The influence of daily air temperature variability on arterial blood pressure: Findings from a Kaunas cohort study

The influence of daily air temperature variability on arterial blood pressure: Findings from a Kaunas cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The influence of daily air temperature variability on arterial blood pressure: Findings from a Kaunas cohort study Jone Vencloviene, Ricardas Radisauskas, Vidmantas Vaiciulis, Dalia Luksiene, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6413166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Several studies reported statistically significant associations of blood pressure (BP) with short-term air temperature variability (TV), but the effect of TV on BP was found to differ in different areas. This study aimed to detect the association between BP and TV in Kaunas, Lithuania. Methods Data of the international HAPIEE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study was used to gather information on the participants' BP during 2006–2008. The TV variables were the diurnal temperature range and the standard deviation (SD) of hourly temperature during the 24 hours (TSD) and during the first 12 hours of the day (TSDF) as well as the SD of daily minimum and maximum temperatures during the exposure days (DTV). A multiple linear regression was used after controlling for potential confounders. Results Among the participants, 45.5% were men, 30.9% were aged > 65 years, and 9.8% were normotensive. A positive association of systolic BP with all TV variables and of diastolic BP with TSD and TSDF was found, a stronger impact being observed in males and physically active participants. The impact of TV was stronger during lower temperatures, and a statistically significant negative interaction term between air temperature and TV variables was found. In May-June, a negative association of DTV with diastolic BP was observed, a stronger effect being found in hypertensive and physically active participants. Conclusions We found a positive association of BP with TV. Sex, the level of physical activity, and air temperature may modify the relationship between TV and BP. Earth and environmental sciences/Environmental sciences Health sciences/Cardiology Health sciences/Risk factors air temperature variability diurnal temperature range arterial blood pressure arterial hypertension Figures Figure 1 Introduction The 2019 Global Burden of Disease study has shown that high systolic blood pressure (BP) is the leading global risk factor for preventable deaths [ 1 ]. Most cardiovascular diseases (CVD) can be effectively prevented through managing and preventing arterial hypertension (AH) [ 2 ], as high BP levels are among the leading risk factors for CVD and mortality [ 3 , 4 ]. Short-term increases in BP have been associated with an immediately increasing risk for cardiovascular events [ 2 ]. Due to the rising global air temperature, changes are also observed in the variation of other weather variables, such as wind speed, air temperature fluctuation, intensification and acceleration of the global hydrological cycle, and the intensity of storms ( https://climate.copernicus.eu/esotc/2021/ ). In the last two decades, the influence of weather changes on cardiovascular health has received increasing attention. The changes in atmospheric pressure, ambient temperature, and relative humidity increase the risk of stroke 5–7]. Air temperature variability (TV) has been associated with an increased risk of mortality/morbidity [ 8 , 9 ]. One of the parameters reflecting air temperature fluctuation is the diurnal temperature range (DTR) – the difference between the maximum and the minimum temperature over 24 hours. In Europe, an increasing trend in DTR has been observed since 1995 [ 10 ], and a positive association between DTR and the atmospheric levels of CO 2 and aerosols was observed in the non-winter period [ 11 ]. Apart from DTR, the other most used measures of intraday TV are the standard deviation (SD) of daily minimum and maximum temperatures (DTV) during the exposure days [ 8 ], and the SD of hourly temperature variability (HTV) [ 12 ]. Several studies reported statistically significant associations of BP with TV in areas with continental climate. Still, the obtained results may be due to different sample sizes, the periods of the lags, and the participants' characteristics. Studies in China showed a positive association of systolic BP with TV variables at lags of 0, 0–1, …, 0–7 days, a positive association of diastolic BP with TV variables at lags of 0, 0–1, 0–2, and 0–3 days, and negative or non-statistically significant association with TV variables at lags of 5, 0–5, or 0–6 days [ 12 , 13 ]. Similar tendencies were observed with DTR at lags of 0–5 days in Seoul [ 14 ], but the lack of statistical significance may be due to the smaller sample size and older age of the study participants. Besides, the participants with elevated BP or detected AH were more sensitive to the effect of DTV and HTV [ 12 , 15 ]. According to the results of these authors, no statistically positive association of systolic BP with TV variables except for DTR at a lag of 0 days for normotensive participants was found [ 12 , 15 ]. One longitudinal study analysed associations of BP with DTR in Europe [ 16 ]. This study found a negative association between DTR and BP at different lags. Therefore, the question remains open about the effects of TV on BP at different lags and their dependence on cardiovascular health status in other areas of Europe. The obtained results may help to identify the physiological mechanisms of the effects of changing weather on the human cardiovascular system. The aim of this study was to detect the association between BP and some variables of TV in Kaunas city, Lithuania. We also assessed the effect modification of air temperature on the association between TV variables and BP and performed the analyses in subgroups. This study was obtained data from the international HAPIEE (Health, Alcohol and Psychosocial Factors in Eastern Europe) study with a high rate of AH. Results The descriptive characteristics of the health variables are summarised in Table 1 . Among the respondents, 3,216 (45.5%) were men, 2,185 (30.9%) were aged > 65 years, 5,654 (79.5%) were overweight or obese, 68.4% had AH, and 39.4% of the respondents had taken drugs for high BP during the last 2 weeks. Most of the surveys were performed in spring (33.3%) and at least in summer (10.8%) – only in June (Table 1 ). A more detailed description of the participants of the survey was presented in the previous work [ 17 ]. During the days of the surveys, the mean daily DTR was 7.64°C. The SD of T calculated on the first half of the day was 2.21°C. Higher mean values of TV variables were observed in the period from May to June (Table S1 ), and lower values – from November to January. The characteristics of the environmental variables during the study period (2006–2008, except for July-August) were similar. Table 1 Participants’ characteristics Characteristic The total number of respondents, N 7,077 Male, N (%) 3,213 (45.4) Age at entry, mean ± SD, years 60.5 ± 7.6 Age > 65 years, N (%) 2,185 (30.9) BMI > 25 kg/m 2 , N (%) 5,654 (79.5) Physically active, N (%) 5,274 (74.7) Normotension (BP < 120/80 mmHg and not being on AHM) 696 (9.8) Prehypertension (BP 120–139/80–89 mmHg and not being on AHM) 1,541 (21.8) Arterial hypertension 4,840 (68.4) Antihypertensive medication, N (%) 2,781 (39.4) Ischemic heart disease, N (%) 1,359 (19.2) Month of the survey January, N (%) 676 (9.6) February, N (%) 625 (8.8) March, N (%) 667 (9.4) April, N (%) 773 (10.9) May, N (%) 919 (12.9) June, N (%) 765 (10.8) September, N (%) 630 (8.9) October, N (%) 751 (10.6) November, N (%) 737 (10.4) December, N (%) 534 (7.5) Systolic BP, mean ± SD, mmHg 141.6 ± 22.2 Diastolic BP, mean ± SD, mmHg 90.4 ± 12.5 BP – blood pressure; BMI – body mass index; AHM - antihypertensive medication According to our database, TSD0, TSDP, and DTR0 were highly correlated (r > 0.9). All these variables negatively correlated with T in winter (r ~ -0.3 and with TV r=-0.46), positively correlated with T during the non-winter period (r ~ 0.5), negatively correlated with RH (r ~ -0.43 and r ~ -0.77 respectively, during both winter and non-winter periods) and WS during the non-winter period (r ~ -0.3), and positively correlated with AP in spring-summer. Apart from this, the used TV variables positively correlated with the daily PM 10 concentration (r ~ 0.2 in autumn-winter and r ~ 0.45 in spring-summer). All the above-mentioned correlations were statistically significant. The results of the multivariate model show an increase in systolic BP on the days of a higher TSD and TSDF than the median and on days of DTR0, DTR02, DTV01, and DTV2 exceeding the first quartile. A stronger effect of TV on diastolic BP was observed on the days of the survey (with TSD and TSDF), and the associations of DBP with DTV01 and DTR02 may be non-linear (Table 2 ). For all the periods of the study, no statistically significant associations were found with TV variables including data of lags exceeding 2 days. Apart from this, interactions between TV variables and air temperature were found. The effect of TSD0 and DTR0 on the day of the test was lower with increasing daily T for all the participants and for some subgroups (Table 3 ). This effect was similar for TSDF, DTV01, and DTV02, and the data of 1–2 previous days were used for cumulative DTR variables. Table 2 The associations between blood pressure and daily temperature variability: results of the multivariate model Variable II quartile III quartile IV quartile Per increase in IQR β (95% CI) β (95% CI) β (95% CI) β (95% CI) Systolic BP TSD0 1.21 (-0.26, 2.68) 1.74 (0.08, 3.39) 3.05 (1.01, 5.09) 1.36 (0.14, 2.58) TSD1 0.03 (-1.42, 1.48) 1.77 (0.14, 3.41) 1.69 (-0.19, 3.57)* 0.90 (-0.23, 2.04) TSDF 0.48 (-0.95, 1.91) 3.29 (1.50, 5.07) 3.77 (1.60, 5.93) 0.19 (-0.01, 2.39)* DTR0 2.31 (0.89, 3.74) 3.17 (1.36, 4.98) 3.56 (1.50, 5.63) 1.29 (0.02, 2.57) DTR1 0.75 (-0.71, 2.21) 2.89 (1.10, 4.69) 2.77 (0.78, 4.76) 0.91 (-0.28, 2.10) DTR01 1.28 (-0.19, 2.76) 1.88 (-0.06, 3.81) 2.12 (0.02, 4.22) 1.43 (0.08, 2.78) DTR02 1.62 (0.16, 3.08) 2.48 (0.63, 4.33) 2.66 (0.68, 4.63) 1.34 (0.05, 2.63) DTV01 1.77 (0.27, 3.28) 2.39 (0.42, 4.36) 2.40 (0.21, 4.60) 1.63 (0.21, 3.06) DTV02 1.53 (0.05, 3.01) 2.58 (0.68, 4.48) 2.22 (0.04, 4.39) 1.29 (-0.14, 2.71)* Diastolic BP TSD0 0.74 (-0.11, 1.59)* 1.07 (0.12, 2.03) 1.67 (0.49, 2.85) 0.87 (0.17, 1.57) TSD1 -0.14 (-0.98, 0.69) 0.21 (-0.74, 1.15) -0.17 (-1.26, 0.92) -0.23 (-0.88, 0.43) TSDF 0.24 (-0.58, 1.07) 1.75 (0.72, 2.78) 1.99 (0.74, 3.24) 0.79 (0.09, 1.48) DTR0 1.37 (0.55, 2.19) 1.34 (0.30, 2.39) 1.73 (0.54, 2.93) 0.66 (-0.08, 1.39)* DTR1 0.41 (-0.43, 1.25) 1.21 (0.17, 2.24) 0.88 (-0.27, 2.03) -0.03 (-0.71, 0.66) DTR01 0.51 (-0.34, 1.36) 0.63 (-0.49, 1.74) 0.67 (-0.55, 1.88) 0.40 (-0.38, 1.18) DTR02 0.88 (0.03, 1.72) 0.95 (-0.12, 2.02)* 1.02 (-0.13, 2.16)* 0.33 (-0.42, 1.07) DTV01 0.89 (0.02, 1.75) 1.24 (0.11, 2.38) 0.83 (-0.44, 2.10) 0.40 (-0.42, 1.22) DTV02 0.72 (-0.14, 1.57) 0.89 (-0.21, 1.99) 0.40 (-0.86, 1.66) 0.15 (-0.67, 0.97) Reference category - first quartile; BP - blood pressure; * - p < 0.1; In the model with interaction (Table 3 ), the impact of TV variables on systolic BP and diastolic BP was stronger on the days of a lower T for all participants, males, and physically active subjects. For non-hypertensive participants, the interaction term of T and TSD0 or DTR0 was non-significant (Table 4 ). According to the regression coefficients, TSD0 (DTR0) was not associated with higher BP if T was over 16°C (14°C) or for participants who took medications for high BP; this cut-off may be about 9°C. Table 3 The effect of the interaction of TSD and DTR with air temperature Group TSD0 TSD0*T DTR0 DTR0*T β (SE) p β (SE) p β (SE) p β (SE) p Systolic BP All 3.10 (0.90) 0.001 -0.19 (0.07) 0.008 3.56 (0.90) < 0.001 -0.27 (0.07) < 0.001 Male 5.30 (1.37) < 0.001 -0.32 (0.11) 0.003 6.36 (1.35) < 0.001 -0.45 (0.11) < 0.001 Female 1.33 (1.19) 0.265 -0.09 (0.09) 0.307 1.16 (1.20) 0.334 -0.15 (0.10) 0.140 AH no 1.42 (0.79) 0.074 -0.04 (0.06) 0.526 1.76 (0.80) 0.027 -0.08 (0.06) 0.235 AH yes 2.09 (1.07) 0.051 -0.17 (0.08) 0.042 3.33 (1.06) 0.002 -0.32 (0.09) 65 2.39 (1.85) 0.196 -0.12 (0.15) 0.432 4.73 (1.85) 0.011 -0.37 (0.16) 0.016 BMI < 25 3.74 (1.83) 0.041 -0.18 (0.14) 0.197 5.11 (1.94) 0.008 -0.32 (0.16) 0.041 BMI ≥ 25 2.94 (1.03) 0.004 -0.19 (0.08) 0.016 3.28 (1.01) 0.001 -0.27 (0.08) 0.001 High PA 3.27 (1.03) 0.001 -0.17 (0.08) 0.038 4.09 (1.03) < 0.001 -0.29 (0.09) 0.001 Low PA 2.64 (1.88) 0.162 -0.23 (0.14) 0.096 2.17 (1.85) 0.242 -0.24 (0.15) 0.104 Diastolic BP All 1.90 (0.52) < 0.001 -0.11 (0.04) 0.007 1.68 (0.52) 0.001 -0.12 (0.04) 0.005 Male 2.51 (0.81) 0.002 -0.14 (0.06) 0.031 2.56 (0.80) 0.001 -0.17 (0.07) 0.014 Female 1.35 (0.67) 0.044 -0.09 (0.05) 0.071 0.90 (0.68) 0.183 -0.10 (0.06) 0.082 AH no 1.09 (0.51) 0.032 -0.05 (0.04) 0.157 1.10 (0.51) 0.031 -0.06 (0.04) 0.158 AH yes 1.09 (0.60) 0.068 -0.08 (0.05) 0.092 1.23 (0.59) 0.038 -0.12 (0.05) 0.014 Age ≤ 65 1.89 (0.62) 0.002 -0.12 (0.05) 0.011 1.17 (0.61) 0.056 -0.09 (0.05) 0.071 Age > 65 0.03 (0.02) 0.156 1.43 (1.01) 0.387 2.46 (1.01) 0.015 -0.20 (0.08) 0.020 BMI < 25 0.05 (0.09) 0.075 -0.02 (0.03) 0.712 2.88 (1.14) 0.011 -0.10 (0.09) 0.286 BMI ≥ 25 1.91 (0.59) 0.001 -0.14 (0.05) 0.003 1.47 (0.58) 0.012 -0.14 (0.05) 0.005 High PA 2.07 (0.60) 0.001 -0.11 (0.05) 0.024 2.05 (0.60) 0.001 -0.14 (0.05) 0.006 Low PA 1.38 (1.07) 0.200 -0.11 (0.08) 0.162 0.68 (1.06) 0.520 -0.08 (0.09) 0.336 BP - blood pressure; AH - arterial hypertension; BMI - body mass index; PA - physical activity A stronger positive association of TV variables with systolic and diastolic BP detected during the cooler period (Tables 4 and 5 ) confirms the results obtained by the multivariate model with an interaction term. In that model, we did not find any statistically significant association of systolic BP with TV variables for females and physically inactive participants. In other subgroups, systolic BP was statistically significantly affected by DTR0 (except for those aged ≤ 65 years) and DTV01 (except for those aged > 65 years), DTR0, DTR01, and DTV01 having a stronger impact on hypertensive participants. Based on beta values, DTV01 had a stronger effect on systolic BP (Table 4 ). In September-April, diastolic BP was mostly statistically positively associated with TV on the day of the survey (Table 5 ). The effect of TV variables calculated by using the minimal and maximal temperatures on BP was stronger in winter, and the effects of TSD and TSDF on systolic BP were stronger during the transition season (Tables 4 and 5 ). Table 4 Associations between temperature variability variables and systolic blood pressure during the whole period and in September-April in subgroups Variables TSD0 TSDF DTR0 DTR01 DTV01 β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) Whole period All 1.36 (0.14, 2.58) 1.19 (-0.01, 2.39) 1.29 (0.02, 2.57) 1.43 (0.08, 2.78) 1.63 (0.21, 3.06) Male 2.27 (0.46, 4.09) 1.50 (-0.29, 3.29) 2.61 (0.73, 4.49) 3.59 (1.56, 5.62) 3.39 (1.27, 5.51) Female 0.46 (-1.18, 2.09 0.85 (-0.75, 2.46) -0.05 (-1.77, 1.67) -0.64 (-2.43, 1.16) -0.23 (-2.14, 1.68) AH no 1.05 (-0.01, 2.12) 1.08 (0.04, 2.11) 1.10 (-0.02, 2.22)* 1.14 (-0.04, 2.33)* 1.53 (0.28, 2.78) AH yes 0.51 (-0.92, 1.95) 0.42 (-1.01, 1.84) 0.71 (-0.79, 2.22) 1.38 (-0.21, 2.98)* 0.48 (-0.20, 3.16)* Age ≤ 65 1.03 (-0.46, 2.51) 1.09 (-0.39, 2.58) 0.74 (-0.79, 2.26) 1.17 (-0.43, 2.76) 1.16 (-0.55, 2.89) Age > 65 1.23 (-0.96, 3.43) 0.50 (-1.61, 2.61) 1.36 (-1.01, 3.73) 0.54 (-2.09, 3.17) 1.05 (-1.63, 3.73) BMI < 25 2.03 (-0.44, 4.49) 3.08 (0.63, 5.53) 2.24 (-0.38, 4.86) 1.43 (-1.44, 4.29) 2.39 (-0.60, 5.38) BMI ≥ 25 1.13 (-0.26, 2.52) 0.72 (-0.65, 2.08) 1.04 (-0.41, 2.49) 1.44 (-0.08, 2.97)* 1.47 (-0.15, 3.08)* High PA 1.42 (0.28, 2.55) 2.03 (0.40, 3.66) 1.70 (0.26, 3.13) 1.75 (0.21, 3.30) 1.95 (0.33, 3.57) Low PA -0.19 (-2.32, 1.95) 0.51 (-2.62, 3.64) 0.17 (-2.55, 2.88) 0.58 (-2.17, 3.34) 0.71 (-2.23, 3.65) September-April All 1.61 (0.42, 2.79) 1.38 (0.27, 2.49) 1.79 (0.59, 2.99) 1.66 (0.25, 3.06) 2.02 (0.61, 3.44) Male 2.85 (1.06, 4.63) 2.36 (0.68, 4.04) 3.33 (1.54, 5.13) 3.60 (1.47, 5.72) 3.73 (1.61, 5.85) Female 0.58 (-1.00, 2.16) 0.56 (-0.91, 2.03) 0.41 (-1.20, 2.02) -0.01 (-1.86, 1.84) 0.43 (-1.46, 2.31) AH no 1.07 (0.40, 2.11) 1.15 (0.19, 2.10) 1.25 (0.20, 2.31) 1.34 (0.11, 2.56) 1.72 (0.48, 2.95) AH yes 0.97 (-0.43, 2.37) 0.99 (-0.32, 2.31) 1.59 (0.18, 3.00) 1.60 (-0.05, 3.25)* 1.80 (0.13, 3.47) Age ≤ 65 1.21 (-0.21, 2.63) 1.10 (-0.25, 2.45) 1.11 (-0.30, 2.53) 1.50 (-0.14, 3.14)* 1.76 (0.09, 3.43) Age > 65 1.54 (-0.70, 3.78) 1.05 (-0.96, 3.06) 2.37 (0.04, 4.70) 0.86 (-1.93, 3.64) 1.33 (-1.46, 4.12) BMI < 25 2.15 (-0.23, 4.52) 2.79 (0.50, 5.08) 2.85 (0.36, 5.35) 2.70 (-0.31, 5.70)* 3.60 (0.63, 6.57) BMI ≥ 25 1.43 (0.07, 2.79) 1.03 (-0.23, 2.30) 1.53 (0.17, 2.90) 1.45 (-0.13, 3.02)* 1.68 (0.07, 3.28) High PA 1.63 (0.30, 2.97) 1.45 (0.19, 2.70) 1.80 (0.45, 3.16) 1.58 (-0.02, 3.18)* 1.96 (0.35, 3.57) Low PA 1.68 (-0.88, 4.25) 1.34 (-1.01, 3.68) 1.93 (-0.64, 4.49) 1.89 (-0.98, 4.75) 2.15 (-0.78, 5.09) Winter All 0.95 (-0.61, 2.52) 0.25 (-1.22, 1.71) 1.34 (0.15, 2.52) 1.46 (0.06, 2.87) 1.35 (0.12, 2.59) β per increase in IQR (for transitional season, IQR of TSD = 2.21 and IQR of TSDF = 2.68; for winter, IQR of DTR = 2.60); BP - blood pressure; AH - arterial hypertension; BMI - body mass index; PA - physical activity Table 5 Associations between temperature variability variables and diastolic blood pressure during the whole period and in September-April in subgroups Variables TSD0 TSDF DTR0 DTR01 DTV01 β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) Whole period All 0.87 (0.17, 1.57) 0.79 (0.09, 1.49) 0.66 (-0.08, 1.39) 0.40 (-0.38, 1.18) 0.40 (-0.42, 1.22) Male 1.21 (0.13, 2.29) 0.61 (-0.46, 1.67) 1.17 (0.05, 2.29) 1.42 (0.22, 2.63) 1.22 (-0.04, 2.48)* Female 0.49 (-0.43, 1.40) 0.90 (-0.01, 1.80) 0.10 (-0.87, 1.07) -0.55 (-1.57, 0.46) -0.47 (-1.54, 0.61) AH no 0.56 (-0.12, 1.25) 0.66 (-0.01, 1.33)* 0.60 (-0.12, 1.31) 0.43 (-0.33, 1.19) 0.52 (-0.29, 1.32) AH yes 0.36 (-0.44, 1.16) 0.25 (-0.54, 1.05) 0.23 (-0.61, 1.06) 0.23 (-0.67, 1.12) 0.15 (-0.78, 1.09) Age ≤ 65 0.81 (-0.07, 1.69) 0.87 (-0.01, 1.75) 0.45 (-0.46, 1.35) 0.48 (-0.46, 1.43) 0.41 (-0.61, 1.43) Age > 65 0.74 (-0.46, 1.93) 0.31 (-0.84, 1.46) 0.68 (-0.61, 1.97) -0.49 (-1.92, 0.94) -0.33 (-1.79, 1.13) BMI < 25 1.62 (0.18, 3.07) 2.21 (0.78, 3.65) 2.00 (0.47, 3.54) 0.74 (0.94, 2.43) 1.05 (-0.71, 2.80) BMI ≥ 25 0.65 (-0.14, 1.45) 0.44 (-0.35, 1.22) 0.34 (-0.50,1.17) 0.33 (-0.54, 1.21) 0.25 (-0.68, 1.18) High PA 1.08 (0.29, 1.87) 1.08 (0.29, 1.86) 0.89 (0.06, 1.73) 0.78 (-0.12, 1.67) 0.76 (-0.18, 1.70) Low PA 0.32 (-1.18, 1.82) -0.02 (-1.47, 1.43) 0.01 (-1.54, 1.55) -0.61 (-2.18, 0.96) -0.64 (-2.31, 1.04) September-April All 1.07 (0.39, 1.76) 0.95 (0.31, 1.59) 1.01 (0.32, 1.70) 0.79 (-0.02, 1.60)* 0.95 (0.14, 1.77) Male 1.22 (0.16, 2.29) 0.93 (-0.07, 1.93) 1.38 (0.31, 2.45) 1.47 (0.21, 2.74) 1.50 (0.23, 2.76) Female 0.94 (0.05, 1.82) 0.93 (0.11, 1.75) 0.65 (-0.25, 1.55) 0.22 (-0.82. 1.26) 0.43 (-0.63, 1.49) AH no 0.57 (-0.08, 1.23)* 0.66 (0.05, 1.27) 0.71 (0.04, 1.38) 0.69 (-0.09, 1.47)* 0.75 (-0.03, 1.54)* AH yes 0.77 (-0.02, 1.55)* 0.76 (0.02, 1.49) 0.88 (0.09, 1.67) 0.73 (-0.20, 1.65) 0.84 (-0.09, 1.78)* Age ≤ 65 0.96 (0.12, 1.80) 0.80 (-0.01, 1.59) 0.73 (-0.10, 1.57) 0.82 (-0.15, 1.79)* 0.94 (-0.06, 1.93)* Age > 65 1.00 (-0.21, 2.22) 0.92 (-0.17, 2.01) 1.30 (0.04, 2.57) 0.28 (-1.23, 1.80) 0.52 (-1.00, 2.04) BMI < 25 1.61 (0.20, 3.02) 1.90 (0.53, 3.26) 2.16 (0.67, 3.64) 1.59 (-0.20, 3.38)* 1.94 (0.17, 3.71) BMI ≥ 25 0.93 (0.15, 1.71) 0.74 (0.01, 1.46) 0.76 (-0.02, 1.54) 0.64 (-0.27, 1.54) 0.75 (-0.17, 1.67) High PA 1.18 (0.40, 1.95) 1.11 (0.38, 1.84) 1.15 (0.37, 0.93) 1.07 (0.14, 2.00) 1.22 (0.29, 2.16) Low PA 0.86 (-0.60, 2.32) 0.58 (-0.75, 1.91) 0.71 (-0.75, 2.17) 0.03 (-1.60, 1.66) 0.17 (-1.49, 1.84) Winter All 0.74 (-0.17, 1.65) 0.35 (-0.50, 1.20) 0.74 (0.06, 1.43) 0.85 (0.04, 1.67) 0.77 (0.05, 1.49) β per increase in IQR (for transitional season, IQR of TSD = 2.21 and IQR of TSDF = 2.68; for winter, IQR of DTR = 2.60); BP - blood pressure; AH - arterial hypertension; BMI -body mass index; PA - physical activity During the warmer period (May-June), systolic BP was negatively associated with DTV03 and DTV04, and diastolic BP was negatively associated with DTR at lags 1, 2, and 3, with cumulative DTR at lags 0–3, 0–4, and 0–5, and with DTV variables (Fig. 1 , A and B). In case of participant subgroups, a stronger effect of DTV on BP was found in hypertensive and physically inactive participants (Fig. 1 , C and D). Figure 1 here Discussion This is the first study on the effects of TV on human physiological parameters using a large database in Lithuania and Eastern Europe. In our study, associations between air TV and arterial BP were detected. We found a positive association of systolic and diastolic BP with TV on the day of the survey, a stronger effect being found for males and physically active participants. Also, the dependence of the impact of TV on daily temperature level was observed: the effect of temperature variability was lower with an increase in daily T. A stronger positive association of systolic BP with TV was found during the cooler period, especially for males who were non-hypertensive and were physically active. In the warmer months (May-June), a negative association of TV with diastolic BP was observed, and a stronger effect was found in hypertensive and physically inactive participants. A difference in temperature variability during different seasons and in different subgroups was observed. The study conducted in Augsburg [ 16 ] showed that an increasing DTR was linked to decreasing systolic BP and diastolic BP, which is in contrast to our studies. These discrepancies may be explained by differences in the mean BP level, study design, and local weather conditions. The participants in the Augsburg study had lower BP (123.3/76.4) (most of the participants were normotensive), while in our study, the mean BP was 141.6/90.4, and 68.4% of participants were hypertensive. Other studies found a negative or non-statistical association between diastolic BP and TV and a positive or non-statistical association between systolic BP and TV [ 12 , 13 , 15 ]. Also, we found a negative association of diastolic BP with DTV and cumulative DTR and a weak negative effect of DTV on systolic BP in May-June. We did not have BP measurements during July-August – the warmest months. The use of data from this period would likely enable us to state a negative association of diastolic BP with TV during non-winter or all study periods. Apart from this, in Augsburg, winters are warmer – the mean monthly temperature is negative (-0.1°C during 1990–2020) only in January. Short-term changes in environmental temperature affect skin temperatures and cause changes in the thermoregulatory and BP regulatory system [ 18 – 20 ]. The results of controlled studies show an increase in BP during acute cold exposure [ 20 ] and a decrease in BP during exposure to heat [ 21 – 23 ]. Therefore, the physical mechanisms of the impact of higher TV on previous 0–5 days on BP changes may be explained by the additional portion of the daily exposure to cold or heat depending on the season. In winter, DTR and other TV variables negatively correlated with T, and at the same average temperature, a higher DTR adds more exposure to more negative temperatures as compared to lower DTR. This same situation is in most days of the two first months of spring and the two last months of autumn when maximal T does not exceed the comfortable temperature. During warmer periods, especially in summer, DTR is positively associated with T, and it is possible that higher exposure to heat occurred on days with a higher DTR at the same average temperature. This can explain the stronger positive association between TV and BP during the cooler period and a negative association of DTV with diastolic BP during May-June. Besides, higher intraday variability was significantly associated with lower sleep efficiency, longer wakefulness after sleep onset, and a shorter total sleep time [ 24 ], and this can affect BP. According to our results, a stronger impact of TV during the cooler period was found in physically active participants. It is possible that these participants were more exposed to the outdoor environment due to their physical activity. We also found a stronger impact of TV during the cooler period in males, although other studies found a stronger effect of TV in females. It is possible that males were more exposed to outdoor environments. However, during the warmer months, a stronger negative association of DTV with BP was found in more susceptible groups – hypertensive and physically inactive participants. We did not detect any stronger effect of TV on susceptible groups during the cooler periods, maybe due to a better possibility of protecting oneself from the effects of cold than from heat. Limitation As one of limitations, it is possible that some of the cases identified and recorded as high BP may in fact have represented a false momentary case due to stress or psychological factors. However, given the extensive data collection protocol, this number is likely to be so small that it does not influence our results. The lack of air pollutants such as NO 2 and SO 2 in the models also can be considered a limitation of the study. In our study, the daily NO 2 and PM 10 concentrations were strongly correlated, and air pollution of SO 2 in Kaunas is very low, thus their role in mediating the effects of meteorological factors was likely small in this study. A limitation of our study may be a lack of data during the warmest months, July and August. Conclusions We found a positive association of systolic and diastolic BP with TV on the day of the survey. Also, a dependence of the impact of TV variables on daily temperature was observed: the effect of TV decreased with an increase in daily air temperature. A stronger positive association of systolic BP with TV was found during the cooler period, while in the warmer months (May-June), a negative association of TV with diastolic BP was observed. Sex, the level of physical activity, the presence of AH, and air temperature may modify the relationship between TV and BP. Under current climate change trends, the impact of air temperature and its variability on human health is becoming increasingly