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The effects of air temperature on office workers' well-being, workload and productivity-evaluated with subjective ratings


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Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate office worker's productivity remains to be a challenge for ergonomists. In this study, the effect of indoor air temperature (17 °C, 21 °C, and 28 °C) on productivity was investigated with 21 volunteered participants in the laboratory experiment. Participants performed computerized neurobehavioral tests during exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroencephalograph (EEG) were also measured. Several subjective rating scales were used to tap participant's emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm discomfort negatively affected participants' well-being and increased the ratio of low frequency (LF) to high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by tasks increased and participants had to exert more effort to maintain their performance and they also had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air temperature had negative influence on office workers' productivity and the subjective rating scales were useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on productivity.
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The effects of air temperature on ofce workerswell-being, workload and
productivity-evaluated with subjective ratings
Li Lan, Zhiwei Lian
School of Mechanical engineering, Shanghai Jiao Tong University, Shanghai 200240, China
article info
Article history:
Received 29 December 2008
Accepted 4 April 2010
Air temperature
Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate
ofce workers productivity remains to be a challenge for ergonomists. In this study, the effect of indoor
air temperature (17
C, 21
C, and 28
C) on productivity was investigated with 21 volunteered partic-
ipants in the laboratory experiment. Participants performed computerized neurobehavioral tests during
exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroen-
cephalograph (EEG) were also measured. Several subjective rating scales were used to tap participants
emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm
discomfort negatively affected participantswell-being and increased the ratio of low frequency (LF) to
high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by
tasks increased and participants had to exert more effort to maintain their performance and they also
had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air
temperature had negative inuence on ofce workersproductivity and the subjective rating scales were
useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on
Ó2010 Elsevier Ltd. All rights reserved.
1. Introduction
Studies (Roelofsen, 2002; Woods, 1989; Lorsch and Ossama,
1994) have shown that productivity bears a close relationship to
the indoor environment quality (heat, cold, noise, light, etc.).
However, howto assess the effect of indoor environment quality on
productivity remains to be the major challenge for ergonomists.
Until now there have been no standard procedures to measure
ofce workers productivity, therefore it has been difcult to
persuade clients to accept the concept of a relationship between
economic productivity benets and indoor environment quality. A
neurobehavioral approach had been proposed to systematically and
quantitatively evaluate the effects of indoor environment quality on
ofce workers productivity by several neurobehavioral tests,
which assessed four classes of neurobehavioral functions involved
in ofce work, including perception, learning and memory,
thinking, and executive functions (Lan et al., 2009a). In the previous
laboratory study some neurobehavioral tests had been used to
investigate the effect of air temperature on productivity for a very
short time (about 30 min). It was found that motivated people
could maintain high performance for a short time under adverse
(hot or cold) environmental conditions. However, it is unclear why
the participants could maintain their performance and whether it
will happen in the routine ofce work environment. In this paper,
several subjective ratings as well as physiological measurements
were made to investigate the effects of thermal discomfort on ofce
workersproductivity. The object was to investigate the effects of
thermal discomfort on occupantsworkload, emotion, well-being,
and motivation, and also the relationship between these activities
and their neurobehavioral performance.
2. Methodologies
The experiment was carried out in an ordinary but low-polluting
ofce (LWH¼645 m), in which participants sat at seven
workstations, each consisting of a table, a chair and a personal
computer (Fig. 1). The ofce was illuminated with eight uorescent
lamps. The room temperature was controlled by an air-conditioner,
which was able to adjust temperature from 16
2.1. Participants
Twenty-one volunteered participants (6 females and 15 males)
were recruited for this experiment. They were recruited based on
*Corresponding author. Tel.: þ86 21 34204263; fax: þ86 21 34206814.
E-mail address: (Z. Lian).
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Applied Ergonomics 42 (2010) 29e36
the following criteria: familiarity with computer, impartiality to the
ofce in which the study was carried out, and absence of chronic
diseases, asthma, allergy, hay-fever and color blind. The participants
were all students, aged 18e20 years (
¼1). Participants
were required to wear long-sleeved sweater, long thick trousers and
long underwear top and bottoms, socks and shoes (an estimated
clothing insulation value of 1.20 Clo, including the insulation of the
chair). They were also asked to have a good rest at the night before
the experiment. The participants were paid a salary for participation
in the experiment at a xed rate per hour. To increase their moti-
vation and especially to encourage them to perform the tests seri-
ously, bonus was paid depending on their performance. Participants
all successfully completed experimental sessions.
All protocols were approvedby the universitys ethics committee
and conformed to the guidelines contained within the Declaration of
Helsinki. Verbal and written informed consent was obtained from
participants before they participated in the experiment.
2.2. Measurements
2.2.1. Physical measurements
The temperature, relativity humidity and velocity of the air were
measured. The mean radiant temperature was estimated from the
globe temperature, which was measured using a 150 mm diameter
black globe thermometer.The illuminant wasmeasured with a digital
luxmeter near each participant at the height of working face.
