Association of Foehn winds with physiological parameters in the general population​

Association of Foehn winds with physiological parameters in the general population​ | 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 Research Article Association of Foehn winds with physiological parameters in the general population​ Selina Hanselmann, Kirsten Grossmann, Ornella Céline Weideli, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7818264/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Foehn winds—warm, dry downslope winds common in alpine regions—may impact human health, especially in weather-sensitive individuals, though evidence is lacking. Wearable devices now enable continuous health monitoring, offering new insights into physiological reactions to such weather conditions. This study investigates the association between Foehn winds and physiological parameters. Methods In a repeated-measures design, healthy adults in Liechtenstein wore medical sensory bracelets for 11 months, recording nightly heart rate (HR), heart rate variability (HRV), wrist skin temperature (WST), respiratory rate (RR), perfusion index (PI) and sleep duration. Foehn exposure was determined using local weather data. Linear mixed-effects models assessed associations, adjusting for confounders. Results A total of 714 participants (59.7% women; mean age 44.0 years) were included. Foehn was associated with changes in HR (+ 0.25 bpm; 95% CI: 0.17, 0.33), PI (+ 0.78%; 95% CI: 0.22, 1.33), and HRV (–0.29%; 95% CI: − 0.55, − 0.04), while RR, WST, and sleep duration remained unchanged. 40.8% disclosed being Foehn-sensitive via questionnaire. They showed a 9.92% lower PI irrespective of Foehn. Effects of Foehn differed by Foehn sensitivity: HR (+ 0.32 bpm vs. +0.15 bpm) and HRV (–0.53% vs. +0.04%) responses were more pronounced in non-sensitive individuals. Conclusions Our findings show that Foehn winds are linked to changes in physiological parameters, suggesting a weather-induced stress response. These effects were independent of sex. Interestingly, Foehn-sensitive individuals showed a lower baseline perfusion index (irrespective of Foehn), indicating a potential physiological predisposition. However, contrary to expectations, stronger physiological responses occurred in non-sensitive individuals. biomonitoring weather sensitivity medical wearables observational cohort study environmental influence Rhein valley Figures Figure 1 Figure 2 Figure 3 1. Introduction It is well-established that weather phenomena can affect human health (Sulman 1984 ). Symptoms experienced during extreme weather conditions or weather changes are varied; including fatigue, sleep disturbances, headaches, rheumatic pain, amongst others (Cooke et al. 2000 ; Yackerson et al. 2012 ; Koszewska et al. 2019 ). Studies have identified temperature, atmospheric pressure, humidity, sunshine, rain, and storms as key factors driving these symptoms (Hollander 1963 ; Sulman et al. 1970 ; Jamison et al. 1995 ). A study showed that 54.5% of the German population are weather-sensitive (Von Mackensen et al. 2005 ). Further research of Dixon et al. suggests that pain in chronic pain patients is exacerbated by increased humidity, higher wind speed, and reduced atmospheric pressure (Dixon et al. 2019 ). Strong, warm, and dry winds occur on the leeward side of mountains due to a pressure gradient, with high pressure on one side and lower pressure on the other. This atmospheric interaction significantly influences regional weather patterns (Jaubert and Stein 2003 ; Richner and Hächler 2008 ). Moreover, such winds are known for their potential to trigger symptoms in weather-sensitive individuals (Von Mackensen et al. 2005 ); for instance Piorecky et al. found that episodes of the warm, dry chinook winds in Canada were significantly associated with an increased frequency of migraine (Piorecky et al. 1997 ). In fact, rapid pressure changes have been discussed previously as a possible cause of the negative health effects induced by Chinook winds (Rudmik et al. 2009 ). In the alpine regions of Switzerland, Austria, Germany and Liechtenstein, this type of leeward wind is called Foehn. Over time, residents in these areas have reported various symptoms during Foehn periods, giving rise to the term “Foehn-illness” (Jaubert and Stein 2003 ; Richner and Hächler 2008 ). In 1933, Rohden conducted the first study examining the symptoms associated with Foehn conditions (Rohden 1933 ), finding that students more frequently experienced headaches, pain, and tiredness during Foehn periods (Rohden 1933 ). Research in southern Germany also found that the incidence of severe traumas is significantly correlated to Foehn events (Greve et al. 2020 ). However, further studies are needed to better understand the physiological reactions underlying these symptoms. Wearable devices have become increasingly popular in recent years. Smart watches, fitness trackers, and similar devices continuously monitor health metrics like heart rate (HR), respiratory rate (RR), and more (Witt et al. 2019 ; Bogu and Snyder 2021 ). The potential of such technology as tools for medical research is immense and extends beyond their original commercial purpose. Notably, the Ava bracelet, a medical device designed for fertility tracking (Goodale et al. 2019 ), has been used to detect COVID-19 symptoms before their onset (Brakenhoff et al. 2021 ; Risch et al. 2022b ; Markovic et al. 2024 ). We conducted a study to explore the associations between Foehn winds and a range of health parameters deriving from the Ava bracelet. We also investigated the evolution of these associations over the course of a Foehn episode and explored possible effect modifiers due to disclosed weather sensitivity or sex differences. To our knowledge, this is the first study to investigate potential associations between Foehn winds and physiological parameters using high-resolution meteorological data in combination with wearable sensor data. 2. Materials and methods 2.1. Study population The GAPP study (Genetic and phenotypic determinants of blood pressure and other cardiovascular risk factors; n = 2,170) is a prospective observational study that started in 2010 (Conen et al. 2013 ). Its aims are to better understand the development of cardiovascular risk factors in young and healthy adults. From the GAPP study cohort, a total of 1,163 participants aged 35–51 years were further recruited in the COVI-GAPP sub-study, which was initiated in 2020 shortly after the outbreak of the COVID-19 pandemic (Fig. 1 ) (Risch et al. 2022b ; Grossmann et al. 2024 ). COVI-GAPP study participants wore a health monitor wristband (Ava bracelet) over the course of 11 months (May 2020 – March 2021), recording physiological parameters during sleep (Risch et al. 2022b ). To investigate the association of Foehn winds with health parameters, we combined participant data from the GAPP and COVI-GAPP studies. To ensure a contrast, only participants from the COVI-GAPP study for whom physiological data were recorded during at least one night with Foehn conditions were included (Fig. 1 ). All participants provided written informed consent prior to participation in the study. The study protocol was approved by the local ethics committee (Kantonale Ethikkommission, Canton of Zürich, (BASEC 2020 − 00786)). 2.2. Physiological data The Ava bracelet was used to track six health parameters during sleep (version 2.0; Ava AG, Zurich, Switzerland): heart rate (HR), heart rate variability (HRV, focusing on SDNN (Standard Deviation of normal-to-normal intervals)), respiratory rate (RR), wrist skin temperature (WST), skin perfusion index (PI), and sleep quantity (total sleep duration). The device uses an accelerometer to measure sleep activity and records high-frequency physiological data using built-in photoplethysmogram (PPG) and temperature sensors (French-Mowat and Burnett 2012 ; Muehlematter et al. 2021 ). Each morning upon waking, the wearer synchronizes their bracelet with a proprietary smartphone app, which processes physiological data from periods of at least four hours of uninterrupted sleep, providing a single representative value for each health parameter for each sleep period. The device was worn as instructed during sleep. The Ava bracelet is CE-certified and FDA-cleared (Risch et al. 2022b ); additional details can be found elsewhere (Risch et al. 2022a ; Grossmann et al. 2024 ). 2.3. Questionnaires Participants completed questionnaires collecting demographic and health-related data, including information on sex (defined as assigned at birth), age, body mass index (BMI), and exact place of residence, which was later used to determine Foehn exposure. As part of the questionnaire, participants were asked whether they feel compromised in their well-being during a Foehn period. Based on their responses, they were categorized into two groups: Foehn-sensitive and non-Foehn-sensitive individuals (Fig. 1 ). Throughout the study, participants also reported the incidence and duration of any disease symptoms that may be associated with COVID-19 (Risch et al. 2022b ). 