important. Our results add to knowledge in this field in Europe. The findings of the study could be used by public health specialists and personal healthcare providers to help patients prepare for periods of high TV or before significant changes in temperature. To adjust the response of the public health sector for local climatic conditions, it is important to carry out similar studies in regions with different TV. In case of significant changes in diurnal temperature, it is essential to ensure suitable BP control measures so that excessive BP elevation could be avoided. Methods Study sample and health variables We used data from the survey performed within the framework of the international HAPIEE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study. The study was conducted in accordance with the Declaration of Helsinki and approved by the Kaunas Regional Biomedical Research Ethics Committee, Lithuania (reference number: 05/09 on 11 January 2005). All participants signed the form of informed consent. We used data of 7,077 residents of Kaunas city (Lithuania) aged 45–72 years from 2006–2008, excluding July-August. Health factors were evaluated using a standard questionnaire and some objective measurements. BP was measured twice using a mercury sphygmomanometer and an appropriately sized cuff on the right arm. The initial measurement was taken after five minutes of rest on the right arm. The second measurement was taken two minutes later. Korotkoff phase 1 (the onset of sound) and Korotkoff phase 5 (the disappearance of sound) were recorded as systolic and diastolic BP. The average of the two readings was used for further analysis. AH was defined as a mean systolic BP of at least 140 mm Hg or a mean diastolic BP of at least 90 mm Hg, or both, and/or the use of antihypertensive medication (AHM) in the previous two weeks. Body mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (kg/m 2 ). We divided the study participants into three different groups: those with normal weight (BMI < 24.99 kg/m 2 ), overweight (BMI 25.0–29.99 kg/m 2 ), and obesity (BMI ⩾30.0 kg/m 2 ) [ 25 ]. Physical activity (PA) was determined by the mean length of time spent per week during leisure time in the autumn-winter and spring-summer seasons for such activities as gardening, maintenance of the house, and other physical activities. The respondents were ranked from the lowest to the highest values and divided into three equal groups (tertiles) according to their PA during leisure activities. The first tertile cut-off (max) was 10 hours. For this reason, we used this cut-off to identify insufficient PA. Other information about the patients’ baseline examination was presented in our previous study [ 17 ]. Environmental variables We obtained hourly data of air temperature (T, °C), relative humidity (RH, %), and the concentration of particulate matter of 10 micrometres or less in diameter (PM 10 , µg/m 3 ) from the National Environmental Department and Municipal Ecological Monitoring Stations. The Municipal Ecological Monitoring Station is located within the residential part of the study region. The daily data of atmospheric pressure at sea level (AP, hPA) and wind speed (WS, knots) were obtained from Kaunas meteorological station ( http://www.geodata.us/weather/ ). The variables of air temperature variability used in our study were the standard deviation (SD) of hourly temperature during the 24 hours (TSD), the SD of hourly temperature during the first part of the day (TSDF), the DTR, and the SD of daily minimum and maximum temperatures on the day of the test and two previous days (DTV). We analysed the impact of TV variables on the day of the test (lag 0) and on the previous 5 days (lag 1–5 days). Statistical analysis Correlations were assessed by using the Spearman correlation coefficient. The associations between the daily TV and systolic BP and diastolic BP were evaluated by applying the multiple linear regression, adjusting for the participant's age, sex, BMI, the use of antihypertensive medications (yes/no), the presence of ischaemic heart disease (IHD) (yes, no), smoking, physical activity, alcohol consumption, and education level. In the model air temperature, RH, both low and high AP, the daily PM 10 concentration, and the year and month were additionally included as categorical predictors. The air TV variables that we used were values of DTR at single-day lags (DTR0, …, DTR5) and cumulative day lags – for example, DTR02 marks DTR during the day of the test and 2 days before the test. By analogy, we used the SDs of daily minimum and maximum temperatures during days from the test day to day 1, 2, …, 5 before the test. For example, DTV02 was calculated as the SD of daily minimum and maximum temperatures on the day of the test and on two previous days. Also, we used TSD on the day of the survey and on the previous day (named TSD0, and TSD1, respectively) and TSDF. These variables were included in the model as continuous or categorised in quartiles. We also assessed the effect modification of air temperature on the association between TV variables and arterial BP by including the interaction term between T and TV variables. The analyses were performed for all participants and separately for males and females, for older (> 65 years) and younger, for those with BMI < 25 kg/m 2 and BMI ≥ 25 kg/m 2 , for hypertensive (systolic BP ≥ 140 mmHg, and/or diastolic BP ≥ 90 mmHg, and/or use of antihypertensive medication (AHM) within 2 weeks) and not, and for physically active and inactive participants. We presented the adjusted beta coefficients per increase in IQR or in °C with their 95% confidence interval (CI) or p-value. Statistical analysis was performed using SPSS 20 software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.). Declarations Funding No funding has been provided by any private or public sources. Acknowledgements The authors would like to thank the Lithuanian Hydrometeorological Service and the Environment Protection Agency for meteorological and air pollution data. Authors Contributions Jone Vencloviene: Writing – original draft, Methodology, Visualisation, Statistical analysis, Conceptualisation. Ricardas Radisauskas: Writing – original draft, Methodology, Investigation. Vidmantas Vaiciulis: Writing - review & editing, Methodology, Visulisation, Environmental data curation. Dalia Luksiene: Writing - review & editing, Methodology, Investigation. Abdonas Tamosiunas: Writing - review & editing, Methodology, Investigation, Medical Data curation, Conceptualisation. Martin Bobak: Writing - review & editing, Methodology, Investigation. Daina Kranciukaite-Butylkiniene: Writing – review & editing, Methodology, Investigation, Medical data curation. All authors reviewed the manuscript. Declaration of Competing Interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. Disclosure statement No potential conflict of interest was reported by the author(s). Data availability statement The half-hourly measurements of air temperature, atmospheric pressure, wind speed, and relative humidity used in the research were collected from the https://www.wunderground.com/history/daily/lt/karm%C4%97lava/EYKA/date/2008-9-9 . The daily concentration of particulate matter of 10 micrometres or less in diameter (PM 10 , μg/m 3 ) and the daily maximal 8-hour ozone concentration were downloaded from the freely available webpage of the Environmental Protection Agency (https://aaa.lrv.lt/lt/veiklos-sritys/oras/oro-kokybes-statistika-ir-duomenys/ ). The medical data cannot be made publicly available upon publication because they contain sensitive personal information. The data that support the findings of this study are available upon reasonable request from the authors. For medical data, please contact with A. Tamosiunas ( [email protected] ) or D. Kranciukaite-Butylkiniene ( [email protected] ) Funding The author(s) reported there is no funding associated with the work featured in this article. References NCD Risk Factor Collaboration (NCD-RisC. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet . 398 (10304), 957-980 (2021). DOI:10.1016/S0140-6736(21)01330-1. Wang. M. C., Lloyd-Jones, D. M. Cardiovascular Risk Assessment in Hypertensive Patients. Am. J. Hypertens. 34 (6), 569-577 (2021). DOI:10.1093/ajh/hpab021. Brunström, M., Carlberg, B. Association of Blood Pressure Lowering with Mortality and Cardiovascular Disease Across Blood Pressure Levels: A Systematic Review and Meta-analysis. JAMA Intern. Med. 178 (1), 28-36 (2018). doi: 10.1001/jamainternmed.2017.6015. Fuchs, F.D., Whelton, P. K. High Blood Pressure and Cardiovascular Disease. Hypertension 75 (2), 285-292 (2020). Jimez-Conde, J. et al. Weather as a Trigger of Stroke. Daily meteorological factors and incidence of stroke subtypes. Cerebrovasc. Dis . 26 (4):348–354 (2008). doi:10.1159/ 000151637. Van Donkelaar, C. E. et al. Atmospheric pressure variation is a delayed trigger for aneurysmal subarachnoid hemorrhage. World Neurosurg . 112 , e783–90 (2018). https://doi.org/10.1016/j.wneu.2018.01.155. Rakers, F. et al. Rapid weather changes are associated with increased ischemic stroke risk: a case-crossover study. Eur. J. Epidemiol . 31 (2), 137–146 (2016). doi: 10.1007/s10654-015-0060-3. Guo, Y. M. et al. Temperature variability and mortality: a multi-country study. Environ. Health Perspect. 124 , 1554–1559 (2016). Danesh Yazdi, M. et al. The effect of long-term exposure to air pollution and seasonal temperature on hospital admissions with cardiovascular and respiratory disease in the United States: A difference-in-differences analysis. Sci. Total Environ . 843 , 156855 (2022). doi: 10.1016/j.scitotenv.2022.156855. Huang, X., Dunn, R. J. H., Li, L. Z. X., McVicar, T. R., Azorin-Molina, C., Zeng, Z. Increasing global terrestrial diurnal temperature range for 1980–2021. Geophys. Res. Let. 50:e2023GL103503 (2023). https://doi.org/10.1029/2023GL103503 Stjern, C. W. et al. How aerosols and greenhouse gases influence the diurnal temperature range. Atmos. Chem. Phys. 20 , 13467–13480 (2020). https://doi.org/10.5194/acp-20-13467-2020. Zhu, W. et al. Ambient temperature variability and blood pressure in a prospective cohort of 50,000 Chinese adults. J. Hum. Hypertens . 37 (9), 818-827 (2022). Zheng, S. et al. The effect of diurnal temperature range on blood pressure among 46609 people in Northwestern China. Sci. Total Environ . 730 , 138987 (2020). Lim, Y. H., Kim, H., Kim, J. H., Bae, S., Hong, Y. C. Effect of diurnal temperature range on cardiovascular markers in the elderly in Seoul, Korea. Int. J. Biometeorol. 57 (4), 597-603 (2013). doi: 10.1007/s00484-012-0587-x. Yan, X. et al. Association between short-term daily temperature variability and blood pressure in the Chinese population: From the China hypertension survey. Environ. Int. 184, 108463 (2024). doi: 10.1016/j.envint.2024.108463. Woeckel, M. et al. Ambient air temperature and temperature variability affecting blood pressure - a repeated-measures study in Augsburg, Germany. Environ. Res. Health . 1 , 035001 (2023). DOI 10.1088/2752-5309/acdf10 Vencloviene, J. et al. The influence of the North Atlantic Oscillation index on arterial blood pressure. J. Hypertens. 37 (3), 513-521 (2019). doi: 10.1097/HJH.0000000000001929. Liu, C. Yavar, Z., Sun, Q. Cardiovascular response to thermoregulatory challenges. Am. J. Physiol. Heart Circ. Physiol . 309 (11), H1793-H1812 (2015). doi:10.1152/ajpheart.00199.2015. Charkoudian, N. Skin blood flow in adult human thermoregulation: how it works, when it does not, and why. Mayo Clin. Proc. 78 (5, :603-612 (2003). doi: 10.4065/78.5.603. Ikäheimo, T. M. Cardiovascular diseases, cold exposure and exercise. Temperature (Austin). 5 (2), 123–146 (2018). doi:10.1080/23328940.2017.1414014. Martinez-Nicolas, A. et al. Daytime variation in ambient temperature affects skin temperatures and blood pressure: Ambulatory winter/summer comparison in healthy young women. Physiol. Behav. 149 , 203-211 (2015). doi: 10.1016/j.physbeh.2015.06.014. Pallubinsky, H., Schellen, L., Kingma, B. R. M., Dautzenberg, B., van Baak, M. A., van Marken Lichtenbelt, W. D. Thermophysiological adaptations to passive mild heat acclimation Temperature (Austin) . 4, 176–186 (2017). Dehghan, H., Bastami, M. T., Mahaki, B. Evaluating combined effect of noise and heat on blood pressure changes among males in climatic chamber. J. Educ. Health Promot. 6 , 39 (2017). doi: 10.4103/jehp.jehp_107_15. Tai, Y., Obayashi, K., Yamagami, Y., Saeki, K. Association between circadian skin temperature rhythms and actigraphic sleep measures in real-life settings. J. Clin. Sleep Med. 19 (7), 1281-1292 (2023). doi: 10.5664/jcsm.10590. World Health Organization. Physical status: the use and interpretation of anthropometry: report of a World Health Organization (WHO) expert committee. Geneva: World Health Organization; 1995. p. 854. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6413166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":457833702,"identity":"5ffd10f3-3447-4d97-8e79-5c1a14566a20","order_by":0,"name":"Jone Vencloviene","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACxgYYix2IE4CYn4HBgIGhgBgtzFAtkg0gLQYGRNjHDKUNDhDQwtzee/ABY5tNHn8z8zGJBxV35IxvJG+TYDD4g9thPeeSDRjb0oolDrOlSSSceWZsdiOtTAKfLYwzcswkGM4cTtzAzGNskNh2OHHbmTNmhLSY/2A48x+q5d/h+s09hLWYMTBUHABpMXyQ2HA4wYC9h4CWnjPGEgkVyYkzDrMlPkg4dthwxvG2YosEA2OcWgzbeww/fDCwS+xvbz5w8EfNYXlg0G288aFCDreWBgZIDKICTBEEkMcjNwpGwSgYBaMAAgD9C1AKIPbnbAAAAABJRU5ErkJggg==","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Jone","middleName":"","lastName":"Vencloviene","suffix":""},{"id":457833705,"identity":"aefd85b9-8f12-407a-a947-e4e0f10a45ce","order_by":1,"name":"Ricardas Radisauskas","email":"","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ricardas","middleName":"","lastName":"Radisauskas","suffix":""},{"id":457833706,"identity":"a4f85cbb-44da-4562-a310-492b11d13bd7","order_by":2,"name":"Vidmantas Vaiciulis","email":"","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vidmantas","middleName":"","lastName":"Vaiciulis","suffix":""},{"id":457833707,"identity":"c945b8c9-0b2b-417b-bdd7-d844da7ae6a2","order_by":3,"name":"Dalia Luksiene","email":"","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dalia","middleName":"","lastName":"Luksiene","suffix":""},{"id":457833708,"identity":"15bcfece-9f60-4a23-87c2-3f5d83e83206","order_by":4,"name":"Abdonas Tamosiunas","email":"","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abdonas","middleName":"","lastName":"Tamosiunas","suffix":""},{"id":457833711,"identity":"38dfb0aa-b867-4bcc-bfae-fa58f771347b","order_by":5,"name":"Martin Bobak","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Bobak","suffix":""},{"id":457833712,"identity":"1451758b-9921-4577-9e5d-168652d32e12","order_by":6,"name":"Daina Kranciukaite-Butylkiniene","email":"","orcid":"","institution":"Lithuanian University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Daina","middleName":"","lastName":"Kranciukaite-Butylkiniene","suffix":""}],"badges":[],"createdAt":"2025-04-09 15:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6413166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6413166/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23031-w","type":"published","date":"2025-11-11T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83144209,"identity":"3e58d366-be72-4562-a57f-a215f604090a","added_by":"auto","created_at":"2025-05-20 12:49:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":645752,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between DTV and systolic BP (A and C) and diastolic BP (B and D) in May-June for all participants (A and B) and in subgroups (C and D).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6413166/v1/645e0e54c43e4520bb9bba56.png"},{"id":96105321,"identity":"22e78cb3-fb4f-4cbe-96ea-3070c1b5e267","added_by":"auto","created_at":"2025-11-17 16:11:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2571918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6413166/v1/6cd2f055-2216-4e48-a23e-5eb51c5b5fbb.pdf"},{"id":83143974,"identity":"06ae6a37-3eec-43fe-b092-1a9ccd679de0","added_by":"auto","created_at":"2025-05-20 12:41:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19265,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementISCIE.docx","url":"https://assets-eu.researchsquare.com/files/rs-6413166/v1/e0b9754ad1adbb3768bdf878.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence of daily air temperature variability on arterial blood pressure: Findings from a Kaunas cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 2019 Global Burden of Disease study has shown that high systolic blood pressure (BP) is the leading global risk factor for preventable deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most cardiovascular diseases (CVD) can be effectively prevented through managing and preventing arterial hypertension (AH) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], as high BP levels are among the leading risk factors for CVD and mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Short-term increases in BP have been associated with an immediately increasing risk for cardiovascular events [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the rising global air temperature, changes are also observed in the variation of other weather variables, such as wind speed, air temperature fluctuation, intensification and acceleration of the global hydrological cycle, and the intensity of storms (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climate.copernicus.eu/esotc/2021/\u003c/span\u003e\u003cspan address=\"https://climate.copernicus.eu/esotc/2021/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In the last two decades, the influence of weather changes on cardiovascular health has received increasing attention. The changes in atmospheric pressure, ambient temperature, and relative humidity increase the risk of stroke 5\u0026ndash;7]. Air temperature variability (TV) has been associated with an increased risk of mortality/morbidity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the parameters reflecting air temperature fluctuation is the diurnal temperature range (DTR) \u0026ndash; the difference between the maximum and the minimum temperature over 24 hours. In Europe, an increasing trend in DTR has been observed since 1995 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and a positive association between DTR and the atmospheric levels of CO\u003csub\u003e2\u003c/sub\u003e and aerosols was observed in the non-winter period [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Apart from DTR, the other most used measures of intraday TV are the standard deviation (SD) of daily minimum and maximum temperatures (DTV) during the exposure days [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and the SD of hourly temperature variability (HTV) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies reported statistically significant associations of BP with TV in areas with continental climate. Still, the obtained results may be due to different sample sizes, the periods of the lags, and the participants' characteristics. Studies in China showed a positive association of systolic BP with TV variables at lags of 0, 0\u0026ndash;1, \u0026hellip;, 0\u0026ndash;7 days, a positive association of diastolic BP with TV variables at lags of 0, 0\u0026ndash;1, 0\u0026ndash;2, and 0\u0026ndash;3 days, and negative or non-statistically significant association with TV variables at lags of 5, 0\u0026ndash;5, or 0\u0026ndash;6 days [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similar tendencies were observed with DTR at lags of 0\u0026ndash;5 days in Seoul [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], but the lack of statistical significance may be due to the smaller sample size and older age of the study participants. Besides, the participants with elevated BP or detected AH were more sensitive to the effect of DTV and HTV [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to the results of these authors, no statistically positive association of systolic BP with TV variables except for DTR at a lag of 0 days for normotensive participants was found [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. One longitudinal study analysed associations of BP with DTR in Europe [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study found a negative association between DTR and BP at different lags. Therefore, the question remains open about the effects of TV on BP at different lags and their dependence on cardiovascular health status in other areas of Europe. The obtained results may help to identify the physiological mechanisms of the effects of changing weather on the human cardiovascular system.\u003c/p\u003e \u003cp\u003eThe aim of this study was to detect the association between BP and some variables of TV in Kaunas city, Lithuania. We also assessed the effect modification of air temperature on the association between TV variables and BP and performed the analyses in subgroups. This study was obtained data from the international HAPIEE (Health, Alcohol and Psychosocial Factors in Eastern Europe) study with a high rate of AH.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe descriptive characteristics of the health variables are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the respondents, 3,216 (45.5%) were men, 2,185 (30.9%) were aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years, 5,654 (79.5%) were overweight or obese, 68.4% had AH, and 39.4% of the respondents had taken drugs for high BP during the last 2 weeks. Most of the surveys were performed in spring (33.3%) and at least in summer (10.8%) \u0026ndash; only in June (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A more detailed description of the participants of the survey was presented in the previous work [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the days of the surveys, the mean daily DTR was 7.64\u0026deg;C. The SD of T calculated on the first half of the day was 2.21\u0026deg;C. Higher mean values of TV variables were observed in the period from May to June (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and lower values \u0026ndash; from November to January. The characteristics of the environmental variables during the study period (2006\u0026ndash;2008, except for July-August) were similar.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe total number of respondents, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,213 (45.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at entry, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65 years, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,185 (30.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,654 (79.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysically active, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,274 (74.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormotension (BP\u0026thinsp;\u0026lt;\u0026thinsp;120/80 mmHg and not being on AHM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e696 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrehypertension (BP 120\u0026ndash;139/80\u0026ndash;89 mmHg and not being on AHM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,541 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,840 (68.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive medication, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,781 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemic heart disease, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,359 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth of the survey January, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e676 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e667 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e773 (10.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e919 (12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e765 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e751 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e737 (10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e534 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eBP \u0026ndash; blood pressure; BMI \u0026ndash; body mass index; AHM - antihypertensive medication\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to our database, TSD0, TSDP, and DTR0 were highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.9). All these variables negatively correlated with T in winter (r ~ -0.3 and with TV r=-0.46), positively correlated with T during the non-winter period (r\u0026thinsp;~\u0026thinsp;0.5), negatively correlated with RH (r ~ -0.43 and r ~ -0.77 respectively, during both winter and non-winter periods) and WS during the non-winter period (r ~ -0.3), and positively correlated with AP in spring-summer. Apart from this, the used TV variables positively correlated with the daily PM\u003csub\u003e10\u003c/sub\u003e concentration (r\u0026thinsp;~\u0026thinsp;0.2 in autumn-winter and r\u0026thinsp;~\u0026thinsp;0.45 in spring-summer). All the above-mentioned correlations were statistically significant.\u003c/p\u003e \u003cp\u003eThe results of the multivariate model show an increase in systolic BP on the days of a higher TSD and TSDF than the median and on days of DTR0, DTR02, DTV01, and DTV2 exceeding the first quartile. A stronger effect of TV on diastolic BP was observed on the days of the survey (with TSD and TSDF), and the associations of DBP with DTV01 and DTR02 may be non-linear (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For all the periods of the study, no statistically significant associations were found with TV variables including data of lags exceeding 2 days. Apart from this, interactions between TV variables and air temperature were found. The effect of TSD0 and DTR0 on the day of the test was lower with increasing daily T for all the participants and for some subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This effect was similar for TSDF, DTV01, and DTV02, and the data of 1\u0026ndash;2 previous days were used for cumulative DTR variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe associations between blood pressure and daily temperature variability: results of the multivariate model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eII quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIII quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIV quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePer increase in IQR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSystolic BP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSD0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (-0.26, 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.74 (0.08, 3.