2.2.2. Physiological measurement
Participantselectrocardiogram (ECG) and electroencephalo-
graph (EEG) were recorded by a Powerlab 8/30 system (AD
Instruments, Australia) and the accessorial materials (e.g. adhesive
pads, EEG or ECG electrodes, etc.). The standard bipolar limb leads
(ML-1340) and adhesive ECG electrodes (MLA-1010) were linked to
the data acquisition system through a dual bioamplier (ML-135)
for recording of ECG. The negative lead and the positive lead were
clung to the right and left wrist, respectively, and the ground lead
was clung to the right ankle. The EEG recordings were made using
the bipolar method: a pair of electrode measured the difference in
electrical potential (voltage) between the two positions above the
brain, one being placed on the right side of the head (60% of
distance from right ear to midpoint of the scalp), another being
placed in the centre of the forehead, and a third electrode being put
on the earlobe as a point of reference. There are three main spectral
peaks of HRV distinguished in a spectrum calculate from 2 to 5 min
ECG recordings: very low frequency (VLF: 0.003e0.04 Hz), low
frequency (LF: 0.04e0.15 Hz), and high frequency (HF: 0.15e0.4 Hz)
(Sayers, 1973). The ratio of low frequency to high frequency (LF/HF)
is usually introduced to infer the sympathetic nervous system
activity (Jaffe et al., 1993). Based on a 5-min record, the relative
power of four EEG bands (
-band EEG: 0.5e4 Hz;
-band EEG:
4e8 Hz;
-band EEG: 8e14 Hz;
-band EEG: 14e35 Hz in
frequency) and the value of LF/HF were calculated.
2.2.3. Performance measurement
Performance refers to the quality and quantity of nished task.
Thirteen computerized neurobehavioral tests were used to evaluate
participants performance. The detailed description of these tests
can be referred to Lan et al. (2009a) and Lan and Lian (2009b).
2.2.4. Subjective measurements Thermal sensation votes. Thermal sensation votes (TSV)
were cast on the ASHRAE/ISO 7-point thermal sensation scale
(ASHRAE, 2001). Thermal comfort (TC) votes were cast on 5-point
numerical scales ecomfortable (0), slightly uncomfortable (1),
uncomfortable (2), very uncomfortable (3), and extremely
uncomfortable (4). The Prole of Mood States. Participantsemotion was
investigated with the Prole of Mood States (POMS). The POMS has
been widely used as a research and clinical instrument. It consists
of 65 adjectives describing feeling and moods that the participant
may have experienced at that moment, or over the previous day,
few days, or week, including key words such as unhappy, tense,
careless, and cheerful (McNair et al., 1992). Each item is scored on
a 5-point Likert-type scale ranging from 0 (not at all) to 4
(extremely) according to participantsresponses. The POMS
consists of six identiable mood states: tension, depression, anger,
vigor, fatigue, and confusion. A composite score, the total mood
disturbance (TMD) score, is computed by summing each of the
individual score for tension, depression, anxiety, fatigue and
confusion, with vigor scores subtracted to indicate participants
total mood disturbance. The higher TMD score indicates higher
negative mood. Well-being and motivation. Motivation refers to the
workers mental drive and enthusiasm to carry out a work. If
a person is reluctant to work, then no matter how accurately he can
perform, the productivity should be deteriorated. Well-being
Fig. 1. (a) The layout of the experimental room (1) consisting of a table, a chair and
a personal computer, (2) air conditioner and (3) waiting room; (b) a picture during the
L. Lan et al. / Applied Ergonomics 42 (2010) 29e3630
indicates the physical and mental health of a worker. Unless
a person feels a positive sense of well-being he will not perform as
effectively, and an optimum level of productivity will not be ach-
ieved. Health factors inuenced by the indoor environment are
(Clements-Croome and Kaluarachchi, 2001): (1) respiratory prob-
lems (dryness, hoarseness, dry/sore throat, changes in voice,
wheezing); (2) skin problems (soreness, itching, dry skin, rashes);
(3) nervous problems (headaches, nausea, drowsiness, tiredness,
lethargy, reduced mental capacity, dizziness, forgetfulness,
fatigue); (4) nasal-related problems (itchy or teary eyes, runny
nose, asthma-like symptoms among non-asthmatics); and (5) odor
complaints (changes in odor, unpleasant odors or tastes).
Motivation scale includes two items relating to workers willing
and enthusiasm to perform task. They were assessed on a 7-point
scale ranging from 1 (very low) to 7 (very high). Well-being was
assessed by 18-item questions covering comfort and health factors
inuenced by the indoor environment on a 5-point Likert-type
scale ranging from 0 (not at all) to 4 (extremely). National aeronautics and space administration-task load
index (NASA-TLX). NASA-TLX is found to be the most valid measure
of subjective workload, to have the highest user acceptance, and to
have the smallest between-subjects variability. NASA-TLX is
a multidimensional, self-reported assessment technique that
provides an estimation of the overall workload associated with task
performance and mental effort (Hart and Staveland, 1988). In TLX,
workload is dened as the cost incurred by human operators to
achieve a specic level of performance.The overall workload score
(OW) is a weighted average of ratings on six underlying psycho-
logical factors, including physical demand (PD), mental demand
(MD), temporal demand (TD), own performance (OP), effort (EF),
and frustration (FR) level. Three dimensions relate to the demands
imposed on the participant (PD, MD, and TD) and three to the
interaction of a participant with the task (EF, FR, and OP). NASA-TLX
involves a two-part evaluation procedure. The rst requirement is
to obtain numerical ratings for each scale that reect the magni-
tude of that factor in a given task on a 7-point bipolar scale ranging
from 1 (low) to 7 (high). Second, participants perform 15 pair-wise
comparisons of six workload scales. The number of times each scale
is rated as contributing more to the workload of a specic task is
used as the weight for that scale. Finally the overall workload score
can be computed by multiplying each rating by the weight given to
that factor. In this research, NASA-TLX was computerized and the
six subscales of workload for a specic neurobehavioral test were
evaluated immediately after the test was nished.
2.3. Experimental procedure
The effect of three indoor air temperatures (17
C, 21
C, and
C) on productivity was studied. The indoorair velocity was kept
under 0.1 m/s, and the relative humidity of air was not dependently
controlled. Within-subject design was applied for this experiment
and balanced Latin-square design was utilized to control the
carryover effects (Zhu, 2000).