2.4. Meteorological data Meteorological data were collected from nine weather stations located in three different regions of Liechtenstein: Unterland (comprising the stations Mauren and Ruggell), Oberland (stations Schaan, Vaduz, Triesen, and Balzers), and Berg (station Triesenberg, Steg, and Malbun). The dataset was provided by Wetterring Liechtenstein and included an hour-by-hour Foehn index, as well as hourly measurements of air temperature (°C), relative humidity (%), air pressure (hPa), wind direction (°), and wind speed (m/s). The Foehn index, which is determined from measured meteorological data by a decision tree algorithm (Fig. 2 ), gives an hour-by-hour indication of the occurrence of Foehn and mixed Foehn air. The latter are typically characterized by low wind speeds, which often already contain a certain proportion of mixed-in cold air (Dürr 2008 ). The most important criterion for the Foehn index is the wind direction at the Guetsch, a mountain in the central Alps with a key weather station for monitoring Foehn winds. The hourly mean wind direction ( dd ) must be between 90° and 240°, ensuring the characteristic air flow from the southeast to the southwest (Dürr 2008 ). To associate the meteorological data with the nocturnal physiological parameters measured by the Ava bracelet, we defined the 24-hour monitoring period from 6:00 AM to 6:00 AM. A Foehn event was considered present if the Foehn index indicated Foehn winds for at least one hour during that period. 2.5. Statistical analysis Meteorological and physiological data were linked by geographic region and the date on which the monitoring period ended. To assess the association of Foehn with changes in health parameters, we applied linear mixed-effects (LME) models, implemented in R (v4.4.1). HRV and PI parameters were log-transformed due to their right-skewed distributions; all other parameters were modelled without transformation. The models controlled for several fixed-effect covariates, including sex, age, BMI, participant-reported disease symptoms, and season. Two binary covariates were included in all models, one accounting for potential behavioral differences on Friday and Saturday nights and another indicating whether participants self-reported sensitivity to Foehn winds. To account for correlations in repeated measurements of health parameters of the same person, each model included a random-effect intercept per participant. To model the temporal progression of physiological responses surrounding Foehn events, we extracted data segments spanning from five days before to one day after each identified Foehn event. Days − 5 to − 2 relative to the start of the Foehn event were defined as the baseline period of the segment, with separate effects estimated for the day preceding the Foehn event, the first day of the Foehn event, any subsequent days of Foehn, and the day after the end of the Foehn. For HR, HRV (SDNN), RR, PI, and WST we fitted a linear mixed-effects model (LME) with fixed-effect covariates for sex, age, BMI, participant-reported disease symptoms, end-of-week, and participant-reported Foehn sensitivity. Random intercepts were included for each segment nested within participant to account for variability between participants and between baseline periods for each segment. We performed sensitivity analyses on participant-declared Foehn sensitivity and sex to explore potential modifications of the Foehn effect. For each health parameter, we fit separate LME models for all subgroups with the same fixed and random effects as in the primary analyses, excluding the fixed-effect defining the subgroup. The difference of effect estimates between subgroups was evaluated using the 95% confidence interval (CI), calculated as \(\:\left({\widehat{\beta\:}}_{1}-{\widehat{\beta\:}}_{2}\right)\pm\:1.96\sqrt{{SE}_{1}^{2}+{SE}_{2}^{2}}\) , where \(\:{\widehat{\beta\:}}_{1}\) and \(\:{\widehat{\beta\:}}_{2}\) are the effect estimates for the two subgroups and \(\:{SE}_{1}\) and \(\:{SE}_{2}\) are their standard errors (Zeka and Schwartz 2004 ). 3. Results 3.1. Descriptive statistics A total of 714 participants were included in the statistical analyses, comprising 427 women and 287 men (Fig. 1 ). The mean age was 44 years (SD = 5.5), and the average BMI was 24.5 kg/m² (SD = 3.9). Participants were distributed across the three main regions of Liechtenstein, with 67 living in the Berg region, 401 in the Oberland, and 246 in the Unterland. The mean baseline nocturnal physiological parameters measured with the AVA bracelet were 60.38 (SD = 7.84) bpm for HR, 59.24 (SD = 15.97) ms for HRV (SDNN), 14.96 (SD = 2.16) breaths per minute (brpm) for RR, 34.07 (SD = 0.93) °C for WST, and 0.81 (SD = 0.38) for PI, with an average sleep duration of 7.38 (SD = 1.23) hours (Table 1 ). Table 1 Description of the physiological parameters measured with the AVA bracelet during the entire study period. Variables Mean SD Heart rate [bpm] 60.38 7.84 Heart rate variability (SDNN) [ms] 59.24 15.97 Respiratory rate [brpm] 14.96 2.16 Wrist skin temperature [°C] 34.07 0.93 Perfusion index 0.81 0.38 Sleep duration [hours] 7.38 1.23 SD, Standard deviation P25, 25th percentile P75, 75th percentile Meteorological data were collected on 338 distinct dates, with 49 of those having a Foehn event in at least one region of Liechtenstein (Table 2 ). Across all participants, we had 132,255 total observations (i.e., nights of participant data), with 9,837 of those corresponding to Foehn events. On average, there were 185.2 observations per participant (SD = 74.4) over the 11-month observation period, of which 13.8 (SD = 7.4) corresponded to nights with Foehn events. Table 2 Number of Foehn events recorded during the study period (May 2020 – March 2021) in the Principality of Liechtenstein, disaggregated by region and season. Region Winter Autumn Spring Summer Berg 58 28 15 10 Oberland 30 15 10 6 Unterland 21 9 9 3 3.2. Effect of Foehn winds on health parameters We found statistically significant associations between Foehn winds and HR, PI, and HRV (SDNN). According to the LME models, Foehn incidents were associated with an increase of 0.25 bpm (95% CI: 0.17, 0.33) in HR. They were also associated with a proportional increase of 0.78% (95% CI: 0.22, 1.33) in PI and a proportional decrease of 0.29% (95% CI: -0.55, -0.04) in HRV (SDNN). We did not find strong evidence for an association between Foehn winds and RR, sleep duration, or WST (Table 3 ). Table 3 Mean changes in the measured parameters during Foehn events with 95% confidence intervals for all participants (n = 714). Variable Change during Foehn incidence 95% CI Heart rate [bpm] + 0.25 0.17, 0.33 Heart rate variability (SDNN) [%] a -0.29 -0.55, -0.04 Respiratory rate [brpm] + 0.01 0.00, 0.03 Wrist skin temperature [°C] + 0.01 0.00, 0.02 Perfusion index [%] a + 0.78 0.22, 1.33 Sleep duration [hours] + 0.02 -0.01, 0.04 a Proportional change (log-transformed response variable) 3.3. Temporal progression of Foehn-related physiological responses To analyze physiological parameter variations before, during, and after a Foehn event, progression curves were calculated (Fig. 3 ). For HR (0.39 bpm; 95% CI: 0.28, 0.49) and HRV (SDNN) (-0.57%; 95% CI: -0.90, -0.23) there is a significant effect on the first day of Foehn, with subsequent days of Foehn not associated with a change. The initial effect on PI (+ 1.03%; 95% CI: 0.31, 1.76) is sustained throughout the Foehn event (+ 1.14%; 95% CI: 0.31, 1.97) (Online Resource 1). 3.4. Sensitivity analyses Participants were stratified by self-reported Foehn sensitivity assessed via questionnaire (n = 291 sensitive; n = 423 non-sensitive). Females were significantly more likely to report being Foehn-sensitive than males, but we found no significant associations between Foehn sensitivity and age, BMI, or region of residence (Table 4 ). Table 4 Participant characteristics of subgroups based on self-identified Foehn sensitivity. Variables (SD) Sensitive a participants n = 291 Non-sensitive a participants n = 423 p-value Sex ratio [F:M] 215:76 211:212 < 0.001 Mean age [years] 44.3 (5.6) 43.8 (5.5) 0.22 Mean BMI [kg/m2] 24.4 (4.1) 24.6 (3.8) 0.30 Region (Berg/ Oberland/ Unterland) 24/162/105 43/239/141 0.58 a Self-identified as Foehn-sensitive / non-Foehn-sensitive BMI, body mass index SD, Standard deviation Comparing the total recorded physiological data of participants classified as Foehn-sensitive versus non-sensitive, we observed a statistically significant association between self-declared Foehn sensitivity and PI. Being Foehn-sensitive was associated with a relative decrease of PI by -9.92% (95% CI: -14.28, -5.33) when controlling for Foehn exposure. We did not find strong evidence that this baseline difference is influenced by sex, nor does Foehn sensitivity modify the observed effect of the Foehn on PI. For the other four physiological parameters we did not find a difference between sensitive and non-sensitive participants. We compared physiological responses to Foehn winds between participants who self-identified as Foehn-sensitive and those who did not. In the non-sensitive subgroup, we found statistically stronger associations between Foehn conditions and both heart rate (HR) and heart rate variability (HRV, SDNN). No statistically significant group differences were observed for the other physiological parameters (Table 5 ). Table 5 Mean changes in the measured parameters in Foehn-sensitive a and non-sensitive a participants during Foehn events with 95% confidence intervals. Variable Sensitive participants a n = 291 Non-sensitive participants a n = 423 95% CI for subgroup difference Heart rate [bpm] + 0.15 [0.02, 0.27] + 0.32 [0.22, 0.43] -0.34, -0.01 Heart rate variability (SDNN) [%] b + 0.04 [-0.35, 0.43] -0.53 [-0.86, -0.19] 0.05, 1.08 Respiratory rate [brpm] + 0.00 [-0.03, 0.02] + 0.03 [0.00, 0.05] -0.07, 0.01 Wrist skin temperature [°C] + 0.02 [0.00, 0.04] + 0.01 [-0.01, 0.02] -0.01, 0.04 Perfusion index [%] b + 0.68 [-0.19, 1.55] + 0.84 [0.12, 1.56] -1.28, 0.96 Sleep duration [hours] + 0.02 [-0.02, 0.05] + 0.01 [-0.01, 0.04] -0.04, 0.05 a Self-identified as Foehn-sensitive /non-Foehn-sensitive b Relative difference When comparing the physiological responses to Foehn winds between men and women, we did not observe differences in the effect of Foehn winds across any of the physiological parameters. 4. Discussion Our study shows that occurrences of Foehn winds are associated with changes in HR, HRV, and PI in the general population. During Foehn episodes, HR and PI increased while HRV decreased, with only the PI remaining elevated throughout the event. Women were more likely to report being Foehn-sensitive, indicating a possible sex-related susceptibility. When controlling for Foehn exposure, we observed statistically significant physiological differences between participants who reported being sensitive to Foehn and those who are not sensitive to Foehn. Most notably, the perfusion index was about 10% lower in the Foehn-sensitive group, suggesting a potential physiological predisposition. Interestingly, Foehn-related changes were more pronounced in individuals who did not identify as Foehn-sensitive. However, the effects were consistent across sexes and showed only a small effect size. Accordingly, our findings provide insight into the underlying physiological reactions that accompany the various symptoms experienced during Foehn episodes. For the first time, we examine changes in physiological parameters during Foehn events, whereas previous research has been limited to reporting correlations with incidence rates and related indicators. For instance, a previous study has examined associations between Foehn winds and mental distress (Mikutta et al. 2022 ). Other investigations have addressed the incidence of acute coronary syndrome (Goerre et al. 2007 ), the occurrence of severe injuries (Maciejczak et al. 2020 ), or the frequency of myocardial infarction (Ambach et al. 1992 ) in the context of Foehn conditions. In contrast, we investigate the association of physiological parameters with Foehn conditions, enabled by continuous weather monitoring in the study region and longitudinal data from a large cohort wearing medical-grade wearable devices over an 11-month period. Physiological monitoring revealed changes in response to Foehn winds, with three out of the six assessed physiological parameters showing significant alterations during Foehn events. Heart rate increased as established marker of stress response (Chrousos and Gold 1992 ). Similarly, a decrease in heart rate variability further supports the hypothesis that Foehn winds trigger a stress response (Chrousos and Gold 1992 ). This may have clinical relevance, as decreased HRV has been suggested as a key pathway linking stress to increased cardiovascular morbidity (Thayer et al. 2010 ). Additionally, stress can act as a disease trigger in individuals vulnerable to cardiovascular conditions and may influence prognosis and outcomes in those with pre-existing cardiovascular or cerebrovascular diseases (Kivimäki and Steptoe 2018 ). Alterations in stress-associated vegetative parameters may also reflect a subcortical pain response, as Chinook winds are a known cause of facial pain related to atmospheric pressure changes (Rudmik et al. 2009 ). Specific sinonasal anatomical variations—such as the presence of a concha bullosa and sphenoethmoidal (Onodi) cell—may predispose individuals to Chinook-associated pain (Rudmik et al. 2009 ) and therefore vegetative reactions. We observed that 40.8% of participants reported an impact on their well-being during Foehn episodes, aligning with previous findings by Mackensen et al., who found that 54.5% of the German population self-identify as weather-sensitive (Von Mackensen et al. 2005 ). Similarly, the observation that women more frequently reported being susceptible to Foehn winds (73.8%) is consistent with their finding that being weather-sensitive is more prevalent among women. When comparing self-identified Foehn-sensitive and non-sensitive individuals, Foehn-sensitive individuals showed a lower baseline perfusion index (PI). Further, we observed stronger Foehn-related changes in heart rate (HR) and HRV (SDNN) among non-sensitive participants. This finding might be contrary to expectations, as individuals who consider themselves sensitive to Foehn might be assumed to exhibit stronger physiological reactions. Despite an observed baseline difference—a higher prevalence of women who reported being Foehn-sensitive—changes in physiological parameters during Foehn episodes did not differ between women and men. Overall, the results suggest a largely uniform physiological response to Foehn conditions across sex, but some variation depending on self-perceived weather sensitivity. A major strength of this study is the use of real-world data from a large cohort, with nearly one year of follow-up and continuous monitoring of physiological parameters via wearable sensors. Nighttime measurements helped minimize behavioral confounding and capture a more stable physiological state. However, wrist skin temperature (WST) is sensitive to wrist position and coverage, which may limit interpretability. Additionally, limiting data collection to nighttime may affect generalizability to daytime conditions. Unlike previous studies with small sample sizes or coarse weather classifications (Greve et al. 2020 ; Maciejczak et al. 2020 ), our study combined a large cohort sample with fine-grained spatial analysis, allowing for more accurate Foehn exposure assessment. This enabled a more precise distinction between affected and non-affected regions and enhanced the accuracy of exposure classification. While the observed effects were statistically significant, they were small in magnitude and may not be clinically relevant. Nevertheless, the "prevention paradox” shows that small effects affecting large populations can still carry meaningful public health implications (Geoffrey 1981 ; Puska and Jaini 2020 ). In conclusion, we provide the first evidence that Foehn winds are associated with changes in physiological parameters, affecting three out of six measured variables. These changes point toward an increased physiological stress response during Foehn periods, suggesting that such weather conditions may act as a physiological stressor in human beings. Moreover, we observed a lower baseline perfusion index and smaller changes in physiological parameters during Foehn in individuals who self-identify as Foehn-sensitive compared with non-sensitive individuals. This indicates that Foehn sensitivity may be linked to a distinct physiological predisposition or phenotype, representing a novel finding that should be further explored. Declarations Competing interests Martin and Lorenz Risch’s family belongs to the largest registered holders of equity securities of Sonic Healthcare, which may be considered a potential competing interest. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was funded by the Princely House of Liechtenstein, the Government of the Principality of Liechtenstein, the Hanela Foundation, the Fürst Franz Josef von Liechtenstein Stiftung, and the Swiss Heart Foundation [FF22104]. Author Contributions Selina Hanselmann: Data curation, Formal Analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing; Kirsten Grossmann: Data curation, Investigation, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing; Ornella Céline Weideli: Data curation, Investigation, Resources, Supervision, Writing – original draft, Writing – review & editing; Vincent Braunack-Mayer: Data curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing; Kenneth Vogt: Data curation, Investigation, Resources; Laura Velez Colorado: Data curation, Formal Analysis, Writing – review & editing; Martina Rothenbhler: Funding acquisition, Investigation, Resources, Writing – review & editing; Oliver Ullrich: Writing – review & editing; Harald Renz: Conceptualization, Supervision, Writing – review & editing; David Conen: Conceptualization, Funding acquisition, Supervision, Writing – review & editing; Lorenz Risch: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing; Martin Risch: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing Acknowledgements We thank the GAPP participants who enrolled in this study. Additionally, the authors thank the following for their contributions to the study: The local study team in Vaduz, FL, the different teams at the Dr Risch medical laboratories in Vaduz and Buchs, CH. We would also like to thank the COVID- 19 remote early detection (COVID- RED) consortium. This work was funded by the Princely House of Liechtenstein, the Government of the Principality of Liechtenstein, the Hanela Foundation, the Fürst Franz Josef von Liechtenstein Stiftung, and the Swiss Heart Foundation [FF22104]. Data availability statement Data that underlie the results reported in the manuscript were collected from study participants from the Principality of Liechtenstein, a very small country, where the risk of subject identification is increased due to the size of the population (less than 40’000 inhabitants). To respect data protection and to prevent the identification of participants, data access is restricted to researchers meeting the criteria for access to confidential data. 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Environ Health Perspect 112:1686–1690. https://doi.org/10.1289/ehp.7286 Supplementary Files Supplementarymaterial1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:16:44","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122785,"visible":true,"origin":"","legend":"","description":"","filename":"IJBMD25006900structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/7743343907406566a0e921f5.xml"},{"id":95565321,"identity":"072479eb-acc1-4a68-ba2b-2b6cb132974e","added_by":"auto","created_at":"2025-11-10 16:14:52","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133538,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/56cf6f5027e489e9d0661945.html"},{"id":95565302,"identity":"4383c1ff-e1b9-4d01-825b-ca35114b74a7","added_by":"auto","created_at":"2025-11-10 16:14:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65843,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population. In the GAPP study, 2,170 participants were enrolled, of whom 1,163 provided written informed consent to participate in the sub-study, the COVI-GAPP study. Data from 714 of these participants were analyzed to investigate the influence of Foehn winds on health parameters, with some participants excluded at this stage because no Foehn event was recorded for them during the observation period. Participants were further classified as sensitive or non-sensitive based on their self-reported sensitivity to Foehn in the questionnaire. The female-to-male ratio is provided in brackets\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/5b73ec62eaf38dab9dd9f9b9.jpg"},{"id":95565304,"identity":"8cae5a83-bc67-45ce-b79d-adef92202512","added_by":"auto","created_at":"2025-11-10 16:14:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73234,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of Foehn indices. The parameters are defined as follows: \u003cstrong\u003edd\u003c/strong\u003e (wind direction in degrees), \u003cstrong\u003eθ\u003c/strong\u003e (potential temperature in Kelvin), \u003cstrong\u003eff\u003c/strong\u003e (wind speed in meters per second), \u003cstrong\u003ef\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/sub\u003e (peak gusts in meters per second) and \u003cstrong\u003eUU\u003c/strong\u003e (relative humidity in percentages). Constants used in the formula: a = 1(K\u003csup\u003e-1\u003c/sup\u003e), b= 1 (s/m) and c = 3/80 (degrees\u003csup\u003e-1\u003c/sup\u003e). Threshold values vary by region; for the region around Vaduz, they are \u003cstrong\u003edd\u003c/strong\u003e = 60-260°, \u003cstrong\u003eUU\u003c/strong\u003e = 48%, \u003cstrong\u003eΔθ\u003c/strong\u003e = -2.8 K, \u003cstrong\u003eff\u003c/strong\u003e = 4.1 m/s and \u003cstrong\u003ef\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/sub\u003e = 7.6 m/s. Wind direction is measured at Guetsch, a mountain in the central Alps with a key Foehn monitoring station\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/612974efe9a3a1e2c7110602.jpg"},{"id":95655735,"identity":"348ea57e-ff83-4d6f-a399-6b7f6955677b","added_by":"auto","created_at":"2025-11-11 16:16:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21509,"visible":true,"origin":"","legend":"\u003cp\u003eProgression of physiological parameters before, during, and after Foehn events. Curves show estimated temporal effects of Foehn winds and their 95% confidence intervals for heart rate, heart rate variability (SDNN) and perfusion index aggregated across all participants (n = 714). The y-axis scale of the heart rate plot corresponds to 15% of the interquartile range of observed values\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/74c09bffd7878937828b2f4e.jpg"},{"id":106959775,"identity":"700128b3-8ec7-486e-bee2-548d3e310545","added_by":"auto","created_at":"2026-04-15 09:14:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1007270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/23d04894-ecc2-473a-82f6-e3b53f30adbf.pdf"},{"id":95565316,"identity":"a981e80c-af72-4ca4-8645-cc8e5079e547","added_by":"auto","created_at":"2025-11-10 16:14:52","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":141632,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7818264/v1/f3b5f9cc9a179298ebf3ce06.pdf"}],"financialInterests":"","formattedTitle":"Association of Foehn winds with physiological parameters in the general population​","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIt is well-established that weather phenomena can affect human health (Sulman \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Symptoms experienced during extreme weather conditions or weather changes are varied; including fatigue, sleep disturbances, headaches, rheumatic pain, amongst others (Cooke et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Yackerson et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Koszewska et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies have identified temperature, atmospheric pressure, humidity, sunshine, rain, and storms as key factors driving these symptoms (Hollander \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Sulman et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Jamison et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). A study showed that 54.5% of the German population are weather-sensitive (Von Mackensen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Further research of Dixon et al. suggests that pain in chronic pain patients is exacerbated by increased humidity, higher wind speed, and reduced atmospheric pressure (Dixon et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStrong, warm, and dry winds occur on the leeward side of mountains due to a pressure gradient, with high pressure on one side and lower pressure on the other. This atmospheric interaction significantly influences regional weather patterns (Jaubert and Stein \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Richner and H\u0026auml;chler \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, such winds are known for their potential to trigger symptoms in weather-sensitive individuals (Von Mackensen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); for instance Piorecky et al. found that episodes of the warm, dry chinook winds in Canada were significantly associated with an increased frequency of migraine (Piorecky et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In fact, rapid pressure changes have been discussed previously as a possible cause of the negative health effects induced by Chinook winds (Rudmik et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the alpine regions of Switzerland, Austria, Germany and Liechtenstein, this type of leeward wind is called Foehn. Over time, residents in these areas have reported various symptoms during Foehn periods, giving rise to the term \u0026ldquo;Foehn-illness\u0026rdquo; (Jaubert and Stein \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Richner and H\u0026auml;chler \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In 1933, Rohden conducted the first study examining the symptoms associated with Foehn conditions (Rohden \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1933\u003c/span\u003e), finding that students more frequently experienced headaches, pain, and tiredness during Foehn periods (Rohden \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1933\u003c/span\u003e). Research in southern Germany also found that the incidence of severe traumas is significantly correlated to Foehn events (Greve et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, further studies are needed to better understand the physiological reactions underlying these symptoms.\u003c/p\u003e\u003cp\u003eWearable devices have become increasingly popular in recent years. Smart watches, fitness trackers, and similar devices continuously monitor health metrics like heart rate (HR), respiratory rate (RR), and more (Witt et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bogu and Snyder \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The potential of such technology as tools for medical research is immense and extends beyond their original commercial purpose. Notably, the Ava bracelet, a medical device designed for fertility tracking (Goodale et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), has been used to detect COVID-19 symptoms before their onset (Brakenhoff et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Risch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Markovic et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe conducted a study to explore the associations between Foehn winds and a range of health parameters deriving from the Ava bracelet. We also investigated the evolution of these associations over the course of a Foehn episode and explored possible effect modifiers due to disclosed weather sensitivity or sex differences.\u003c/p\u003e\u003cp\u003eTo our knowledge, this is the first study to investigate potential associations between Foehn winds and physiological parameters using high-resolution meteorological data in combination with wearable sensor data.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study population\u003c/h2\u003e\u003cp\u003eThe GAPP study (Genetic and phenotypic determinants of blood pressure and other cardiovascular risk factors; n\u0026thinsp;=\u0026thinsp;2,170) is a prospective observational study that started in 2010 (Conen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Its aims are to better understand the development of cardiovascular risk factors in young and healthy adults. From the GAPP study cohort, a total of 1,163 participants aged 35\u0026ndash;51 years were further recruited in the COVI-GAPP sub-study, which was initiated in 2020 shortly after the outbreak of the COVID-19 pandemic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Risch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Grossmann et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). COVI-GAPP study participants wore a health monitor wristband (Ava bracelet) over the course of 11 months (May 2020 \u0026ndash; March 2021), recording physiological parameters during sleep (Risch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo investigate the association of Foehn winds with health parameters, we combined participant data from the GAPP and COVI-GAPP studies. To ensure a contrast, only participants from the COVI-GAPP study for whom physiological data were recorded during at least one night with Foehn conditions were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e All participants provided written informed consent prior to participation in the study. The study protocol was approved by the local ethics committee (Kantonale Ethikkommission, Canton of Z\u0026uuml;rich, (BASEC 2020\u0026thinsp;\u0026minus;\u0026thinsp;00786)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Physiological data\u003c/h2\u003e\u003cp\u003eThe Ava bracelet was used to track six health parameters during sleep (version 2.0; Ava AG, Zurich, Switzerland): heart rate (HR), heart rate variability (HRV, focusing on SDNN (Standard Deviation of normal-to-normal intervals)), respiratory rate (RR), wrist skin temperature (WST), skin perfusion index (PI), and sleep quantity (total sleep duration). The device uses an accelerometer to measure sleep activity and records high-frequency physiological data using built-in photoplethysmogram (PPG) and temperature sensors (French-Mowat and Burnett \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Muehlematter et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Each morning upon waking, the wearer synchronizes their bracelet with a proprietary smartphone app, which processes physiological data from periods of at least four hours of uninterrupted sleep, providing a single representative value for each health parameter for each sleep period. The device was worn as instructed during sleep. The Ava bracelet is CE-certified and FDA-cleared (Risch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e); additional details can be found elsewhere (Risch et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Grossmann et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Questionnaires\u003c/h2\u003e\u003cp\u003eParticipants completed questionnaires collecting demographic and health-related data, including information on sex (defined as assigned at birth), age, body mass index (BMI), and exact place of residence, which was later used to determine Foehn exposure. As part of the questionnaire, participants were asked whether they feel compromised in their well-being during a Foehn period. Based on their responses, they were categorized into two groups: Foehn-sensitive and non-Foehn-sensitive individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Throughout the study, participants also reported the incidence and duration of any disease symptoms that may be associated with COVID-19 (Risch et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Meteorological data\u003c/h2\u003e\u003cp\u003eMeteorological data were collected from nine weather stations located in three different regions of Liechtenstein: Unterland (comprising the stations Mauren and Ruggell), Oberland (stations Schaan, Vaduz, Triesen, and Balzers), and Berg (station Triesenberg, Steg, and Malbun). The dataset was provided by Wetterring Liechtenstein and included an hour-by-hour Foehn index, as well as hourly measurements of air temperature (\u0026deg;C), relative humidity (%), air pressure (hPa), wind direction (\u0026deg;), and wind speed (m/s). The Foehn index, which is determined from measured meteorological data by a decision tree algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), gives an hour-by-hour indication of the occurrence of Foehn and mixed Foehn air. The latter are typically characterized by low wind speeds, which often already contain a certain proportion of mixed-in cold air (D\u0026uuml;rr \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The most important criterion for the Foehn index is the wind direction at the Guetsch, a mountain in the central Alps with a key weather station for monitoring Foehn winds. The hourly mean wind direction (\u003cem\u003edd\u003c/em\u003e) must be between 90\u0026deg; and 240\u0026deg;, ensuring the characteristic air flow from the southeast to the southwest (D\u0026uuml;rr \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo associate the meteorological data with the nocturnal physiological parameters measured by the Ava bracelet, we defined the 24-hour monitoring period from 6:00 AM to 6:00 AM. A Foehn event was considered present if the Foehn index indicated Foehn winds for at least one hour during that period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eMeteorological and physiological data were linked by geographic region and the date on which the monitoring period ended. To assess the association of Foehn with changes in health parameters, we applied linear mixed-effects (LME) models, implemented in R (v4.4.1). HRV and PI parameters were log-transformed due to their right-skewed distributions; all other parameters were modelled without transformation.\u003c/p\u003e\u003cp\u003eThe models controlled for several fixed-effect covariates, including sex, age, BMI, participant-reported disease symptoms, and season. Two binary covariates were included in all models, one accounting for potential behavioral differences on Friday and Saturday nights and another indicating whether participants self-reported sensitivity to Foehn winds. To account for correlations in repeated measurements of health parameters of the same person, each model included a random-effect intercept per participant.\u003c/p\u003e\u003cp\u003eTo model the temporal progression of physiological responses surrounding Foehn events, we extracted data segments spanning from five days before to one day after each identified Foehn event. Days \u0026minus;\u0026thinsp;5 to \u0026minus;\u0026thinsp;2 relative to the start of the Foehn event were defined as the baseline period of the segment, with separate effects estimated for the day preceding the Foehn event, the first day of the Foehn event, any subsequent days of Foehn, and the day after the end of the Foehn. For HR, HRV (SDNN), RR, PI, and WST we fitted a linear mixed-effects model (LME) with fixed-effect covariates for sex, age, BMI, participant-reported disease symptoms, end-of-week, and participant-reported Foehn sensitivity. Random intercepts were included for each segment nested within participant to account for variability between participants and between baseline periods for each segment.\u003c/p\u003e\u003cp\u003eWe performed sensitivity analyses on participant-declared Foehn sensitivity and sex to explore potential modifications of the Foehn effect. For each health parameter, we fit separate LME models for all subgroups with the same fixed and random effects as in the primary analyses, excluding the fixed-effect defining the subgroup. The difference of effect estimates between subgroups was evaluated using the 95% confidence interval (CI), calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left({\\widehat{\\beta\\:}}_{1}-{\\widehat{\\beta\\:}}_{2}\\right)\\pm\\:1.96\\sqrt{{SE}_{1}^{2}+{SE}_{2}^{2}}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{\\beta\\:}}_{2}\\)\u003c/span\u003e\u003c/span\u003e are the effect estimates for the two subgroups and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SE}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SE}_{2}\\)\u003c/span\u003e\u003c/span\u003e are their standard errors (Zeka and Schwartz \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive statistics\u003c/h2\u003e\u003cp\u003eA total of 714 participants were included in the statistical analyses, comprising 427 women and 287 men (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age was 44 years (SD\u0026thinsp;=\u0026thinsp;5.5), and the average BMI was 24.5 kg/m\u0026sup2; (SD\u0026thinsp;=\u0026thinsp;3.9). Participants were distributed across the three main regions of Liechtenstein, with 67 living in the Berg region, 401 in the Oberland, and 246 in the Unterland.\u003c/p\u003e\u003cp\u003eThe mean baseline nocturnal physiological parameters measured with the AVA bracelet were 60.38 (SD\u0026thinsp;=\u0026thinsp;7.84) bpm for HR, 59.24 (SD\u0026thinsp;=\u0026thinsp;15.97) ms for HRV (SDNN), 14.96 (SD\u0026thinsp;=\u0026thinsp;2.16) breaths per minute (brpm) for RR, 34.07 (SD\u0026thinsp;=\u0026thinsp;0.93) \u0026deg;C for WST, and 0.81 (SD\u0026thinsp;=\u0026thinsp;0.38) for PI, with an average sleep duration of 7.38 (SD\u0026thinsp;=\u0026thinsp;1.23) hours (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDescription of the physiological parameters measured with the AVA bracelet during the entire study period.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate [bpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate variability (SDNN) [ms]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate [brpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWrist skin temperature [\u0026deg;C]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerfusion index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration [hours]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eSD, Standard deviation\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eP25, 25th percentile\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eP75, 75th percentile\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeteorological data were collected on 338 distinct dates, with 49 of those having a Foehn event in at least one region of Liechtenstein (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Across all participants, we had 132,255 total observations (i.e., nights of participant data), with 9,837 of those corresponding to Foehn events. On average, there were 185.2 observations per participant (SD\u0026thinsp;=\u0026thinsp;74.4) over the 11-month observation period, of which 13.8 (SD\u0026thinsp;=\u0026thinsp;7.4) corresponded to nights with Foehn events.\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\u003eNumber of Foehn events recorded during the study period (May 2020 \u0026ndash; March 2021) in the Principality of Liechtenstein, disaggregated by region and season.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBerg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOberland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnterland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Effect of Foehn winds on health parameters\u003c/h2\u003e\u003cp\u003eWe found statistically significant associations between Foehn winds and HR, PI, and HRV (SDNN). According to the LME models, Foehn incidents were associated with an increase of 0.25 bpm (95% CI: 0.17, 0.33) in HR. They were also associated with a proportional increase of 0.78% (95% CI: 0.22, 1.33) in PI and a proportional decrease of 0.29% (95% CI: -0.55, -0.04) in HRV (SDNN). We did not find strong evidence for an association between Foehn winds and RR, sleep duration, or WST (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMean changes in the measured parameters during Foehn events with 95% confidence intervals for all participants (n\u0026thinsp;=\u0026thinsp;714).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eChange during Foehn incidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate [bpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17, 0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate variability (SDNN) [%]\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.55, -0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate [brpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00, 0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWrist skin temperature [\u0026deg;C]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00, 0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerfusion index [%]\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22, 1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration [hours]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01, 0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eProportional change (log-transformed response variable)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Temporal progression of Foehn-related physiological responses\u003c/h2\u003e\u003cp\u003eTo analyze physiological parameter variations before, during, and after a Foehn event, progression curves were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For HR (0.39 bpm; 95% CI: 0.28, 0.49) and HRV (SDNN) (-0.57%; 95% CI: -0.90, -0.23) there is a significant effect on the first day of Foehn, with subsequent days of Foehn not associated with a change. The initial effect on PI (+\u0026thinsp;1.03%; 95% CI: 0.31, 1.76) is sustained throughout the Foehn event (+\u0026thinsp;1.14%; 95% CI: 0.31, 1.97) (Online Resource 1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Sensitivity analyses\u003c/h2\u003e\u003cp\u003eParticipants were stratified by self-reported Foehn sensitivity assessed via questionnaire (n\u0026thinsp;=\u0026thinsp;291 sensitive; n\u0026thinsp;=\u0026thinsp;423 non-sensitive). Females were significantly more likely to report being Foehn-sensitive than males, but we found no significant associations between Foehn sensitivity and age, BMI, or region of residence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eParticipant characteristics of subgroups based on self-identified Foehn sensitivity.