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.05 (1.01, 5.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.36 (0.14, 2.58)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (-1.42, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.77 (0.14, 3.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (-0.19, 3.57)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 (-0.23, 2.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (-0.95, 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.29 (1.50, 5.07)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.77 (1.60, 5.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 (-0.01, 2.39)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.31 (0.89, 3.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.17 (1.36, 4.98)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.56 (1.50, 5.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.29 (0.02, 2.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (-0.71, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.89 (1.10, 4.69)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.77 (0.78, 4.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (-0.28, 2.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (-0.19, 2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88 (-0.06, 3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.12 (0.02, 4.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.43 (0.08, 2.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.62 (0.16, 3.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.48 (0.63, 4.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.66 (0.68, 4.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.34 (0.05, 2.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTV01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.77 (0.27, 3.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.39 (0.42, 4.36)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.40 (0.21, 4.60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.63 (0.21, 3.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTV02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.53 (0.05, 3.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.58 (0.68, 4.48)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.22 (0.04, 4.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29 (-0.14, 2.71)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic BP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSD0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (-0.11, 1.59)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.07 (0.12, 2.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.67 (0.49, 2.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.87 (0.17, 1.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14 (-0.98, 0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (-0.74, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17 (-1.26, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23 (-0.88, 0.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (-0.58, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.75 (0.72, 2.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.99 (0.74, 3.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.79 (0.09, 1.48)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.37 (0.55, 2.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.34 (0.30, 2.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.73 (0.54, 2.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66 (-0.08, 1.39)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (-0.43, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.21 (0.17, 2.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.88 (-0.27, 2.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03 (-0.71, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (-0.34, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (-0.49, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (-0.55, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (-0.38, 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTR02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.88 (0.03, 1.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (-0.12, 2.02)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (-0.13, 2.16)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (-0.42, 1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTV01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.89 (0.02, 1.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.24 (0.11, 2.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (-0.44, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (-0.42, 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTV02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (-0.14, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (-0.21, 1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40 (-0.86, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15 (-0.67, 0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReference category - first quartile; BP - blood pressure; * - p\u0026thinsp;\u0026lt;\u0026thinsp;0.1;\u003c/p\u003e \u003cp\u003eIn the model with interaction (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the impact of TV variables on systolic BP and diastolic BP was stronger on the days of a lower T for all participants, males, and physically active subjects. For non-hypertensive participants, the interaction term of T and TSD0 or DTR0 was non-significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the regression coefficients, TSD0 (DTR0) was not associated with higher BP if T was over 16\u0026deg;C (14\u0026deg;C) or for participants who took medications for high BP; this cut-off may be about 9\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of the interaction of TSD and DTR with air temperature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTSD0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTSD0*T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDTR0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eDTR0*T\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eSystolic BP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.56 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.27 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.30 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.36 (1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.45 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.15 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.33 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.32 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.79 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.52 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.73 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.37 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.18 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.11 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.32 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.28 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.27 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.27 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.09 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.29 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.23 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.24 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic BP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.12 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.56 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.10 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.12 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.09 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.20 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.88 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.10 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.14 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.05 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.14 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBP - blood pressure; AH - arterial hypertension; BMI - body mass index; PA - physical activity\u003c/p\u003e \u003cp\u003eA stronger positive association of TV variables with systolic and diastolic BP detected during the cooler period (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) confirms the results obtained by the multivariate model with an interaction term. In that model, we did not find any statistically significant association of systolic BP with TV variables for females and physically inactive participants. In other subgroups, systolic BP was statistically significantly affected by DTR0 (except for those aged\u0026thinsp;\u0026le;\u0026thinsp;65 years) and DTV01 (except for those aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years), DTR0, DTR01, and DTV01 having a stronger impact on hypertensive participants. Based on beta values, DTV01 had a stronger effect on systolic BP (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In September-April, diastolic BP was mostly statistically positively associated with TV on the day of the survey (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The effect of TV variables calculated by using the minimal and maximal temperatures on BP was stronger in winter, and the effects of TSD and TSDF on systolic BP were stronger during the transition season (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between temperature variability variables and systolic blood pressure during the whole period and in September-April in subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSD0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTR0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDTR01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDTV01\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhole period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.36 (0.14, 2.58)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (-0.01, 2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.29 (0.02, 2.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.43 (0.08, 2.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.63 (0.21, 3.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.27 (0.46, 4.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (-0.29, 3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.61 (0.73, 4.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.59 (1.56, 5.62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.39 (1.27, 5.51)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (-1.18, 2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (-0.75, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (-1.77, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.64 (-2.43, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.23 (-2.14, 1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (-0.01, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.08 (0.04, 2.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (-0.02, 2.22)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (-0.04, 2.33)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.53 (0.28, 2.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (-0.92, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42 (-1.01, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (-0.79, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (-0.21, 2.98)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.48 (-0.20, 3.16)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (-0.46, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (-0.39, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (-0.79, 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (-0.43, 2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.16 (-0.55, 2.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (-0.96, 3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (-1.61, 2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (-1.01, 3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54 (-2.09, 3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.05 (-1.63, 3.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03 (-0.44, 4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.08 (0.63, 5.53)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24 (-0.38, 4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43 (-1.44, 4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.39 (-0.60, 5.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (-0.26, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (-0.65, 2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (-0.41, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44 (-0.08, 2.97)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.47 (-0.15, 3.08)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.42 (0.28, 2.55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.03 (0.40, 3.66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.70 (0.26, 3.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.75 (0.21, 3.30)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.95 (0.33, 3.