Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in hypertensive individuals

Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in hypertensive individuals | 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 Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in hypertensive individuals Ádrya Aryelle Ferreira, Raphael Martins de Abreu, Pedro Igor Lustosa Roriz, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6363534/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 Objective Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in patients with systemic arterial hypertension (SAH) was evaluated in a cross-sectional study of 52 individuals. Methods Participants were allocated to two groups based on COVID-19 history. They were underwent heart rate variability on the 24-hour Holter, lung function was assessed by spirometry and functional capacity (CF) was assessed by the cardiopulmonary exercise test. Results Was revealed worsened lung function in COVID-19-recovered SAH patients, indicated by lower forced expiratory volume in the first second (FEV1) rates [2.3 (1.9–2.6) vs. 2.5 (2.2–3.0), p < 0.05)] and FEV1/[81.8 (77.5–83.9) vs . 84.6 (80.8–87.7) p < 0.05)], with 30% showing restrictive disorder. However, no significant differences were found in cardiac autonomic control. A positive and moderate association between VO 2peak and FEV1 in COVID-19-recovered SAH patients was noted (r = 0.50 p < 0.05), and between VO 2peak and the 0V% index was noted a negative and moderate association (r =-0,55 p < 0.05). Conclusion Findings suggest mild COVID-19 in SAH patients may not cause significant long-term HRV changes. However, there is worsening of lung function, with the presence mainly of restrictive disorder in 30% of cases. COVID-19 SARS-CoV-2 arterial hypertension autonomic nervous system disease functional capacity Figures Figure 1 Figure 2 Figure 3 Introduction The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019 (COVID-19), caused a global pandemic declared by the World Health Organization (WHO) in 2020. COVID − 19 is marked by its remarkable transmission capacity and high level of morbidity and mortality [ 1 – 3 ]. In general, most infected individuals are asymptomatic or present mild symptoms such as dry cough, fever, headache, runny nose, anosmia and mild fatigue, without the need for hospitalization. However, in 15% of cases the condition can progress to severe respiratory failure requiring admission to the Intensive Care Unit (ICU) [ 4 ]. After the acute phase of SARS-CoV-2 infection, survivors often experience persistent symptoms like fatigue, dyspnea, chest pain, arrhythmias, tachycardia, autonomic dysfunction, anxiety, and depression for over 3 months [ 5 ]. “Long COVID” describes symptoms developing 3 months post-infection and lasting over two months [ 6 , 7 ], attributed to immune dysregulation, autoimmune manifestations, persistent viral reservoirs, and neuroinvasive effects [ 8 , 9 ]. Chen et al. [ 10 ] found “long COVID” in approximately 43% of recovered individuals from 50 studies. Identifying these long-term sequelae is crucial for developing treatment strategies [ 4 ]. During the active period of the disease, the exacerbated inflammatory response known as “cytokine storm” can cause direct damage to the cardiovascular and pulmonary systems and, in severe cases, resulting in Acute Respiratory Distress Syndrome (ARDS) and acute myocardial injury [ 11 , 12 ]. In addition to the intense inflammatory process, some studies indicate that the severity of the COVID-19 condition may also be related to age and the presence of pre-existing comorbidities [ 13 , 14 ]. It is known that individuals with Cardiovascular Diseases (CVDs) and their risk factors, such as Systemic Arterial Hypertension (SAH), have a strong association with the severity and mortality by COVID-19 [ 3 , 15 , 16 ]. A likely pathophysiological explanation is the high affinity of SARS-CoV-2 for the Angiotensin Converting Enzyme 2 (ACE2) receptor, present both in lung cells and in the vascular endothelium, which functions as a mechanism of entry for the virus into the cell [ 11 ]. This enzyme is highly expressed in these individuals with CVD, making them more susceptible to the disease [ 13 ]. Among CVDs, SAH affects more than 30% of the world population, with a prevalence of approximately 30 million in Brazil [ 17 ]. This disease is defined as a multifactorial clinical condition characterized by a sustained increase in Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) levels [ 18 ]. According to previous studies, SAH was the most prevalent chronic disease among those infected with SARS-CoV-2 [ 19 ]. Therefore, it can provide greater chances of unfavorable outcomes such as the development of a severe form of the disease and post-infection sequelae [ 1 , 20 ]. Furthermore, SAH is associated with an increase in sympathetic nervous activity and vagal suppression, which characterizes an autonomic imbalance [ 21 , 22 ]. Studies already indicate that COVID-19 can cause sequelae in the autonomic nervous system (ANS) that are not limited to critically ill patients, including orthostatic hypotension (OH), postural orthostatic tachycardia syndrome (POTS), dysfunction in body thermoregulation, in addition to exacerbation of pre-existing sympathovagal imbalance conditions [ 23 , 24 ]. However, there are still no studies investigating the long-term effects of COVID-19 on the ANS of patients with SAH [ 25 – 27 ]. The cardiac autonomic control can be assessed non-invasively through Heart Rate Variability (HRV) [ 28 ]. HRV corresponds to the study of temporal oscillations of consecutive RR intervals (iRR) from the electrocardiogram [ 29 , 30 , 31 ]. A high resting HRV has been linked to efficient cardiac autonomic modulation, capable of responding to different internal and external stimuli. On the other hand, a low HRV at rest signals an abnormal and insufficient adaptation of the ANS, resulting in health impairments, due to poor physiological functioning, low physical capacity and the presence of CVDs, such as SAH, as well as COVID-19 [ 22 , 32 ]. In addition to dysautonomia, studies indicate that individuals who have been hospitalized due to COVID-19 face a significant decrease in lung function and functional capacity (FC), which can persist for months after recovery [ 38 , 39 ]. This impairment appears to be intrinsically linked to specific injuries caused by viral inflammation, especially in the respiratory system, with a consequent reduction in lung function analyzed by indices such as forced vital capacity (FVC), total lung capacity (TLC) and diffusion capacity [ 40 ]. Additionally, deficits in FC have been associated with direct damage to the ANS and respiratory system. Previous studies show that 85% of those recovered from COVID-19 have an impaired exercise response during cardiopulmonary exercise testing (CPET) [ 41 , 42 ]. This intricate network of changes in the ANS, cardiovascular and respiratory systems highlights the need for cardiorespiratory rehabilitation, which demonstrates the fundamental importance of evaluating pulmonary and functional sequelae [ 43 ]. However, there is a lack of evidence investigating the long-term impact of COVID-19 on SAH patients. Therefore, the main objective of this study was to evaluate the long-term impact of COVID-19 on cardiac and pulmonary autonomic function in patients with AH. Furthermore, the results of this study can provide information to various health professionals, in order to help them guide and qualify rehabilitation programs for these patients. We hypothesize that individuals with SAH after COVID-19 infection, even those who experienced mild symptoms, not requiring hospitalization, may present greater impairment in cardiovascular autonomic balance and lung function, in addition, that these impairments may be associated with a worse FC. Materials and methods Study design This is a cross-sectional observational study, described in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative, translated into Portuguese [ 44 ], carried out from March/2023 to September/2023 and approved by the Research Ethics Committee of the University of Pernambuco (UPE), (CAAE − 66973322.0.0000.5191). All participants provided voluntary consent before participation, signing the Free and Informed Consent Form (TCLE). Population and sample selection Fifty-two individuals with AH were evaluated, of both sexes, aged between 40 and 75 years, who had or had not been infected with the SARS-CoV-2 virus. The sample selection was carried out based on publicity on radio, television and digital media. Elegibility criteria Participants diagnosed with SAH for at least one year, using continuous and unchanged antihypertensive medication for at least 3 months, aged between 40 and 75 years of both sexes, were included. For (G1-), individuals with AH who did not have a confirmed history of COVID-19 infection, and for (G2+), individuals previously infected by SARS CoV-2 and with diagnostic confirmation, there are at least 6 and at most 18 months, who had mild symptoms of COVID-19, without the need for hospital admission, whether in a ward or ICU. Individuals who presented any physical or mental limitation at the time of the assessment that prevented the examinations from being carried out, or had SBP/DBP greater than 180/100 mmHg were not included; as well as pregnant women, as physical tests, even at submaximal levels, may not be safe for this population. Patients who had severe symptoms of COVID-19 and needed to be admitted with ventilator support in the ICU or wards were also not included in the research. Volunteers who were unable to perform the tests or complete the protocols were excluded from the study. In the anamnesis, data such as age, sex, body mass (BM), height, body mass index (BMI) were recorded, in addition to the presence of diseases (heart disease, liver disease, neoplasms, pulmonary, metabolic, renal, cerebral, vascular diseases), lifestyle habits (smoking, alcoholism, physical activity) and vital signs [SBP/DBP, Heart Rate (HR) and Peripheral Oxygen Saturation (SpO2)] of the subjects. All volunteers underwent assessments of long-term autonomic control, lung function and maximum functional capacity. Sample size This study was based on an intentional sample, in which the researchers played the role of carefully outlining the profile of participants to be included. The sample size was calculated using the GPower 3.1.9.4 software, with significance levels α set at 0.05 and statistical power β set at 0.80, resulting in a total of 42 individuals, with each group consisting of 21 participants. It is worth noting that, at the end of the study, the sample number was increased, totaling 52 individuals, distributed between groups G1- (n = 25) and G2+ (n = 27). S trategy and operationalizationof the study Participants underwent assessments of cardiac autonomic function, pulmonary function and maximum functional capacity in the afternoon from 2 to 6 pm. The individuals were evaluated individually at the Cardiorespiratory Physiotherapy Laboratory (LAFIC) of the University of Pernambuco (UPE) Campus Petrolina by a researcher, in order to ensure that they were in good clinical and/or health conditions to carry out the tests and exams. On the first day, anamnesis was carried out with the collection of the participant's personal data (name, age, sex, contact telephone number) and anthropometric data (BM, height, BMI), as well as the presence of associated diseases, lifestyle habits and measurement of vital signs (SBP/DBP, HR and SpO2). The participant also informed whether they had any limiting symptoms or complaints before starting the assessment, if so, the assessments were rescheduled. Afterwards, a pulmonary function test was performed and a 24-hour Holter was installed. In the following day, the patient returned in order to have the 24-hour Holter removed and the researchers proceeded with data extraction, ensuring the effectiveness of the recording. In the same day, the team scheduled the CPET for the following week. This strategy aimed to avoid interference from a bad night's sleep after Holter and other discomforts generated by 24 hours with the equipment. Long-term HRV recording Long-term HRV was performed by recording a 24-hour electrocardiogram using the device (CARDIOS, São Paulo, SP, Brazil), with a sampling rate of 800 points per second (pps) with 12-bit resolution, to assess heart rate variations over a 24-hour period. The electrocardiogram was recorded using 4 channels fixed to the patient's chest and connected to the device, which is located on the patient's waist and records the information transmitted by these electrodes. After installation, individuals were instructed to carry out their activities normally during the 24 hours of the exam, except taking a shower. In addition, everyone received a sheet to record the time they went to sleep and the time they woke up, in addition to any changes that occurred during the 24 hours ( e.g .: cardiac, respiratory symptoms, headaches), as well as the time they occurred. After 24 hours, the device was removed to pre-process the data that was recorded on the equipment. The iRR time series was transferred to a personal computer and processed, after computerized primary analysis, all recordings were reviewed and edited manually by a trained team to carefully eliminate ectopic beats and artifacts, cleaning of artifacts did not exceed 5% of the sample iRR. All records had a minimum of 18 hours of iRR with sinus rhythm, divided into sleep and wake periods [ 45 ]. To analyze the sleep period, the hours informed in the volunteer's record were selected, with the first and last hours being discarded to guarantee the majority of