest with patients.
It would be beneficial to have under counter
mounted lighting in the workstations. They can
be turned on/off or even dimmed to each nurses
liking.
3
Fall 5E
Job Stress,
Coworkers
My coworkers are wonderful and makes the
biggest difference in my work environment. We
have some of the friendliest hard-working staff
on 5E. Out manager however can make it very
difficult to work here. He is short tempered and
never provides help to our busy unit (esp when
we are short) His lack of insight and poor
management skill adds to the stress of our nurses.
Also we have a shortage of staff (again the
manager is not doing anything to hire anyone)
This staffing shortage makes it very stressful to
work. If we could fix our management and
staffing issue this unit would run smoother.
4
Fall 5E
Thermal, Air,
Cleanliness
Hard to control temp. in rooms and work nurse
station. Air quality is bad, increase allergies, very
dusty. A lot of bacteria on table tops. I break out
in rash if my arms touch counters that’s why I
wear long sleeves.
5
Fall 5W Job Stress
Nursing in general, is stressful Although not
always. There are a number of factors that effect
the stress including wage, numbers of years of
experience, coworkers, staffing, position(lead, rn,
etc.)
6
Fall 7W Cleanliness
Cleanlieness of the unit esp the floors are not
cleaned or cluttered in the nursing unit, affects
my workflow. Appears to not be cleaned
routinely, would like at least once a shift. I
always have to call EVS to mop/sweep the
nursing station at least once a week as I feel I am
becoming a bother to them.
116

7
Fall 7W
Space,
Equipment,
Privacy
Limited availability for computers in am as MD’s
come in and use all computers in nursing unit.
MD’s also interrupt breaks by putting their
belongings in our break room
8
Fall 7W Thermal
Sometimes it gets too cold in the unit. I am
unable to focus on my work and get sleepy.
9
Fall 7W
Space, Job
Stress
Not enough work space. MD’s take over nurses
station in the Am making it difficult for us to
chart and complete our work on time.
10
Fall 7W Space, Privacy
We need a break area outside of the inside work
environment that is outdoors and away from
visitors to be calm peaceful and relaxing. This
must be eveningly accessible to all staff and not
to far walk from the work area because our
breaks are short.
11
Fall 7W Air So much dust. Allergies act up at work
12
Fall 7W Space
Very important to be have drinks at the nurses
station. If the lids are secure it should be allowed
13
Fall 7W Space
Having a beverage ie coffee is important. Is
having coffee in the nursing station really
compromise infection risk to pts.
14
Fall 8 E
Space, Job
Stress
9 E/W is a great place to work as long as
everyone works as a team.
15
Fall 8E equipment
I want my workstation more organized and well
kept. I want a cabinet to keep the charts and
binders.
16
Fall 8W Thermal Please lower the overall temperature in 3 south
17
Fall
9
E/W
Noise, Light Less noise more lighting
18
Fall
9
E/W
Patients, Air
Please clean air filters. Patients always complain
of allergies in their rooms. Should be changed at
least every 3 months. But it only gets changed
when there is an inspection.
19
Fall
9
E/W
Air, Window
access
Air Quality is horrible, widow access would be
relaxing, enjoy open floor plan.
117

20
Spring 5 E
Air, Space,
Privacy,
Patients
I would prefer better air quality for our patients
be addressed before the nurses station. Also
would appreciate an outdoor area for pt’s where
they are safely monitored by staff in private away
from the hustle and bustle of the front fountain
area. We have pts that may stay indoors for 1-2
months.
21
Spring
5
E/W
Light, Air,
Privacy
Nice lighting and privacy. Also, clean-very
important.
22
Spring 5 N
Job Created
Stress
Very stressful job as a nurse. Mostly short staff.
Management not team friendly, Management
treats us like we are $$ dollar signs and does not
being over budget.
23
Spring 5 N
Spaces are
dated.
New equipment(computers, chairs, desks,
etc.)Upgrade the overall look of the dept.
24
Spring 5N Air Clean our vents please!
25
Spring 5N
Space,
equipment
Having more workspace and computers would
increase my job satisfaction.
26
Spring 5N
Equipment,
patient
Upgrade to new equipment/furniture in station
and pt rooms
27
Spring 5N
Space,
Equipment
Not enough space at the nursing station 5N, only
a handful of computers. There is a lack of
equipment.
28
Spring 5S Space Work Space very tight!
29
Spring 5S Space, patient
Our work station is very small and very limited
computers for the number of employees at a
given time. Patients are always unorganized.
There is not even a space to talk to families
privately.
30
Spring 5W Coworkers The coworkers create a quality environment.
31
Spring
6
E/W
None No comment
32
Spring
6
E/W
Light
Natural lighting affects my mood and
productivity positively.
118

