Author: Mr Pedro Torres STSM Period: 2019-12-01 – 2019-12-14


Hosting institution: Technical University of Košice

From ITC: Yes



The ageing of the population is a phenomenon that affects most of the European territories, being more pressing in countries such as Spain and Portugal and, above all, in its border areas that today are characterized by the ageing of their communities. This trend explains the growing demand for long-term care services, such as Elderly Care Centers (ECC). In addition, considering that older people spend a considerable part of their lives inside, their well-being and their thermal comfort (TC) cannot be ignored. Determine the influential parameters in TC is relevant both to design spaces and to ensure the welfare and health of the occupants of buildings. In the current context of climate change and global warming, the inclusion of the concept of adaptive thermal comfort in TC standards allows adopting new energy efficiency strategies and consistently meet the requirements of sustainable development.

My stay is linked to the project ConTerMa which aims to evaluate the parameters that affect TC in elderly and to develop an adaptive method for this group of population. This project evaluates the TC of elderly people in ECC located in the Atlantic and Mediterranean climatic areas. Different spaces of ECC (eight in Portugal and five in Spain) in each climatic area and in the different seasons of the year were analysed. Environmental parameters (air temperature, mean radiant temperature, air velocity and air humidity) of the selected spaces and the external environmental conditions were collected. At the same time, residents were surveyed to determine their Thermal Sensation (TS), Preference (P) and degree of acceptability (A). Data about physical activity, clothing, age, weight, body mass index, sex, among others were also collected. The aim of my stay is to unify the analysis of two samples collected in different climatic conditions. TC will be evaluated first by correlation among operation conditions and survey results, then by correlation among residents’ characteristics and survey results and finally by regression analysis to obtain the comfort temperature based on these variables. These analyses will be done for each different climatic zone and in the different seasons. The difference between samples (elderly caregivers, men-women, etc.) will be also evaluated by means of an Anova test. Finally, the TC results obtained from the questionnaire survey and those obtained from existing methods (PMV) will be contrasted and compared by means of a t-test. The Statistical software SPSS will be used to carry out all statistical analysis.


The following description of the work done during the STSM conforms to the main objectives:

  1. i) Presentation and discussion of data collected by the two project teams and verification of similarities and differences, not only in the information itself, but also in the methodology for its collection and presentation. In this initial phase, we analysed the characterizations of the study sites and the population, the physical data collected directly by the equipment, the answers obtained by the questionnaires and the other additional information collected by the two teams during the monitoring.
  2. ii) Structuring of data processing methodology and statistical analysis: selection of statistical methods to verify significant correlations between variables and their influence on the final equations of thermal comfort; methodological adaptation of data processing according to the different information limitations presented by the two teams; definition of expected results. Given that between the two teams there were different emphases given to the selected variables in the information collection and given that the realities of the different populations influenced the size and typology of the information collected, there was a need to create a third database that presented the information considered most important. important for the creation of the model, where both teams could feed the database equally. This situation usually occurs when teams from different fields of scientific expertise work together, fostering multidisciplinary in scientific development.

(iii) Data processing and statistical data analysis using data from summer 2019. At an early stage it was preferable to process only part of the information so that after evaluating the methods used to process the information it would be possible to process all of the information. the information collected efficiently. For the treatment of information, there was the initial stage of descriptive statistics, where it was possible to conclude some points about the main expected influences for the thermal comfort evaluation model. With the application of statistical methods of assessment of correlation between variables, preliminary results take shape.


The main results of this STSM were:

– Understanding the different realities studied by the two teams, in the Porto Metropolitan Area and in the east coast of Spain. Not only is the size of the sample studied quite different, but the functioning of the intended services for the elderly population presents quite important differences for the analysis of thermal comfort. The Spanish team approaches institutions with a number of residents much higher than the Portuguese institutions and not only the infrastructures but also the logistics involved in the residents’ daily life differ greatly between the teams.

– Project planning adapted to the need to address the lack of information for winter 2019, and it was decided that the Portuguese team would carry out an extra monitoring campaign for winter 2020.

– An action plan was devised to collect missing information, such as the average daily temperatures of 2019, anthropometric measurements of residents and their relevant medical history.

– With the processing of data relating to the summer of 2019, a protocol of statistical analysis of the collected information was structured so that, in a cohesive and efficient way, all the information of all seasons of the year was treated. With the planning of the desired layout for the characterization of the residents and the spaces, and with the intermediate results from the statistical analysis, it was possible to sharpen some edges in the data processing methodology for the elaboration of the mathematical model presented as the final objective of the project.

After preliminary analysis of the data for the selected season, it was found that the behaviour of the Portuguese non-resident population sample differs greatly from the Portuguese resident population sample and is not taken into account for the structuring of the mathematical model for Portuguese team.

– At the level of the statistical analysis itself, it was found that certain variables will play a key role in the final equations of the mathematical model, such as age, operating temperature, indoor air temperature, metabolic activity and thermal insulation by clothing.

– As the project embraces work from different teams in different parts of the world, face-to-face work opportunities with both teams become vital for efficient work synchronization, establishing a solid work structure with agreed planning and a strong knowledge bridge between teams.

– Given that the variables contemplated in the databases cover several areas of study (from civil engineering, health, architecture, and environment), the analysis of multivariate correlations provide secondary results that will enable publications in various areas.


In addition to outlining the work required for project development, certain areas of study were identified as possible branches of scientific development within the project, such as Psychology and Sociology and areas of study related to the logistics involved in this type of care services. As such, it opened the possibility of including secondary variables in the project that could trigger new lines of study and thinking about the conditions of thermal comfort.

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