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitive\u003csup\u003ea\u003c/sup\u003e participants\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;291\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-sensitive\u003csup\u003ea\u003c/sup\u003e participants\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;423\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex ratio [F:M]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215:76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211:212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003eMean age [years]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.3 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.8 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean BMI [kg/m2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.4 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.6 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion (Berg/\u003c/p\u003e\u003cp\u003eOberland/\u003c/p\u003e\u003cp\u003eUnterland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24/162/105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43/239/141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSelf-identified as Foehn-sensitive / non-Foehn-sensitive\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eBMI, body mass index\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eSD, Standard deviation\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e Comparing the total recorded physiological data of participants classified as Foehn-sensitive versus non-sensitive, we observed a statistically significant association between self-declared Foehn sensitivity and PI. Being Foehn-sensitive was associated with a relative decrease of PI by -9.92% (95% CI: -14.28, -5.33) when controlling for Foehn exposure. We did not find strong evidence that this baseline difference is influenced by sex, nor does Foehn sensitivity modify the observed effect of the Foehn on PI. For the other four physiological parameters we did not find a difference between sensitive and non-sensitive participants.\u003c/p\u003e\u003cp\u003e We compared physiological responses to Foehn winds between participants who self-identified as Foehn-sensitive and those who did not. In the non-sensitive subgroup, we found statistically stronger associations between Foehn conditions and both heart rate (HR) and heart rate variability (HRV, SDNN). No statistically significant group differences were observed for the other physiological parameters (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\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\u003eMean changes in the measured parameters in Foehn-sensitive\u003csup\u003ea\u003c/sup\u003e and non-sensitive\u003csup\u003ea\u003c/sup\u003e participants during Foehn events with 95% confidence intervals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eSensitive participants\u003csup\u003ea\u003c/sup\u003e n\u0026thinsp;=\u0026thinsp;291\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-sensitive participants\u003csup\u003ea\u003c/sup\u003e n\u0026thinsp;=\u0026thinsp;423\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI for subgroup difference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate [bpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.15 [0.02, 0.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.32 [0.22, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.34, -0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate variability (SDNN) [%]\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.04 [-0.35, 0.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.53 [-0.86, -0.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05, 1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate [brpm]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.00 [-0.03, 0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.03 [0.00, 0.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.07, 0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWrist skin temperature [\u0026deg;C]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.02 [0.00, 0.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.01 [-0.01, 0.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01, 0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerfusion index [%]\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.68 [-0.19, 1.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.84 [0.12, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.28, 0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration [hours]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.02 [-0.02, 0.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;0.01 [-0.01, 0.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.04, 0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eSelf-identified as Foehn-sensitive /non-Foehn-sensitive\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eRelative difference\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen comparing the physiological responses to Foehn winds between men and women, we did not observe differences in the effect of Foehn winds across any of the physiological parameters.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study shows that occurrences of Foehn winds are associated with changes in HR, HRV, and PI in the general population. During Foehn episodes, HR and PI increased while HRV decreased, with only the PI remaining elevated throughout the event. Women were more likely to report being Foehn-sensitive, indicating a possible sex-related susceptibility. When controlling for Foehn exposure, we observed statistically significant physiological differences between participants who reported being sensitive to Foehn and those who are not sensitive to Foehn. Most notably, the perfusion index was about 10% lower in the Foehn-sensitive group, suggesting a potential physiological predisposition. Interestingly, Foehn-related changes were more pronounced in individuals who did not identify as Foehn-sensitive. However, the effects were consistent across sexes and showed only a small effect size. Accordingly, our findings provide insight into the underlying physiological reactions that accompany the various symptoms experienced during Foehn episodes.\u003c/p\u003e\u003cp\u003eFor the first time, we examine changes in physiological parameters during Foehn events, whereas previous research has been limited to reporting correlations with incidence rates and related indicators. For instance, a previous study has examined associations between Foehn winds and mental distress (Mikutta et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other investigations have addressed the incidence of acute coronary syndrome (Goerre et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the occurrence of severe injuries (Maciejczak et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or the frequency of myocardial infarction (Ambach et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) in the context of Foehn conditions. In contrast, we investigate the association of physiological parameters with Foehn conditions, enabled by continuous weather monitoring in the study region and longitudinal data from a large cohort wearing medical-grade wearable devices over an 11-month period.\u003c/p\u003e\u003cp\u003ePhysiological monitoring revealed changes in response to Foehn winds, with three out of the six assessed physiological parameters showing significant alterations during Foehn events. Heart rate increased as established marker of stress response (Chrousos and Gold \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Similarly, a decrease in heart rate variability further supports the hypothesis that Foehn winds trigger a stress response (Chrousos and Gold \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). This may have clinical relevance, as decreased HRV has been suggested as a key pathway linking stress to increased cardiovascular morbidity (Thayer et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, stress can act as a disease trigger in individuals vulnerable to cardiovascular conditions and may influence prognosis and outcomes in those with pre-existing cardiovascular or cerebrovascular diseases (Kivim\u0026auml;ki and Steptoe \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlterations in stress-associated vegetative parameters may also reflect a subcortical pain response, as Chinook winds are a known cause of facial pain related to atmospheric pressure changes (Rudmik et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Specific sinonasal anatomical variations\u0026mdash;such as the presence of a concha bullosa and sphenoethmoidal (Onodi) cell\u0026mdash;may predispose individuals to Chinook-associated pain (Rudmik et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and therefore vegetative reactions.\u003c/p\u003e\u003cp\u003eWe observed that 40.8% of participants reported an impact on their well-being during Foehn episodes, aligning with previous findings by Mackensen et al., who found that 54.5% of the German population self-identify as weather-sensitive (Von Mackensen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Similarly, the observation that women more frequently reported being susceptible to Foehn winds (73.8%) is consistent with their finding that being weather-sensitive is more prevalent among women. When comparing self-identified Foehn-sensitive and non-sensitive individuals, Foehn-sensitive individuals showed a lower baseline perfusion index (PI). Further, we observed stronger Foehn-related changes in heart rate (HR) and HRV (SDNN) among non-sensitive participants. This finding might be contrary to expectations, as individuals who consider themselves sensitive to Foehn might be assumed to exhibit stronger physiological reactions. Despite an observed baseline difference\u0026mdash;a higher prevalence of women who reported being Foehn-sensitive\u0026mdash;changes in physiological parameters during Foehn episodes did not differ between women and men. Overall, the results suggest a largely uniform physiological response to Foehn conditions across sex, but some variation depending on self-perceived weather sensitivity.