Epidemiological studies investigating the relationship between temperature and mortality and morbidity have revealed a widespread pattern of intrinsic, individual, and extrinsic socioeconomic and environmental factors that are associated with increased mortality and morbidity during heat events in addition to high temperatures (Fernandez Milan and Creutzig, 2015). Intrinsic, individual factors for higher mortality found across studies are, in particular, old age and high physiologic susceptibility because of pre-existing health problems or medication use, and also being confined to bed (Conti et al., 2007; Fouillet et al., 2006; Gronlund, 2014; Madrigano et al., 2013; Vandentorren et al., 2006; Zanobetti et al., 2013). Vandentorren et al. (2006) demonstrated additionally the positive effects of individual heat-protective behavior during the heat wave in 2003, e.g., of cooling the body or going outside during cooler times of the day. Extrinsic factors for higher mortality associated with the socioeconomic status are living alone (Fouillet et al., 2006; Uejio et al., 2011), in some cases being female (Borell et al., 2006; D'Ippoliti et al., 2010; Zanobetti et al., 2013), and living in census tracts with a lower socioeconomic status and higher poverty rate (Madrigano et al. 2013; Xu et al., 2013; Zanobetti et al., 2013). Studies in the US observed that ethnicity and linguistic isolation additionally played a role for both higher mortality and heat distress calls (Gronlund, 2014; Uejio et al., 2011). Regarding the urban built environment, increased mortality and hospital admission rates were observed in large cities with high temperatures also during the night (Conti et al., 2007; Grize et al., 2005; Laaidi et al., 2012), in areas with little surrounding green space, and in dense urban structures (Gabriel and Endlicher, 2011; Scherber et al., 2014; Xu et al., 2013). Further studies identified housing conditions, such as living in an area with low property values, in a building with low insulation standard, or having the bedroom in the attic floor, as being associated with higher mortality rates (Smargiassi et al., 2013; Vandentorren et al., 2004, 2006; Xu et al., 2013). Similarly, Harlan et al. (2006) found the combination of dense urban settlement structures and low socioeconomic status to be a factor for vulnerability to heat stress.

To measure the impact of heat on the human body before its consequences become visible in the form of increased morbidity and mortality, biometeorological studies have used indices of thermal discomfort to approach physiological heat stress (Blazejczyk et al., 2012; Jendritzky et al., 2012; Laschewski and Jendritzky, 2002; Staiger et al., 2012). Since individuals spend most of their time indoors and can be exposed to and affected by high temperatures at home or at work, it is necessary to consider also indoor temperatures to understand potential factors for individual heat stress. Several studies therefore explored the outdoor–indoor temperature relationship for different building types and, in some cases, combined meteorological parameters with surveys on heat perception or behavior during heat waves in relation to the indoor temperature (Franck et al., 2013; Semenza et al., 2008; White-Newsome et al., 2012). Their results underpin the small-scale variability of indoor temperatures depending on sensitivities of building materials to outdoor temperatures, characteristics of the building, and its surroundings. Studies for German cities additionally showed that indoor temperatures in the analyzed buildings increased with higher floor level (Franck et al., 2013; Langner et al., 2014). Comparing temperature measurements with survey results on subjective heat perception during a heat wave in Leipzig, Germany, Franck et al. (2013) found that the evening temperatures measured in the bedroom dominated the heat perception, whereas other parameters, such as urban structure type and green space showed no clear relation to heat perception. In their in-depth study of 29 homes, White-Newsome et al. (2011) showed that heat-protective behavior was significantly associated with increasing indoor temperatures, and that residents in high-rise buildings and impervious areas showed a higher rate of behavior changes to adjust to the heat.

A recently growing number of social science studies have explored subjective heat stress and vulnerability to heat using – more or less explicitly – various theoretical frameworks and concepts of the natural hazards and climate change research community. Some of them focused on the risk perception and the response to heat warnings of vulnerable groups such as the elderly (Abrahamson et al., 2009; Hansen et al., 2011; Sampson et al., 2013; Sheridan, 2007) and their social networks (Wolf et al., 2010). Others tried to understand the social dimensions of subjective heat stress and behavior during heat (Großmann et al., 2012) or the social production of heat wave impacts (Klinenberg, 2002). The studies employed qualitative or quantitative research designs and because of their timing, they differ as well regarding the temporal relation to heat experience. In some of them, data collection took place during a heat wave (Großmann et al., 2012; Klinenberg, 2002; Sheridan, 2007), in others in the summer season without pronounced heat (Abrahamson et al., 2009; White-Newsome et al., 2011), in late spring (Pfaffenbach and Siuda, 2010), or during winter (Kalkstein and Sheridan, 2007).