The experiment was performed in three days, each day for 8.5 h
from morning to afternoon. Twenty-one participants were sepa-
rated into 3 groups with 7 participants (5 males and 2 females,
respectively) in a group, each group being exposed to the three
temperature conditions in one day. A standardized three by three
balanced Latin-square design was shown in Table 1, in which factor
Twas the experimental variable (T
C, T
C, T
factor Areferred to the three participant groups, and factor B
referred to the three successive time periods of each group. The
schedules of the whole day and each temperature condition are
shown in Fig. 2. There were two pauses among the three temper-
ature conditions: from 11:00 to 12:30 was lunch time, participants
being supplied with the same Chinese foods; and from 14:30 to
15:30 was the second interval time, during which participants went
out of the ofce for a break and they could consume non-carbon-
ated water and biscuits which were freely available at the waiting
room. Each temperature condition lasted for a total of 120 min.
First, participants entered the ofce reading book or playing games
for 40 min to adapt to the indoor environment. During this period,
physical parameters were measured. Following the exposure,
participants spent about 60 min on performing the computerized
neurobehavioral tests and assessing workload. After completing
the neurobehavioral tests, participants were instructed to assess
their general perceptions of the environment, emotions, well-
being, and motivation to work by lling in questionnaires for
10 min. Then the physiological parameters of one of the seven
participants were measured for 10 min. Participants were told
before the experiment that they could not leave the room until the
120 min session had reached.
A week before starting the experiment, participants practiced
the neurobehavioral test battery for 1 h. They were also instructed
on how to ll out the questionnaires.
2.4. Statistical analysis
The SPSS 13.0 (SPSS Inc., Chicago, IL, USA) program was
employed to make the statistical analysis. The data wererst tested
for normality using ShapiroeWilksWtest; the signicance level
Table 1
Balanced Latin-square design of this experiment.
Fig. 2. Schedule of the experiment: (a) schedule of the whole day and (b) schedule of
each exposure.
L. Lan et al. / Applied Ergonomics 42 (2010) 29e36 31
was set to be 0.05 (P<0.05). Normally distributed data were
subjected to analysis of variance in a repeated measures design
with each participant as her own control, thus excluding any
differences in experience, training, intellectual skills, etc., which
can inuence performance greatly. HuynheFeldt statistics were
used to adjust the violation of sphericity. HuynheFeldtsP-values
were based on corrected degrees of freedom, though the original
degrees of freedom were reported. Whenever necessary, post hoc
comparisons (Turkey HSD test) were then performed. Not normally
distributed data were analyzed using Friedmans analysis of vari-
ance or Wilcoxon Matched Pairs test.
3. Results
3.1. Results of thermal condition
The measured physical parameters describing the indoor
climate of the ofce are shown in Table 2. They did not deviate from
the intended level, and also there were no large differences in the
relative humidity among the three conditions. The relationship
between thermal sensation votes and air temperature is shown in
Fig. 3. The relationship between thermal comfort votes and air
temperature is shown Fig. 4. The size of the plot in Figs. 3 and 4
represents the number of replies. It can be seen that participants
attained thermal neutrality and comfort at 21
C; they felt slightly
cool at 17
C and slightly warm at 28
C and most participants felt
slight discomfort at these two conditions.
3.2. Results of emotion
The effectof air temperatureon participantsemotion is illustrated
in Table 3. The reliability (Cronbachs
disturbance and subscales (ranging from 0.65 to 0.92) suggests that
the POMS is a reliable scale being used to investigate the effect of
thermal environment on occupants emotion. It should be noted that
the higher score of negative moods, such as tension, depression,
anger, fatigue, and confusion, indicates higher negative emotion,
while the higher score of vigor indicates higher positive emotion. It
can be seen from Table 3 that participantstension and anger emotion
and total mood disturbance were signicantly affected by air
temperature (Repeated-measure ANOVA, P<0.05), they experi-
encing more negative moods at 28
C and less negative moods at 21
(Post hoc test, P<0.05). No signicant emotional difference was
observed between 17
C, or between 17
3.3. Results of well-being and motivation
The effect of air temperature on occupantswell-being and
motivation is shown in Fig. 5. It was found that occupantspercep-
tion of well-being and their motivation to work were also signi-
cantly affected by air temperature(Friedman ANOVA, P<0.05). They
perceived better well-being at cool condition and neutral condition
than at warm (Wilcoxon Matched Pairs test, P<0.01). As to moti-
vation, they had signicantly higher motivation to do work at
neutral condition than at warm condition (Wilcoxon Matched Pairs
test, P<0.05). No signicance difference in well-being or motivation
was found between the neutral condition and the cool condition.
3.4. Results of workload and performance of neurobehavioral tests
The overall workload and the six subscales imposed by the
neurobehavioral tests under different temperature conditions are
Table 2
Mean values and STD of physical parameters inside the ofce.
Parameter 28 21 17
Air temperature (
C) 28.2 0.5 21.1 0.6 18.0 0.5
Relative humidity (%) 62.6 7.9 71.3 4.3 63.2 3.9
Air velocity (m/s) 0.1 0.03 0.08 0.02 0.1 0.03
Mean radiant temperature (
C) 28.1 0.4 21.0 0.5 18.1 0.5
Illuminant (lx) 560.2 56.3 561.4 54.7 562.1 54.0
Fig. 3. Change in thermal sensation votes with air temperature.
Fig. 4. Change in thermal comfort votes with air temperature.