33
Spring
6
E/W
Air,
Cleanliness
The air ducts need to be cleaned. I know there is
dust and allergens in the ducts. I have post nasal
drip and sneezing only when I am working on 6
E/W. I do not normally have allergies or these
symptoms.
34
Spring
6
E/W
Job stress
I hate I can’t hydrate as much because I cannot
have water with me.
35
Spring 6 S
Spaces are not
equal
I work in the float pool, not all units are the same.
Some units are better. 6 south is OK.
36
Spring 6N Thermal I am so cold all the time.
37
Spring 6N Space Need more space and computers
38
Spring 6N None Nothing at this time.
39
Spring 6N
Space, Thermal
comfort, Noise
Never enough room to write like filling out this
survey was difficult. It is too cold all the time, I
have no way to control temperature # my fingers
ache from the cold. There is constant noise, that
alone increases my stress.
40
Spring 6N
Space,
equipment
Better workspace and better computers
41
Spring 6N Thermal, Noise
Our temperature can be unpredictable. During
winter season, the nurses station is very cold.
Noise level can be very high esp. During daytime
when a lot of staff are in the unit.
42
Spring 6S Cleanliness Can you make it cleaner, this unit is filthy!
43
Spring 6S
Cleanliness,
Air, Space,
Equipment
Our unit is very dirty. Visible dirt on the floor.
Trash, overflow most of the time. Light quality.
Computer and chair should be replaced for good
posture. Beside table almost impossible to move.
44
Spring 6S
Window
Access,
Equipment,
Light
Xenex machines, when windows are not covered,
cause light sensitivity/headaches
45
Spring 6S
Noise,
Distraction,
Light
Noise level is high especially in the mornings,
floor buffer (Zamboni looking cart) usually on at
7-8 am, some pt’s still sleeping and c/o loud
noise. The floor uses fluorescent lighting fixtures,
makes my eyes-tired midday on. Do not like
fluorescent lighting.
119

46
Spring 6S Staff
Making sure there is enough staff to
accommodate to the census is important and
needs to be followed as per pct contract.
47
Spring 6S Staff
13 pcts all of the time and not to take away one
pct because one sitter on the floor.
48
Spring 6W
Window
Access, View
Lack of windows does not help with job
satisfaction.
49
Spring 6W
Space, dated
equipment,
View outside,
Window access
Our unit and nurses’ station is dated and
extremely cramped. There is peeling of the
laminate which looks ugly and cheap, the floors
look dirty even when clean and the environment
is generally very unappealing. We are constantly
fighting for adequate space to chart and many
days I go 12 hours without seeing outside.
50
Spring 7N
Spaces are
small
Need more space and workstations for computers.
51
Spring 7N
Natural light,
Spaces are not
equal
I work on many different floors. The availability
of computers also impacts my productivity and
ability to chart. Enjoy the openness of 6 E/W and
their availability of computers vs 7
th
floors N/S.
There is a lack of natural light in building for the
working staff in general and often I try to take my
lunch break outside because of this.
52
Spring 7N Job Stress
Very stressful workload, high quantity with less
assistance.
53
Spring 7N
Space,
Equipment
Computer should be hidden underneath the
countertop. Computer doesn’t have the
confidential screen.
54
Spring 7N
Parking, Job
stress
Parking for nursing staff is too far. We work 12
hour shifts, 7am-7pm/ 7pm-7am, we are tired
from patient work, then we walk or take shuttle.
We suggest that after personal hour works 8-4pm
should park in san Pablo and valley parking lot
because they’re hours are very flexible.
55
Spring 7N Air
Air quality needs to be better now I notice more.
Dry nose/coughing and dry throat of me and my
colleagues.
120