Despite these conceptual and methodical differences, the common explicit or implicit understanding underlying the research activities is that heat and subjective heat stress are problems that unfold in the context of everyday social life and that a number of individual and social characteristics interacting with health and urban spatial structures affect subjective heat stress and coping behavior. In contrast to the previously mentioned biometeorological approach using thermal discomfort indices (Harlan et al., 2006; Langner et al., 2014), subjective heat stress (SHS) refers here to the subjective and individual experiencing of heat as stress that is measured with the statements expressed by individual study participants.

This definition and measurement has been used in previous empirical studies on SHS among residents of the German cities Leipzig (Großmann et al., 2012), Aachen (Pfaffenbach and Siuda, 2010), and Nuremberg (Wittenberg et al., 2012). These clearly indicated that SHS in everyday life is not solely an issue at home and in the residential environment, but also at work. As the most common expression of SHS at work, the studies found a decreased ability to concentrate due to the heat (Großmann et al., 2012; Pfaffenbach and Siuda, 2010). Similarly, Sampson et al. (2013) pointed to the negative effect of the heat on the energy level and the heat's negative effect to function normally also in daily activities. Other impairments and health-related problems reported by study participants in the context of heat included circulatory complaints, but also headaches, disturbed sleep, exhaustion, and respiratory diseases (Pfaffenbach and Siuda, 2010; Wittenberg et al., 2012). Pfaffenbach and Siuda (2010) found higher SHS rates among respondents with a chronic respiratory or cardiovascular disease. Furthermore, the studies mentioned found significant differences in SHS according to elements of the urban spatial structure (building type and inhabited level in the building, settlement density, and surrounding green space) that correspond to the results of heat discomfort studies outlined above.

Regarding sociodemographic characteristics that make a difference to the intensity of SHS, results of previous studies diverged in particular for elderly persons reporting higher SHS levels than the younger respondents (Pfaffenbach and Siuda, 2010) versus elderly persons reporting lower SHS levels than younger persons (Großmann et al., 2012). Studies among elderly citizens in the UK (Abrahamson et al., 2009; Wolf et al., 2010), the US (Sampson et al., 2013; Sheridan, 2007), and Australia (Hansen et al., 2011) suggested that they did not perceive themselves as vulnerable to heat just because of their chronological age in years. Moreover, as Großmann et al. (2012) found that retired respondents more often changed their daily routines during the heat than the younger and economically active ones, they raised the question as to whether and how the elderly might balance their higher susceptibility to heat with higher coping capacity through their freedom from the constraints of working life.

Studies in several countries showed that people employed various measures during hot-weather periods to cope with the heat (Abrahamson et al., 2009; Kalkstein and Sheridan, 2007; Sampson et al., 2013; Sheridan, 2007). The measures reported throughout the studies can be categorized into three types: changes in routines (for example, drinking more fluids) and changes of daily routines themselves (for example, shifting activities to other parts of the day, seeking cooler places). The third group is the use of available technical or structural measures to modify the indoor environment (ventilation, shading, fans, and primarily in the studies in the US, air conditioning). The observed implementation rate of the various measures to adjust and cope with the heat, however, was rather variable. It ranged from a third of respondents (Pfaffenbach and Siuda, 2010), a half of respondents (Kalkstein and Sheridan, 2007; Sheridan, 2007), to two-thirds of respondents (Großmann et al., 2012). At the same time, beliefs and attitudes also turned out to be relevant in the context of behavior during heat. A few studies (Kalkstein and Sheridan, 2007; Wolf et al., 2010) referred to respondents' reasoning that hot weather is normal during summer and to respondents' narratives that behavior changes in summer are just “common sense” (Kalkstein and Sheridan, 2007). Additionally, Wittenberg et al. (2012) observed that respondents perceived persistent heat as a problem one is helplessly exposed to, indicating only a moderate attitude that heat stress is a problem one can actively cope with.