Table 3
The effects of air temperature on subjective emotional ratings.
Mean (SD) P
Temperature (
C) 17 21 28 17 21 28
Total mood
disturbance (TMD)
0.87 0.83 0.88 26.8(5.1) 24.8(4.8) 32.6(5.1) 0.05*
Tension 0.80 0.75 0.70 7.8(1.0) 6.5 (0.7) 8.6(0.9) 0.05*
Depression 0.91 0.83 0.85 11.3 (1.8) 10.1(1.7) 12.1(1.7) 0.14
Anger 0.87 0.81 0.81 7.5(1.2) 6.3(0.9) 8.7(1.4) 0.04*
Vigor 0.85 0.90 0.83 15.5(1.2) 14.7(1.3) 14.5(1.3) 0.54
Fatigue 0.89 0.87 0.92 7.6(0.9) 8.6(1.1) 9.5(1.1) 0.06
Confusion 0.68 0.65 0.67 9.0(0.7) 8.9(0.7) 9.3(0.7) 0.79
L. Lan et al. / Applied Ergonomics 42 (2010) 29e3632
shown in Fig. 6. The air temperature had signicant effects on the
perceived overall workload as well as the subscales (except the
physical demand subscale) (Friedman ANOVA, P<0.05). It can be
seen that participants perceived that workloads were signicantly
higher at 17
C and 28
C than that at 21
C. The results of the six
subscales indicate that the neurobehavioral tests were character-
ized by signicantly higher mental demand than physical demand
(Repeated-measure ANOVA, P<0.05), and well represent the
characteristics of current ofce works. Signicant effect of air
temperature on effort was observed (Repeated-measure ANOVA,
P<0.05). Post hoc test shows that participants had to perform tests
with signicantly more effort at 17
C than at 21
Fig. 7 shows the relationship between indoor air temperature
and the performance of neurobehavioral tests based on regression
analysis. The real lines indicate the variation of accuracy and speed
with air temperature, and the broken lines illustrate the 95%
condence interval. It can be seen from Fig. 7 the trend that the
accuracy and speed of neurobehavioral test decreased in the
moderately uncomfortable environment. However, no signicant
effect of air temperature can be observed on the performance of
most neurobehavioral tests.
The correlations between workload and performance of neu-
robehavioral tests are shown in Table 4. The correlation factors
were analyzed with Spearman coefcients. It can be seen that
participants had to exert more effort to perform tasks when the
mental demand and time demand were higher. However, negative
correlation was found between exerted effort and the accuracy and
speed of neurobehavioral tests, indicating that although partici-
pants had exert more effort to do task no improvement on
performance could be achieved. Moreover, both accuracy and speed
of neurobehavioral tests decreased with the increase of overall
workload, that is, participants having poorer performance when
the workload was higher.
Table 5 illustrates correlations of overall workload (OW),
emotion (TMD), well-being (WB), motivation (MT), and the
performance (accuracy and speed) of neurobehavioral tests. The
correlation factors were analyzed with Spearman coefcients.
There is a positive correlation between motivation and well-being,
both of which decreased with increase of total mood disturbance
score and overall workload. But no signicant correlation can be
found between the performance of neurobehavioral tests and
emotion, well-being, or motivation.
Fig. 5. Change in (a) perception of well-being and (b) motivation to work with air temperature.
Fig. 6. Change in overall workload and the six subscales with air temperature.
L. Lan et al. / Applied Ergonomics 42 (2010) 29e36 33
3.5. Results of HRV and EEG
In this study the physiological parameters of 3 participants were
measured. Fig. 8 shows the variation of LF/HF with air temperature. It
can be seen that the LF/HF increased with air temperature although
no signicant statistical temperature effects was found; great
increase in the LF/HF ratio was found at the 28
C compared with
another two conditions. The global relative power of the four EEG
bands is shown in Fig. 9. The power of
-band EEG was signicantly
affected by air temperature (Repeated-measure ANOVA, P<0.02).
-band EEG decreased at the 17 or 28
C compared with the
neutral condition (Post hoc test, P<0.05). No signicant changes of
other three EEG bands were found but it can be seen from Fig. 9 that
-band and
-band EEG increased at the 17 and 28
4. Discussion
This study suggests that participants maintained their perfor-
mance byexerting more effort when the workload demand increased
in thermal discomfort environment. Haneda et al. (2009) found
similar results when examining the effects of thermal discomfort on
the performance of ofce work.Just as it is dened, effort, consciously
protecting the level of task performance by trying hard, can coun-
teract an impaired operator state as well as enable dealing with
increased task demands (Hockey, 1986). On the other hand, studies
have validated the existence of a cognitive reserve,whereby
participants have at their disposal a certain amount of neural
resources that could be allocated to the performance of tasks and
activities. Performance of these tasks and activities would deteriorate
when the amount of resources was insufcient to deal with both the
task demands and thermal stress, such that participants would be
able to maintain their performance level until the resources were
overloaded (Hocking et al., 2001). Moreover, participants would like
to exert more effort to maintain performance if needed when they
were supplied with extra bonus for better performance. The invest-
ment of effort has the advantage that task performance remains at
a certain target level, but there are costs for this achievement. Short-
lasting effort investment is probably without health consequences
and is one of the advantages of human exibility to deal with in
demands. However, prolonged, continuous effort compensation can
be a threat to good health, as it has been suggested that repetitive
activation of the cardiovascular defense response may lead to
hypertension (Johnson and Anderson, 1990).