\u003c/p\u003e\u003cp\u003eA major strength of this study is the use of real-world data from a large cohort, with nearly one year of follow-up and continuous monitoring of physiological parameters via wearable sensors. Nighttime measurements helped minimize behavioral confounding and capture a more stable physiological state. However, wrist skin temperature (WST) is sensitive to wrist position and coverage, which may limit interpretability. Additionally, limiting data collection to nighttime may affect generalizability to daytime conditions. Unlike previous studies with small sample sizes or coarse weather classifications (Greve et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Maciejczak et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our study combined a large cohort sample with fine-grained spatial analysis, allowing for more accurate Foehn exposure assessment. This enabled a more precise distinction between affected and non-affected regions and enhanced the accuracy of exposure classification. While the observed effects were statistically significant, they were small in magnitude and may not be clinically relevant. Nevertheless, the \"prevention paradox\u0026rdquo; shows that small effects affecting large populations can still carry meaningful public health implications (Geoffrey \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Puska and Jaini \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn conclusion, we provide the first evidence that Foehn winds are associated with changes in physiological parameters, affecting three out of six measured variables. These changes point toward an increased physiological stress response during Foehn periods, suggesting that such weather conditions may act as a physiological stressor in human beings. Moreover, we observed a lower baseline perfusion index and smaller changes in physiological parameters during Foehn in individuals who self-identify as Foehn-sensitive compared with non-sensitive individuals. This indicates that Foehn sensitivity may be linked to a distinct physiological predisposition or phenotype, representing a novel finding that should be further explored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eMartin and Lorenz Risch\u0026rsquo;s family belongs to the largest registered holders of equity securities of Sonic Healthcare, which may be considered a potential competing interest. All other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was funded by the Princely House of Liechtenstein, the Government of the Principality of Liechtenstein, the Hanela Foundation, the F\u0026uuml;rst Franz Josef von Liechtenstein Stiftung, and the Swiss Heart Foundation [FF22104].\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eSelina Hanselmann: Data curation, Formal Analysis, Methodology, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Kirsten Grossmann: Data curation, Investigation, Project administration, Resources, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Ornella C\u0026eacute;line Weideli: Data curation, Investigation, Resources, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Vincent Braunack-Mayer: Data curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Kenneth Vogt: Data curation, Investigation, Resources; Laura Velez Colorado: Data curation, Formal Analysis, Writing \u0026ndash; review \u0026amp; editing; Martina Rothenbhler: Funding acquisition, Investigation, Resources, Writing \u0026ndash; review \u0026amp; editing; Oliver Ullrich: Writing \u0026ndash; review \u0026amp; editing; Harald Renz: Conceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing; David Conen: Conceptualization, Funding acquisition, Supervision, Writing \u0026ndash; review \u0026amp; editing; Lorenz Risch: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; Martin Risch: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe thank the GAPP participants who enrolled in this study. Additionally, the authors thank the following for their contributions to the study: The local study team in Vaduz, FL, the different teams at the Dr Risch medical laboratories in Vaduz and Buchs, CH. We would also like to thank the COVID- 19 remote early detection (COVID- RED) consortium.\u003c/p\u003e\u003cp\u003eThis work was funded by the Princely House of Liechtenstein, the Government of the Principality of Liechtenstein, the Hanela Foundation, the F\u0026uuml;rst Franz Josef von Liechtenstein Stiftung, and the Swiss Heart Foundation [FF22104].\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eData that underlie the results reported in the manuscript were collected from study participants from the Principality of Liechtenstein, a very small country, where the risk of subject identification is increased due to the size of the population (less than 40\u0026rsquo;000 inhabitants). To respect data protection and to prevent the identification of participants, data access is restricted to researchers meeting the criteria for access to confidential data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmbach E, Tributsch W, Mairinger T, Steinacker R, Reinegger G (1992) Fatal myocardial infarction and Tyrolean winds (the Foehn). 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Environ Health Perspect 112:1686\u0026ndash;1690. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1289/ehp.7286\u003c/span\u003e\u003cspan address=\"10.1289/ehp.7286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biomonitoring, weather sensitivity, medical wearables, observational cohort study, environmental influence, Rhein valley","lastPublishedDoi":"10.21203/rs.3.rs-7818264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7818264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFoehn winds\u0026mdash;warm, dry downslope winds common in alpine regions\u0026mdash;may impact human health, especially in weather-sensitive individuals, though evidence is lacking. Wearable devices now enable continuous health monitoring, offering new insights into physiological reactions to such weather conditions. This study investigates the association between Foehn winds and physiological parameters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn a repeated-measures design, healthy adults in Liechtenstein wore medical sensory bracelets for 11 months, recording nightly heart rate (HR), heart rate variability (HRV), wrist skin temperature (WST), respiratory rate (RR), perfusion index (PI) and sleep duration. Foehn exposure was determined using local weather data. Linear mixed-effects models assessed associations, adjusting for confounders.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 714 participants (59.7% women; mean age 44.0 years) were included. Foehn was associated with changes in HR (+\u0026thinsp;0.25 bpm; 95% CI: 0.17, 0.33), PI (+\u0026thinsp;0.78%; 95% CI: 0.22, 1.33), and HRV (\u0026ndash;0.29%; 95% CI: \u0026minus;\u0026thinsp;0.55, \u0026minus;\u0026thinsp;0.04), while RR, WST, and sleep duration remained unchanged. 40.8% disclosed being Foehn-sensitive via questionnaire. They showed a 9.92% lower PI irrespective of Foehn. Effects of Foehn differed by Foehn sensitivity: HR (+\u0026thinsp;0.32 bpm vs. +0.15 bpm) and HRV (\u0026ndash;0.53% vs. +0.04%) responses were more pronounced in non-sensitive individuals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur findings show that Foehn winds are linked to changes in physiological parameters, suggesting a weather-induced stress response. These effects were independent of sex. Interestingly, Foehn-sensitive individuals showed a lower baseline perfusion index (irrespective of Foehn), indicating a potential physiological predisposition. However, contrary to expectations, stronger physiological responses occurred in non-sensitive individuals.\u003c/p\u003e","manuscriptTitle":"Association of Foehn winds with physiological parameters in the general population​","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 16:14:47","doi":"10.21203/rs.3.rs-7818264/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cfee334-9421-4c90-ab24-bb679d3b55c1","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T12:32:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 16:14:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7818264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7818264","identity":"rs-7818264","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}