In sum, the outlined results from previous surveys suggest that SHS is an issue relevant in different contexts of daily life. Second, they underline that various sociodemographic characteristics, health and behavior factors, and factors related to the built environment help explain why individuals experience and report more or less SHS. These results, however, were obtained in bivariate analyses and comparisons. The previous studies thus limit conclusions across the factors regarding their effects on SHS, and they limit conclusions on what factors play a major or minor role as determinants for SHS in different contexts of daily life. Additionally, the question is still open as to what proportion of the observed variability in SHS they actually explain. Furthermore, as for some of the mentioned studies the data collection took place without preceding pronounced hot-weather periods, the actual weather conditions and the fact that respondents had to rely on their memories of heat experience might have influenced their responses (Abrahamson et al., 2009).

The study concept based on previous studies (Großmann et al., 2012; Wittenberg et al., 2012) approached SHS as a problem individuals experience and respond to in their everyday life settings in an urban environment. SHS was operationalized as subjective heat stress in general, at home, and at work as the three key dependent variables. SHS was included for 12 additional typical daily activities to account for the variety of everyday life contexts in which people are exposed to heat (see Table 1). For all of them, SHS was measured using the following question in an expressed-preferences approach. “During a hot weather period, to what extent do you experience heat as stress (…in general/at home/at work/etc.)?”

To analyze what makes a difference to subjective heat stress and to identify the main determinants of SHS in general, at home, and at work, the study concept adopted and combined individual, social, behavioral, and environmental factors associated with SHS from previous explorative studies (Großmann et al. 2012; Wittenberg et al., 2012). Adjusting the heuristic framework for vulnerability assessment of Birkmann et al. (2013) to subjective heat stress in everyday life, these factors are related to the exposure to heat in a daily life context, the susceptibility of individuals, and their ability to cope with the heat. The study concept therefore included the factors health, coping attitude, and behavior during the heat, building elements and spatial structures in the urban built environment, and finally a number of sociodemographic characteristics (Table 1). Characteristics of the type of work and work place were also addressed.

Health impairments referring to symptoms suffered during heat, subjective health status, and the negative coping attitude, i.e., the feeling of being helpless against the heat, were adopted from Wittenberg et al. (2012). The measures to cope with the heat referred to changed behavior in response to the heat that can be performed immediately in the “here and now” (Birkmann et al., 2013, p. 193). We hereby used the terms coping behavior and coping measures to distinguish them from long-term adaptation measures to prepare for more future heat waves on an individual or institutional level, such as investments in heat protection and thermal insulation of buildings, urban planning, organization of work processes, and economic production (Ginski et al., 2013). The coping measures were derived from public information material on behavior during heat and from previous research, in particular from the studies by Großmann et al. (2012) and Abrahamson et al. (2009). The elements of the urban built environment covered housing conditions and urban spatial structures tested in previous studies in German cities (Großmann et al., 2012; Pfaffenbach and Siuda, 2010; Wittenberg et al., 2012). We included also heat protection and thermal insulation of buildings that help keep indoor temperature at an acceptable level and opportunities to relax outside and recover from the heat during the cooler times of the day without leaving home. This study concept was translated into a questionnaire with 28 questions that combined standardized questions with Likert scales, with ordinally and categorically coded answers (Table 1).

Karlsruhe, a city with approximately 300 000 inhabitants, is located in the Rhine Valley in southwest Germany. It belongs to the warmest regions in Germany. Until 5 July 2015, Karlsruhe held the temperature record for Germany of 40.2 ∘C measured during the European heat wave in August 2003

On 5 July 2015, a new official German temperature record of 40.3 ∘C was measured at the DWD weather station Kitzingen.

In 2013, all summer months June, July, and August at the Rheinstetten weather station of the German Weather Service (DWD) just south of Karlsruhe had a positive temperature anomaly compared to the climatological reference period 1961 to 1990. In July, 11 hot days with a maximum temperature of 30 ∘C or more were measured between 16 and 27 July, with only 1 day below 30 ∘C in between (see Fig. 1). This period was followed by 4 hot days in the first 6 days of August with a maximum temperature of 36.8 ∘C (German Weather Service, 2014; Mühr, 2014). During these 3 weeks, heat warnings for 6 and 7 consecutive days had been issued by the DWD (see Fig. 1). As there is no standard definition of heat waves (Fischer and Schär, 2010; Lissner et al., 2012; Robinson, 2001; Tinz et al., 2008), we use the term heat wave in our study if heat warnings by the DWD were issued during 3 or more consecutive days. The DWD heat warnings are based on the perceived temperature (PT, Staiger et al., 2012), with warnings of great heat stress with +32 ≤ PT < 38 ∘C and of extreme heat stress with PT > 38 ∘C. The weather conditions were therefore appropriate to study the subjective experiencing of heat as stress. As the Rheinstetten DWD station is not located directly within an urban area, it can be assumed that the temperatures in parts of the city of Karlsruhe were even higher due to the urban heat island effect (Oke, 1973).