56
Spring 7N
Space,
Distractions
Nursing station is too small and the nurses refuse
to use the WOW in the patient’s rooms. Auxiliary
staff use the units nurses station as a
hangout/meeting area. They stand over us to talk
shop while we are trying to answer phones and
call lights. Its really rude.
57
Spring 7N
Space,
Equipment
It would be better if there is a hydration area at
the nurses station. Should have enough computers
for staff. Need privacy screen on computer. Need
more BP machines(portable) on the unit. Need
pulse oximetry(portable) on the unit.
58
Spring 7N
Thermal
comfort
No hot water to wash hands sometimes.
59
Spring 7N Light Computer glare is very strong, daylight is too strong.
60
Spring 7S
Space, Privacy,
Light, View,
Job Stress
Our job is getting harder throughout the years.
Mostly we are so overwhelmed that do not think
about air pollution or view from windows. We
get distracted continuously and do not have
privacy at all. Not even when eating lunch.
61
Spring 7S Job Stress Not enough equipment, not enough help
62
Spring 7W None N/A
63
Spring 7W Privacy, Space
Privacy increase and workspace increase would
be good.
64
Spring 7W None N/A
65
Spring 8 E
Cleanliness,
Space
There are too many boards with scattered information
on them which I feel looks unprofessional and
unorganized. I feel our nurses station gets dirty and
not kept up daily. The carpet in our nurse’s station
huddle room is extremely dirty and unsanitary.
66
Spring 8 E None Not at this time
67
Spring 8E
Privacy, Job
Stress
Phone calls all damn day. I can’t complete any
task because I have to answer the phone.
68
Spring 8W
Space,
Equipment
Need more computers and chairs
69
Spring 8W Space, Air
Move workstation around. Air quality is filled
with debris.
121

E) KECK Floor Plans

5
th
Floor

6
th
floor

7
th
Floor

8
th
Floor

9
th
Floor

70
Spring
9
E/W
Window
Access,
Thermal
Comfort
No windows in area. The floor is usually very
cold.
122

F) Hand Measurements

Floor Desk Assignment Temperature On Desk Air Speed Under Desk Air Speed Above Desk Air speed Light On Desk Light Facing Computer Humidity Decibels(sound)
6 1 25.5 0.01 0.02 0.05 3 1 25.1 83.3
6 2 25.4 0.01 0.03 0.01 1 0 25.5 48
6 3 25 0.01 0 0.03 3 0 26.3 49
6 4 24.8 0.03 0.04 0.03 1 0 27.4 49.4
6 5 24.8 0.02 0.01 0.01 1 0 24.7 49.4
6 6 24.6 0 0 0 2 0 28.2 49
6 7 24.5 0.01 0.02 0.01 1 0 28.2 90.6
6 8 24.3 0 0.02 0.03 1 0 28.3 49.1
6 9 24.4 0.02 0.01 0 1 0 28.6 49.1
6 10 24.5 0 0 0.01 0 0 28.9 49.1
6 11 24.5 0 0 0 1 0 28.5 49
5 1 24.7 0.03 0 0.05 1 0 28.2 49.2
5 2 24.7 0 0 0 2 0 28.2 49.3
5 3 24.6 0.03 0.16 0.12 1 0 27.9 49.3
5 4 24.6 0.03 0 0.03 0 0 28.5 49.1
5 5 24.7 0.04 0 0.09 1 0 27.8 84.1
5 6 24.6 0.02 0.12 0.03 12 0 27.9 49.3
5 7 24.5 0 0 0 1 0 28.8 49.1
5 8 24.6 0 0 0 20 0 29.2 49.2
5 9 24.2 0 0 0 29 0 28 49.3
5 10 24.2 0 0 0 2 0 28.3 49.3
5 11 24.3 0 0 0 2 0 28.6 49.3
5 12 24.3 0 0 0 2 0 28.2 49
5 13 24.2 0 0 0 1 0 29.5 49
7 1 25.9 0.01 0.01 0.01 0.01 0.01 34.5 49.8
7 2 25.4 0 0 0 0.01 0.01 36.5 50.1
7 3 25.2 0 0 0 0.01 0.01 36.1 50
7 4 24.2 0 0.01 0 0.01 0.01 37.5 90
7 5 25.1 0 0 0 0.01 0.01 39.9 49.9
7 6 24.6 0 0 0 0.01 0.01 38.5 51
7 7 24.9 0 0 0 0.01 0.01 36.8 42
7 8 24.5 0 0 0 0.01 0.01 37.1 92
7 9 24.4 0.02 0.01 0 0.01 0.01 38.8 50
6 1 23.9 0 0 0 0.01 0.01 39.3 50.1
6 2 25.2 0 0 0 0.01 0.01 38.6 50.1
6 3 24.4 0 0 0 0.01 0.01 38.9 50.1
6 4 24 0 0.01 0 0.01 0.01 40.7 50.5
6 5 23.6 0 0 0 0.01 0.01 39.2 50
6 6 23.8 0 0 0 0.01 0.01 38 50.1
123

