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Indoor temperature - office work performance database

Citation

Porras-Salazar, Jose Ali et al. (2021), Indoor temperature - office work performance database, Dryad, Dataset, https://doi.org/10.6078/D1G42R

Abstract

The objective of developing this database was to summarise all relevant published studies that have linked the thermal environment to office work performance within the most representative temperature range for office buildings (20 °C to 30 °C). We conducted a comprehensive literature review and collected the relevant published data into our database. A variety of combinations of keywords including temperature, thermal sensation, work, cognitive, and task performance, and office and commercial buildings, were used.

In total, we found thirty-five studies, in 29 peer-reviewed journal publications and 571 measures of performance, met our inclusion criteria. We normalised these measures using a method originally proposed by Seppänen et al. (2006; 2006). This method uses the change in work performance per 1 °C increment in temperature (λ%), which is measured in percentage per degree Celsius (%/°C). Our database comprised a total of 358 data points for λ%.

Furthermore, we developed a web-based interactive tool with an easy-to-use interface to visualize the relationship between temperature and office work performance. This tool can automatically calculate the model’s equation and accuracy metrics via different data subsets and regression models.

Methods

Our literature search strategies and data collection criteria are specified below:

  • We searched electronic databases of scientific publications from September 2019- March 2020, including Google Scholar, Web of Science, Elsevier, PubMed, and ProQuest.      
  • A variety of combinations of keywords were used.
  • We looked only for peer-reviewed journal articles that reported both thermal environment measurement data and the subject’s performance of office work. Diverse measures were considered to describe office work performance including diagnostic tests, simulated office work tasks, and existing outcome metrics.
  • We used air temperature as a proxy of the thermal environment in our research scope because it was extensively measured in most of the studies.
  • We did not include the following conditions in our database:
    • Physiological measurements providing information on cognitive load EEG, ECG, heart rate variability, and pupillary responses;
    • Studies that reporting only self-estimated performance;
    • Proxies for reduced performance, such as the prevalence and intensity of acute health symptoms, especially for fatigue, difficulty in concentrating, sleepiness, or headaches;
    • Data from factory workers, university students, or primary/secondary school children;
    • Any experimental conditions that have a low-temperature condition (TL) below 17 °C or the high-temperature condition (TH) above 36 °C;
    • Studies where thermal stress was induced by any means other than the indoor thermal conditions (e.g., exercise or water immersion).

From each study, we retrieved the year of publication, journal, and information regarding the study location, whether the study was or was not performed in a controlled environment, the sample size, age group, occupation of the participants, clothing, and physical activity level. We also collected the tasks or tests used to measure performance, the performance metrics and outcomes, and the temperature conditions to which the participants were exposed.

We normalised the performance data obtained from the studies using the method proposed by Seppänen et al.(2006). This approach assumes that performance changes linearly within the high temperature (TH) and low temperature (TL) range examined in each study regardless of the performance measure used and the temperature range. We calculated the change in work performance in % per 1 °C increment in temperature (λ%), whereby positive λ% indicates an increase in performance with increasing temperature; while negative λ% indicates a decrease in performance with increasing temperature. The detailed normalisation process can be seen in Porras-Salazar et al. (2021).

References:

Seppänen, O., & Fisk, W. J. (2006). Some quantitative relations between indoor environmental quality and work performance or health. HVAC and R Research, 12(4), 957–973. https://doi.org/10.1080/10789669.2006.10391446

Seppänen, O., Fisk, W. J., & Lei, Q. H. (2006). Effect of Temperature on Task Performance in Office Environment. Proceedings of 5th International Conference on Cold ClimateHeating, Ventilating and Air Conditioning. Moscow, Russia.

Porras-Salazar, J.A.; Schiavon, S.; Wargocki, P.; Cheung, T. & Tham, K.W. (2021) Meta-Analysis of 35 Studies Examining the Effect of Indoor Temperature on Office Work Performance. Building and Environment, 203. https://doi.org/10.1016/j.buildenv.2021.108037

Usage Notes

Parameter Description

Name of column

Description

Study

Study code

Authors

Authors of the study

Source

Journal where the study was published

City_Region

City or region where the study was conducted

Climate

Climate classification according to the city or region where the study was conducted. We used the main climate groups (A: Tropical, B: Dry, C: Temperate, and D: Continental) of the Köppen-Geiger climate categorisation.

Number_Participants

The number of people who participated in the study

Male

The number of males who participated in the study

Female

The number of females who participated in the study

Age

Mean age of the people who participated in the study

Occupation

Main occupation of the people who participated in the study

Anthropometric_Data

Tells if anthropometric data of the participants were reported in the study (Yes/No). Anthropometric data refers to height, weight or body mass index (BMI)

Health_Status

Tells if the health status of the participants was reported in the study (Yes/No)

Clothing_Insulation

Shows information about the clothing insulation as was reported in the studies

Clothing_Insulation2

Shows the clothing insulation in Clo units. For those studies where only the participants’ attire was reported, we estimated the corresponding insulation in Clo units using the CBE Thermal Comfort Tool. https://comfort.cbe.berkeley.edu/

Exposure_Time

Duration of exposure per temperature condition. Time in minutes

Measure

Name given in the study to the measure of performance. E.g., Serial digit learning, Text typing, Talk time, etc.

Measure_Type

We classified the measures of performance into 23 different types

Metric

Metrics used in the studies to measure the participants’ individual performance. FP: False positives, RMSE: Root Mean Square Error, SC: Scores, NOL: Number of lags, RE: Percentage of correct, MI: Missed, NOC: Number of correct, NER: Number of errors, REER: Percentage of errors, SP: Span, NOE: Number of exercises, RT: Reaction time, T: Time.

SA

We classified the metrics into Accuracy or Speed based on the information provided in the publications

TSLow

Mean thermal sensation of the study’s participants under the low thermal condition

TSHigh

Mean thermal sensation of the study’s participants under the high thermal condition

TLow_C

Mean temperature at the low thermal condition in degree Celsius (°C)

THigh_C

Mean temperature at the high thermal condition in degree Celsius (°C)

TLow_F

Mean temperature at the low thermal condition in degree Fahrenheit (°F)

THigh_F

Mean temperature at the high thermal condition in degree Fahrenheit (°F)

PLow

Mean performance of the study’s participants under the low thermal condition

PHigh

Mean performance of the study’s participants under the high thermal condition

 

Funding

National Research Foundation Singapore