The cost of effort investment can be partly illustrated by
participantsmotivation to work: they had lower motivation to
work when they had to exert more effort to perform a task. Moti-
vation plays an important role in worker productivity. Heerwagen
(1998) showed that the relationship between buildings and
worker performance was related to both motivation and ability. An
individual has to want to do the task and then has to be capable of
doing it. In addition to effort investment, well-being is also related
to motivation to work. As to the health condition, participants
reported signicantly more SBS symptoms at the warm condition.
Fang et al. (2004) also found the perception of air freshness and
acceptability improved greatly as temperature decreased and the
intensity of fatigue, headache and difculty in thinking clearly
decreased at lower levels of air temperature. The conditioning of
the mucous membrane in the upper respiratory tract induced by
the temperature and water vapor difference between the inspired
air and the surface of the nasal passage potentially alleviated the
perceptions of poor air quality in the neutral and cool environment
(Berglund and Cain, 1989; Fang et al., 2004). Due to the above
reasons, high air temperature signicantly reduced participants
motivation to do their work. In fact, the condence that the ght for
a healthy work environment and healthy workers is a prerequisite
for innovation and productivity in a knowledge-based economy, is
gaining more and more ground in companies (Hermans and
Peteghem, 2006).
Fig. 7. Change in performance of neurobehavioral tests: (a) accuracy; (b) speed with air temperature.
Table 4
Correlations among workload and performance of neurobehavioral tests.
PD MD TD OP EF FR Accuracy Speed
OW 0.26** 0.64** 0.52** 0.52** 0.76** 0.71** 0.19** 0.14**
PD 0.04 0.10** 0.10** 0.05 0.25** 0.02 0.02
MD 0.27** 0.14** 0.47** 0.36** 0.16** 0.09**
TD 0.06 0.30** 0.26** 0.02 0.02
OP 0.21** 0.37** 0.28** 0.11**
EF 0.51** 0.08*0.12**
FR 0.10** 0.11**
Accuracy 0.01
*P<0.05; **P<0.01.
L. Lan et al. / Applied Ergonomics 42 (2010) 29e3634
The role of emotion has been largely neglected in productivity
research. Frijda (1996) suggested that the ultimate function of
emotions is to potentiate or stimulate behavioral responses that, in
turn, inuence the personeenvironment relation in the service of
some adaptive goal. Damasio (1994) also claimed that emotion can
provide an early warning to the cognitive system as to whether
things are going well or badly and indicate which things need
attention. In particular, intense and negative emotions may reduce
performance efciency in several ways: (a) they may disrupt the
state regulation, which makes it less optimal for task performance
because of over-reactivity; (b) they may be so distracting that they
directly interfere with the processing of the task information; or (c)
they may cause psychosomatic complaints that also demand
attention. The correlations between emotion and motivation show
that negative emotion reduced participantsmotivation to do work.
Participants experienced more negative moods at the warm
condition, so they even did not want to exert as much effort as the
slightly cool condition when they felt that the environment was
unfavorable and it was hard to maintain their performance.
However, no signicant correlations can be found between
performanceof neurobehavioral tests and emotion, or well-being, or
motivation. There are several explanations for this dissociation; the
time lag between takingperformance measuresand subjectiverating,
or the dynamic interaction between people and their overt perfor-
mance. There is a continuous and dynamic interaction between
people and their surroundings that produces physiological and
psychological strain on the person. The humansbody is not a passive
system that responds to an environmental input in a way that is
monotonically related to the level of the physical stimulus (Parsons,
2000). Instead, acting as an adaptive agent, humans regulate the
effect of indoor environment imposed on their productivity. They
keep on appraising their environment consciously or unconsciously
and choose coping strategies and self-regulative processes that are
believed to be appropriate. Productivity is the nal result of those
behaviorally manifestations of human psychological, physiological,
and neural functioning changes. Due to the exibility of human
beings, the effects of indoor environment on productivity may not be
illustrated overtly on neurobehavioral performance during the
experimental period. In this study, participants succeeded to main-
tain their performance by exerting more effort in the moderately
uncomfortable environment when they were encouraged by extra
bonus. On the other hand, they also realized that they could leave the
environment after the short experiment, which helped them to
overcome the effects of adverse thermal environment during the
experimental period. This is probably one of the main limitations of
laboratory study on productivity. However, the lower motivation to
do work, higher perceived workload, and more negative emotions
might indicate that productivity will be reduced in an uncomfortable
environment in real life. Moreover, when subjective assessments are
taken together with performance measures, whether dissociating or
not, reveals more about task performance than performance
measures taken in isolation.
The HRV index has been shown to be closely related to thermal
comfort sensations; the LF/HF was higher when the participants
stayed in the unpleasant thermal environment (Yao et al., 2009). In
this study, when stayed in the warm environment, participantsLF/
HF was twice as much as that when they were neutral or slightly
cool, which may suggest that they felt discomfort at the warm
environment. However, it is not clear why no increase of LF/HF value
was found in the slightly cool condition. Just like Yao et al.s (2009)
research, the present study also found an increase of
-band EEG
and decrease of
-band EEG in the neutral environment. The
EEG is usually associated with slow-wave sleep. This result may
indicate that participants were tired after 2 h work and they could
have a good relax in the comfortable but not in the uncomfortable
environment. One limitation of this study is that only limited
samples (3 participants) of physiological measurements were made
after the tasks; it would be better if they were made along with tasks
at the same time. However, the results still support useful evidence
together subjective assessments that thermal discomfort has
negative inuence on occupantsproductivity.
5. Conclusions
In the eld laboratory experiment subjective rating scales and
physiological measurements were made to evaluate the effects of
air temperature on productivity. It can be concluded that:
Table 5
Correlations among emotion, well-being, motivation, and performance.