The survey took place from 9 August until 25 September 2013 immediately after the two heat waves. Data were collected with an online version of the questionnaire that was available on the web page of the South German Climate Office at KIT and that was advertised in the local and social media. Parallel to the online survey, the senior citizens' office of the City of Karlsruhe sent a paper–pencil version of the questionnaire to groups of senior citizens.

In total, 323 respondents living in Karlsruhe participated in the survey, 249 of them online and 74 using the paper–pencil questionnaire. 159 of the respondents were female, 158 male. The age of the respondents ranged from 17 to 94 years. Compared to the Karlsruhe population, the sample was somewhat younger, and the proportion of respondents with an academic degree higher. The respondents represent almost all districts of Karlsruhe (see Fig. 2a). Corresponding to the high education level, most of the economically active respondents worked in mentally challenging jobs (89.4 %), mainly sitting (85.8 %) indoors (95.3 %). Only 6.3 % carried out physically demanding jobs and 3.6 % worked outside. In sum, data collection resulted in a random sample suitable to explore relevant potential determinants of subjective heat stress in general and at home. However, the possibility of analyzing SHS at work is limited because of too little response spread in the characteristics of work type and work place.

Data were analyzed using the program IBM SPSS version 21. First, a descriptive univariate analysis was carried out to explore respondents' SHS in different contexts of daily life, health impairments experienced during heat, their coping attitude, and what measures they used to cope with the heat. In order to understand what makes a difference to low or high SHS in general, at home, and at work, in the next step bivariate correlations and significant differences were tested with variables representing health, sociodemographic characteristics, coping measures, and elements of the spatial built environment. As the three dependent variables of the study, SHS in general, at home, and at work, and also a number of other variables were either not normally distributed or coded on an ordinal level, Spearman's rank correlation coefficients and nonparametric statistical tests were applied.

Finally, three multiple regressions for SHS in general, SHS at home, and SHS at work as dependent variables were performed to identify their main statistic determinants. Those variables referring to health, beliefs, coping measures, sociodemographic variables, and the built environment were entered into the regression models as independent variables that yielded significant results in bivariate analysis and that were plausible given the empirical evidence gained in other studies. The resulting three regression models for SHS in general, at home, and at work showed a good model fit with Durbin Watson values of 1.99, 2.03, and 1.99, respectively. To avoid collinearity, the independent variables had been accepted only if they fulfilled the criteria of a tolerance measure > 0.25 and a variance inflation factor, VIF < 5. Observed tolerance values of > 0.5 and VIF < 2 were the case for all independent variables.

The analysis of SHS at work considered only the economically active respondents (54.5 %) and the students/trainees (21.7 %). Students were included based on the assumption that in the perspective of everyday life experience, the students' and trainees' time and performance requirements in the course of the day correspond to requirements of working life independently of earning an income with their work.

A number of variables first required transformation into scores to finally test them as determinants of SHS (see also Table 1). The health impairments score was developed according to the summated score by Wittenberg et al. (2012) as a measure for the overall health impact during heat for each respondent. We refined their calculation and related the summated frequency of health impairments reported to the total number of health impairments with valid answers. Thermal insulation of buildings and heat protection help keep indoor temperature at home acceptable, and having a balcony or a garden provides opportunities to relax and recover from the heat during the cooler times of the day without leaving home. Our intention was to test the effects of having these possibilities at home for SHS and not primarily the effect of single elements as analyzed in previous studies (Großmann et al., 2012; Pfaffenbach and Siuda, 2010; Wittenberg et al., 2012). The scores for the heat protection, outdoor recreation, and insulation elements therefore each counted the number of elements available in the respondents' residence.