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19 (-2.32, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (-2.62, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 (-2.55, 2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58 (-2.17, 3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.71 (-2.23, 3.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeptember-April\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.61 (0.42, 2.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.38 (0.27, 2.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.79 (0.59, 2.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.66 (0.25, 3.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.02 (0.61, 3.44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.85 (1.06, 4.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.36 (0.68, 4.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.33 (1.54, 5.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.60 (1.47, 5.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.73 (1.61, 5.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 (-1.00, 2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (-0.91, 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (-1.20, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (-1.86, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.43 (-1.46, 2.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.07 (0.40, 2.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.15 (0.19, 2.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.25 (0.20, 2.31)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.34 (0.11, 2.56)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.72 (0.48, 2.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (-0.43, 2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (-0.32, 2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.59 (0.18, 3.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.60 (-0.05, 3.25)*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.80 (0.13, 3.47)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (-0.21, 2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (-0.25, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (-0.30, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50 (-0.14, 3.14)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.76 (0.09, 3.43)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (-0.70, 3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (-0.96, 3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.37 (0.04, 4.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (-1.93, 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.33 (-1.46, 4.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 (-0.23, 4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.79 (0.50, 5.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.85 (0.36, 5.35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70 (-0.31, 5.70)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.60 (0.63, 6.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.43 (0.07, 2.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (-0.23, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.53 (0.17, 2.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45 (-0.13, 3.02)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.68 (0.07, 3.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.63 (0.30, 2.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.45 (0.19, 2.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.80 (0.45, 3.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (-0.02, 3.18)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.96 (0.35, 3.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 (-0.88, 4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (-1.01, 3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93 (-0.64, 4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89 (-0.98, 4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.15 (-0.78, 5.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (-0.61, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (-1.22, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.34 (0.15, 2.52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.46 (0.06, 2.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.35 (0.12, 2.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eβ per increase in IQR (for transitional season, IQR of TSD\u0026thinsp;=\u0026thinsp;2.21 and IQR of TSDF\u0026thinsp;=\u0026thinsp;2.68; for winter, IQR of DTR\u0026thinsp;=\u0026thinsp;2.60); BP - blood pressure; AH - arterial hypertension; BMI - body mass index; PA - physical activity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between temperature variability variables and diastolic blood pressure during the whole period and in September-April in subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSD0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDTR0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDTR01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDTV01\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhole period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.87 (0.17, 1.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.79 (0.09, 1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (-0.08, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (-0.38, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40 (-0.42, 1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.21 (0.13, 2.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 (-0.46, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.17 (0.05, 2.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.42 (0.22, 2.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22 (-0.04, 2.48)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (-0.43, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (-0.01, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10 (-0.87, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.55 (-1.57, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.47 (-1.54, 0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (-0.12, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (-0.01, 1.33)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (-0.12, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 (-0.33, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (-0.29, 1.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36 (-0.44, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (-0.54, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (-0.61, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (-0.67, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15 (-0.78, 1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (-0.07, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (-0.01, 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45 (-0.46, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48 (-0.46, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (-0.61, 1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (-0.46, 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (-0.84, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (-0.61, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.49 (-1.92, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33 (-1.79, 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.62 (0.18, 3.07)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.21 (0.78, 3.65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.00 (0.47, 3.54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.94, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (-0.71, 2.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (-0.14, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (-0.35, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (-0.50,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (-0.54, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25 (-0.68, 1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.08 (0.29, 1.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.08 (0.29, 1.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.89 (0.06, 1.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 (-0.12, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76 (-0.18, 1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (-1.18, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-1.47, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (-1.54, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.61 (-2.18, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.64 (-2.31, 1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeptember-April\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.07 (0.39, 1.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.95 (0.31, 1.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.01 (0.32, 1.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (-0.02, 1.60)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.95 (0.14, 1.77)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.22 (0.16, 2.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (-0.07, 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.38 (0.31, 2.45)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.47 (0.21, 2.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.50 (0.23, 2.76)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.94 (0.05, 1.82)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.93 (0.11, 1.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (-0.25, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22 (-0.82. 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43 (-0.63, 1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (-0.08, 1.23)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.66 (0.05, 1.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.71 (0.04, 1.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69 (-0.09, 1.47)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (-0.03, 1.54)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (-0.02, 1.55)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.76 (0.02, 1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.88 (0.09, 1.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73 (-0.20, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84 (-0.09, 1.78)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.96 (0.12, 1.80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (-0.01, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (-0.10, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82 (-0.15, 1.79)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94 (-0.06, 1.93)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (-0.21, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (-0.17, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.30 (0.04, 2.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28 (-1.23, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (-1.00, 2.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.61 (0.20, 3.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.90 (0.53, 3.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.16 (0.67, 3.64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59 (-0.20, 3.38)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.94 (0.17, 3.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.93 (0.15, 1.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.74 (0.01, 1.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 (-0.02, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64 (-0.27, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (-0.17, 1.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.18 (0.40, 1.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.11 (0.38, 1.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.15 (0.37, 0.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.07 (0.14, 2.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.22 (0.29, 2.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (-0.60, 2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58 (-0.75, 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (-0.75, 2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03 (-1.60, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17 (-1.49, 1.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (-0.17, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (-0.50, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.74 (0.06, 1.43)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.85 (0.04, 1.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.77 (0.05, 1.