the patient's sleep, a period where it is expected that there will be greater vagal tone, and for wakefulness, the hours from waking up until removing the equipment. Long-term HRV processing The HRV measurements were processed using specific routines for linear and non-linear analysis, developed by a bioengineer Prof. Dr. Alberto Porta (Università degli Studi di Milano, Milan, Italy). For the linear analysis in the time domain, the following indices were used: (1): the average of all normal RR intervals in milliseconds (ms); (2) the variance of all normal iRR in milliseconds (ms²). In the frequency domain, the autoregressive model was applied to the iRR sequence data. Two spectral components were considered: low frequency (LF - from 0.04 to 0.15 Hz) and high frequency (HF - from 0.15 to 0.40 Hz), which represent cardiac sympathetic and parasympathetic modulation, respectively [ 31 ]. The spectral components were also expressed in normalized units (LFnu and HFnu). Normalization consists of dividing the power of a given spectral component (HF or LF) by the total power minus the power below 0.04 Hz, and multiplying the ratio by 100. This strategy is extremely important for the BF index, having Since it represents a mixed modulation, normalization helps to remove the influence of other bands, which makes it a predominantly sympathetic index [ 31 , 46 ]. For non-linear analysis, these iRR were transformed into a sequence of symbols (numbers) ranging from 0 to 5. Next, the patterns of a sequence of 3 beats were constructed. The distribution of patterns was calculated by Shannon entropy (SE). This index describes the shape of the distribution of patterns [ 33 ]. To perform symbolic analysis (SA), all patterns were grouped into four families, described as follows: (a) patterns without variation (0V); (b) patterns with a variation (1V: 2 consecutive symbols are the same and one symbol is different); (c) patterns with two similar variations (2LV); (d) 2 different variations (2UV: 3 symbols that form a peak or a valley). The occurrence rate of each pattern is defined as 0V%, 1V%, 2LV%, and 2UV%. Where 0V% can be considered as a marker of sympathetic modulation, 1V% as a marker of mixed modulation, 2LV% and 2UV% as markers of vagal modulation [ 36 ]. According to Porta et al., [ 36 ] conditional entropy (CE) measures the amount of information per new sample that cannot be obtained from a sequence L of values. EC is evaluated by the complexity index (CI). Additionally, to calculate normalized CI (NCI), it was normalized by the SE of the RRi and ranges from 0 (null information) to 1 (maximum information). The higher the CI and NCI, the greater the complexity and the lower the regularity of the series. Spirometry The pulmonary function test was performed to evaluate and quantify the presence of ventilatory disorders, with the analysis of airflows, volumes and lung capacities. The procedure was conducted in accordance with the standards of the American Thoracic Society/European Respiratory Society (ATS/ERS) [ 47 ]. The evaluation was carried out using a spirometer (Quark CPET, Cosmed, Italy). Prior to the day of the assessment, individuals were instructed to avoid ingesting heavy foods, caffeine or performing vigorous exercise in the 2 hours prior to the test. At the time of the assessment, participants were positioned comfortably in a chair with a straight back without wheels, keeping their feet well supported on the floor. Next, the evaluator provided guidance and demonstrated how to perform the deep inspiration and forced expiration maneuver to ensure familiarity with the process. The following variables were analyzed: forced vital capacity (FVC), which represents the maximum volume of air that can be exhaled after a maximum inspiration, its reduced value may characterize a restrictive disorder; forced expiratory volume in the first second (FEV1), which indicates the amount of air exhaled in the first second of forced expiration, being one of the main indicators of pulmonary function essential for classifying ventilatory disorders, and the ratio (FEV1/FVC), which represents the Tiffeneau index. The values ​​obtained were compared with the values ​​predicted by Pereira, Sato and Rodrigues (2007) [ 48 ]. CPET The CPET was performed in order to analyze FC, was conducted according to the criteria established by the European Respiratory Society (ERS) [ 49 ], carried out using a horizontal exercise bike (Quinton Corival 400, USA), with ergospirometric parameters monitored by a gas analyzer (Quark CPET, Cosmed, Italy). The team always consisted of 3 experienced examiners, including a cardiologist. Prior to the assessment day, participants were instructed to avoid ingesting stimulant drinks (coffee, energy drinks, green tea) and heavy foods in the 3 hours prior to the exam, as well as avoiding strenuous exercise in the previous 24 hours. In addition, they were instructed on the appropriate clothing and footwear to perform the test, and in the case of male participants, they were asked to perform a trichotomy for adequate adhesion of the electrodes. On the day of the evaluation, the patients were evaluated regarding their health conditions, in addition to measuring their vital signs. In case of important changes in them, the exam was rescheduled. Moreover, patients were continuously monitored by an 12 leads ECG. The test protocol consisted in 1 min at rest with the volunteer properly positioned on the exercise bike, followed by a 2-minute warm-up with free load (approximately 4 W), using a staggered protocol with a progressive increase in workload every minute. according to a total exercise time between 8 and 12 min. A load of 15 Watts/min was standardized for all individuals (50). A cycling frequency of 60–65 rotations per minute (rpm) had to be maintained, and every 2 min after the start of the load increment, the variables of SBP/DBP and subjective perception of exertion for dyspnea and limb fatigue were recorded by the Modified Borg Scale [ 51 ]. The test ended with a 2 min cool down. The examination was interrupted if maximum voluntary exhaustion was reached or in case of important electrocardiographic changes and/or limiting symptoms. To determine cardiorespiratory capacity and power, 2 independent researchers were responsible for identifying the ventilatory anaerobic threshold (VAT) and respiratory compensation point, both using the analysis data of VE/VO2, VE/VCO2, PetO2, PetCO2 and the V-slope method. The following variables were used for analysis: (1) VO2peak (maximum O2 consumption achieved during CPET) (2) Maximum power achieved (watts) and (3) VO2 at VAT. Statistical analysis Statistical analysis was performed using the IBM® SPSS® Statistics version 22.0 program. The normality of data from the studied population was analyzed using the Kolmogorov–Smirnov test. Parametric and non-parametric variables were expressed as mean ± standard deviation and median (1st – 3rd interquartile), respectively. Intragroup differences between periods of sleep and wakefulness were analyzed using the Wilcoxon Signed Rank test for related samples and for intergroup comparisons, the Mann-Whitney U test was used for independent samples. The association of categorical variables was tested using Chi-square. Correlations between HRV, CF and Spirometry indices, were performed using the Spearman correlation test. The classification of Spearman's correlation coefficients was carried out according to Munro (2001) and grouped as small correlation: 0 to 0.25; low: 0.26 to 0.49; moderate: 0.50 to 0.69; high: 0.70 to 0.89 and very high: 0.90 to 1.00. In addition, subanalyses were performed with baseline characteristics to deal with confounding factors such as age and medications. Results whose descriptive levels ( p values) were below 0.05 were considered as statistically significant. In some situations, results whose p-values ​​are between 0.05 and 0.08 will be referred as trends. Results The sample consisted of 52 individuals with an average age of (55.62 ± 9.09), with a higher prevalence of females (78.8%), divided into G1- (n = 25; 19 women/6 men) and G2+ (n = 27; 22 women/5 men). Of these, two participants from G1- were excluded during the CPET phase, but continued to evaluate the results of cardiac and pulmonary autonomic function. The flowchart of study is shown in Fig. 1 . Table 1 presents the general characteristics of the sample. In relation to the baseline, with the variables of age, sex, height, body mass and BMI, there was no statistical difference between the groups. It is worth noting that all individuals were continuously using antihypertensives and 8 (19.2%) were using beta blockers associated with AH treatment. No significant differences were detected between risk factors, comorbidities and medication use. It is noteworthy that, among the 52 participants analyzed, 33 (63.4%) were obese, with a higher prevalence in G2+, individuals with a history of COVID-19 infection, totaling 18 (54.5%) participants. Table 1 Characteristics of the studied population (n = 52) G1- (n = 25) G2+ (n = 27) p Age (years) 53.32 ± 7.51 57,74 ± 10,01 0,10 Female, n (%) 19 (76,0%) 22 (81,4%) - Vital signs PAS/PAD (mmHg) FC (bpm) Anthropometric data Height (m) 1.62 ± 0.85 1.60 ± 0.78 0,66 Massa corporal (Kg) 85,17 ± 14,07 82,91 ± 18,15 0,55 BMI (Kg/m²) 32.61 ± 6.16 32.03 ± 5.62 0,89 Risk factors Smoking 1 (4,0%) 0 (0%) 0,29 Dyslipidemia 7 (28,0%) 12 (44,4%) 0,21 Grade I and II obesity 10 (40,0%) 16 (59,2%) 0,16 Grade III obesity 5 (20,0%) 2 (7,4%) 0,18 Associated comorbidities Diabetes Mellitus 1 (4,0%) 5 (18,5%) 0,10 Asthma (self-related) 1 (4,0%) 3 (11,1%) 0,95 Class of medications Beta blockers 2 (8,0%) 6 (22,2%) 0,51 Calcium channel blockers 4 (16,0%) 9 (33,3%) 0,14 ACE inhibitors 2 (8,0%) 1 (3,7%) 0,50 Data expressed as mean ± standard deviation. SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; BMI: body mass index; ACE: angiotensin converting enzyme. Nominal variables were presented as absolute value and relative frequency (%). The long-term HRV indices of the studied groups are presented in Table 2 . There was no statistical difference in any indices of the linear and non-linear analyzes between the groups. However, when examining the intragroup disparity between periods of sleep and wakefulness, statistically significant discrepancies were identified in all HRV indices, presented in Table 3 . These variations align in a physiologically expected manner, since there is a prevalence of the system vagal nervous system during sleep, contrasting with the predominance of the sympathetic system during wakefulness. Table 2 Comparison of linear and non-linear indices of long-term HRV between groups (n = 52) G1- (n = 25) G2+ (n = 27) p Sleep Vigil Sleep Vigil Sleep Vigil Linear analysis Time Domain Mean RRi (ms) 865,9 (790,5–1021,1) 715,9 (628,9–760,8) 932,6 (814,5–998,9) 738,4 (654,4–851,2) 0,41 0,17 RRi variance (ms²) 1854,0 (819,4–4870,1) 1331,4 (665,9–3248,3) 1853,0 (991,9–4730,3) 1253,0 (705,9–2525,6) 0,99 0,80 Frequency domain LF (ms) 30,8 (23,3–41,4) 44,6 (30,4–60,3) 25,6 (19,5–42,1) 39,2 (28,4–49,6) 0,27 0,29 HF (ms) 384,7 (134,7–1371,1) 114,4 (44,4–421,6) 386,0 (213,1–85,5) 130,9 (56,8–354,2) 0,80 0,59 Nonlinear analysis Symbolic analysis 0V% 28,4 (24,2–38,6) 50,5 (40,3–57,5) 26,7 (16,0–40,6) 50,8 (39,1–60,5) 0,64 0,85 1V% 45,8 (41,0–48,6) 35,8 (31,5–42,8) 45,5 (38,3–47,3) 35,1(27,7–43,1) 0,54 0,44 2LV% 6,0 (2,8–8,7) 2,0 (1,0–2,5) 5,0 (2,7–9,0) 2,0 (1,0–3,0) 0,94 0,81 2UV% 13,0 (11,8–18,1) 10,7 (9,0–13,0) 15,4 (9,7–23,1) 11,4 (8,3–16,0) 0,70 0,74 Shannon's Entropy SE 3,3 (3,0–3,4) 2,9 (2,5–3,1) 3,3 (2,8–3,5) 2,8 (2,1–3,2) 0,80 0,59 Conditional Entropy CI 0,7 (0,6–0,7) 0,6 (0,5–0,6) 0,7 (0,6–0,7) 0,6 (0,6–0,7) 0,56 0,40 NCI 1,1 (0,9–1,2) 0,9 (0,8–1,0) 1,1 (0,9–1,2) 0,8 (0,7–1,1) 0,86 0,59 Data expressed as median (1st − 3rd interquartile). HF: high frequency in absolute units, LFun: low frequency in normalized units, SE: Shannon Entropy, CI: complexity index, NCI: normalized complexity index, RRi: RR intervals, 0V%: patterns without variations, 1V%: patterns with one variation, 2LV%: patterns with two identical variations, 2UV%: patterns with two different variations. Table 3 Intragroup comparison of HRV indices between periods of sleep and vigil (n = 52) G1- (n = 25) p G2+ (n = 27) p Are Vigil Are Vigil Linear analysis Time Domain Mean RRi (ms) 865,9 (790,5–1021,1) 715,9 (628,9–760,8) 0,00 932,6 (814,5–998,9) 738,4 (654,4–851,2) 0,00 RRi variance (ms²) 1854,0 (819,4–4870,1) 1331,4 (665,9–3248,3) 0,05 1853,0 (991,9–4730,3) 1253,0 (705,9–2525,6) 0,06 Frequency domain LF (one) 30,8 (23,3–41,4) 44,6 (30,4–60,3) 0,03 25,6 (19,5–42,1) 39,2 (28,4–49,6) 0,01 HF (ms) 384,7 (134,7–1371,1) 114,4 (44,4–421,6) 0,00 386,0 (213,1–85,5) 130,9 (56,8–354,2) 0,00 Nonlinear analysis Symbolic analysis 0V% 28,4 (24,2–38,6) 50,5 (40,3–57,5) 0,00 26,7 (16,0–40,6) 50,8 (39,1–60,5) 0,00 1V% 45,8 (41,0–48,6) 35,8 (31,5–42,8) 0,00 45,5 (38,3–47,3) 35,1(27,7–43,1) 0,00 2LV% 6,0 (2,8–8,7) 2,0 (1,0–2,5) 0,00 5,0 (2,7–9,0) 2,0 (1,0–3,0) 0,00 2UV% 13,0 (11,8–18,1) 10,7 (9,0–13,0) 0,00 15,4 (9,7–23,1) 11,4 (8,3–16,0) 0,00 Shannon's Entropy SE 3,3 (3,0–3,4) 2,9 (2,5–3,1) 0,00 3,3 (2,8–3,5) 2,8 (2,1–3,2) 0,00 Conditional Entropy CI 0,7 (0,6–0,7) 0,6 (0,5–0,6) 0,00 0,7 (0,6–0,7) 0,6 (0,6–0,7) 0,00 NCI 1,1 (0,9–1,2) 0,9 (0,8–1,0) 0,00 1,1 (0,9–1,2) 0,8 (0,7–1,1) 0,00 Data expressed as median (1st − 3rd interquartile). HF: high frequency in absolute units, LFun: low frequency in normalized units, SE: Shannon Entropy, CI: complexity index, NCI: normalized complexity index, RRi: RR intervals, 0V%: patterns without variations, 1V%: patterns with one variation, 2LV%: patterns with two identical variations, 2UV%: patterns with two different variations. p < 