Regarding the residential district of the respondents, the 27 city districts of Karlsruhe were classified into four categories (see Fig. 2b). These categories correspond to settlement density and heat loading as modeled for Karlsruhe based on an urban climate model (Nachbarschaftsverband Karlsruhe, 2013) and on a combined approach using weather stations and remote sensing data (Bach et al., 2013). In the presented study, the two districts in the city center represent the districts with the highest settlement density and highest heat loading. The adjacent four urban districts (south, southwest, east, and west of the city center) represent the category with dense urban settlement and high heat loading. Eleven districts with urban and suburban characteristics form the third category, corresponding to a moderate heat loading. Ten suburban districts that are either located out of town or close to adjacent forests correspond to the category with the lowest heat loading.

The majority of the 323 respondents experienced heat as stress in general, at home, and at work to a rather high extent (Fig. 3). At the same time, the box plots indicate a high individual variability for all of the three SHS variables. Wilcoxon signed-rank tests, however, showed that the SHS at home was significantly lower than the overall general SHS (z= -4.036, p= 0.000) and the SHS at work (z=-2.529, p= 0.011).

Figure 4 displays how respondents experienced heat as stress in 12 typical situations and activities in daily life. More than half of the respondents reported strong or very strong heat stress in public transport, in the city center, and almost half of them at home while sleeping at night, and at work. The lowest percentages of heat experienced as stress were reported for being outside in gardens, parks, or pools/lakes, while doing shopping, and while being in the car. For the two latter this can be explained by the fact that most cars and shops or shopping centers are equipped with air conditioning. The responses shown in Fig. 4 also reveal that due to individually different daily routines and life styles, the activities or situations in which heat is experienced did not apply equally to all respondents.

The majority of respondents reported either a very good (29.4 %) or good (43.3 %) subjective health status. Respondents aged 65 years and older significantly more often expressed an impaired or strongly impaired state of health than the younger ones, χ2 (24, n= 319) = 128.66, p= 0.000. Respondents most often reported excessive sweating, feeling tired, sleep disturbances, and concentration problems from the heat (see Fig. 5). Number and frequency of health impairments suffered during the heat was summarized in the health impairments score. The score mean of 2.40 (standard deviation – SD = 0.60, score range 1 to 4) indicates that the majority of respondents reported a modest overall rate of health impairments. Respondents with a lower, impaired, or strongly impaired subjective health status reported a higher frequency of worsening of existing diseases (χ2= 111.34, p= 0.000), circulatory problems (χ2= 66.66, p= 0.000), headaches (χ2= 26.96, p= 0.008), and feeling sick (χ2= 25.11, p= 0.014). Elderly persons above 65 years more often reported worsening of existing diseases (χ2= 35.5960, p= 0.000) and having circulatory problems (χ2= 25.49, p= 0.013), but less often having concentration problems (χ2= 43.80, p= 0.000; chi-square tests with 12 degrees of freedom in each case, n= 305 to 314).

The agreement among the respondents to the negative coping attitude, i.e., their being subjected to the heat without being able to do anything against it, was rather high (mean = 3.36, SD = 1.10 on a scale from 1 to 5). Despite this, the majority of them implemented measures to cope with the heat. As can be seen in Fig. 6, almost all participants employed basic behavioral measures that focused on physical well-being during the heat and that could be easily integrated into the daily routine, such as drinking plenty of fluids, wearing light clothes, and eating lighter meals. Among the other behavioral measures that imply changes or alternations in daily routines and thus may require certain flexibility, were avoiding the direct sun, cooling the body, and avoiding exertion or exercise; these were implemented most by the respondents. To a lesser extent, the respondents sought cooler places, allowed themselves breaks, and slowed down or shifted work or activities to other (cooler) times of the day. At the same time, approximately up to a third of all respondents would have employed the three latter measures if they had had the possibility to do so. Out of the structural and technical measures to keep the indoor temperature at a tolerable level, almost all respondents used ventilation and shading of their rooms. Fans and in particular air conditioning (9.8 %) were used less frequently. However, with 43.6 %, a reasonable percentage of respondents would have switched on the air conditioning if they had had the possibility to do so.

Female respondents more frequently changed over to lighter meals (χ2= 7.37, p= 0.007) and lighter clothing (χ2= 3.89, p= 0.049) and more often avoided exertion or exercise (χ2= 4.77, p= 0.029). Male respondents more often used air conditioning (χ2= 6.32, p= 0.012; df = 1 in each case, N= 301 to 316). Regarding age, in particular the respondents aged 65 years and older shifted their activities more often to other times of the day (χ2= 41.605, p= 0.000) or allowed breaks and slowing down (χ2= 55.88, p= 0.000). They also more often sought cooler places to evade the heat (χ2= 26.89, p= 0.000), and more often avoided direct sun and exertion (χ2= 11.75, p= 0.019, χ2= 31.83, p= 0.000 respectively; df = 4 in each case, n= 307 to 318).