TMD WB MT Accuracy Speed
OW 0.24** 0.25** 0.12** 0.19** 0.14**
TMD 0.48** 0.27** 0.02 0.01
WB 0.64** 0.03 0.01
MT 0.06 0.10
Accuracy 0.01
Fig. 8. Ratio of low frequency to high frequency (LF/HF) under different air
Fig. 9. Effect of air temperature on EEG power of different bands.
L. Lan et al. / Applied Ergonomics 42 (2010) 29e36 35
Participants had lower motivation to do work and their
EEG decreased in the moderately uncomfortable environment.
Warm discomfort increased the LF/HF value and negatively
affected participantswell-being.
The workload imposed by neurobehavioral tests increased in
the moderately uncomfortable environment and participants
had to exert more effort to maintain their performance with
the increase of workload.
Thermal discomfort caused by high or low air temperature had
negative inuence on ofce workersproductivity and the
subjective rating scales were important supplements of neu-
robehavioral performance measures when evaluating the
effects of IEQ on productivity.
In the future experiments, more participants will be involved,
longer exposure session (for example, at least 4 h) should be
investigated, and the extra bonus may not be supplied anymore to
make the experiment environment more like the actual work
environment. An effort should be also made to integrate the two
indices of accuracy and speed into one index, as productivity
concerns not only the quantity of work, but also the qualityof work.
The project was nancially supported by the National Natural
Science Foundation of China (No. 50878125) and the Major
Program of National Natural Science Foundation of China (No.
50838009). The authors would like to thank the participants who
volunteered for this study. The authors also would like to thank the
anonymous reviewers for their useful and valuable comments on
this paper.
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... The workers who experienced heat stress at work were more likely to be associated with well-being, physical, and mental health problems. Previous studies have found that heat stress at work provide negative impacts on work motivation, productivity, and increased injury risk [23] [24]. ...
... From the experimental test, the heart rate is a general indicator of stress on the body [23]. Heart rate is the safest index because it is the earliest response of physiological strain [24]. ...
... Heart rate is the safest index because it is the earliest response of physiological strain [24]. Earlier research reported that the standard heart rates for performing heavy work in a hot and humid environment were in the range of 120-160 bpm [23,25]. Our findings of heart rate reinforce the results of the previous studies. ...
Heat stress normally known as a hidden cause of accidents in construction sectors. To ensure the productivity and health of workers in the construction site, it is necessary to evaluate the effects of temperature and relative humidity on the workers’ physiology under hot conditions. Hence, the aims of this paper are: (i) to investigate the knowledge of heat stress and workers perceptions on workers performance in construction site, (ii) to identify the environment factor of heat stress in the construction site and (iii) explore the measurement physiology parameters for heat stress. Heat stress questionnaires and experiment test were combined to extract useful information. An online survey was undertaken with a representative sample (N=292) from Malaysia construction sector. While, the experiment was carried out in a well-controlled climate chamber to obtain datasets with four conditions combining air temperature and relative humidity (32 °C/70 %, 34 °C/92, 34 °C/74% and 38 °C/83%). At a climate chamber, the subjects doing a job such as lifting and carry the 10 kg workload were exposed to different combinations of air temperatures and relative humidity. The subject’s physiological responses to the environment were then investigated. The survey’s finding showed 71.9% of the workers understand about heat stress. 22.6% of the workers perceive that the temperature is hot and quite hot and relative humidity result showed that 50.9 % of the workers perceived that part of their mouth and throat are dry while working. Besides, the experiment study showed that workers physical demands varies according to their work task with a combination of the influences from individual and environmental factors.
... In the built environment, indoor thermal comfort conditions may influence health [1,2], office work performance [3][4][5][6], learning performance [7][8][9], well-being [10,11], and the overall satisfaction of occupants [6,12,13]. An analysis done by Graham et al. (2021) based on approximately 90,000 occupant satisfaction survey responses found that roughly 40% of them are dissatisfied with their thermal environment [14]. ...
... Each approach is weighted before their summation into an affinity matrix with and respectively, where + = 1. Spectral clustering is then run on the affinity matrix on a multiple number of cohorts (e.g., = [2,10]). The best is the one with the highest Silhouette Score. ...
Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using two personal comfort datasets containing longitudinal data from 55 people. The datasets comprise more than 6,000 unique right-here-right-now thermal comfort surveys. The results show that a CCM that uses only the one-time onboarding survey information of an individual occupant has generally as good or better performance as compared to conventional general-purpose models, but uses no historical longitudinal data as compared to personalized models. If up to ten historical personal preference data points are used, CCM increased the thermal preference prediction by 8% on average and up to 36% for half of the occupants in the first of the tested datasets. In the second dataset, one-third of the occupants increased their thermal preference prediction by 5% on average and up to 46%. CCM can be an important step toward the development of personalized thermal comfort models without the need to collect a large number of datapoints per person.
... Ear drum temperature, skin temperature, heart rate and body weight loss have been shown to significantly increase at 35 0 C in comparison to 26 0 C (Liu et al., (2017). In addition, there are several research which their results shows that high air temperature can affect thermal comfort, health and performance (Srinavin & Mohammed, 2003;Langkulsen et al., 2010;Lan, et al., 2010;Venugopal et al., 2020;Somanathan et al., 2021). ...
... Atmospherics and Affective Wellbeing. In the reviewed (largely experimental) studies (see Table 2) certain lighting, thermal and noise conditions were reported to affect employee (positive and negative) mood (e.g., Hoffmann et al. 2008;Lan et al. 2010;Lamb and Kwok 2016;Zhu et al. 2019), which, to some extent, depended on gender (Knez 1995) and age (Knez and Kers 2000), or was mediated by perceived office attractiveness (Veitch et al. 2013). Air quality effects on mood were insignificant (Snow et al. 2019). ...