6 7 23.8 0 0.03 0 0.01 0.01 39.5 50
6 8 23.8 0.01 0.01 0 0.01 0.01 40.1 50.1
6 9 23.6 0 0.01 0 0.01 0.01 42.6 49.7
6 10 23.5 0 0 0 0.01 0.01 43 51
9 1 24.0 0 0.01 0 0.01 0.01 45 42.4
9 2 24.0 0.01 0.01 0.01 0.01 0.01 34.5 45.5
9 3 22.9 0 0 0 0.01 0.01 36.5 34.6
9 4 24.0 0 0 0 0.01 0.01 36.1 42.4
9 5 24.2 0.01 0 0 0.01 0.01 37.5 47.6
9 6 24.4 0 0 0 0.01 0.01 39.9 41.4
9 7 23.5 0 0 0 0.01 0.01 38.5 36.4
9 8 23.8 0 0 0 0.01 0.01 36.8 35.1
9 9 24.1 0 0 0 0.01 0.01 37.1 40.1
9 10 24.3 0.01 0 0.02 0.01 0.01 38.8 35
9 11 23.8 0 0 0 0.01 0.01 39.3 38
9 12 24.1 0 0 0 0.01 0.01 38.6 53.6
9 13 22.3 0 0 0 0.01 0.01 38.9 42.1
8 1 23.7 0.01 0 0 0.01 0.01 40.7 52.9
8 2 23.2 0 0 0 0.01 0.01 39.2 44.2
8 3 24.2 0 0 0 0.01 0.01 38 43.1
8 4 24.4 0.03 0 0 0.01 0.01 39.5 46.1
8 5 23.1 0.01 0 0.01 0.01 0.01 40.1 42.2
8 6 23.5 0.01 0 0 0.01 0.01 42.6 42.2
8 7 23.4 0 0 0 0.01 0.01 43 44.4
8 8 23.2 0 0 0 0.01 0.01 39.9 33.2
8 9 22.9 0.01 0 0.02 0.01 0.01 38.5 38.4
8 10 23.1 0 0 0 0.01 0.01 36.8 41.8
8 11 22.6 0 0 0 0.01 0.01 37.1 38.4
8 12 22.5 0 0 0 0.01 0.01 38.8 45.3
8 13 22.3 0.01 0 0 0.01 0.01 39.3 45.7
7 1 23.1 0 0 0 0.01 0.01 38.6 46.6
7 2 22.8 0 0 0 0.01 0.01 38.9 50
7 3 23.4 0.03 0 0 0.01 0.01 40.7 52.1
7 4 23.2 0.01 0 0.01 0.01 0.01 39.2 33.8
7 5 23.4 0.01 0 0 0.01 0.01 38 35.6
7 6 23.2 0 0 0 0.01 0.01 39.5 40.8
7 7 22.2 0 0 0 0.01 0.01 40.1 55.7
7 8 22.3 0 0 0 0.01 0.01 42.6 52.5
7 9 22.0 0 0 0 0.01 0.01 43 32.3
7 10 23.0 0.01 0 0.01 0.01 0.01 39.9 41.4
7 11 22.4 0.01 0 0 0.01 0.01 38.5 32.8
7 12 24.0 0 0 0 0.01 0.01 36.8 34.7
8 1 24.1 0 0.01 0 0.02 0.01 25.2 49.3
124