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eβ per increase in IQR (for transitional season, IQR of TSD\u0026thinsp;=\u0026thinsp;2.21 and IQR of TSDF\u0026thinsp;=\u0026thinsp;2.68; for winter, IQR of DTR\u0026thinsp;=\u0026thinsp;2.60); BP - blood pressure; AH - arterial hypertension; BMI -body mass index; PA - physical activity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring the warmer period (May-June), systolic BP was negatively associated with DTV03 and DTV04, and diastolic BP was negatively associated with DTR at lags 1, 2, and 3, with cumulative DTR at lags 0\u0026ndash;3, 0\u0026ndash;4, and 0\u0026ndash;5, and with DTV variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, A and B). In case of participant subgroups, a stronger effect of DTV on BP was found in hypertensive and physically inactive participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, C and D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study on the effects of TV on human physiological parameters using a large database in Lithuania and Eastern Europe. In our study, associations between air TV and arterial BP were detected. We found a positive association of systolic and diastolic BP with TV on the day of the survey, a stronger effect being found for males and physically active participants. Also, the dependence of the impact of TV on daily temperature level was observed: the effect of temperature variability was lower with an increase in daily T. A stronger positive association of systolic BP with TV was found during the cooler period, especially for males who were non-hypertensive and were physically active. In the warmer months (May-June), a negative association of TV with diastolic BP was observed, and a stronger effect was found in hypertensive and physically inactive participants. A difference in temperature variability during different seasons and in different subgroups was observed.\u003c/p\u003e \u003cp\u003eThe study conducted in Augsburg [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] showed that an increasing DTR was linked to decreasing systolic BP and diastolic BP, which is in contrast to our studies. These discrepancies may be explained by differences in the mean BP level, study design, and local weather conditions. The participants in the Augsburg study had lower BP (123.3/76.4) (most of the participants were normotensive), while in our study, the mean BP was 141.6/90.4, and 68.4% of participants were hypertensive. Other studies found a negative or non-statistical association between diastolic BP and TV and a positive or non-statistical association between systolic BP and TV [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Also, we found a negative association of diastolic BP with DTV and cumulative DTR and a weak negative effect of DTV on systolic BP in May-June. We did not have BP measurements during July-August \u0026ndash; the warmest months. The use of data from this period would likely enable us to state a negative association of diastolic BP with TV during non-winter or all study periods. Apart from this, in Augsburg, winters are warmer \u0026ndash; the mean monthly temperature is negative (-0.1\u0026deg;C during 1990\u0026ndash;2020) only in January.\u003c/p\u003e \u003cp\u003eShort-term changes in environmental temperature affect skin temperatures and cause changes in the thermoregulatory and BP regulatory system [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results of controlled studies show an increase in BP during acute cold exposure [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and a decrease in BP during exposure to heat [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, the physical mechanisms of the impact of higher TV on previous 0\u0026ndash;5 days on BP changes may be explained by the additional portion of the daily exposure to cold or heat depending on the season. In winter, DTR and other TV variables negatively correlated with T, and at the same average temperature, a higher DTR adds more exposure to more negative temperatures as compared to lower DTR. This same situation is in most days of the two first months of spring and the two last months of autumn when maximal T does not exceed the comfortable temperature. During warmer periods, especially in summer, DTR is positively associated with T, and it is possible that higher exposure to heat occurred on days with a higher DTR at the same average temperature. This can explain the stronger positive association between TV and BP during the cooler period and a negative association of DTV with diastolic BP during May-June. Besides, higher intraday variability was significantly associated with lower sleep efficiency, longer wakefulness after sleep onset, and a shorter total sleep time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and this can affect BP.\u003c/p\u003e \u003cp\u003eAccording to our results, a stronger impact of TV during the cooler period was found in physically active participants. It is possible that these participants were more exposed to the outdoor environment due to their physical activity. We also found a stronger impact of TV during the cooler period in males, although other studies found a stronger effect of TV in females. It is possible that males were more exposed to outdoor environments. However, during the warmer months, a stronger negative association of DTV with BP was found in more susceptible groups \u0026ndash; hypertensive and physically inactive participants. We did not detect any stronger effect of TV on susceptible groups during the cooler periods, maybe due to a better possibility of protecting oneself from the effects of cold than from heat.\u003c/p\u003e"},{"header":"Limitation","content":"\u003cp\u003eAs one of limitations, it is possible that some of the cases identified and recorded as high BP may in fact have represented a false momentary case due to stress or psychological factors. However, given the extensive data collection protocol, this number is likely to be so small that it does not influence our results. The lack of air pollutants such as NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e in the models also can be considered a limitation of the study. In our study, the daily NO\u003csub\u003e2\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations were strongly correlated, and air pollution of SO\u003csub\u003e2\u003c/sub\u003e in Kaunas is very low, thus their role in mediating the effects of meteorological factors was likely small in this study. A limitation of our study may be a lack of data during the warmest months, July and August.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe found a positive association of systolic and diastolic BP with TV on the day of the survey. Also, a dependence of the impact of TV variables on daily temperature was observed: the effect of TV decreased with an increase in daily air temperature. A stronger positive association of systolic BP with TV was found during the cooler period, while in the warmer months (May-June), a negative association of TV with diastolic BP was observed. Sex, the level of physical activity, the presence of AH, and air temperature may modify the relationship between TV and BP.\u003c/p\u003e \u003cp\u003eUnder current climate change trends, the impact of air temperature and its variability on human health is becoming increasingly important. Our results add to knowledge in this field in Europe. The findings of the study could be used by public health specialists and personal healthcare providers to help patients prepare for periods of high TV or before significant changes in temperature. To adjust the response of the public health sector for local climatic conditions, it is important to carry out similar studies in regions with different TV. In case of significant changes in diurnal temperature, it is essential to ensure suitable BP control measures so that excessive BP elevation could be avoided.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy sample and health variables\u003c/h2\u003e \u003cp\u003eWe used data from the survey performed within the framework of the international HAPIEE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study. The study was conducted in accordance with the Declaration of Helsinki and approved by the Kaunas Regional Biomedical Research Ethics Committee, Lithuania (reference number: 05/09 on 11 January 2005). All participants signed the form of informed consent. We used data of 7,077 residents of Kaunas city (Lithuania) aged 45\u0026ndash;72 years from 2006\u0026ndash;2008, excluding July-August. Health factors were evaluated using a standard questionnaire and some objective measurements.\u003c/p\u003e \u003cp\u003eBP was measured twice using a mercury sphygmomanometer and an appropriately sized cuff on the right arm. The initial measurement was taken after five minutes of rest on the right arm. The second measurement was taken two minutes later. Korotkoff phase 1 (the onset of sound) and Korotkoff phase 5 (the disappearance of sound) were recorded as systolic and diastolic BP. The average of the two readings was used for further analysis. AH was defined as a mean systolic BP of at least 140 mm Hg or a mean diastolic BP of at least 90 mm Hg, or both, and/or the use of antihypertensive medication (AHM) in the previous two weeks.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) was calculated as the weight in kilograms divided by the height in meters squared (kg/m\u003csup\u003e2\u003c/sup\u003e). We divided the study participants into three different groups: those with normal weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24.99 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (BMI 25.0\u0026ndash;29.99 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (BMI ⩾30.0 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysical activity (PA) was determined by the mean length of time spent per week during leisure time in the autumn-winter and spring-summer seasons for such activities as gardening, maintenance of the house, and other physical activities. The respondents were ranked from the lowest to the highest values and divided into three equal groups (tertiles) according to their PA during leisure activities. The first tertile cut-off (max) was 10 hours. For this reason, we used this cut-off to identify insufficient PA. Other information about the patients\u0026rsquo; baseline examination was presented in our previous study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental variables\u003c/h2\u003e \u003cp\u003eWe obtained hourly data of air temperature (T, \u0026deg;C), relative humidity (RH, %), and the concentration of particulate matter of 10 micrometres or less in diameter (PM\u003csub\u003e10\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) from the National Environmental Department and Municipal Ecological Monitoring Stations. The Municipal Ecological Monitoring Station is located within the residential part of the study region. The daily data of atmospheric pressure at sea level (AP, hPA) and wind speed (WS, knots) were obtained from Kaunas meteorological station (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geodata.us/weather/\u003c/span\u003e\u003cspan address=\"http://www.geodata.us/weather/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The variables of air temperature variability used in our study were the standard deviation (SD) of hourly temperature during the 24 hours (TSD), the SD of hourly temperature during the first part of the day (TSDF), the DTR, and the SD of daily minimum and maximum temperatures on the day of the test and two previous days (DTV). We analysed the impact of TV variables on the day of the test (lag 0) and on the previous 5 days (lag 1\u0026ndash;5 days).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCorrelations were assessed by using the Spearman correlation coefficient. The associations between the daily TV and systolic BP and diastolic BP were evaluated by applying the multiple linear regression, adjusting for the participant's age, sex, BMI, the use of antihypertensive medications (yes/no), the presence of ischaemic heart disease (IHD) (yes, no), smoking, physical activity, alcohol consumption, and education level. In the model air temperature, RH, both low and high AP, the daily PM\u003csub\u003e10\u003c/sub\u003e concentration, and the year and month were additionally included as categorical predictors. The air TV variables that we used were values of DTR at single-day lags (DTR0, \u0026hellip;, DTR5) and cumulative day lags \u0026ndash; for example, DTR02 marks DTR during the day of the test and 2 days before the test. By analogy, we used the SDs of daily minimum and maximum temperatures during days from the test day to day 1, 2, \u0026hellip;, 5 before the test. For example, DTV02 was calculated as the SD of daily minimum and maximum temperatures on the day of the test and on two previous days. Also, we used TSD on the day of the survey and on the previous day (named TSD0, and TSD1, respectively) and TSDF. These variables were included in the model as continuous or categorised in quartiles. We also assessed the effect modification of air temperature on the association between TV variables and arterial BP by including the interaction term between T and TV variables.\u003c/p\u003e \u003cp\u003eThe analyses were performed for all participants and separately for males and females, for older (\u0026gt;\u0026thinsp;65 years) and younger, for those with BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e and BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, for hypertensive (systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, and/or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, and/or use of antihypertensive medication (AHM) within 2 weeks) and not, and for physically active and inactive participants.\u003c/p\u003e \u003cp\u003eWe presented the adjusted beta coefficients per increase in IQR or in \u0026deg;C with their 95% confidence interval (CI) or p-value. Statistical analysis was performed using SPSS 20 software (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp.).