Despite the awareness that employees spend at least half of their awake time at work, knowledge about how the physical office work environment (POWE) shapes employee wellbeing remains fragmented, inconsistent and scattered across disciplines. We provide a narrative review of the empirical literature to summarise the current state of the science and lay the groundwork for advancing a more holistic and nuanced theoretical understanding of the mediating mechanisms underlying the POWE‐wellbeing relationship. To do so, we propose an updated taxonomy of POWE features, incorporating a new dimension – exposure to nature, and use this extended taxonomy to examine the evidence base on the relationship between POWE features and five dimensions of wellbeing: affective, physical, social, cognitive and professional. Based on our findings, we extend a meta‐theoretical model which identifies three distinct theoretically‐driven mediating pathways – relatedness, energy and functional discomfort – through which POWE features differentially influence wellbeing dimensions. In doing so, we integrate the organizational behaviour theory of Job Demands‐Resources and the environmental psychology framework of POWE functions to argue that POWE functions can be both demands and resources‐generating, and can, therefore, have simultaneous positive and negative consequences for employee wellbeing. We conclude with a critical examination of theoretical, methodological and practical implications for future research.
... Many mathematical models have been established to evaluate the effect of room temperature on workplace productivity. [10][11][12][13][14][15][16][17][18] However, it has been shown that deviation from the thermal neutral state decreases workplace productivity. ...
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... The role of temperature in the workplace was also established by Seppänen et at. [12] in their study of the effect of temperature on task performance and was discovered that performance increases with temperature up to 21-22 o C, and decreases with a temperature above 23-24 o C. The highest productivity is at a temperature of around 22 o C. Lan et al. [13] based their experiment on the effects of different levels of temperature and discovered that thermal discomfort caused by air temperature had a negative effect on workers' performance and productivity. ...
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Non-territorial offices have been a growing architectural trend as they save costs and space, while maximizing the number of available workstations. The change to desk sharing is even more significant after the pandemic, with more companies switching to hybrid mode. The risk with non-territorial offices lies in lack of attachment to workplace in the employees, as these spaces scrap away any chance for personalization to the environment. Attachment as an affective bond to environments and places has a deep psychological implication in workplaces. The fundamental characteristic of the concept of attachment (and place attachment in particular) is the proximity-seeking behavior that draws the person closer to the attachment subject. Place attachment has been said to rely on social features and physical features. Attachment to workplace results in employees’ comfort, job satisfaction, development of commitment and organizational citizenship behaviors. Several models of people–place relationships have been proposed, including the PPP model by Scannell and Gifford which highlights place loss and resulting emotion, attitudes, and behaviors relevant for workplace change processes. From a behavioral perspective, one can study attachment as a habit formation behavior. This motivational/emotional behavior therefore underpins the mesolimbic dopaminergic system involved in reward mechanisms as well as seeking mechanisms. Considering the appraisal theory, the affective bond shapes because the environment (1) is predictable to offer security and support survival (2) supports achievement of physical and cognitive goals (3) matches one’s personal values (4) supports one’s expectations based on past experiences. Operationalization of place attachment helps architects to design attractive work environments that evoke this emotion.
This paper reports an experimental study on college students in a climate chamber, analyzed the scenes of measured skin temperature, EEG activity and task performance of subjects exposed to three temperatures. The concentration performances of the subject task were evaluated by measured neurophysiological parameters. Results shown that there were gender differences in local skin temperatures and EEG-based concentration index at the indoor temperatures to which they were exposed, females’ mean skin temperature is more sensitive to temperature steps. Accuracy divided by response time was proposed as a performance index, the influence of exposed temperature on two indicators depends on the type of cognitive task. The concentration index was more affected by cold in the male subjects. Experimental results strongly confirmed that EEG-based concentration index should be affected by indoor temperatures. Furthermore, a correlational analysis identified the close relationship between concentration index and performance index.