The correlation coefficients (Spearman's rho) listed in Table 2 show that SHS in general correlated rather highly with the health impairment score, the agreement to the statement “one is helplessly subjected to the heat”, and only weakly correlated with the subjective health status, which in turn was weakly correlated with the health impairment score. For SHS at home and at work, the respective correlation coefficients are somewhat lower, and there is no significant correlation with the subjective health status.

Regarding sociodemographic and economic characteristics, the respondents mainly differed in their experiencing heat as stress at home, and to a lesser extent for SHS in general, and not at all for SHS at work. Table 3 lists the test results for SHS at home. Male, young (up to 24 years) respondents, and respondents living as single parents or in shared flats reported higher SHS levels at home. Students and trainees reported higher SHS levels than employed respondents (full- and part-time). Conversely, the retired respondents and those aged 65 years and older reported the lowest levels of SHS at home. Similar to SHS at home, the retired also reported lower SHS in general than the rest of the sample (χ2 (4, n= 319) = 11.293, p= 0.046; Kruskal–Wallis test).

Only 5 out of the 12 coping measures listed in the questionnaire showed significant differences in SHS (Table 4). Seeking cooler places, allowing oneself to rest, and using the air conditioning were more often associated with lower SHS at work. In comparison to this, the other significant differences point in the opposite direction: avoiding the sun was more often associated with higher SHS levels in all three contexts, using the air conditioning with higher SHS levels in general and at home, and using the fan with higher SHS levels at home.

The respondents differed significantly in their SHS at home for almost all elements of the residential building and the surrounding urban environment included in the questionnaire, but not for SHS in general or at work. The test results for SHS at home in Table 5 show that higher heat loadings of the residential district are associated with higher SHS. Respondents with a one- or two-family home and respondents who have the possibility to use multiple floors in their home reported lower SHS levels than those living in multiple-unit dwellings or in apartment towers. Respondents in apartments in the upper levels and in particular in attics expressed higher SHS at home than those living at ground level. Based on the median values listed in Table 5, however, a steadily increasing average value of SHS with increasing building level was not observed.

Regarding the scores for heat-protective elements, the possibilities to sit outside, and the known insulation elements, the results in Table 5 show that having no or a low number of elements in each case is associated with higher levels of SHS experienced at home; hereby, the respondents who do not have any heat-protective elements (neither shutters nor blinds mounted outside to shade the window, nor air conditioning) disproportionately often lived in apartments in the attic (χ2 (12, n= 308) = 25.50, p= 0.012). Only for the walking distance to the next public green space, could no significant difference in the level of SHS at home be observed.

Table 6 lists the results of the multiple regression analyses to identify which of the variables making a difference at the bivariate level are the main statistic determinants for SHS in general, SHS at home, and SHS at work on a multivariate level of analysis. With resulting determination coefficients Rcorr2 of 0.567 for SHS in general, 0.458 for SHS at home, and 0.379 for SHS at work, the regression models yielded moderate, yet satisfactory results in terms of the variance explained.

As it can be seen from the standardized beta coefficients in Table 6, first, not all of the variables that yielded significant differences in bivariate analyses turned out to also have a significant effect in the multiple regressions. Second, health impairments and the feeling of being helplessly exposed to the heat had a significant positive (increasing) effect for all three SHS variables. The other determinants varied for SHS in general, at home, and at work. A number of characteristics of the residence significantly influenced the SHS at home only. Both the heat loading of the district and the level used in the building had an effect, whereby living in districts with higher heat loading or living on a higher level in a building increased SHS at home. The possibility to use more than one level at home, a higher number of available heat protection elements, a higher number of possibilities to sit outside, and a higher number of known insulation elements showed a decreasing effect on SHS at home.

Among the coping measures, seeking cooler places and using air conditioning had a significant negative, thus decreasing, effect on SHS at work. In comparison to this and as already observed on the bivariate analysis level, avoiding the sun had a weak positive, thus increasing effect for the SHS in general, and using a fan had a positive effect for SHS at home. The tested demographic variables, age and gender, did not show significant effects in the models, as was the case for the subjective health status.