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding has been provided by any private or public sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Lithuanian Hydrometeorological Service and the Environment Protection Agency for meteorological and air pollution data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJone Vencloviene: Writing \u0026ndash; original draft, Methodology, Visualisation, Statistical analysis, Conceptualisation. Ricardas Radisauskas: Writing \u0026ndash; original draft, Methodology, Investigation. Vidmantas Vaiciulis: Writing - review \u0026amp; editing, Methodology, Visulisation, Environmental data curation. Dalia Luksiene: Writing - review \u0026amp; editing, Methodology, Investigation. Abdonas Tamosiunas: Writing - review \u0026amp; editing, Methodology, Investigation, Medical Data curation, Conceptualisation. Martin Bobak: Writing - review \u0026amp; editing, Methodology, Investigation. Daina Kranciukaite-Butylkiniene: Writing \u0026ndash; review \u0026amp; editing, Methodology, Investigation, Medical data curation. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDisclosure statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The half-hourly measurements of air temperature, atmospheric pressure, wind speed, and relative humidity used in the research were collected from the https://www.wunderground.com/history/daily/lt/karm%C4%97lava/EYKA/date/2008-9-9 .\u003c/p\u003e\n\u003cp\u003eThe daily concentration of particulate matter of 10 micrometres or less in diameter (PM\u003csub\u003e10\u003c/sub\u003e, \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e) and the daily maximal 8-hour ozone concentration were downloaded from the freely available webpage of the Environmental Protection Agency (https://aaa.lrv.lt/lt/veiklos-sritys/oras/oro-kokybes-statistika-ir-duomenys/ ).\u003c/p\u003e\n\u003cp\u003eThe medical data cannot be made publicly available upon publication because they contain sensitive personal information. The data that support the findings of this study are available upon reasonable request from the authors. For medical data, please contact with A. Tamosiunas ([email protected]) or D. Kranciukaite-Butylkiniene ([email protected])\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) reported there is no funding associated with the work featured in this article.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNCD Risk Factor Collaboration (NCD-RisC. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. \u003cem\u003eLancet\u003c/em\u003e. \u003cstrong\u003e398\u003c/strong\u003e(10304), 957-980 (2021). DOI:10.1016/S0140-6736(21)01330-1.\u003c/li\u003e\n\u003cli\u003eWang. M. C., Lloyd-Jones, D. M. Cardiovascular Risk Assessment in Hypertensive Patients. \u003cem\u003eAm. J. Hypertens.\u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e(6), 569-577 (2021). DOI:10.1093/ajh/hpab021.\u003c/li\u003e\n\u003cli\u003eBrunstr\u0026ouml;m, M., Carlberg, B. Association of Blood Pressure Lowering with Mortality and Cardiovascular Disease Across Blood Pressure Levels: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e\u003cstrong\u003e178\u003c/strong\u003e(1), 28-36 (2018). doi: 10.1001/jamainternmed.2017.6015. \u003c/li\u003e\n\u003cli\u003eFuchs, F.D., Whelton, P. K. High Blood Pressure and Cardiovascular Disease. \u003cem\u003eHypertension\u003c/em\u003e\u003cstrong\u003e75\u003c/strong\u003e(2), 285-292 (2020). \u003c/li\u003e\n\u003cli\u003eJimez-Conde, J. et al. Weather as a Trigger of Stroke. Daily meteorological factors and incidence of stroke subtypes. \u003cem\u003eCerebrovasc. Dis\u003c/em\u003e. \u003cstrong\u003e26\u003c/strong\u003e(4):348\u0026ndash;354 (2008). doi:10.1159/ 000151637.\u003c/li\u003e\n\u003cli\u003eVan Donkelaar, C. E. et al. Atmospheric pressure variation is a delayed trigger for aneurysmal subarachnoid hemorrhage. \u003cem\u003eWorld Neurosurg\u003c/em\u003e. \u003cstrong\u003e112\u003c/strong\u003e, e783\u0026ndash;90 (2018). https://doi.org/10.1016/j.wneu.2018.01.155.\u003c/li\u003e\n\u003cli\u003eRakers, F. et al. Rapid weather changes are associated with increased ischemic stroke risk: a case-crossover study. \u003cem\u003eEur. J. Epidemiol\u003c/em\u003e. \u003cstrong\u003e31\u003c/strong\u003e(2), 137\u0026ndash;146 (2016). doi: 10.1007/s10654-015-0060-3.\u003c/li\u003e\n\u003cli\u003eGuo, Y. M. et al. Temperature variability and mortality: a multi-country study. \u003cem\u003eEnviron. Health Perspect.\u003c/em\u003e\u003cstrong\u003e124\u003c/strong\u003e, 1554\u0026ndash;1559 (2016).\u003c/li\u003e\n\u003cli\u003eDanesh Yazdi, M. et al. The effect of long-term exposure to air pollution and seasonal temperature on hospital admissions with cardiovascular and respiratory disease in the United States: A difference-in-differences analysis. \u003cem\u003eSci. Total Environ\u003c/em\u003e. \u003cstrong\u003e843\u003c/strong\u003e, 156855 (2022). doi: 10.1016/j.scitotenv.2022.156855. \u003c/li\u003e\n\u003cli\u003eHuang, X., Dunn, R. J. H., Li, L. Z. X., McVicar, T. R., Azorin-Molina, C., Zeng, Z. Increasing global terrestrial diurnal temperature range for 1980\u0026ndash;2021. Geophys. Res. Let. 50:e2023GL103503 (2023). https://doi.org/10.1029/2023GL103503\u003c/li\u003e\n\u003cli\u003eStjern, C. W. et al. How aerosols and greenhouse gases influence the diurnal temperature range. \u003cem\u003eAtmos. Chem. Phys.\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 13467\u0026ndash;13480 (2020). https://doi.org/10.5194/acp-20-13467-2020.\u003c/li\u003e\n\u003cli\u003eZhu, W. et al. Ambient temperature variability and blood pressure in a prospective cohort of 50,000 Chinese adults. \u003cem\u003eJ. Hum. Hypertens\u003c/em\u003e. \u003cstrong\u003e37\u003c/strong\u003e(9), 818-827 (2022).\u003c/li\u003e\n\u003cli\u003eZheng, S. et al. The effect of diurnal temperature range on blood pressure among 46609 people in Northwestern China. \u003cem\u003eSci. Total Environ\u003c/em\u003e. \u003cstrong\u003e730\u003c/strong\u003e, 138987 (2020).\u003c/li\u003e\n\u003cli\u003eLim, Y. H., Kim, H., Kim, J. H., Bae, S., Hong, Y. C. Effect of diurnal temperature range on cardiovascular markers in the elderly in Seoul, Korea. Int. J. Biometeorol. \u003cstrong\u003e57\u003c/strong\u003e(4), 597-603 (2013). doi: 10.1007/s00484-012-0587-x.\u003c/li\u003e\n\u003cli\u003eYan, X. et al. Association between short-term daily temperature variability and blood pressure in the Chinese population: From the China hypertension survey. \u003cem\u003eEnviron. Int.\u003c/em\u003e 184, 108463 (2024). doi: 10.1016/j.envint.2024.108463.\u003c/li\u003e\n\u003cli\u003eWoeckel, M. et al. Ambient air temperature and temperature variability affecting blood pressure - a repeated-measures study in Augsburg, Germany. \u003cem\u003eEnviron. Res. Health\u003c/em\u003e. \u003cstrong\u003e1\u003c/strong\u003e, 035001 (2023). DOI 10.1088/2752-5309/acdf10\u003c/li\u003e\n\u003cli\u003eVencloviene, J. et al. The influence of the North Atlantic Oscillation index on arterial blood pressure. J. Hypertens. \u003cstrong\u003e37\u003c/strong\u003e(3), 513-521 (2019). doi: 10.1097/HJH.0000000000001929.\u003c/li\u003e\n\u003cli\u003eLiu, C. Yavar, Z., Sun, Q. Cardiovascular response to thermoregulatory challenges. \u003cem\u003eAm. J. Physiol. Heart Circ. Physiol\u003c/em\u003e. \u003cstrong\u003e309\u003c/strong\u003e(11), H1793-H1812 (2015). doi:10.1152/ajpheart.00199.2015.\u003c/li\u003e\n\u003cli\u003eCharkoudian, N. Skin blood flow in adult human thermoregulation: how it works, when it does not, and why. \u003cem\u003eMayo Clin. Proc.\u003c/em\u003e\u003cstrong\u003e78\u003c/strong\u003e(5, :603-612 (2003). doi: 10.4065/78.5.603.\u003c/li\u003e\n\u003cli\u003eIk\u0026auml;heimo, T. M. Cardiovascular diseases, cold exposure and exercise. \u003cem\u003eTemperature (Austin).\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e(2), 123\u0026ndash;146 (2018). doi:10.1080/23328940.2017.1414014.\u003c/li\u003e\n\u003cli\u003eMartinez-Nicolas, A. et al. Daytime variation in ambient temperature affects skin temperatures and blood pressure: Ambulatory winter/summer comparison in healthy young women. \u003cem\u003ePhysiol. Behav.\u003c/em\u003e\u003cstrong\u003e149\u003c/strong\u003e, 203-211 (2015). doi: 10.1016/j.physbeh.2015.06.014. \u003c/li\u003e\n\u003cli\u003ePallubinsky, H., Schellen, L., Kingma, B. R. M., Dautzenberg, B., van Baak, M. A., van Marken Lichtenbelt, W. D. Thermophysiological adaptations to passive mild heat acclimation \u003cem\u003eTemperature (Austin)\u003c/em\u003e. 4, 176\u0026ndash;186 (2017).\u003c/li\u003e\n\u003cli\u003eDehghan, H., Bastami, M. T., Mahaki, B. Evaluating combined effect of noise and heat on blood pressure changes among males in climatic chamber. \u003cem\u003eJ. Educ. Health Promot.\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 39 (2017). doi: 10.4103/jehp.jehp_107_15.\u003c/li\u003e\n\u003cli\u003eTai, Y., Obayashi, K., Yamagami, Y., Saeki, K. Association between circadian skin temperature rhythms and actigraphic sleep measures in real-life settings. \u003cem\u003eJ. Clin. Sleep Med.\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e(7), 1281-1292 (2023). doi: 10.5664/jcsm.10590.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Physical status: the use and interpretation of anthropometry: report of a World Health Organization (WHO) expert committee. Geneva: World Health Organization; 1995. p. 854.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"air temperature variability, diurnal temperature range, arterial blood pressure, arterial hypertension","lastPublishedDoi":"10.21203/rs.3.rs-6413166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6413166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSeveral studies reported statistically significant associations of blood pressure (BP) with short-term air temperature variability (TV), but the effect of TV on BP was found to differ in different areas. This study aimed to detect the association between BP and TV in Kaunas, Lithuania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData of the international HAPIEE (Health, Alcohol, and Psychosocial Factors in Eastern Europe) study was used to gather information on the participants' BP during 2006\u0026ndash;2008. The TV variables were the diurnal temperature range and the standard deviation (SD) of hourly temperature during the 24 hours (TSD) and during the first 12 hours of the day (TSDF) as well as the SD of daily minimum and maximum temperatures during the exposure days (DTV). A multiple linear regression was used after controlling for potential confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the participants, 45.5% were men, 30.9% were aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years, and 9.8% were normotensive. A positive association of systolic BP with all TV variables and of diastolic BP with TSD and TSDF was found, a stronger impact being observed in males and physically active participants. The impact of TV was stronger during lower temperatures, and a statistically significant negative interaction term between air temperature and TV variables was found. In May-June, a negative association of DTV with diastolic BP was observed, a stronger effect being found in hypertensive and physically active participants.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe found a positive association of BP with TV. Sex, the level of physical activity, and air temperature may modify the relationship between TV and BP.\u003c/p\u003e","manuscriptTitle":"The influence of daily air temperature variability on arterial blood pressure: Findings from a Kaunas cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 12:41:34","doi":"10.21203/rs.3.rs-6413166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-03T05:34:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T14:37:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190913601020705603021682279443512117667","date":"2025-06-25T09:46:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T06:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326721864787813279794037632930691130064","date":"2025-06-23T11:00:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-15T11:30:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T11:28:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-11T10:57:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T10:18:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-09T14:59:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c05c720b-28bc-43f2-ac71-f48725e8ba2d","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48658773,"name":"Earth and environmental sciences/Environmental sciences"},{"id":48658774,"name":"Health sciences/Cardiology"},{"id":48658775,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-17T16:08:01+00:00","versionOfRecord":{"articleIdentity":"rs-6413166","link":"https://doi.org/10.1038/s41598-025-23031-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-11 15:57:55","publishedOnDateReadable":"November 11th, 2025"},"versionCreatedAt":"2025-05-20 12:41:34","video":"","vorDoi":"10.1038/s41598-025-23031-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23031-w","workflowStages":[]},"version":"v1","identity":"rs-6413166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6413166","identity":"rs-6413166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}