Extreme heat wave events (EHE) are one of the most unprecedented threats posed by climate change. The escalation of global temperature is expected to cause more frequent and prolong heat waves which may have a disastrous impact on the human population and the ecosystem. Currently, more than 50% of the world population lives in urban areas and is expected to increase to 6.4 billion by 2050. Global climate change with the local urban heating phenomenon in cities is expected to impose immense threat in terms of increased surface temperature affecting the urban well-being and livability status. While the different aspects of climate change may affect urban health through direct and indirect pathways, the implications of heat toward mental health are less studied. High ambient temperatures have a range of mental health effects. A study conducted in 19 different countries suggested a significant relationship between heat and psychological conditions where heat stress and consistent exposure to high temperature were found to cause depression and anxiety. Other scholarly studies found an increase in mental and behavioral disorder reports during heat wave period. The strongest evidence was found for increased suicide risk. Six broad mental health outcome categories were identified: suicide and heat; bipolar disorder, mania and depression, and heat; schizophrenia and heat; organic mental health outcomes and heat including dementia; alcohol and substance misuse and heat; and multiple mental health outcomes/mental health service usage and heat. A review from fifteen studies showed an increased suicide risk with heat (relative risk (RR) 1.014–1.37 per 1 °C, P < 0.05; r = 0.10–0.64, P < 0.05). Increased risks of mental health-related admissions and emergency department visits at higher temperatures were also found. Despite mounting evidence that mental health treatments are essential, especially during EHE, mental health services are found to be scarce in most developing and low-income nations. Raising public awareness of the consequences of EHE on mental health is critical to educate people on how to reduce risks and safeguard mental health. However, understanding the people's attitudes and behaviors on EHE is the first step in developing a more targeted public awareness program. As a result, individual-level research is required to identify vulnerable populations and assist in the development of adaption strategies, such as heat action plans. In summary, evidence for the impact of heat on other mental health outcomes was mixed. Knowledge gaps exist on the impact of high temperatures on many common mental health disorders. Mental health impacts should be incorporated into plans for the public health response to high temperatures.KeywordExtreme heat eventMental healthSustainable citiesUrban healthUrban heat island
Local Strong Thermal Radiant environment is widely present in industrial buildings, resulting in reduced cognitive abilities and thermal comfort for workers who perform monitoring operations. However, the effect is difficult to quantify, especially for jobs that are near hyperthermal radiant sources. In this study, ten young male subjects were directly exposed to five different radiation panel temperatures for 60 min, i.e., 100, 150, 200, 250, and 300 °C, at 0.8 m from the radiant panel. Subjects were asked to perform cognitive tasks and subjective questionnaires and monitor 16 channels of EEG signals in a chamber. Based on the assessment of the EEG characteristics, the impacts of Local Strong Thermal Radiant on cognitive abilities and EEG are investigated. The results indicated that cognitive abilities were improved while the radiant temperature was below 250 °C. As the radiation temperature increases, the normalized power of β activity and α activity increases, and the vigilance and frontal EEG asymmetry increase. When it reaches 300 °C, all these features decrease and show an inverted “U” shape. The Local Strong Thermal Radiant arouses β activity and motivation for a short period and enhances cognitive abilities. The thermal comfort scores and thermal sensation scores rise with increasing temperature. Several international standards and literature can verify the findings of this study. This study's findings provided a foundation for assessing the cognitive abilities and inducing the mental states of hyperthermal radiant environment young workers to advance effective safety management in the industrial workshop.
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A subjective experiment was conducted in a climate chamber to investigate the effects of thermal discomfort (feeling too warm) on the performance of office work. Twenty-seven Danish female subjects were exposed in a climate chamber to four conditions with different levels of thermal discomfort provided by a combination of operative temperature and amount of clothing. Thermal sensation votes towards the end of exposures were neutral, slightly warm, warm and very warm. More symptoms indicating mental fatigue were observed with increased thermal discomfort. The subjects reported that more effort was necessary when they felt thermally warm compared to conditions in which they felt thermally neutral and slightly warm. Performance of proof-reading, addition and text-typing tasks was not affected by thermal discomfort. This suggests that the subjects were able to maintain their performance but as a result they got more tired and the mental work load increased.
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One of the fundamental human requirements is a working environment that allows people to perform their work optimally under comfortable conditions. Given that buildings and air conditioning systems are designed on the basis of a certain level of discomfort, this raises the key question ‘What is the effect of the level of comfort on the productivity of people working in office environments?’ The purpose of this paper is to quantify this relationship as an aid to making choices regarding the working environment at strategic level within the facilities management process, with particular emphasis on thermal conditions.
The present study describes three recent large studies of office-worker productivity measurements and relates productivity to building costs. A general agreement that improved working conditions are found to increase productivity. However, determining a quantitative relationship between environment and productivity proved to be highly controversial. In an office, spending money on improving the work environment may be the most cost-effective way to improve worker productivity since the cost of the people is an order-of-magnitude higher than the cost of maintaining and operating the building.
One of the central themes of this book is the adaptive nature of human response to stress and environmental demands. The basis of adaptive behaviour has not been systematically studied, though it may provide the key to the understanding of individual differences in stress proneness and health-related behaviour. It may also prove fundamental in the development of a general theory of effects of stress on performance and efficiency, for example by focusing attention on the processes which underlie successful and unsuccessful environmental management. The present paper puts forward a model of regulatory activity underlying stress management and coping, based on the “variable state theory” interpretation of stress effects (Hamilton, Hockey & Rejman, 1977). The initial analysis of stress effects leads to a proposal of a broader, more widely — applicable mechanism for adaptive regulation of behaviour. This theory suggests a number of possible sources for the derivation of individual differences. These may arise within the control system itself, or in intrinsic variability in both cognitive and energetical resources.
Spontaneous variability of heart-rate has been related to three major physiological originating factors: quasi-oscillatory fluctuations thought to arise in blood-pressure control, variable frequency oscillations due to thermal regulation, and respiration; frequency selective analysis of cardiac interbeat interval sequences allows the separate contributions to be isolated. Using this method, a laboratory and field study of the effects of mental work load on the cardiac interval sequence has been carried Out. Results suggest that mean heart rate and variance are unreliable measures, but that consistent changes in interval spectrum occur; these have been traced to alterations mainly in the 0·1 Hz region, perhaps originating with changes in the patterns of respiration which interact with the 0·1 Hz vasomotor activity,
provide psychophysiologists with a strategy for selecting physiological (or biochemical) parameters to be measured as dependent variables(s) associated with a manipulated or measured psychological variable(s) the concepts of stress and arousal are complex and defy simple definitions / our approach to defining these terms will be to show how they evolved historically and how experimental findings provide pressure for their continued evolution since one mainstream of psychophysiology has focused extensively on the stress activation model of analysis, section 2 of this chapter will be directed at understanding the emergence and current status of stress activation research / section 3 will discuss rationales for selecting physiological variables for psychophysiological research / section 4 will show how judicious selection of physiological measures can be especially effective in generating important experimental findings in stress activation research (PsycINFO Database Record (c) 2012 APA, all rights reserved)