0.05. In Table 4 , the results of the comparison of spirometry indices between the groups are presented. The measured FEV1 variable, as well as the % achieved from the predicted value, were higher in G1-, in individuals who did not have a COVID-19 infection, with a statistically significant difference (P < 0.05). Furthermore, the Tiffeneau index (FEV1/FVC) and its % of predicted were also higher in G1- when compared to G2+, which demonstrates better lung function in individuals who did not have a history of COVID-19 infection. Spirometry results were interpreted according to Pereira, Sato and Rodrigues [ 48 ] to assess the presence of ventilatory disorders. Despite the absence of evident respiratory symptoms during follow-up, it was found that, among the 52 individuals evaluated, 11 (21.1%) showed some form of change. The highest prevalence of such changes was observed in the G2 + group (91%), among them 30% showed mild restrictive disorder, due to the reduction in FVC. Regarding the functional capacity assessed by CPET, although G1- presented a better performance in numerical terms, no significant differences were observed between the groups, as shown in Table 4 . Despite the minimum predicted value calculated by Wasserman [ 75 ], only 11 (21.1%) of the sample had a VO2peak < 85% of predicted, 8 (72.7%) in G1-, all were classified with a level of cardiorespiratory fitness (CRF) very weak or weak according to the classification by Herdy and Caixeta (2016). The main reasons for interrupting the test were: lower limb fatigue (64%) and dyspnea (20%). Table 4 Comparison of spirometric indices between groups (n = 52) G1- (n = 25) G2+ (n = 27) p FVC measured (L) 2,9 (2,6–3,7) 2,8 (2,4–3,1) 0,15 FVC, predicted (L) 3,2 (2,9–4,0) 3,1 (2,9–3,5) 0,42 % of predicted, FVC 91 (79–95) 85 (79–103) 0,51 FEV1, measured (L) 2,5 (2,2–3,0) 2,3 (1,9–2,6) 0,02* FEV1, predicted (L) 2,7 (2,3–3,2) 2,6 (2,3–2,8) 0,50 % of predicted, FEV1 95 (89–102) 86 (76–99) 0,00* FEV1/FVC, measured (%) 84,6 (80,8–87,7) 81,8 (77,5–83,9) 0,00* FEV1/FVC, predicted (%) 80,9 (79,8–81,8) 80,1 (78,7–81,2) 0,19 % of predicted, FEV1/FVC 105 (100–107) 103 (98–104) 0,01* Data expressed as median (1st − 3rd interquartile). FVC: forced vital capacity; FEV1: forced expired volume in the first second; FEV1/FVC: Tiffeneau Index. * p < 0.05. Table 5 Comparison of FC indices assessed by CPET between groups (n = 50) G1- (n = 23) G2+ (n = 27) p VO 2 peak (mL/min) 1344 (1027–1646) 1213 (1103–1400) 0,45 VO 2 max, predicted (mL/min) 1344 (1230–1505) 1322 (1073–1425) 0,18 VO 2 peak (mL/min/Kg) 16,4 (13,3–20,0) 15,7 (14,0–17,7) 0,70 VO 2 max predicted (mL/min/Kg) 16,9 (15,0–19,5) 15,3 (14,4–16,9) 0,10 % of predicted VO 2 peak 94 (82–110) 97 (91–103) 0,49 Maximum Power (Watt) 100 (90–130) 99 (77–115) 0,26 VO 2 max at VAT ( mL//min/Kg) 10,4 (8,9–13,3) 10,4 (9,5–12,1) 0,70 Cardiorespiratory fitness level Very weak 10 (43,5%) 10 (37,0%) 0,65 Weak 13 (56,5%) 17 (63,0%) 0,64 Data expressed as median (1st − 3rd interquartile). VO2max (maximum oxygen consumption); VAT (ventilatory anaerobic threshold). Nominal variables were presented as absolute value and relative frequency (%). Figure 2 shows the relationship between the measured FEV1 variable and VO2peak. There was a positive and moderate correlation only in G2+. Demonstrating that the greater the FEV1 the greater the VO2peak in individuals with a history of COVID-19 infection. Figure 3 shows the relationship between VO2peak and the 0V% index of HRV measured during the waking period, which represents sympathetic modulation. There was a negative correlation in both groups, however this correlation was low in G1- and moderate in G2+. Demonstrating that the higher the VO2peak, the lower the sympathetic predominance during the waking period. The other variables did not show a significant correlation in both groups. Discussion This was the first study to evaluate the long-term impact of COVID-19 on cardiac and pulmonary autonomic function in individuals with SAH who had mild symptoms during the active period of the disease. The main finding was that individuals with SAH who had COVID-19 had worse lung function when compared to individuals without a history of the disease. Therefore, we can observe that even in the long term, after 6 to 18 months, hypertensive individuals infected with SARS-CoV-2 who developed a mild clinical condition may present pulmonary sequelae. Furthermore, the disorders presented were mostly of a restrictive nature, with a reduction in the FVC index on examination. Lung function Previous evidence indicates that the lungs were the organs most affected by the SARS-CoV-2 virus, due to its ability to directly invade lung tissues and that even after 6 months patients may present changes in lung function as post-infection sequelae [52– 54 ]. Since then, spirometry has stood out as the most used tool for evaluating lung function in patients recovered from COVID-19. This method has highlighted restrictive disorders as one of the most frequent respiratory sequelae, with a reduction in the FVC index [ 38 , 55 ]. However, most studies are limited to investigating pulmonary sequelae in individuals with a history of hospitalization. Nirmal et al. (2022) evaluated 39 patients with an average age of 49 years, three months after hospital discharge, and observed through spirometry the presence of restrictive disorder in 66.6% of the sample [ 56 ]. Other studies have shown residual changes in lung function also with restrictive defects, but all after severe or critical COVID-19 [ 57 – 60 ]. This contrasts with our approach, which focused exclusively on individuals who had a mild form of the disease, without the need for hospital admission, which makes direct comparisons of the results obtained in our study with those involving more severe cases difficult. Our hypothesis is that, due to the direct damage to the respiratory system caused by the virus, even patients who have had a mild form of the disease may experience lasting impacts on lung function. As evidenced in our results, when comparing the groups, we observed that the G2 + group demonstrated worse lung function, indicated by lower FEV1 and Tiffeneau index values. It is worth mentioning that despite the reduction in these values, only two individuals in G2 + presented mild obstructive disorder. Cardiac autonomic function Regarding cardiac autonomic function, our study demonstrated the absence of long-term impairment, when analyzing the 24-hour HRV indices in the analyzed population, both for linear and non-linear indices. The literature has demonstrated that COVID-19 infection can generate cardiovascular sequelae associated with dysfunction of the autonomic nervous system [ 61 , 62 ]. One of the hypotheses for this autonomic dysfunction is that the inflammatory storm caused by the SARS-CoV-2 virus, with the release of cytokines such as interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α), can cause an autonomic imbalance, due to local and systemic damage that affects the afferent and efferent receptors of the ANS. The afferent receptors, responsible for transmitting sensory information to the central nervous system, and the efferent receptors, which convey motor commands from the central nervous system to the body, become impaired. This impairment disrupts the homeostasis maintained by the ANS, resulting in an autonomic imbalance that manifests in various symptoms observed in conditions such as long COVID [ 3 , 61 ]. Furthermore, studies indicate that conditions associated with a sympathovagal imbalance, such as SAH, are associated with the severity of COVID-19, due to the possible inability of an appropriate anti-inflammatory response, which in turn depends on adequate vagal signaling [ 3 , 63 , 64 ]. In the study conducted by Acanfora et al. [ 68 ], cardiac autonomic control was assessed using 24-hour Holter monitoring, after 12 weeks of COVID-19 infection in 30 patients, 63.3% of whom were hypertensive, and compared with a control group of 20 individuals, of which 55% were hypertensive. Of the total participants, 21 had a mild to moderate illness and 9 developed a severe form. The authors demonstrated an autonomic imbalance in the post-COVID-19 group characterized by a reduction in the linear indices of SDNN and SDANN, in addition to vagal impairment with a reduction in HF indices and an increase in the LF/HF ratio. Corroborating these findings, Kurtoglu et al. [ 69 ], evaluated non-hypertensive patients, recovered from mild COVID-19 after 20 weeks of infection, aged (40.8 ± 10.3 years) and also observed a reduction in vagal tone with a reduction in rMSSD, pNN50 and HF indices. On the other hand, Asarcikli et al. [ 67 ], evaluated HRV with 24-hour Holter in healthy individuals recovered from COVID-19 with mild to moderate symptoms after 12 to 26 weeks and observed a significant increase in parasympathetic tone due to the increase in linear indices rMSSD, pNN50 and HF in the group post-COVID-19. The authors suggest that the persistence of vagal activity may be associated with fatigue and affects the physical capacity of patients. It is important to highlight that, in contrast to our investigation, most studies do not include hypertensive patients, and when they do, this is generally restricted to a limited portion of the sample. An additional study, involving 65 male patients, after a period of 4 to 6 weeks post-mild COVID-19, with a mean age of 22.6 ± 3.4 years, was conducted in comparison to a control group consisting of 26 patients. In this analysis, an increase in the activity of the parasympathetic nervous system was observed, evidenced by the increase in the 2LV% index obtained by the 24-hour HRV [ 65 ]. Contrary to what was observed in several studies, we did not observe any change in HRV indices, also obtained by 24-hour Holter monitoring, but only evaluating those who had a mild illness. In a previous study [ 66 ], it was observed that the majority of studies that evaluated post-COVID-19 HRV recruited patients who had the disease for a maximum of 6 months, with a very heterogeneous sample profile in relation to comorbidities and despite all of them presenting changes of autonomic control, the results remain contradictory, limited to short and medium-term impacts post-infection [ 65 , 67 – 69 ]. Furthermore, many studies have emphasized the evaluation of patients with a history of hospitalization [ 70 , 71 ]. We hypothesize that the lack of significant findings regarding changes in cardiac autonomic control in our study is due to the profile of the patients evaluated. Hypertensive patients already present with COVID-19 a sympathovagal imbalance, due to the clinical condition of those assessed being mild, the autonomic changes may not have been severe enough to persist in the long term [ 21 , 63 ]. Therefore, the impact on the ANS in hypertensive patients may be limited to the short and medium term periods after COVID-19 infection. Although we used validated indices to evaluate changes in cardiac autonomic control, it is known that the ANS is influenced by several afferent and efferent neural physiological mechanisms, which come from the respiratory systems, baroreceptors, chemoreceptors, mechanoreceptors, among others. Therefore, future studies should include multivariate analysis approaches, considering not only the iRR, but also biological signals, such as respiratory and beat-to-beat blood pressure oscillations, as well as analyzes that consider the causality of biological signals to better understand the impact of disease on the ANS [ 72 – 74 ]. Exercise capacity Regarding the assessment of functional capacity using CPET, we also did not observe significant differences between the groups. Exercise capacity has been assessed by CPET in patients recovered from COVID-19 with and without a history of hospitalization. Njoten et al. (2023), evaluating 65 patients recovered from COVID-19, with no history of hospitalization, aged (39.0 ± 11.8), 83% female, identified through CPET that 2/3 of the participants had normal exercise capacity by VO2peak values. On the other hand, an observational study carried out by Almázan and collaborators (2022), which included 72 patients in the post-mild COVID-19 period, with a mean age of (45.5 ± 9.0), identified that half of these individuals had VO2peak less than 85% of predicted. The research also revealed that patients who experience persistent symptoms after a mild COVID-19 infection may suffer functional limitations, not necessarily due to sequelae of the disease, but rather related to the level of cardiorespiratory fitness (CRF) prior to the infection. Despite differences regarding the inclusion of patients with persistent post-COVID-19 symptoms and VO2peak performance (> 85% of predicted in both groups), our study identified an ACR level classified as weak or very weak across the entire spectrum. sample. This finding allows us to infer that this result can be attributed to pre-existing conditions, such as hypertension, obesity and sedentary lifestyle, which, prior to the infection, may have contributed to a worse performance in the exercise test. It is important to highlight the lack of data on CRF prior to infection in most studies, including ours, which makes it difficult to identify whether the low level of CRF is due to COVID-19 infection. Despite the lack of findings regarding functional capacity between the groups, we identified a moderate positive correlation between FEV1 and VO2peak in G2+, indicating that the greater the expiratory volume in the first second, the greater the functional capacity measured by CPET. The literature shows that the reduction in FEV1 values ​​indicates limitation of expiratory flow and may contribute to a lower ventilatory reserve and consequently exercise intolerance [ 75 , 76 ]. With this, we can suggest that better FEV1 values ​​indicate better lung function, which contributes to a better oxygen transport capacity, causing a direct impact on aerobic capacity measured by VO2peak. Another significant correlation identified was between VO2peak and the 0V% index during the waking period, referring to sympathetic modulation. This negative correlation was low for G1- and moderate for G2+. These results indicate that as functional capacity increases, sympathetic modulation during wakefulness reduces. This observation is consistently supported by adaptations of the cardiovascular system in favor of better FC. Previous studies such as that by Liguori et al. (2014), have already highlighted that improvements in the FC and efficiency of the cardiovascular system can result in a reduction in the activation of the sympathetic nervous system during rest. Furthermore, it is crucial to consider that sympathetic hyperactivation, often associated with a higher HR and greater energy consumption at rest, can compromise the overall efficiency of the cardiovascular system and negatively impact aerobic performance [ 77 ]. Therefore, this could explain the existence of a relationship between greater functional capacity through VO2peak and lower sympathetic activity at rest. Limitations Although we exerted rigorous efforts to conduct this research, it is critical to recognize the inherent limitations of the study. Among them, we would like to highlight that whether or not the history of COVID-19 was confirmed was self-reported, and that we cannot say that patients in G1- did not have asymptomatic symptoms of the disease. Furthermore, we do not have the spirometry history of individuals prior to SARS-CoV-2 infection, which limits the conclusion that the worsening of lung function is actually due to COVID-19. Furthermore, it is important to highlight that the sample of this study was restricted to a specific profile of participants with SAH, who presented mild COVID-19 without the need for hospitalization, with an average age of 55 years and a higher prevalence of females (78 .8%), which may limit the extrapolation of results to other groups. Conclusion According to our observations, we can conclude that there is no change in the ANS or HR in patients with hypertension who recovered from COVID-19, but there is a worsening of lung function, with the presence mainly of restrictive disorder that can negatively contribute to CPET performance. Therefore, the importance of a comprehensive approach in the post-infection evaluation of hypertensive patients is highlighted. These findings provide valuable insights for health professionals and researchers, such as the clinical importance of evaluating the pulmonary component in these individuals after mild COVID-19, with pre-existing conditions, such as SAH and instituting Cardiorespiratory Rehabilitation programs. We suggest that new studies be carried out evaluating patients recovered from COVID-19 in different populations, including ANS dysfunction as a post-infection sequelae. Declarations Declaration of conflict of interest The authors declare that there is no conflict of interest regarding the publication of this article. Funding source This work was supported by the Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – under grant number 001. 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Disponível em: /pmc/articles/PMC7236826/ Frija-Masson J, Debray MP, Gilbert M, Lescure FX, Travert F, Borie R, Duhamel A, Le Guern R, Braune S, Valeyre D, Tiberghien P, Le Roux G, Papon JF (2020) Functional characteristics of patients with SARS-CoV-2 pneumonia at 30 days post-infection. Eur Respir J 56(2). Disponível em: /pmc/articles/PMC7301832/ Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, Zhang L, Zhang X, Cheng Z, Li Y, Li H, Liu P, Xiao M, Shen Y, Lu R, Zhao Z, Hu Y, Yang L, Li S, Zhao Y (2021) RETRACTED: 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet 397(10270):220. Disponível em: /pmc/articles/PMC7833295/ Li X, Wang C, Kou S, Luo P, Zhao M, Yu K (2020) Lung ventilation function characteristics of survivors from severe COVID-19: a prospective study. Crit Care 24(1). 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Disponível em: https://bioelecmed.biomedcentral.com/articles/10.1186/s42234-020-00058-0 Soliński M, Pawlak A, Petelczyc M, Buchner T, Aftyka J, Gil R, Chmielarz P, Zawisza D, Sosnowski M, Kwiatkowski A, Kwiecień M (2022) Heart rate variability comparison between young males after 4-6 weeks from the end of SARS-CoV-2 infection and controls. Sci Rep 12(1). Disponível em: https://pubmed.ncbi.nlm.nih.gov/35614330/ Ferreira ÁA, Abreu RM de, Teixeira RS, da Silva Neto HR, Roriz PIL, Silveira MS, Dantas FMNA, Andrade AD, Scwingel PA, Neves VR (2024) Applicability of heart rate variability for cardiac autonomic assessment in long-term COVID patients: A systematic review. J Electrocardiol. 82:89–99. Asarcikli LD, Hayiroglu Mİ, Osken A, Keskin K, Kolak Z, Aksu T (2022) Heart rate variability and cardiac autonomic functions in post-COVID period. J Interv Card Electrophysiol 63(3):715–21. 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Disponível em: https://pubmed.ncbi.nlm.nih.gov/35571200/ Bai T, Zhou D, Yushanjiang F, Wang D, Zhang D, Liu X, Zheng L, Zhang Y, Hu M (2022) Alternation of the Autonomic Nervous System Is Associated With Pulmonary Sequelae in Patients With COVID-19 After Six Months of Discharge. Front Physiol 12. Disponível em: https://pubmed.ncbi.nlm.nih.gov/35126184/ Bajić D, Ðajić V, Milovanović B (2021) Entropy Analysis of COVID-19 Cardiovascular Signals. Entropy 2021, Vol 23, Page 87 23(1):87. Disponível em: https://www.mdpi.com/1099-4300/23/1/87/htm Dick TE, Hsieh YH, Dhingra RR, Baekey DM, Galán RF, Wehrwein E, Liao W, McDougal D, Paton JF (2014) Cardiorespiratory Coupling: Common Rhythms in Cardiac, Sympathetic, and Respiratory Activities. Prog Brain Res 209:191–205. Malik M, Camm AJ, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, Hoes AW, Lanza GA, Pinna GD, Spataro A (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17(3):354–81. Wasserman K, Hansen JE, Sue DY, Stringer WW, Casaburi R (2011) Principles of exercise testing and interpretation: Including pathophysiology and clinical applications: Fifth edition. Disponível em: https://www.researchgate.net/publication/304853582_Principles_of_exercise_testing_and_interpretation_Including_pathophysiology_and_clinical_applications_Fifth_edition Da Silva R, Pulmonar F, Henrique S, Ramalho R, Correa A, Balbuena De Lima G, Lima R, Ferreira R, Rabelo F (2022) Relationship of Lung Function and Inspiratory Strength with Exercise Capacity and Prognosis in Heart Failure. Arq Bras Cardiol 118(4):680–91. Disponível em: https://doi.org/10.36660/abc.20201130 Goldstein DS, Robertson D, Esler M, Straus SE, Eisenhofer G (2002) Dysautonomias: clinical disorders of the autonomic nervous system. Ann Intern Med 137(9):753–63. Disponível em: https://pubmed.ncbi.nlm.nih.gov/12416949/ 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6363534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449248450,"identity":"3c581706-595d-4bea-bb11-95e1bc3b6596","order_by":0,"name":"Ádrya Aryelle Ferreira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFCCBBAhB2YeYGCwAdFsxGgxhmlJI1ELEBwmrIW/PfeZxA8GAznz2YcPHq6oOZ+4nb2B7XEFHi0SZ56bSfYwGBjLnEtLOHjm2O3EnT0H2A3P4LPmRhqbBA/Dn8QZPDwGBxvYbiduuJHAJtmAR4c8UIvkHwaD+hk8/B8ONvw7l7jh/gP8WgyAWqR5GAwSJHh4GA42th0A2sKAX4vhmWfM1jIGBoYzeNgMDjb2JRvv7ElsN8SnRe54GuPNNxUG8hI8zI8/Nnyzk93OfvjYQ3xagIBFgsEA2akMjAQ0MDAwf0DhGuBQNgpGwSgYBSMXAADVUUzIcp7LOAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-2404-6624","institution":"Universidade de Pernambuco - 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Campus de Petrolina","correspondingAuthor":false,"prefix":"","firstName":"Fabianne","middleName":"Maisa de Novaes Assis","lastName":"Dantas","suffix":""},{"id":449248461,"identity":"43bc0f60-4131-40e1-974d-de68bce8654b","order_by":11,"name":"Armele de Fátima Dornellas de Andrade","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Armele","middleName":"de Fátima Dornellas","lastName":"de Andrade","suffix":""},{"id":449248462,"identity":"a009662d-c0b1-4c1f-b36a-f9d51ecf38dc","order_by":12,"name":"Victor Ribeiro Neves","email":"","orcid":"","institution":"Universidade de Pernambuco - Campus de Petrolina","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Ribeiro","lastName":"Neves","suffix":""}],"badges":[],"createdAt":"2025-04-02 17:55:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6363534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6363534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82141781,"identity":"b5aa9397-2cf6-4e16-85c9-d220e8078415","added_by":"auto","created_at":"2025-05-07 06:35:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46284,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart illustrates the process of inclusion and exclusion of samples throughout the study. Initially, 52 participants were recruited and underwent pulmonary and autonomic function assessments, being divided into two groups: G1- (n = 25) and G2+ (n = 27). Both groups underwent cardiopulmonary testing, and two exclusions occurred in the G1- group during the analysis process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6363534/v1/a9e52d526318b78d772e4aa5.png"},{"id":82143852,"identity":"03e9798e-d27c-4f56-aff0-929f07e53d59","added_by":"auto","created_at":"2025-05-07 06:43:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57100,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between FEV1 and VO2peak\u003c/p\u003e\n\u003cp\u003eLegend: The figure shows a scatterplot illustrating a positive correlation between FEV1 and VO2peak across groups, with values ​​representing significant data for G2+ participants.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6363534/v1/e40cfcee04f66cb90da76c31.png"},{"id":82141782,"identity":"db3f02ad-9841-41db-85f8-c5b558af292f","added_by":"auto","created_at":"2025-05-07 06:35:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55465,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between VO2peak and HRV indices\u003c/p\u003e\n\u003cp\u003eLegend: The figure shows a scatterplot illustrating a negative correlation between VO2peak and 0V% index of HRV across groups, with values ​​representing significant data for both groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6363534/v1/ee8360938f28c4c3d3fefdd4.png"},{"id":86026810,"identity":"4512d2e2-1c1d-45d3-b37e-0c209d9a7d24","added_by":"auto","created_at":"2025-07-04 13:21:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1301928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6363534/v1/8bab0de2-36c0-4b96-89ce-a2950d8122fc.pdf"}],"financialInterests":"","formattedTitle":"Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in hypertensive individuals","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eThe outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease 2019 (COVID-19), caused a global pandemic declared by the World Health Organization (WHO) in 2020. COVID \u0026minus;\u0026thinsp;19 is marked by its remarkable transmission capacity and high level of morbidity and mortality [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In general, most infected individuals are asymptomatic or present mild symptoms such as dry cough, fever, headache, runny nose, anosmia and mild fatigue, without the need for hospitalization. However, in 15% of cases the condition can progress to severe respiratory failure requiring admission to the Intensive Care Unit (ICU) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter the acute phase of SARS-CoV-2 infection, survivors often experience persistent symptoms like fatigue, dyspnea, chest pain, arrhythmias, tachycardia, autonomic dysfunction, anxiety, and depression for over 3 months [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u0026ldquo;Long COVID\u0026rdquo; describes symptoms developing 3 months post-infection and lasting over two months [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], attributed to immune dysregulation, autoimmune manifestations, persistent viral reservoirs, and neuroinvasive effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Chen et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] found \u0026ldquo;long COVID\u0026rdquo; in approximately 43% of recovered individuals from 50 studies. Identifying these long-term sequelae is crucial for developing treatment strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the active period of the disease, the exacerbated inflammatory response known as \u0026ldquo;cytokine storm\u0026rdquo; can cause direct damage to the cardiovascular and pulmonary systems and, in severe cases, resulting in Acute Respiratory Distress Syndrome (ARDS) and acute myocardial injury [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to the intense inflammatory process, some studies indicate that the severity of the COVID-19 condition may also be related to age and the presence of pre-existing comorbidities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is known that individuals with Cardiovascular Diseases (CVDs) and their risk factors, such as Systemic Arterial Hypertension (SAH), have a strong association with the severity and mortality by COVID-19 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A likely pathophysiological explanation is the high affinity of SARS-CoV-2 for the Angiotensin Converting Enzyme 2 (ACE2) receptor, present both in lung cells and in the vascular endothelium, which functions as a mechanism of entry for the virus into the cell [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This enzyme is highly expressed in these individuals with CVD, making them more susceptible to the disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong CVDs, SAH affects more than 30% of the world population, with a prevalence of approximately 30\u0026nbsp;million in Brazil [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This disease is defined as a multifactorial clinical condition characterized by a sustained increase in Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) levels [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. According to previous studies, SAH was the most prevalent chronic disease among those infected with SARS-CoV-2 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, it can provide greater chances of unfavorable outcomes such as the development of a severe form of the disease and post-infection sequelae [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, SAH is associated with an increase in sympathetic nervous activity and vagal suppression, which characterizes an autonomic imbalance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies already indicate that COVID-19 can cause sequelae in the autonomic nervous system (ANS) that are not limited to critically ill patients, including orthostatic hypotension (OH), postural orthostatic tachycardia syndrome (POTS), dysfunction in body thermoregulation, in addition to exacerbation of pre-existing sympathovagal imbalance conditions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, there are still no studies investigating the long-term effects of COVID-19 on the ANS of patients with SAH [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The cardiac autonomic control can be assessed non-invasively through Heart Rate Variability (HRV) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. HRV corresponds to the study of temporal oscillations of consecutive RR intervals (iRR) from the electrocardiogram [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A high resting HRV has been linked to efficient cardiac autonomic modulation, capable of responding to different internal and external stimuli. On the other hand, a low HRV at rest signals an abnormal and insufficient adaptation of the ANS, resulting in health impairments, due to poor physiological functioning, low physical capacity and the presence of CVDs, such as SAH, as well as COVID-19 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to dysautonomia, studies indicate that individuals who have been hospitalized due to COVID-19 face a significant decrease in lung function and functional capacity (FC), which can persist for months after recovery [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This impairment appears to be intrinsically linked to specific injuries caused by viral inflammation, especially in the respiratory system, with a consequent reduction in lung function analyzed by indices such as forced vital capacity (FVC), total lung capacity (TLC) and diffusion capacity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, deficits in FC have been associated with direct damage to the ANS and respiratory system. Previous studies show that 85% of those recovered from COVID-19 have an impaired exercise response during cardiopulmonary exercise testing (CPET) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This intricate network of changes in the ANS, cardiovascular and respiratory systems highlights the need for cardiorespiratory rehabilitation, which demonstrates the fundamental importance of evaluating pulmonary and functional sequelae [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there is a lack of evidence investigating the long-term impact of COVID-19 on SAH patients. Therefore, the main objective of this study was to evaluate the long-term impact of COVID-19 on cardiac and pulmonary autonomic function in patients with AH. Furthermore, the results of this study can provide information to various health professionals, in order to help them guide and qualify rehabilitation programs for these patients. We hypothesize that individuals with SAH after COVID-19 infection, even those who experienced mild symptoms, not requiring hospitalization, may present greater impairment in cardiovascular autonomic balance and lung function, in addition, that these impairments may be associated with a worse FC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis is a cross-sectional observational study, described in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative, translated into Portuguese [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], carried out from March/2023 to September/2023 and approved by the Research Ethics Committee of the University of Pernambuco (UPE), (CAAE \u0026minus;\u0026thinsp;66973322.0.0000.5191). All participants provided voluntary consent before participation, signing the Free and Informed Consent Form (TCLE).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and sample selection\u003c/h3\u003e\n\u003cp\u003eFifty-two individuals with AH were evaluated, of both sexes, aged between 40 and 75 years, who had or had not been infected with the SARS-CoV-2 virus. The sample selection was carried out based on publicity on radio, television and digital media.\u003c/p\u003e\n\u003ch3\u003eElegibility criteria\u003c/h3\u003e\n\u003cp\u003eParticipants diagnosed with SAH for at least one year, using continuous and unchanged antihypertensive medication for at least 3 months, aged between 40 and 75 years of both sexes, were included. For (G1-), individuals with AH who did not have a confirmed history of COVID-19 infection, and for (G2+), individuals previously infected by SARS CoV-2 and with diagnostic confirmation, there are at least 6 and at most 18 months, who had mild symptoms of COVID-19, without the need for hospital admission, whether in a ward or ICU.\u003c/p\u003e \u003cp\u003eIndividuals who presented any physical or mental limitation at the time of the assessment that prevented the examinations from being carried out, or had SBP/DBP greater than 180/100 mmHg were not included; as well as pregnant women, as physical tests, even at submaximal levels, may not be safe for this population. Patients who had severe symptoms of COVID-19 and needed to be admitted with ventilator support in the ICU or wards were also not included in the research. Volunteers who were unable to perform the tests or complete the protocols were excluded from the study.\u003c/p\u003e \u003cp\u003eIn the anamnesis, data such as age, sex, body mass (BM), height, body mass index (BMI) were recorded, in addition to the presence of diseases (heart disease, liver disease, neoplasms, pulmonary, metabolic, renal, cerebral, vascular diseases), lifestyle habits (smoking, alcoholism, physical activity) and vital signs [SBP/DBP, Heart Rate (HR) and Peripheral Oxygen Saturation (SpO2)] of the subjects.\u003c/p\u003e \u003cp\u003eAll volunteers underwent assessments of long-term autonomic control, lung function and maximum functional capacity.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThis study was based on an intentional sample, in which the researchers played the role of carefully outlining the profile of participants to be included. The sample size was calculated using the GPower 3.1.9.4 software, with significance levels α set at 0.05 and statistical power β set at 0.80, resulting in a total of 42 individuals, with each group consisting of 21 participants. It is worth noting that, at the end of the study, the sample number was increased, totaling 52 individuals, distributed between groups G1- (n\u0026thinsp;=\u0026thinsp;25) and G2+ (n\u0026thinsp;=\u0026thinsp;27).\u003c/p\u003e \u003cp\u003e \u003cb\u003eS\u003c/b\u003e \u003cb\u003etrategy and operationalizationof the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParticipants underwent assessments of cardiac autonomic function, pulmonary function and maximum functional capacity in the afternoon from 2 to 6 pm. The individuals were evaluated individually at the Cardiorespiratory Physiotherapy Laboratory (LAFIC) of the University of Pernambuco (UPE) Campus Petrolina by a researcher, in order to ensure that they were in good clinical and/or health conditions to carry out the tests and exams.\u003c/p\u003e \u003cp\u003eOn the first day, anamnesis was carried out with the collection of the participant's personal data (name, age, sex, contact telephone number) and anthropometric data (BM, height, BMI), as well as the presence of associated diseases, lifestyle habits and measurement of vital signs (SBP/DBP, HR and SpO2). The participant also informed whether they had any limiting symptoms or complaints before starting the assessment, if so, the assessments were rescheduled. Afterwards, a pulmonary function test was performed and a 24-hour Holter was installed. In the following day, the patient returned in order to have the 24-hour Holter removed and the researchers proceeded with data extraction, ensuring the effectiveness of the recording. In the same day, the team scheduled the CPET for the following week. This strategy aimed to avoid interference from a bad night's sleep after Holter and other discomforts generated by 24 hours with the equipment.\u003c/p\u003e\n\u003ch3\u003eLong-term HRV recording\u003c/h3\u003e\n\u003cp\u003eLong-term HRV was performed by recording a 24-hour electrocardiogram using the device (CARDIOS, S\u0026atilde;o Paulo, SP, Brazil), with a sampling rate of 800 points per second (pps) with 12-bit resolution, to assess heart rate variations over a 24-hour period. The electrocardiogram was recorded using 4 channels fixed to the patient's chest and connected to the device, which is located on the patient's waist and records the information transmitted by these electrodes. After installation, individuals were instructed to carry out their activities normally during the 24 hours of the exam, except taking a shower. In addition, everyone received a sheet to record the time they went to sleep and the time they woke up, in addition to any changes that occurred during the 24 hours (\u003cem\u003ee.g\u003c/em\u003e.: cardiac, respiratory symptoms, headaches), as well as the time they occurred. After 24 hours, the device was removed to pre-process the data that was recorded on the equipment.\u003c/p\u003e \u003cp\u003eThe iRR time series was transferred to a personal computer and processed, after computerized primary analysis, all recordings were reviewed and edited manually by a trained team to carefully eliminate ectopic beats and artifacts, cleaning of artifacts did not exceed 5% of the sample iRR. All records had a minimum of 18 hours of iRR with sinus rhythm, divided into sleep and wake periods [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To analyze the sleep period, the hours informed in the volunteer's record were selected, with the first and last hours being discarded to guarantee the majority of the patient's sleep, a period where it is expected that there will be greater vagal tone, and for wakefulness, the hours from waking up until removing the equipment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLong-term HRV processing\u003c/h2\u003e \u003cp\u003eThe HRV measurements were processed using specific routines for linear and non-linear analysis, developed by a bioengineer Prof. Dr. Alberto Porta (Universit\u0026agrave; degli Studi di Milano, Milan, Italy). For the linear analysis in the time domain, the following indices were used: (1): the average of all normal RR intervals in milliseconds (ms); (2) the variance of all normal iRR in milliseconds (ms\u0026sup2;). In the frequency domain, the autoregressive model was applied to the iRR sequence data. Two spectral components were considered: low frequency (LF - from 0.04 to 0.15 Hz) and high frequency (HF - from 0.15 to 0.40 Hz), which represent cardiac sympathetic and parasympathetic modulation, respectively [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The spectral components were also expressed in normalized units (LFnu and HFnu). Normalization consists of dividing the power of a given spectral component (HF or LF) by the total power minus the power below 0.04 Hz, and multiplying the ratio by 100. This strategy is extremely important for the BF index, having Since it represents a mixed modulation, normalization helps to remove the influence of other bands, which makes it a predominantly sympathetic index [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor non-linear analysis, these iRR were transformed into a sequence of symbols (numbers) ranging from 0 to 5. Next, the patterns of a sequence of 3 beats were constructed. The distribution of patterns was calculated by Shannon entropy (SE). This index describes the shape of the distribution of patterns [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To perform symbolic analysis (SA), all patterns were grouped into four families, described as follows: (a) patterns without variation (0V); (b) patterns with a variation (1V: 2 consecutive symbols are the same and one symbol is different); (c) patterns with two similar variations (2LV); (d) 2 different variations (2UV: 3 symbols that form a peak or a valley). The occurrence rate of each pattern is defined as 0V%, 1V%, 2LV%, and 2UV%. Where 0V% can be considered as a marker of sympathetic modulation, 1V% as a marker of mixed modulation, 2LV% and 2UV% as markers of vagal modulation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to Porta et al., [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] conditional entropy (CE) measures the amount of information per new sample that cannot be obtained from a sequence L of values. EC is evaluated by the complexity index (CI). Additionally, to calculate normalized CI (NCI), it was normalized by the SE of the RRi and ranges from 0 (null information) to 1 (maximum information). The higher the CI and NCI, the greater the complexity and the lower the regularity of the series.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpirometry\u003c/h3\u003e\n\u003cp\u003eThe pulmonary function test was performed to evaluate and quantify the presence of ventilatory disorders, with the analysis of airflows, volumes and lung capacities. The procedure was conducted in accordance with the standards of the American Thoracic Society/European Respiratory Society (ATS/ERS) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The evaluation was carried out using a spirometer (Quark CPET, Cosmed, Italy). Prior to the day of the assessment, individuals were instructed to avoid ingesting heavy foods, caffeine or performing vigorous exercise in the 2 hours prior to the test. At the time of the assessment, participants were positioned comfortably in a chair with a straight back without wheels, keeping their feet well supported on the floor. Next, the evaluator provided guidance and demonstrated how to perform the deep inspiration and forced expiration maneuver to ensure familiarity with the process.\u003c/p\u003e \u003cp\u003eThe following variables were analyzed: forced vital capacity (FVC), which represents the maximum volume of air that can be exhaled after a maximum inspiration, its reduced value may characterize a restrictive disorder; forced expiratory volume in the first second (FEV1), which indicates the amount of air exhaled in the first second of forced expiration, being one of the main indicators of pulmonary function essential for classifying ventilatory disorders, and the ratio (FEV1/FVC), which represents the Tiffeneau index. The values ​​obtained were compared with the values ​​predicted by Pereira, Sato and Rodrigues (2007) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCPET\u003c/h3\u003e\n\u003cp\u003eThe CPET was performed in order to analyze FC, was conducted according to the criteria established by the European Respiratory Society (ERS) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], carried out using a horizontal exercise bike (Quinton Corival 400, USA), with ergospirometric parameters monitored by a gas analyzer (Quark CPET, Cosmed, Italy). The team always consisted of 3 experienced examiners, including a cardiologist.\u003c/p\u003e \u003cp\u003ePrior to the assessment day, participants were instructed to avoid ingesting stimulant drinks (coffee, energy drinks, green tea) and heavy foods in the 3 hours prior to the exam, as well as avoiding strenuous exercise in the previous 24 hours. In addition, they were instructed on the appropriate clothing and footwear to perform the test, and in the case of male participants, they were asked to perform a trichotomy for adequate adhesion of the electrodes.\u003c/p\u003e \u003cp\u003eOn the day of the evaluation, the patients were evaluated regarding their health conditions, in addition to measuring their vital signs. In case of important changes in them, the exam was rescheduled. Moreover, patients were continuously monitored by an 12 leads ECG.\u003c/p\u003e \u003cp\u003eThe test protocol consisted in 1 min at rest with the volunteer properly positioned on the exercise bike, followed by a 2-minute warm-up with free load (approximately 4 W), using a staggered protocol with a progressive increase in workload every minute. according to a total exercise time between 8 and 12 min. A load of 15 Watts/min was standardized for all individuals (50). A cycling frequency of 60\u0026ndash;65 rotations per minute (rpm) had to be maintained, and every 2 min after the start of the load increment, the variables of SBP/DBP and subjective perception of exertion for dyspnea and limb fatigue were recorded by the Modified Borg Scale [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The test ended with a 2 min cool down. The examination was interrupted if maximum voluntary exhaustion was reached or in case of important electrocardiographic changes and/or limiting symptoms.\u003c/p\u003e \u003cp\u003eTo determine cardiorespiratory capacity and power, 2 independent researchers were responsible for identifying the ventilatory anaerobic threshold (VAT) and respiratory compensation point, both using the analysis data of VE/VO2, VE/VCO2, PetO2, PetCO2 and the V-slope method. The following variables were used for analysis: (1) VO2peak (maximum O2 consumption achieved during CPET) (2) Maximum power achieved (watts) and (3) VO2 at VAT.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using the IBM\u0026reg; SPSS\u0026reg; Statistics version 22.0 program. The normality of data from the studied population was analyzed using the Kolmogorov\u0026ndash;Smirnov test. Parametric and non-parametric variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and median (1st \u0026ndash; 3rd interquartile), respectively. Intragroup differences between periods of sleep and wakefulness were analyzed using the Wilcoxon Signed Rank test for related samples and for intergroup comparisons, the Mann-Whitney U test was used for independent samples. The association of categorical variables was tested using Chi-square. Correlations between HRV, CF and Spirometry indices, were performed using the Spearman correlation test. The classification of Spearman's correlation coefficients was carried out according to Munro (2001) and grouped as small correlation: 0 to 0.25; low: 0.26 to 0.49; moderate: 0.50 to 0.69; high: 0.70 to 0.89 and very high: 0.90 to 1.00. In addition, subanalyses were performed with baseline characteristics to deal with confounding factors such as age and medications. Results whose descriptive levels (\u003cem\u003ep\u003c/em\u003e values) were below 0.05 were considered as statistically significant. In some situations, results whose p-values ​​are between 0.05 and 0.08 will be referred as trends.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe sample consisted of 52 individuals with an average age of (55.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09), with a higher prevalence of females (78.8%), divided into G1- (n\u0026thinsp;=\u0026thinsp;25; 19 women/6 men) and G2+ (n\u0026thinsp;=\u0026thinsp;27; 22 women/5 men). Of these, two participants from G1- were excluded during the CPET phase, but continued to evaluate the results of cardiac and pulmonary autonomic function. The flowchart of study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the general characteristics of the sample. In relation to the baseline, with the variables of age, sex, height, body mass and BMI, there was no statistical difference between the groups. It is worth noting that all individuals were continuously using antihypertensives and 8 (19.2%) were using beta blockers associated with AH treatment. No significant differences were detected between risk factors, comorbidities and medication use. It is noteworthy that, among the 52 participants analyzed, 33 (63.4%) were obese, with a higher prevalence in G2+, individuals with a history of COVID-19 infection, totaling 18 (54.5%) participants.\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\u003eCharacteristics of the studied population (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eG1- (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eG2+ (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e53.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e57,74\u0026thinsp;\u0026plusmn;\u0026thinsp;10,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e19 (76,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e22 (81,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAS/PAD (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnthropometric data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMassa corporal (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e85,17\u0026thinsp;\u0026plusmn;\u0026thinsp;14,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e82,91\u0026thinsp;\u0026plusmn;\u0026thinsp;18,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e32.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e32.03\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1 (4,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e7 (28,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e12 (44,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade I and II obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e10 (40,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e16 (59,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade III obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e5 (20,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2 (7,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociated comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1 (4,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e5 (18,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma (self-related)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1 (4,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3 (11,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass of medications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2 (8,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e6 (22,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium channel blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e4 (16,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e9 (33,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2 (8,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e1 (3,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; BMI: body mass index; ACE: angiotensin converting enzyme. Nominal variables were presented as absolute value and relative frequency (%).\u003c/p\u003e \u003cp\u003eThe long-term HRV indices of the studied groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There was no statistical difference in any indices of the linear and non-linear analyzes between the groups. However, when examining the intragroup disparity between periods of sleep and wakefulness, statistically significant discrepancies were identified in all HRV indices, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These variations align in a physiologically expected manner, since there is a prevalence of the system vagal nervous system during sleep, contrasting with the predominance of the sympathetic system during wakefulness.\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\u003eComparison of linear and non-linear indices of long-term HRV between groups (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eG1- (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eG2+ (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVigil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVigil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVigil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinear analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean RRi (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865,9 (790,5\u0026ndash;1021,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e715,9 (628,9\u0026ndash;760,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e932,6 (814,5\u0026ndash;998,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e738,4 (654,4\u0026ndash;851,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRi variance (ms\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1854,0 (819,4\u0026ndash;4870,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1331,4 (665,9\u0026ndash;3248,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1853,0 (991,9\u0026ndash;4730,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1253,0 (705,9\u0026ndash;2525,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency domain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,8 (23,3\u0026ndash;41,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44,6 (30,4\u0026ndash;60,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,6 (19,5\u0026ndash;42,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39,2 (28,4\u0026ndash;49,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384,7 (134,7\u0026ndash;1371,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114,4 (44,4\u0026ndash;421,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e386,0 (213,1\u0026ndash;85,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130,9 (56,8\u0026ndash;354,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNonlinear analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSymbolic analysis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0V%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,4 (24,2\u0026ndash;38,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50,5 (40,3\u0026ndash;57,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26,7 (16,0\u0026ndash;40,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50,8 (39,1\u0026ndash;60,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1V%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45,8 (41,0\u0026ndash;48,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,8 (31,5\u0026ndash;42,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45,5 (38,3\u0026ndash;47,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,1(27,7\u0026ndash;43,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2LV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,0 (2,8\u0026ndash;8,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,0 (1,0\u0026ndash;2,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,0 (2,7\u0026ndash;9,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,0 (1,0\u0026ndash;3,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2UV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,0 (11,8\u0026ndash;18,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,7 (9,0\u0026ndash;13,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,4 (9,7\u0026ndash;23,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,4 (8,3\u0026ndash;16,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eShannon's Entropy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,3 (3,0\u0026ndash;3,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,9 (2,5\u0026ndash;3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,3 (2,8\u0026ndash;3,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,8 (2,1\u0026ndash;3,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConditional Entropy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,7 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,6 (0,5\u0026ndash;0,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,7 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,6 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,1 (0,9\u0026ndash;1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,9 (0,8\u0026ndash;1,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,1 (0,9\u0026ndash;1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,8 (0,7\u0026ndash;1,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData expressed as median (1st \u0026minus;\u0026thinsp;3rd interquartile). HF: high frequency in absolute units, LFun: low frequency in normalized units, SE: Shannon Entropy, CI: complexity index, NCI: normalized complexity index, RRi: RR intervals, 0V%: patterns without variations, 1V%: patterns with one variation, 2LV%: patterns with two identical variations, 2UV%: patterns with two different variations.\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\u003eIntragroup comparison of HRV indices between periods of sleep and vigil (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eG1- (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eG2+ (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVigil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVigil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinear analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean RRi (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865,9 (790,5\u0026ndash;1021,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e715,9 (628,9\u0026ndash;760,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e932,6 (814,5\u0026ndash;998,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e738,4 (654,4\u0026ndash;851,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRi variance (ms\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1854,0 (819,4\u0026ndash;4870,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1331,4 (665,9\u0026ndash;3248,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1853,0 (991,9\u0026ndash;4730,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1253,0 (705,9\u0026ndash;2525,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency domain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF (one)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30,8 (23,3\u0026ndash;41,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e44,6 (30,4\u0026ndash;60,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25,6 (19,5\u0026ndash;42,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39,2 (28,4\u0026ndash;49,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384,7 (134,7\u0026ndash;1371,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e114,4 (44,4\u0026ndash;421,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e386,0 (213,1\u0026ndash;85,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e130,9 (56,8\u0026ndash;354,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNonlinear analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSymbolic analysis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0V%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,4 (24,2\u0026ndash;38,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e50,5 (40,3\u0026ndash;57,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,7 (16,0\u0026ndash;40,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50,8 (39,1\u0026ndash;60,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1V%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45,8 (41,0\u0026ndash;48,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35,8 (31,5\u0026ndash;42,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45,5 (38,3\u0026ndash;47,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35,1(27,7\u0026ndash;43,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2LV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,0 (2,8\u0026ndash;8,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,0 (1,0\u0026ndash;2,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,0 (2,7\u0026ndash;9,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,0 (1,0\u0026ndash;3,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2UV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,0 (11,8\u0026ndash;18,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10,7 (9,0\u0026ndash;13,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,4 (9,7\u0026ndash;23,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,4 (8,3\u0026ndash;16,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eShannon's Entropy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,3 (3,0\u0026ndash;3,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,9 (2,5\u0026ndash;3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,3 (2,8\u0026ndash;3,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,8 (2,1\u0026ndash;3,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConditional Entropy\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,7 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0,6 (0,5\u0026ndash;0,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,7 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,6 (0,6\u0026ndash;0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,1 (0,9\u0026ndash;1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0,9 (0,8\u0026ndash;1,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,1 (0,9\u0026ndash;1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,8 (0,7\u0026ndash;1,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData expressed as median (1st \u0026minus;\u0026thinsp;3rd interquartile). HF: high frequency in absolute units, LFun: low frequency in normalized units, SE: Shannon Entropy, CI: complexity index, NCI: normalized complexity index, RRi: RR intervals, 0V%: patterns without variations, 1V%: patterns with one variation, 2LV%: patterns with two identical variations, 2UV%: patterns with two different variations. \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the results of the comparison of spirometry indices between the groups are presented. The measured FEV1 variable, as well as the % achieved from the predicted value, were higher in G1-, in individuals who did not have a COVID-19 infection, with a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the Tiffeneau index (FEV1/FVC) and its % of predicted were also higher in G1- when compared to G2+, which demonstrates better lung function in individuals who did not have a history of COVID-19 infection.\u003c/p\u003e \u003cp\u003eSpirometry results were interpreted according to Pereira, Sato and Rodrigues [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] to assess the presence of ventilatory disorders. Despite the absence of evident respiratory symptoms during follow-up, it was found that, among the 52 individuals evaluated, 11 (21.1%) showed some form of change. The highest prevalence of such changes was observed in the G2\u0026thinsp;+\u0026thinsp;group (91%), among them 30% showed mild restrictive disorder, due to the reduction in FVC.\u003c/p\u003e \u003cp\u003eRegarding the functional capacity assessed by CPET, although G1- presented a better performance in numerical terms, no significant differences were observed between the groups, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eDespite the minimum predicted value calculated by Wasserman [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], only 11 (21.1%) of the sample had a VO2peak\u0026thinsp;\u0026lt;\u0026thinsp;85% of predicted, 8 (72.7%) in G1-, all were classified with a level of cardiorespiratory fitness (CRF) very weak or weak according to the classification by Herdy and Caixeta (2016). The main reasons for interrupting the test were: lower limb fatigue (64%) and dyspnea (20%).\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\u003eComparison of spirometric indices between groups (n\u0026thinsp;=\u0026thinsp;52)\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1- (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG2+ (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC measured (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,9 (2,6\u0026ndash;3,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,8 (2,4\u0026ndash;3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC, predicted (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,2 (2,9\u0026ndash;4,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,1 (2,9\u0026ndash;3,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of predicted, FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (79\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (79\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, measured (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,5 (2,2\u0026ndash;3,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,3 (1,9\u0026ndash;2,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,02*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1, predicted (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,7 (2,3\u0026ndash;3,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,6 (2,3\u0026ndash;2,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of predicted, FEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (89\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (76\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,00*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, measured (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84,6 (80,8\u0026ndash;87,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81,8 (77,5\u0026ndash;83,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,00*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC, predicted (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80,9 (79,8\u0026ndash;81,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80,1 (78,7\u0026ndash;81,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of predicted, FEV1/FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (100\u0026ndash;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (98\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,01*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData expressed as median (1st \u0026minus;\u0026thinsp;3rd interquartile). FVC: forced vital capacity; FEV1: forced expired volume in the first second; FEV1/FVC: Tiffeneau Index. \u003cb\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/b\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\u003eComparison of FC indices assessed by CPET between groups (n\u0026thinsp;=\u0026thinsp;50)\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1- (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG2+ (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003epeak (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1344 (1027\u0026ndash;1646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1213 (1103\u0026ndash;1400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003emax, predicted (mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1344 (1230\u0026ndash;1505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1322 (1073\u0026ndash;1425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003epeak (mL/min/Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,4 (13,3\u0026ndash;20,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,7 (14,0\u0026ndash;17,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003emax predicted (mL/min/Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,9 (15,0\u0026ndash;19,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,3 (14,4\u0026ndash;16,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% of predicted VO\u003csub\u003e2\u003c/sub\u003epeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (82\u0026ndash;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (91\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum Power (Watt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (90\u0026ndash;130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (77\u0026ndash;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003emax at VAT ( mL//min/Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,4 (8,9\u0026ndash;13,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,4 (9,5\u0026ndash;12,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiorespiratory fitness level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery weak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (43,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (37,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (56,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (63,0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData expressed as median (1st \u0026minus;\u0026thinsp;3rd interquartile). VO2max (maximum oxygen consumption); VAT (ventilatory anaerobic threshold). Nominal variables were presented as absolute value and relative frequency (%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e shows the relationship between the measured FEV1 variable and VO2peak. There was a positive and moderate correlation only in G2+. Demonstrating that the greater the FEV1 the greater the VO2peak in individuals with a history of COVID-19 infection.