8 2 24 0 0 0 0.02 0.01 25.5 49.2
7 1 25.3 0 0 0.01 0.01 0.01 24.8 49.3
7 2 25.4 0.01 0.01 0 0.01 0 24.4 49.3
7 3 25.2 0.01 0.01 0 0 0.01 24.2 49.1
7 4 25.1 0 0 0 0.01 0.01 24 53.4
7 5 25.2 0.01 0 0 0 0.01 23.9 75.8
7 6 25.2 0.01 0.01 0 0 0.01 23.4 52.7
7 7 25.4 0.02 0.01 0.01 0.02 0.02 23.3 50.02
7 8 25.3 0 0.01 0 0.01 0.01 23.4 49.8
7 9 25.3 0.01 0 0 0.01 0.01 23.4 49.3
7 10 25.9 0.01 0 0.01 0.02 0.02 23.3 48.8
7 11 25.8 0.01 0 0 0 0.01 23.7 50.5
7 12 25.3 0 0 0 0.01 0.01 24.4 49.8
7 13 25.1 0 0 0 0.01 0.01 24.2 49.9
e-BOT measurements
All are within the ASHRAE standards. No detectable issues present.
125

Figure 39: Humidity Comfort Range
Humidity never falls below 10% and is for the most part locked in between 12 and 17%. The
few measurements above that are likely outliers caused by some special condition created by
occupants.
126

T-hospital: Comfort Ranges

Figure 40: Lighting Comfort Levels

Figure 41: Thermal comfort Ranges based on ASHRAE
-300
200
700
1200
68 69 70 71 72 73 74 75 76 77 78
Amount Recorded
Temperature
Heating Season
-300
200
700
1200
74 75 76 77 78 79 80 81 82
Cooling Season

Abstract (if available)

Abstract Hospitals are the place where people go to get over an ailment with the expectation of receiving proper treatment with a quick recovery time. Among all of the staff at the hospital, Nurses are one of the fundamental workers towards patient recovery. Nurses are involved in all stages of a patient’s care, from signing the patient in to walking them out the door. Nurses are the front line of defense for a hospital, ensuring that patients receive proper treatment. Nurses are essential to patient recovery. Therefore, their comfort should be a major concern as it directly linked to their productivity. A hospital should respond to their nursing staff’s comfort needs to produce higher results from its nurses. By minimizing nurse discomfort a hospital can expect a nurse to have higher productivity values. Nurses are essential to patient recovery. Nurses should not be an afterthought when it comes to setting up the buildings comfort control systems. It is of the utmost importance that nurses be given respect inside their space in terms of comfort. Using IEQ driven data, occupant surveying, and observational information allows us to understand how a Nurse is considered in their space. The goal of this research is to determine the impacts of IEQ on hospitals nursing staff. In doing so determine the IEQ satisfaction and workplace productivity of the users. ❧ The study adopted several data collections methods to produce relevant data. The first data set came from installing and monitoring sensors around the common work areas of the nursing staff to collect the IEQ of the space. The second data set came from the nurse comfort surveys. Lastly the third data set came from architectural observation. Data collection occurred over several days to ensure that anomalies were minimized in any of the data sets. Each data set provides additional layers of information about what is occurring inside of the hospital. The direct monitoring of the IEQ of the space told us the present conditions of the workstations, once compared to the occupant survey a more complete picture can be made and the problems start to become clear. After that the architectural observation is used to see what features appear to cause the most discomfort among the staff. Analyzing and comparing the data sets, makes it possible to evaluate how comfortable nurses are in their environment. The third step was to produce design guide recommendations based on the analysis results. In conclusion nurse’s complaints were in alignment with one another and overall, it was found that IEQ factors were insufficient to some degree and impacted them negatively. Future research should use a larger data set generated from several hospitals. To proof the work done in this thesis is able to be used in other hospitals.

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Asset Metadata

Creator Bradley, Brandt Alex (author)

Core Title Nursing comfort study: are hospitals performing for nurses? IEQ analysis of working conditions inside hospitals

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Publication Date 07/25/2019

Defense Date 05/08/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Comfort,data-driven,Hospital,IEQ,nurse,OAI-PMH Harvest,occupant comfort,occupant productivity,survey,thermal,user-satisfaction

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Choi, Joon-Ho (committee chair), Baldwin-Rodriquez, Brooke (committee member), Konis, Kyle (committee member), Schiler, Marc E. (committee member)

Creator Email babradle@usc.edu,brandtbradley@sbcglobal.net

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-193845

Unique identifier UC11662627

Identifier etd-BradleyBra-7631.pdf (filename),usctheses-c89-193845 (legacy record id)

Legacy Identifier etd-BradleyBra-7631.pdf

Dmrecord 193845

Document Type Thesis

Format application/pdf (imt)

Rights Bradley, Brandt Alex

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA