Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta-analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision-making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high-performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ-based performance gains that can directly inform cost-benefit analyses of building design and renovation.

1. Introduction

The built environment—the buildings where people live, work, and spend time—can impact human health in a variety of ways [1]. The term “Healthy Buildings” has been used to describe buildings that are designed to promote an indoor environment that has a positive impact on human health [2]. The success of any organization relies heavily on the productivity and efficiency of its workforce [3]. Building managers face a pertinent challenge when it comes to providing targeted information supporting the renovation of interior spaces with the goal of enhancing office work performance. Limited access to resources poses a significant hurdle in their efforts to curate healthy work environments that foster increased performance and well-being among employees. Although there are many facets of work performance of employees in the workplace, this research considers performance through the lens of only efficiency-based tasks—direct measures of work speed and output. Unlike other performance metrics like score on a test or the subjective quality of work output, efficiency metrics have more explicit monetization and business case implications, for example through the “lost wages” salary conversion method, and, therefore, have understood value for many stakeholders.

This research stems from the development of the Healthy Buildings and Energy Support Tool (H-BEST), which is publicly available for use by building maintenance professionals and the general public [4]. H-BEST is an analytical tool that estimates the benefits of conducting building renovations or interventions in pursuit of an optimal indoor environment from indoor environmental quality (IEQ) data entered by the user. It assesses the potential improvements possible when increasing IEQ conditions to high-performance targets that are associated with improved occupant satisfaction and performance. This tool is intended for use in real-world applications for building owners and managers who are considering investing in building system improvements and renovations and would benefit from understanding the potential health and financial return on investment, which is why productivity was selected as the output metric of interest. This work presents the meta-analyses that provide a basis for the performance improvement content in H-BEST.

1.1. Influence of IEQ on Office Performance

There are many mechanisms for which IEQ can influence work task performance. Four major IEQ categories form the basis of the regression models in this research: indoor air quality, thermal comfort, lighting, and acoustic environment. These were defined within the scope of H-BEST as metrics of interest, which is what prompted their use in this analysis. There is one metric that was selected under each of the four IEQ categories. One metric cannot wholly capture the performance of any of these broad IEQ categories, and it is possible that other metrics in each category may yield stronger correlations. This section provides a short background of each category, the metric chosen to represent it, and some other important design factors within each category.

1.1.1. Indoor Air Quality

Ventilation rate is a comprehensive metric for indoor air quality. Ventilation rate refers to the amount of outdoor air introduced to indoor spaces. Ventilation dilutes indoor air contaminants, such as particulate matter, volatile organic compounds, and human bioeffluents, assuming the outdoor air is properly filtered of contaminants. Higher ventilation rates have been associated with reduced absenteeism, sick building syndrome symptoms, and odor complaints, as well as improved task performance [5].

Another common metric for indoor air quality is carbon dioxide (CO₂), which is easier to measure in buildings and has an inherent relationship to ventilation rate. Indoors, CO₂ has been shown to act as a surrogate for ventilation rate [6] and indoor-generated pollutant levels [7], rather than being an exposure metric for impacts on human health directly [8]. Those who want to measure CO₂ directly while utilizing the ventilation regression in this report could convert their steady state indoor CO₂ to a ventilation rate using the method described by Persily [9]. There are many indoor air contaminants that likely affect work performance, but ventilation rate was chosen for this research as outdoor air removes all indoor contaminants. It is important that the outdoor air quality is good, or that there is efficient filtration at the outdoor air intake, for ventilation rate to be a useful metric of indoor air quality.

1.1.2. Thermal Comfort

The human body continuously dissipates and regulates heat in relation to ambient environmental conditions, and air temperature is a major factor in such interactions (in addition to humidity, clothing level, airflow, and other factors). Depending on the air temperature, the human body undergoes either vasodilation (expansion of blood vessels to increase skin blood flow and heat dissipation) or vasoconstriction (the opposite of vasodilation) to minimize discomfort. This mechanism is called the human thermoregulation. The level of physical activity when occupants are walking, sitting, sleeping, etc. plays a crucial role in the interaction of the human body with the air temperature. Several studies have demonstrated that air temperature significantly influences office employees’ productivity (e.g., [10, 11]). Predicted mean vote (PMV) is another metric for thermal comfort that considers air temperature as well as radiant temperature, relative humidity, air velocity, metabolic rate, and clothing level. Air temperature was chosen as the metric for this category because there are more research studies available to include as most of the studies done with PMV use temperature as the experimental variable. It is important when using temperature to evaluate the work performance of thermal comfort the other five variables in the PMV model are not creating uncomfortable conditions that would confound air temperature.

1.1.3. Lighting

There are many metrics and components for the field of lighting in buildings. Horizontal illuminance is the density of light (lumens) per unit of horizontal surface area and is measured in lux (lumens per square meter) or footcandles (lumens per square foot). An appropriate illuminance is important for visual tasks being performed on a desk or horizontal work surface. It is not well understood whether horizontal illuminance is the best metric for computer-based work, which occurs on the vertical plane to the eye; however, some studies have found an increase in productivity in computer-based work associated with higher horizontal illuminance levels [12, 13]. The literature investigation in this research did not yield a significant number of vertical illuminance studies to construct its own regression model, and the majority of studies that were identified were conducted at very high illuminances over 2000 lx, which is not reasonably achievable for everyday electric lighting in offices. Although there have been mixed results, the majority of existing research has found significant improvements to occupants’ alertness when exposed to bright lighting conditions during the daytime [14]. A study by Barnaby [15] found a 2.8% increase in productivity by changing the horizontal illuminance from 50 footcandles (500 lx) to 100 footcandles (1000 lx) and an 8.1% increase when changing to 150 footcandles (1500 lx). A literature review of 11 studies of visual performance found a significant rise in speed and accuracy of various work tasks when environments were modified from low to medium light levels and smaller improvements to performance at higher illumination levels [16]. There are many other important metrics in the lighting field, like vertical illuminance, that do not have as much existing research available on their impact to work productivity compared to horizontal illuminance, including contrast, uniformity, disruptive glare, illuminance from daylight, circadian rhythm factors, and spectral composition. Horizontal illuminance was chosen as the metric to represent this category, but it is important in the context of this research that these other factors are not creating uncomfortable or disruptive conditions that would confound horizontal illuminance as a design metric.

1.1.4. Acoustics

Noise level refers to sound pressure level, measured in decibels, which is a logarithmic scale based on the ratio of the measured sound pressure to a reference sound pressure, typically 20 μPa, which is based on the absolute threshold for normal hearing. Background noise in an office environment impacts speech intelligibility and speech privacy [17] and can impact a worker’s ability to focus and perform tasks efficiently [18]. Exposure to very high noise levels over 85 dB for enough time can lead to hearing loss [19]. Much lower noise levels in the range of 50–60 dBA may not be harmful to humans but can still have impacts to focus and performance in work settings [20, 21]. The “A” in dBA indicates a frequency weighting that is often used in applications related to human hearing and most indoor environment literature, and most building performance standards use dBA as the noise metric of choice. Noise in an office environment has several dimensions, such as frequency content, reverberance, temporal variation (e.g., frequency of interruptions), speech noise characteristics, and overall average noise level. Overall average noise level was selected as the metric of interest in this work because of the relatively large volume of experimental studies with this metric, but it is important to consider these other components of noise that could create work environments unconducive to performance and productivity that could overshadow or confound noise level.

Each of these metrics are elements of larger, more complex categories, with additional factors and characteristics that interact to influence human health. While it is acknowledged that a single metric cannot fully characterize an entire topic area, for example, temperature representing thermal comfort, studying and benchmarking isolated metrics can provide some insight into a bigger picture of health impacts and interactions [22–24]. Despite the complexity of these categories, clear patterns emerged in three of the four metrics studied (ventilation rate, air temperature, and horizontal illuminance), suggesting that sufficient data for a single metric may yield usable results across a larger sample despite confounding factors.

1.2. Existing Productivity Meta-Analyses

Although there are many IEQ design resources available, there is no comprehensive guidance on quantifying health benefits of renovations or improvements for a specific building. Additionally, there is no universal definition or measurement of work performance or productivity, and the concept varies depending on the context of the job function [25]. Prior studies have explored the connection between various IEQ metrics and performance, but there are two key limitations as applicable to the objectives of this research: (1) previous literature has combined multiple building use types or work types and (2) previous literature has combined a wide variety of performance metrics, such as performance scores and accuracy, which Keene and Jung [26] suggest may not be directly proportional in impact to the efficiency-based definition of productivity used for this research. Existing meta-analysis studies to quantify performance as it relates to IEQ conditions are summarized here with their potential limitations.

Seppanen et al. [27] conducted a meta-analysis of eight studies on the effects of ventilation rate on task performance. They found an increase in performance with increasing ventilation rates, with diminishing returns at higher ventilation rates. In the ventilation range 6.5–10 L/s per person, the increase in performance is 2.0%–3.5% per 10 L/s per person increase, and in the range 10–20 L/s per person, there is a 1.0%–2.0% per 10 L/s per person increase. This analysis included one study that was conducted in a school setting and one study included a creative thinking performance score that does not fall under the efficiency-based definition of task performance.

Seppanen et al. [28] conducted a meta-analysis to establish a correlation between human performance and air temperature. Their findings revealed an incremental rise in performance with increasing temperature until reaching a peak around 21°C–22°C, and a consistent decline in performance was observed as temperatures exceeded 24°C–26°C. A 9% reduction in performance was observed at an air temperature of 30°C compared to the optimum of 21.7°C. Their meta-analysis included 24 studies conducted in a mix of office and school settings. The analysis also utilizes a diverse set of performance metrics, including cognitive test scores that do not fall under the task speed-based definition of performance.

Porras-Salazar et al. [29] conducted a meta-analysis study of the impact of temperature on office work performance and included 35 studies. The study found a low proportion of variation in performance that was explained by air temperature. The analysis also utilized a diverse set of metrics, including accuracy and various unnormalized performance scores (e.g., error score, number of lags, number of false alarms, and percentage attempted) that do not fall under the task speed-based definition of performance.

Yeganeh et al. [30] correlated air temperature to cognitive performance in a meta-analysis review with 28 controlled laboratory studies. The results were reported in mean change from control temperature in each study rather than absolute temperature, which makes it difficult to compare with other meta-analyses, but the study found a maximum ~8% performance improvement at extreme cold and warm temperatures 20°C–25°C from control temperature and ~6% performance improvement at a more moderate 7°C–10°C deviation both warmer and colder from the control temperature. This study only included office-related cognitive tasks, but some of the metrics do not fall under the task speed-based definition of performance, such as accuracy, memory string length, and number of false alarms.

Konstantzos et al. [31] conducted a meta-analysis of lighting studies, including studies correlating horizontal illuminance levels to work output. The analysis found a positive relationship between increasing horizontal illuminance and work output but with significant variation as to the extent of this relationship. The study presented a curve with a 2.0% work output increase per 10 lx increase between 0 and 100 lx and about 0.15% work output increase per 10 lx increase above 100 lx. Their analysis used mostly studies that measured the production assembly speed of industrial workers, and the study noted the theoretical contrast to modern office settings, for which it did not present a regression equation specifically.

Szalma and Hancock [32] conducted a meta-analysis looking at effect sizes of noise attributes (e.g., duration, intensity, and speech vs. nonspeech) on human performance (e.g. perception, cognition, and communication); however, their analysis was not specific to office environments nor did it conduct a regression analysis. Notably, they found a wide variation in the impact of noise intensity (noise level) on performance.

Hongisto [33] and later Roelofsen [34] used regressions to quantify the impact of speech intelligibility on performance loss, finding that decreasing the intelligibility of background speech and increasing sound absorption could result in productivity improvements of ~7% but neither study conducted a regression analysis based on overall noise level specifically, so no direct comparisons can be made.

1.3. Analysis Objectives

This research builds upon existing research in IEQ regression modeling by isolating a high-impact application (task performance in office settings) rather than homogenizing various contextual settings or disparate types of metrics. This research includes new studies that have been published since some of the older meta-analyses were conducted and includes four major pillars of IEQ (air quality, thermal comfort, lighting, and acoustics) under one standardized analysis procedure that allows for direct comparison for prioritizing areas to invest building capital budgets in real buildings.

The outcome of this work is a regression meta-analysis of existing literature relating distinct IEQ conditions, specifically ventilation rate, temperature, horizontal illuminance, and noise level, to task performance. The data collection includes only efficiency-based tasks conducted in office settings to understand specifically how IEQ impacts employee time-based performance. The regression models are intended be used in real life settings to estimate the benefits of improving IEQ conditions to the defined high-performance targets. These results will benefit stakeholders who are considering implementing office building upgrades or operational improvements that contribute to a healthy and productive indoor environment.

2. Methods

This work is a review article that systematically surveys existing literature and performs a quantitative synthesis of IEQ studies within the defined areas of interest that consider impacts on office performance. A meta-analysis with a regression framework is then applied to the literature collection.

2.1. Data Collection

In order to collect articles that investigated the impact of IEQ on task performance in office buildings, literature databases and search engines were leveraged, including ScienceDirect, Wiley, and Google Scholar to identify peer-reviewed publications. Figure 1 shows the literature collection and filtering methodology, described subsequently.

Details are in the caption following the image — **Figure 1**
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Flowchart outlining the literature collection and filtering methodology for the meta-analysis.

The following keywords combinations were used, pairing office performance keywords with IEQ metric keywords:

1.
Performance keywords: office, productivity, performance, cognitive score, self-reported productivity
2.
IEQ keywords: thermal comfort, temperature, ventilation, carbon dioxide, lighting, horizontal illuminance, and noise.

In the resulting literature, the title and the abstract of each study were read to see whether they aligned with the research interests. In addition, existing literature review papers and meta-analyses, such as Seppanen et al. [28], Wang et al. [35], and Szalma and Hancock [32], were leveraged by adding the studies that those papers identified to this meta-analysis (snowball sampling) in order to form a comprehensive collection of existing literature.

2.2. Inclusion Criteria

The main criteria for including a study in this literature collection were the following:

1.
It measured a specific change to ventilation rate, air temperature, horizontal illuminance, or noise level (e.g., a general retrofit to the building was excluded since it would simultaneously impact multiple IEQ categories with no way to isolate the impact),
2.
It measured performance in task speed, reaction time, call handling time, or self-reported productivity (efficiency-based definition discussed in the introduction of this paper and by Keene and Jung [26]),
3.
It recorded the before and after IEQ conditions and task performance outcomes in a table or figure, and
4.
The study was conducted in an office or office-like environment on adult participants.

Data points were included regardless of their statistical significance, because insignificant (close to zero) changes in performance should still be reported to accurately account for all findings within the ranges of interest. However, some studies did not report values if there was no statistical significance, and so the data from these studies could not be included without reported values. The following, more detailed steps for selecting studies and datapoints within studies were taken:

For self-reported task performance studies, only questionnaires that assessed task performance on a continuous numeric scale (e.g., 0–10 or 0–100) were included. Studies with qualitative survey options were excluded as they could not be objectively integrated with the quantitative nature of this research.

Only studies where the participants were assessed for task performance under each of the IEQ testing conditions were included; for example, for horizontal illuminance, studies investigating “bright light therapy” by exposing participants to high illuminances for a short period of time and then measuring cognition directly after exposure under dimmer lighting conditions were excluded from analysis. In addition, for horizontal illuminance studies, only testing conditions for daytime workers were included in this research as this is the typical occupancy for office buildings. Studies that investigated whether high illuminance exposure in the evening or at night interfered with sleep quality and cognition were excluded for this reason.

For acoustics, while studies have investigated the impact of specific characteristics of noise on human productivity, such as office noise versus speech noise [36], to enable systematic comparison, this work only considers studies that report task performance under at least two different measured overall noise levels, recognizing that this only captures a single dimension of acoustics. Studies considering background music were excluded as this is not a common practice in office settings.

Finally, if a study reported conditions for control variables outside a reasonably expected indoor value, those data points were excluded from analysis because the extreme conditions may overshadow the impacts of the testing variable. Extreme indoor values were defined as follows:

•
< 15°C and > 31°C for temperature studies
•
> 2000 lx for horizontal illuminance studies
•
> 85 dBA for noise studies

2.3. Data Organization

Most studies included tables that reported the various IEQ conditions and resulting task performance measurements, which were directly utilized in the meta-analyses. The data for a small number of publications that showed the results in a graphical format and not accompanied with a tabular format were extrapolated and recorded based on the graphic presentation of the data. The studies that collected multiple efficiency metrics from the same study population, within the same timeframe, and under the same set of IEQ conditions were combined, and the task performance results were averaged. For studies that reported task performance results intermittently within a longer period of exposure to a particular IEQ metric, the values corresponding to the full reported time span were used as one data point instead of including multiple data points for each intermittent time span because the purpose of the models is to quantify task performance impacts in real-life work environments where employers would be concerned with long-term performance.

For studies that tested multiple IEQ dimensions, data points for a given metric where the other metric was held constant were used as the testing condition. For example, if a study reported task performance for multiple temperatures and multiple lighting conditions, the temperature performance data points were taken between two different temperatures within the same lighting condition. For studies with more than two IEQ conditions, the values closest together of the data points were selected. For example, for a study with three data points at 5, 10, and 15 L/s per person, the authors recorded variation in performance between 5 and 10 L/s per person and then 10–15 L/s per person and did not record the variation between 5 and 15 L/s per person.

2.4. Data Processing

The first step in processing the data was to apply a weighting factor to studies based on sample sizes and study durations. Some existing regression analyses use weighting factors as well; for example, Seppänen et al. [28] gave studies with 100 or more participants a factor of 1.0 (as to prevent large studies from having excessive influence) and divided smaller studies’ sample sizes by 100 to create a scale from 0 to 1. For this study, a logarithmic function, whose nature is to dampen the impact of extreme values, was used in addition to a scaling factor like the one used by Seppänen et al. [28] so as not to overshadow studies with small numbers of participants according to Equation (1). This equation produces a weighting factor on a scale of 1–10. Studies that took place in less than 1 day (e.g., several hours) were recorded as 1 day. The duration refers to the number of days of performance measurements, not the number of days of exposure to the environmental conditions.

(1)

W is the weighting factor, _n is the n^th data point in the set, D is the duration of measurement (days), P is the number of participants, and _MAX is the maximum value in the set for given regression model.

The approach used in this work to investigate relationships between IEQ metrics and human performance was similar to calculations conducted in Wargocki et al. [37], Seppänen et al. [28], and Seppänen et al. [27]. The process is outlined in Equation (2) and Equation (3) and involves taking the unit change of task performance values from studies and then integrating the area under the curve to get absolute performance potential improvement.

Four values were extracted from each data point: the “before” and “after” IEQ conditions (x₁ and x₂) and the “before” and “after” performance values (p(x₁) and p(x₂)). To maintain consistency in the orientation of the results, the convention x₂ > x₁ (e.g., lower IEQ value is the “before” condition and higher is the “after”) was used so that a positive impact to performance is associated with increasing IEQ values, even if increasing IEQ is not considered “better.”

Equation (2) shows a lower-case p for the performance values in each publication, whether it is typing speed, number of work tasks completed per hour, or another efficiency-based productivity metric. To combine these multiple metrics, the percent change was calculated by subtracting the “before” value from the “after” value and dividing by the “before” value (numerator in Equation (2) divided by p(x₁)). To normalize the IEQ range of the study, the percent change in performance was divided by the difference in IEQ conditions (x₁ − x₂) in the denominator of Equation (2) to yield the rate of change of performance per unit increase in IEQ (ΔP) and then plotted at the midpoint in IEQ conditions (x_mid).

Studies where a lower performance value (p) is considered better, like reaction time, were multiplied by negative one in Equation (2). Outliers were removed, defined as datapoints where ΔP is less than the first quartile minus 1.5 times the interquartile range or greater than the third quartile plus 1.5 times the interquartile range.

A polynomial regression equation was fitted to the set of points calculated in Equation (2) (performance gain per unit IEQ) and then integrated to obtain relative performance in Equation (3). The potential terms of the polynomial regression equation were selected from exponents in the set {−2, −1, −0.5, 0, 0.5, 1, 2} where 0 represents the natural logarithm. For example, (−1, 0.5, 2) signifies the equation y = a∗x⁻¹ + b∗x^0.5 + c∗x² + d. It was found that more than two terms would over fit the data based on visual observation by adding extreme curvature between data points, and so every combination of two terms was evaluated and the equation with the lowest residual standard error was used for each IEQ regression.

(2)

(3)

p is the absolute performance value from study at specific IEQ value (x), ΔP is the rate in change of performance per unit increase in IEQ between two IEQ values, P is the absolute performance gain between IEQ value and optimum, x₁, x₂ are the IEQ values (e.g., CO₂ level) at the two study conditions, and x_mid is the IEQ value at the midpoint of x₁ and x₂.

The relative performance curves were capped at the extents of where data points exist to create the models as to not extrapolate the behavior beyond existing data. Since the integral in Equation (3) leaves a free variable, commonly denoted as “+C”, where C indicates an arbitrary constant, the equation can be given any reference value to represent 100% relative performance, and all other values would be interpreted as performance increments or decrements in relation to that value. For this research, 100% relative performance is defined for each metric’s high-performance target or range defined in the subsequent section.

2.5. Benchmarking

The results of these regression models—relative task performance versus each IEQ metric—depends on defining what IEQ value to associate with 100% relative performance. The methodology for this research needs to establish an IEQ value associated with a baseline (100%) value for performance, and the performance at all other IEQ values is reported as a relative percentage from that point. For this research, the authors reviewed available standards and certification systems that define benchmarks associated with promoting occupant health and selected the “best-in-class” of the available values. A summary of the high-performance targets for each regression analysis in the results is shown in Table 1.

Table 1. High-performance targets for IEQ metrics used in this analysis.

Metric	High-performance target	Units	Source
Ventilation rate	> 13.6	Liter/second/person	ASHRAE 62.1, WELL
Air temperature	20–24.4	°C	OSHA
Horizontal illuminance	> 500	Lux	IES Lighting Handbook 10th Edition
Noise level	< 40	dB (A)	ANSI S12.2

The high-performance target for ventilation rate for this research is from WELL [38], which recommends 60% above the ASHRAE 62.1 minimum standard of 8.5 L/s/person for office spaces, or 13.6 L/s/person. This work used a high-performance range based for temperature based on the OSHA guidance of 20°C–24.4°C [39]. Considering thermal comfort sensitivity in individuals [40], this entire range was taken into consideration. The Illuminating Engineering Society (IES) Lighting Handbook 10th Edition generally recommends 300–500 lx for visual comfort in office settings [41]. This research sets the upper end of this range, 500 lx, as the high-performance target. The WELL building standard recommends an average of 50 dBA and a maximum of 60 dBA for concentration in open work areas [21]. Work based on the American National Standard Institute (ANSI) S12.2: American National Standard Criteria for Evaluating Room Noise recommends 45–55 dBA for open offices and 40–45 dBA for private offices [42]. The ANSI lower limit recommendation for noise is 40 dBA and was used as the benchmark in the noise result section.

3. Results and Discusion

Tables A1, A2, A3, and A4 in the Appendix section present an overview of the data and attributes extracted from studies examining the impact of IEQ metrics on human productivity in office and office-like environments. This study encompasses a comprehensive analysis that incorporates a total of 130 data points extracted from 65 relevant studies, aiming to examine the impact of four specific metrics, namely, ventilation rate, air temperature, illuminance, and noise on productivity outcomes. The selected studies span a wide timeframe, from 1978 to 2022, and encompass data collected from both real work environments and controlled laboratory settings, as long as the tasks being done by the participants relate to office work.

3.1. Ventilation Rate

The impact of ventilation rate on task performance was examined through regression analysis, considering a set of 18 data points from 10 studies listed in Table A1 in the Appendix section. Two of these points were identified as outliers using the process mentioned in the methodology and removed from the analysis. Figure 2 depicts the results of the regression analysis investigating the relationship between ventilation rate (measured in liter/second/person) and the corresponding task performance improvement. Figure 2a showcases a scatter plot with a polynomial regression curve depicting the relationship between ventilation rate and task performance per unit ventilation rate increase. The two regression lines are presented to demonstrate the analysis conducted both with and without the incorporation of a weighting factor, as per Equation (1). It is evident from the trendlines in Figure 2a that the inclusion of a weighting factor did not significantly alter the observed trend.

Below a threshold of 36 L/s/person, the trendline in Figure 2a exhibits a positive impact on task performance as the ventilation rate increases. Conversely, above this threshold, the trendline indicates negative impacts on task performance as ventilation rate increases; however, the data is limited, comprising only four data points that are all close to the horizontal axis. It is evident that the ventilation rate has a relationship with task performance. Since the scope of this work is only interested in the behavior of the curve below the high-performance target of 13.6 L/s/person, this behavior will not be apparent in the final performance curve in Figure 2b.

Figure 2b presents the relative performance model derived from the regression equation depicted in Figure 2a using Equation (3), showing the performance gain attained by increasing the ventilation rate to the recommended target of 13.6 L/s/person, as specified in Table 1. The high-performance target was assigned a value of 1.0 (100%). The lowest data point used to create the model is 4.5 L/s/person, and so the range of plotted values is from 4.5 to 13.6 L/s/person.

The weighted polynomial regression model in Figure 2a is taken, given its fitness to the collected datapoints. The results are shown in Equation (4).

(4)

where ΔP is the rate in change of performance per unit decrease in ventilation rate and x is the ventilation rate (liter/second/person).

The integrated equation for relative performance in Figure 2b is documented in Equation (5). The regression model suggests that there can be a 4% improvement to task performance on average for buildings changing from the lowest value in the dataset (4.5 L/s/person) to the high-performance target of 13.6 L/s/person. Seppanen et al. [27] created a regression model with an approximate 2% improvement to task performance by increasing from 6.5 to 13.6 L/s/person, and the model from the present research found a similar results with approximately 3% improvement from the same change. The results also suggest that maintaining ventilation rates above the high-performance target may yield increasingly positive task performance benefits, up to 35 L/s/person, which is the intercept point in the graph. These findings underscore the critical role of adequate ventilation in fostering a productive work environment and highlight the potential detrimental effects of insufficient airflow.

For x > 4.5 L/s/person and x < 13.6 L/s/person,

(5)

where P is the relative performance and x is the ventilation rate (liter/second/person).

A different high-performance target value would change the results. For example, if the ASHRAE 62.1 minimum ventilation level of 8.5 L/s/person were used instead of the 13.6 L/s/person, the equation would change to P(x) = 0.19x⁻¹ + 7.3∗10⁻²ln(x)–1.9∗10⁻³x + 0.84. The corresponding relative performance value at 4.5 L/s/person would be 0.98 instead of 0.96, which signals there is less opportunity for improvement but associated with smaller increase in ventilation. If using a much more ambitious ventilation target, for example, two times the 13.6 L/s/person value of 27.2 L/s/person, the relative performance value at 4.5 L/second/person would be 0.95 instead of 0.96, signaling slightly more improvement opportunity. It is important that the results are seen as relative performance in context to the selected high-performance targets.

3.2. Air Temperature

The impact of air temperature levels on task performance was examined through regression analysis, considering a substantial dataset of 64 data points from 28 studies listed in Table A2 in the Appendix section. Four of these points were identified as outliers and removed from the analysis. Figure 3 depicts the results of a regression analysis investigating the correlation between air temperature and the corresponding task performance improvement. Figure 3a presents a scatter plot with a polynomial regression curve to visualize the relationship between air temperature and task performance per unit temperature increase. The two regression lines are presented to demonstrate the analysis conducted both with and without the incorporation of a weighting factor, as per Equation (1). The trendlines depicted in Figure 3a reveal that the inclusion of the weighting factor had minimal impact on the observed trend.

In Figure 3a, positive values on the y-axis indicate improved performance in relation to increasing temperature, while negative values indicate decreased performance as temperature increases. The depicted trendline shows a decline from lower to higher temperatures across the range of data points. The y-intercept is around 21°C, which indicates that if the temperature is below this value, increasing it will yield an improvement to performance and if the temperature is above this value, increasing it will yield a decrement to performance.

There is a large disparity in results, but a trend emerged showing a potential increase in task performance as optimal temperatures are approached from either cooler or hotter temperatures. All points below the high-performance temperature range (below 20°C) have positive impacts to performance, and the majority of points above 26.5°C have negative impacts to performance as a result of increasing temperature as expected. Variation in positive and negative impacts to performance was expected within the high-performance range due to personal preferences, but there is also notable variation in positive and negative data points outside the high-performance range from 24.4°C to 26.5°C. Further investigation into these data points revealed that these studies found task speed increased at these higher temperatures, although accuracy decreased (e.g., [83]). This suggests that some people may have elevated work efficiency at warmer temperatures up to 26.5°C but potentially at the expense of task accuracy. The temperature regression had the largest body of literature of the four IEQ metrics studied but also had the largest variation in results, which is likely because this metric is confounded by seasonality, geographic region, airflow, metabolic rate, humidity, and other factors. This regression approach synthesizes a wide variety of studies to discover what trends emerge from within the scattered data.

Figure 3b presents the relative performance model derived from the regression equation depicted in Figure 3a using Equation (3), showing the performance gain achieved by improving to a high-performance temperature range of 20°C–24.4°C, as specified in Table 1. The high-performance range was assigned a value of 1.0 (100%). The lowest temperature value used to create the regression is 16°C, and the highest value used to create the regression is 31°C, and so points outside this range are not plotted.

The weighted polynomial regression model shown in Figure 3a is taken, given its fitness to the collected datapoints. The results of the weighted function are shown in Equation (6).

(6)

where ΔP is the rate in change of performance per unit increase in temperature and x is the temperature (degree Celsius).

The integrated equation for relative performance shown in Figure 3b is documented in Equation (7). The regression model suggests that there can be a 6% performance gain on average for buildings decreasing temperature from the warmest value in the data (31°C) to the high-performance range of 20°C–24.4°C and a 4% performance gain from increasing temperature from the coolest value (16°C) to the high-performance range. The regression model by Seppanen et al. [28] found a potential performance improvement of up to 15% achievable by reducing the temperature from its warmest recorded value of 34°C, and a 9% potential improvement when transitioning from the coldest recorded value of 15°C to the temperature associated with peak performance (21.7°C). Yaganeh et al. [30] found a 6%–8% potential performance improvement from temperatures warmer and colder than the control temperature for each study. The regression model from present study found about 6% and 4% potential improvement from too warm and too cold temperatures, respectively, which is significantly less than the Seppanen study and slightly less than the Yageneh study. Porros-Salazer et al. [29] found significant variation in impact, both positive and negative, to performance at all temperatures with data, whereas these results show a notable variation in positive and negative impacts to task performance only within the more limited range of 22°C–26.5°C and clearer trends at warmer and cooler temperatures.

For x > 16^°C and x < 20^°C,

And for x > 24.4°C and x < 31°C,

(7)

where P is the absolute performance potential increase and x is the temperature (degree Celsius).

3.3. Horizontal Illuminance

The impact of horizontal illuminance levels on task performance was examined through regression analysis, considering a dataset of 17 data points from 10 studies listed in Table A3 in the Appendix section. One of these points was identified as an outlier and removed from the analysis. The results depicted in Figure 4 provide insights into the relationship between horizontal illuminance and task performance improvement. Figure 4a presents a scatter plot with a polynomial regression curve to depict the relationship between horizontal illuminance and task performance per unit increase to horizontal illuminance. The two regression lines are presented to demonstrate the analysis conducted both with and without the incorporation of a weighting factor, as per Equation (1). The inclusion of a weighting factor did not significantly alter the observed trend.

The trendline in Figure 4a shows the impact to task performance as a result of increasing horizontal illuminance. A relationship between illuminance and task performance is evident, with a positive impact to task performance as illuminance increases throughout the entire limits of data and a greater magnitude of performance benefit per unit illuminance increase at lower illuminance values. The trendline never crosses the horizontal axis, although the majority of points tend to approach the horizontal axis at higher illuminance values.

Figure 4b presents the relative performance model derived from the regression equation depicted in Figure 4a using Equation (3), depicting the performance gain achieved by improving indoor conditions to reach the high-performance horizontal illuminance level of 500 lx, as specified in Table 1. The peak performance was set to 1.0 (100%), corresponding to the high-performance target of 500 lx. The lowest data point used to create the model is 80 lx, so the range of plotted values is from 80 to 500 lx.

The weighted polynomial regression model for Figure 4a is taken, given its fitness to the collected datapoints. The results of the weighted function are shown in Equation (8).

(8)

where ΔP is the rate in change of performance per unit increase in horizontal illuminance and x is the horizontal illuminance level (lux).

The integrated equation for relative performance from Figure 4b is documented in Equation (9). The regression model suggests that there can be a 7% performance gain on average for buildings increasing from 80 lx to the high-performance target of 500 lx. There is no existing horizontal illuminance meta-analysis regression identified for office work for comparing the magnitude of these results.

This research does not discriminate between paper-based (horizontal work plane) and computer-based (vertical work plane) office tasks in the studies that are included. Horizontal illuminance may not be as appropriate of a metric for computer-based work as vertical illuminance; however, the regression data found a positive relationship between increasing horizontal illuminance for both computer and paper tasks, which may be due to an increase in vertical illuminance when increasing horizontal illuminance from overhead light sources and task lighting. The studies that increase horizontal illuminance and did measure vertical illuminance reported significantly higher vertical illuminance values as well as horizontal, which underscores the importance of provided sufficient vertical illuminance when designing for high-performance horizontal illuminance. There was not a large enough sample of existing vertical illuminance studies at a wide range of illuminance values to construct a robust vertical illuminance regression model.

For x > 80 lx and x < 500 lx,

(9)

where P is the absolute performance potential increase and x is the horizontal illuminance level (lux).

3.4. Noise Level

The impact of noise levels on task performance was examined through regression analysis, considering a dataset of 31 data points from 17 studies listed in Table A4 in the Appendix section. Seven of these points were identified as outliers and removed from the analysis. The results depicted in Figure 5 provide insights into the relationship between noise level and task performance improvement. Figure 5 presents a scatter plot with a polynomial regression curve to visualize the relationship between noise and task performance per unit decrease to noise level. The two regression lines are presented to demonstrate the analysis conducted both with and without the incorporation of a weighting factor, as per Equation (2). The trendlines in Figure 5 show that the inclusion of a weighting factor did not significantly alter the observed trend for this metric.

The regression in Figure 5 does not appear to present a clear trend regarding potential performance gains. There are points above and below the y-axis throughout the range of noise levels, suggesting that the association between noise level and task performance is not clear based on literature. These results are unexpected, as the authors hypothesized that there would be an evident negative performance trend as each of the IEQ metrics strayed further from the high-performance values. The inconclusive nature of the results may be explained in part by the variety of noise characteristics that can be present in overall noise levels. As described in the Introduction, previous studies have found that the type and intensity of both the work being performed and additional characteristics of the noise itself can influence the relationship of noise and task performance [32, 64, 67], which could be factors influencing these results. In addition, some studies [64, 93] found that high levels of noise increased the speed of completing high workload tasks although accuracy decreased. Since this work considers only efficiency metrics and not accuracy metrics, some of the inconclusive findings may be explained in part by this concept. As a result, no relative performance chart is included in the figure.

Although it does not fit the data well and not recommended for use, the weighted polynomial regression model for the graph on the left of Figure 4 showed the best fitness to the collected datapoints. In studying the literature for comparable works, the authors did not identify any existing meta-analysis regression models for noise levels on office work for direct comparison.

(10)

where ΔP is the rate in change of performance per unit increase in noise level and x is the noise level (decibels A).

4. Conclusions

This research provides a streamlined process for estimating task performance benefits in real-life buildings and situations across the major pillars of IEQ (air quality, thermal comfort, lighting, and noise) with models that focus on office work only and efficiency-based definitions of performance. For ventilation rate, Figure 2 suggests a positive impact on office performance (up to 4% improvement) when ventilation rate is increased to the high-performance level. Considering air temperature, the model from this work in Figure 3 estimated up to 4%–6% performance gain potential when modifying indoor temperature toward the optimal comfort range, with conflicting results for temperatures in the range of 24.4°C–26.5°C. For horizontal illuminance, a potential performance increase of up to 7% was found in the model in Figure 4, and supplementing that with sufficient vertical illuminance, especially for computer-based tasks, was identified as an important element for that performance increase from several of the studies used to make the model. While the noise model in Figure 5 did not produce clear results correlating simple noise level to performance, this does not mean that noise is unrelated to performance; rather, noise level alone did not emerge above other factors for a prevalent impact on performance, which underscores the importance of future research in the field of acoustics to better understand its role on human performance. Readers are encouraged to utilize the large repository of articles reviewed here to explore the nuances of frequency, temporal variation, and other noise characteristics and their impact on performance.

Some studies used to create the regression models did not report values if there was no statistical significance, and so the data from these studies could not be used, which may introduce bias. In addition, it is possible that studies that show positive associations between task performance and improving IEQ values are more likely to be published than ones with little effects or negative effects compared to what is expected, which also could introduce publication bias.

This work is limited to office environments, but even within office jobs, there exists a variety of task types and job categories. This research does not differentiate between possible subsets of office workers (e.g., administrative, analytical, or managerial) and does not make claims about how this may impact applicability. Additionally, some individuals will experience higher or lower performance losses than others, and the extent to which people adjust to performance losses by working extra hours is a potential real-life component that may not be reflected in studies.

This work included studies of various durations, and it is important to note that people may adapt over time and behave differently after repeated exposure to a condition. Task performance depends on a wide variety of external factors, and it is challenging to feasibly control for every confounding variable, especially in field studies. In addition, isolated metrics cannot fully represent the intricacies of the impact of IEQ on health and performance. While it is impossible to remove all confounding factors, this research still yielded a clear trend in three of the four models. By synthesizing work from a large body of existing studies, it becomes increasingly possible to see correlations emerge despite the unavoidable complexities of human factors in indoor environments. To help account for confounding factors or factors that could overshadow the impact of the selected metrics, these regressions should be used in conjunction with well-established building design principals and strategies. For ventilation, it is important to have good outdoor air quality or efficient filtration at the outdoor air intake. For temperature, it is important to reduce sources of radiant heat, maintain relative humidity in a comfortable range, and consider the impact of airflow, occupants’ clothing level, and occupants’ metabolic rate for their expected work activities. For lighting, it is important to have uniform overhead lighting with little contrast, access to daylight, and reduce sources of disruptive glare.

These regressions are used in H-BEST, a tool that considers recommendations for office building updates based on impacts to health, well-being, and performance. This tool is publicly available and can be used by stakeholders interested in quantifying the potential task performance gains in their building by improving to the high-performance IEQ benchmarks. Alternatively, the regression models from this research can be used as a standalone resource for estimating the potential task performance improvements using data collected from commercial IEQ monitors.

Beyond analytical value, practical applications of the outcomes of this paper include support for prioritizing and planning building retrofits or in the project planning phase for new construction. The multifaceted approach from this research allows building decision makers to directly compare between four major IEQ categories for high level decision-making support on where to make building investments and operational changes. For example, if they are considering between upgrading the building HVAC system to have more reliable and programmable outdoor air dampers that support a high-performance ventilation rate during regular occupancy or an integrated LED luminaire with daylight harvesting and scheduling controls that support a high-performing illuminance levels during occupancy that balance daylight and electric light, the results of an IEQ assessment with these regression models would determine which option might provide the greatest value to occupant work performance under the same regression methodology. In addition to prioritization, the estimated performance benefits can be used as rationale for building upgrade costs. For example, the added cost of more localized temperature setpoint controls with a variable air volume system could be justified in part by the potential for increased office performance of more occupants working in a thermally comfortable range. Empowering building stakeholders with information supporting the potential impact of the indoor environment on occupants allows for better decisions related to building design, operation, and maintenance.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This research was funded by the U.S. Department of Energy (DOE) and the U.S. General Services Administration (GSA).

Acknowledgments

The authors would like to thank DOE project manager Allison Ackerman and GSA project manager Brian Gilligan for their continued support throughout the project.

Appendix

Table A1. References used to construct performance versus ventilation regression.

Title	Author (year)	Data points from unique IEQ testing conditions	Average weighting factor	Performance metric category
Productivity Is Affected by the Air Quality in Offices	Wargocki et al. (2000) [43]	2	4.42	Task speed
Windows and Offices: A Study of Office Worker Performance and the Indoor Environment	Heschong (2003) [44]	2	8.63	Call handling time
Human Perception, SBS Symptoms, and Performance of Office Work During Exposure to Air Polluted by Building Materials and Personal Computers	Bako-Biro (2004) [45]	1	4.38	Task speed and self-reported productivity
Worker Performance and Ventilation in a Call Center: Analyses of Work Performance Data for Registered Nurses	Federspiel et al. (2004) [46]	3	9.30	Call handling time
Effects of Temperature and Outdoor Air Supply Rate on the Performance of Call Center Operators in the Tropics	Tham (2004) [11]	1	7.00	Call handling time
The Performance and Subjective Responses of Call-Centre Operators With New and Used Supply Air Filters at Two Outdoor Air Supply Rates	Wargocki et al. (2004) [47]	1	6.92	Call handling time
Thermal and Indoor Air Quality Effects on Physiological Responses, Perception and Performance of Tropically Acclimatized People	Willem (2006) [48]	3	9.78	Call handling time
The Effects of Outdoor Air Supply Rate on Work Performance During 8-h Work Period	Park and Yoon (2011) [49]	2	4.19	Task speed
Effects of Exposure to Carbon Dioxide and Bioeffluents on Perceived Air Quality, Self-Assessed Acute Health Symptoms and Cognitive Performance	Zhang et al. (2017) [50]	2	4.23	Task speed and response time
The Effect of Low Ventilation Rate With Elevated Bioeffluent Concentration on Work Performance, Perceived Indoor Air Quality, and Health Symptoms	Maula et al. (2017) [51]	1	4.60	Task speed and response time

Table A2. References used to construct performance versus air temperature regression.

Title	Author (year)	Data points from unique IEQ testing conditions	Average weighting factor	Performance metric category
The Effect of Air Temperature on Labour Productivity in Call Centres—A Case Study	Niemelä et al. (2002) [69]	3	6.84	Call handling time
Windows and Offices: A Study of Office Worker Performance and the Indoor Environment	Heschong (2003) [44]	3	6.70	Call handling time
Effects of Temperature and Outdoor Air Supply Rate on the Performance of Call Center Operators in the Tropics	Tham (2004) [11]	1	5.74	Call handling time
Productivity and Fatigue	Tanabe and Nishihara (2004) [70]	2	3.93	Task speed
The Effects of Moderate Heat Stress and Open-Plan Office Noise Distraction on SBS Symptoms and on the Performance of Office Work	Witterseh et al. (2004) [57]	2	4.25	Task speed and self-reported productivity
Worker Performance and Ventilation in a Call Center: Analyses of Work Performance Data for Registered Nurses	Federspiel et al. (2004) [46]	5	7.56	Call handling time
Thermal and Indoor Air Quality Effects on Physiological Responses, Perception and Performance of Tropically Acclimatized People	Willem (2006) [48]	3	5.72	Task speed and call handling time and self-reported productivity
Effect of Overcooling on Productivity Evaluated by the Long Term Field Study	Nishihara et al. (2007) [71]	1	6.66	Call handling time
Evaluation Method for Effects of Improvement of Indoor Environmental Quality on Productivity	Kawamura et al. (2007) [72]	1	2.83	Self-reported productivity
Performance Evaluation Measures for Workplace Productivity	Tanabe et al. (2007) [73]	1	8.99	Task speed
The Influence of Exposure to Multiple Indoor Environmental Parameters on Human Perception, Performance and Motivation	Balazova et al. (2007) [74]	1	4.19	Self-reported productivity and task speed
Neurobehavioral Approach for Evaluation of Office Workers’ Productivity: The Effects of Room Temperature	Lan et al. (2009) [75]	3	3.52	Response time
Occupant Responses and Office Work Performance in Environments With Moderately Drifting Operative Temperatures	Kolarik et al. (2009) [76]	2	4.13	Task speed
Use of Neurobehavioral Tests to Evaluate the Effects of Indoor Environment Quality on Productivity	Lan and Lian (2009) [77]	2	3.42	Response time
Comfort, Perceived Air Quality, and Work Performance in a Low-Power Task-Ambient Conditioning System	Zhang et al. (2010) [78]	4	3.29	Task speed
The Effects of Air Temperature on Office Workers’ Well-Being, Workload and Productivity-Evaluated With Subjective Ratings	Lan et al. (2010) [79]	2	3.42	Task speed
Quantitative Measurement of Productivity Loss due to Thermal Discomfort	Lan et al. (2011) [80]	1	5.07	Task speed and self-reported productivity
Influence of Indoor Air Temperature on Human Thermal Comfort, Motivation and Performance	Cui et al. (2013) [81]	4	3.84	Task speed and self-reported productivity
The Effect of Indoor Office Environment on the Work Performance, Health and Well-Being of Office Workers	Vimalanathan and Ramesh Babu (2014) [12]	2	2.83	Response time
Workplace Productivity and Individual Thermal Satisfaction	Tanabe et al. (2015) [82]	2	4.97	Task speed and self-reported productivity
Performance, Acute Health Symptoms and Physiological Responses During Exposure to High Air Temperature and Carbon Dioxide Concentration	Liu et al. (2017) [83]	1	2.97	Task speed
The Impact of Morning Light Intensity And Environmental Temperature on Body Temperatures and Alertness	Te Kulve et al. (2017) [84]	2	3.34	Response time
The Impact of Thermal Environment on Occupant IEQ Perception and Productivity	Geng et al. (2017) [85]	9	3.42	Response time
Reduced Cognitive Function During a Heat Wave Among Residents of Nonair-Conditioned Buildings: An Observational Study of Young Adults in the Summer of 2016	Cedeño Laurent et al. (2018) [86]	1	5.55	Task speed and response time
Evaluation of the Combined Effects of Heat and Lighting on the Level Of Attention and Reaction Time: Climate Chamber Experiments in Iran	Mohebia et al. (2018) [87]	1	4.65	Response time
Investigating the Effect of Indoor Thermal Environment on Occupants’ Mental Workload and Task Performance Using Electroencephalogram	Wang et al. (2019) [88]	2	3.15	Response time
The Effects of Noise on Human Cognitive Performance and Thermal Perception Under Different Air Temperatures	Sepehri et al. (2019) [63]	2	3.52	Response time
Perceived Air Quality and Cognitive Performance Decrease at Moderately Raised Indoor Temperatures Even When Clothed for Comfort	Lan et al. (2020) [89]	1	2.97	Self-reported productivity and task speed

Table A3. References used to construct performance versus horizontal illuminance regression.

Title	Author (year)	Data points from unique IEQ testing conditions	Average weighting factor	Performance metric category
Lighting, Productivity, and the Work Environment	Hughes and Mcnelis (1978) [90]	1	6.18	Task speed
Lighting for Productivity Gains	Barnaby (1980) [15]	1	5.15	Task speed
Windows and Offices: A Study of Office Worker Performance and the Indoor Environment	Heschong (2003) [44]	6	9.08	Call handling time
Productivity and Fatigue	Tanabe and Nishihara (2004) [70]	1	4.84	Task speed
Evaluation Method for Effects of Improvement of Indoor Environmental Quality on Productivity	Kawamura et al. (2007) [72]	1	4.19	Self-reported productivity
Effects of Four Workplace Lighting Technologies on Perception, Cognition and Affective State	Hawes et al. (2012) [13]	1	7.33	Response time and task speed
A Higher Illuminance Induces Alertness Even During Office Hours: Findings on Subjective Measures, Task Performance and Heart Rate Measures	Smolders et al. (2012) [91]	1	5.80	Response time
Bright Light and Mental Fatigue: Effects on Alertness, Vitality, Performance and Physiological Arousal	Smolders and de Kort (2014) [92]	1	5.62	Response time
The Effect of Indoor Office Environment on the Work Performance, Health and Well-Being of Office Workers	Vimalanathan and Ramesh Babu (2014) [12]	2	4.19	Response time
Evaluation of the Combined Effects of Heat and Lighting on the Level of Attention and Reaction Time: Climate Chamber Experiments in Iran	Mohebian et al. (2018) [87]	2	7.37	Response time

Table A4. References used to construct performance versus noise regression.

Title	Author (year)	Data points from unique IEQ testing conditions	Average weighting factor	Performance metric category
Performance Effects of Noise Intensity, Psychological Set, and Task Type and Complexity	Gawron (1982) [52]	2	4.38	Response time
The Effects of Noise, Cognitive Set and Gender on Mental Arithmetic Performance	Gulian and Thomas (1986) [53]	1	7.94	Task speed
The Combined Effects of Occupational Health Hazards: An Experimental Investigation of the Effects of Noise, Nightwork and Meals	Smith and Miles (1987) [54]	1	6.16	Task speed
Effects of Noise, Heat and Indoor Lighting on Cognitive Performance and Self-Reported Affect	Hygge and Knez (2001) [55]	1	8.88	Task speed
The Effects of Road Traffic Noise and Meaningful Irrelevant Speech on Different Memory Systems	Hygge et al. (2003) [56]	1	8.41	Task speed
The Effects of Moderate Heat Stress and Open-Plan Office Noise Distraction on SBS Symptoms and on the Performance of Office Work	Witterseh et al. (2004) [57]	1	7.65	Self-reported productivity and task speed
Open-Plan Office Environments: A Laboratory Experiment to Examine the Effect of Office Noise and Temperature on Human Perception, Comfort and Office Work Performance	Balážová et al.(2008) [58]	4	5.40	Self-reported productivity and task speed
Open-Plan Offices: Task Performance and Mental Workload	Smith-Jackson and Klein (2009) [59]	1	7.47	Task speed
Effect of Open-Plan Office Noise on Occupant Comfort and Performance	Toftum et al. (2012) [60]	1	8.44	Self-reported productivity and task speed
Is Noise Always Bad? Exploring the Effects of Ambient Noise on Creative Cognition	Mehta et al. (2012) [61]	3	7.78	Task speed and response time
Performance, Fatigue and Stress in Open-Plan Offices: The Effects of Noise and Restoration on Hearing Impaired and Normal Hearing Individuals	Jahncke and Halin (2012) [62]	1	5.78	Response time
The Effects of Noise on Human Cognitive Performance and Thermal Perception Under Different Air Temperatures	Sepehri et al. (2019) [63]	2	6.16	Response time
Attention and Short-Term Memory During Occupational Noise Exposure Considering Task Difficulty	Golmohammadi et al. (2020) [64]	2	6.57	Response time
Does Background Sounds Distort Concentration and Verbal Reasoning Performance in Open-Plan Office?	Liu et al. (2021) [65]	3	8.09	Task speed
The Effect of Background Noise on a “Studying for an Exam” Task in an Open-Plan Study Environment: A Laboratory Study	Braat-Eggen et al. (2021) [66]	3	7.80	Task speed
Gender Differences in Cognitive Performance and Psychophysiological Responses During Noise Exposure and Different Workloads	Abbasi et al. (2022) [67]	3	5.50	Response time
Task Performance and Perception of Noise Under Different Office Noise Conditions	Zhou (2022) [68]	1	5.59	Response time

Open Research

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Meta-Analysis and Regression Modeling of the Impacts of Four Indoor Environmental Quality Metrics on Office Performance

Abstract

1. Introduction