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the relationship between VO2peak and the 0V% index of HRV measured during the waking period, which represents sympathetic modulation. There was a negative correlation in both groups, however this correlation was low in G1- and moderate in G2+. Demonstrating that the higher the VO2peak, the lower the sympathetic predominance during the waking period. The other variables did not show a significant correlation in both groups.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis was the first study to evaluate the long-term impact of COVID-19 on cardiac and pulmonary autonomic function in individuals with SAH who had mild symptoms during the active period of the disease. The main finding was that individuals with SAH who had COVID-19 had worse lung function when compared to individuals without a history of the disease. Therefore, we can observe that even in the long term, after 6 to 18 months, hypertensive individuals infected with SARS-CoV-2 who developed a mild clinical condition may present pulmonary sequelae. Furthermore, the disorders presented were mostly of a restrictive nature, with a reduction in the FVC index on examination.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLung function\u003c/h2\u003e \u003cp\u003ePrevious evidence indicates that the lungs were the organs most affected by the SARS-CoV-2 virus, due to its ability to directly invade lung tissues and that even after 6 months patients may present changes in lung function as post-infection sequelae [52\u0026ndash; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Since then, spirometry has stood out as the most used tool for evaluating lung function in patients recovered from COVID-19. This method has highlighted restrictive disorders as one of the most frequent respiratory sequelae, with a reduction in the FVC index [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, most studies are limited to investigating pulmonary sequelae in individuals with a history of hospitalization. Nirmal et al. (2022) evaluated 39 patients with an average age of 49 years, three months after hospital discharge, and observed through spirometry the presence of restrictive disorder in 66.6% of the sample [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Other studies have shown residual changes in lung function also with restrictive defects, but all after severe or critical COVID-19 [\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This contrasts with our approach, which focused exclusively on individuals who had a mild form of the disease, without the need for hospital admission, which makes direct comparisons of the results obtained in our study with those involving more severe cases difficult.\u003c/p\u003e \u003cp\u003eOur hypothesis is that, due to the direct damage to the respiratory system caused by the virus, even patients who have had a mild form of the disease may experience lasting impacts on lung function. As evidenced in our results, when comparing the groups, we observed that the G2\u0026thinsp;+\u0026thinsp;group demonstrated worse lung function, indicated by lower FEV1 and Tiffeneau index values. It is worth mentioning that despite the reduction in these values, only two individuals in G2\u0026thinsp;+\u0026thinsp;presented mild obstructive disorder.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCardiac autonomic function\u003c/h2\u003e \u003cp\u003eRegarding cardiac autonomic function, our study demonstrated the absence of long-term impairment, when analyzing the 24-hour HRV indices in the analyzed population, both for linear and non-linear indices. The literature has demonstrated that COVID-19 infection can generate cardiovascular sequelae associated with dysfunction of the autonomic nervous system [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. One of the hypotheses for this autonomic dysfunction is that the inflammatory storm caused by the SARS-CoV-2 virus, with the release of cytokines such as interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α), can cause an autonomic imbalance, due to local and systemic damage that affects the afferent and efferent receptors of the ANS. The afferent receptors, responsible for transmitting sensory information to the central nervous system, and the efferent receptors, which convey motor commands from the central nervous system to the body, become impaired. This impairment disrupts the homeostasis maintained by the ANS, resulting in an autonomic imbalance that manifests in various symptoms observed in conditions such as long COVID [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, studies indicate that conditions associated with a sympathovagal imbalance, such as SAH, are associated with the severity of COVID-19, due to the possible inability of an appropriate anti-inflammatory response, which in turn depends on adequate vagal signaling [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In the study conducted by Acanfora et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], cardiac autonomic control was assessed using 24-hour Holter monitoring, after 12 weeks of COVID-19 infection in 30 patients, 63.3% of whom were hypertensive, and compared with a control group of 20 individuals, of which 55% were hypertensive. Of the total participants, 21 had a mild to moderate illness and 9 developed a severe form. The authors demonstrated an autonomic imbalance in the post-COVID-19 group characterized by a reduction in the linear indices of SDNN and SDANN, in addition to vagal impairment with a reduction in HF indices and an increase in the LF/HF ratio. Corroborating these findings, Kurtoglu et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], evaluated non-hypertensive patients, recovered from mild COVID-19 after 20 weeks of infection, aged (40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3 years) and also observed a reduction in vagal tone with a reduction in rMSSD, pNN50 and HF indices.\u003c/p\u003e \u003cp\u003eOn the other hand, Asarcikli et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], evaluated HRV with 24-hour Holter in healthy individuals recovered from COVID-19 with mild to moderate symptoms after 12 to 26 weeks and observed a significant increase in parasympathetic tone due to the increase in linear indices rMSSD, pNN50 and HF in the group post-COVID-19. The authors suggest that the persistence of vagal activity may be associated with fatigue and affects the physical capacity of patients. It is important to highlight that, in contrast to our investigation, most studies do not include hypertensive patients, and when they do, this is generally restricted to a limited portion of the sample.\u003c/p\u003e \u003cp\u003eAn additional study, involving 65 male patients, after a period of 4 to 6 weeks post-mild COVID-19, with a mean age of 22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 years, was conducted in comparison to a control group consisting of 26 patients. In this analysis, an increase in the activity of the parasympathetic nervous system was observed, evidenced by the increase in the 2LV% index obtained by the 24-hour HRV [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Contrary to what was observed in several studies, we did not observe any change in HRV indices, also obtained by 24-hour Holter monitoring, but only evaluating those who had a mild illness.\u003c/p\u003e \u003cp\u003eIn a previous study [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], it was observed that the majority of studies that evaluated post-COVID-19 HRV recruited patients who had the disease for a maximum of 6 months, with a very heterogeneous sample profile in relation to comorbidities and despite all of them presenting changes of autonomic control, the results remain contradictory, limited to short and medium-term impacts post-infection [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Furthermore, many studies have emphasized the evaluation of patients with a history of hospitalization [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe hypothesize that the lack of significant findings regarding changes in cardiac autonomic control in our study is due to the profile of the patients evaluated. Hypertensive patients already present with COVID-19 a sympathovagal imbalance, due to the clinical condition of those assessed being mild, the autonomic changes may not have been severe enough to persist in the long term [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Therefore, the impact on the ANS in hypertensive patients may be limited to the short and medium term periods after COVID-19 infection.\u003c/p\u003e \u003cp\u003eAlthough we used validated indices to evaluate changes in cardiac autonomic control, it is known that the ANS is influenced by several afferent and efferent neural physiological mechanisms, which come from the respiratory systems, baroreceptors, chemoreceptors, mechanoreceptors, among others. Therefore, future studies should include multivariate analysis approaches, considering not only the iRR, but also biological signals, such as respiratory and beat-to-beat blood pressure oscillations, as well as analyzes that consider the causality of biological signals to better understand the impact of disease on the ANS [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExercise capacity\u003c/h2\u003e \u003cp\u003eRegarding the assessment of functional capacity using CPET, we also did not observe significant differences between the groups. Exercise capacity has been assessed by CPET in patients recovered from COVID-19 with and without a history of hospitalization. Njoten et al. (2023), evaluating 65 patients recovered from COVID-19, with no history of hospitalization, aged (39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8), 83% female, identified through CPET that 2/3 of the participants had normal exercise capacity by VO2peak values.\u003c/p\u003e \u003cp\u003eOn the other hand, an observational study carried out by Alm\u0026aacute;zan and collaborators (2022), which included 72 patients in the post-mild COVID-19 period, with a mean age of (45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0), identified that half of these individuals had VO2peak less than 85% of predicted. The research also revealed that patients who experience persistent symptoms after a mild COVID-19 infection may suffer functional limitations, not necessarily due to sequelae of the disease, but rather related to the level of cardiorespiratory fitness (CRF) prior to the infection.\u003c/p\u003e \u003cp\u003eDespite differences regarding the inclusion of patients with persistent post-COVID-19 symptoms and VO2peak performance (\u0026gt;\u0026thinsp;85% of predicted in both groups), our study identified an ACR level classified as weak or very weak across the entire spectrum. sample. This finding allows us to infer that this result can be attributed to pre-existing conditions, such as hypertension, obesity and sedentary lifestyle, which, prior to the infection, may have contributed to a worse performance in the exercise test. It is important to highlight the lack of data on CRF prior to infection in most studies, including ours, which makes it difficult to identify whether the low level of CRF is due to COVID-19 infection.\u003c/p\u003e \u003cp\u003eDespite the lack of findings regarding functional capacity between the groups, we identified a moderate positive correlation between FEV1 and VO2peak in G2+, indicating that the greater the expiratory volume in the first second, the greater the functional capacity measured by CPET. The literature shows that the reduction in FEV1 values ​​indicates limitation of expiratory flow and may contribute to a lower ventilatory reserve and consequently exercise intolerance [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. With this, we can suggest that better FEV1 values ​​indicate better lung function, which contributes to a better oxygen transport capacity, causing a direct impact on aerobic capacity measured by VO2peak.\u003c/p\u003e \u003cp\u003eAnother significant correlation identified was between VO2peak and the 0V% index during the waking period, referring to sympathetic modulation. This negative correlation was low for G1- and moderate for G2+. These results indicate that as functional capacity increases, sympathetic modulation during wakefulness reduces. This observation is consistently supported by adaptations of the cardiovascular system in favor of better FC.\u003c/p\u003e \u003cp\u003ePrevious studies such as that by Liguori et al. (2014), have already highlighted that improvements in the FC and efficiency of the cardiovascular system can result in a reduction in the activation of the sympathetic nervous system during rest. Furthermore, it is crucial to consider that sympathetic hyperactivation, often associated with a higher HR and greater energy consumption at rest, can compromise the overall efficiency of the cardiovascular system and negatively impact aerobic performance [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Therefore, this could explain the existence of a relationship between greater functional capacity through VO2peak and lower sympathetic activity at rest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAlthough we exerted rigorous efforts to conduct this research, it is critical to recognize the inherent limitations of the study. Among them, we would like to highlight that whether or not the history of COVID-19 was confirmed was self-reported, and that we cannot say that patients in G1- did not have asymptomatic symptoms of the disease. Furthermore, we do not have the spirometry history of individuals prior to SARS-CoV-2 infection, which limits the conclusion that the worsening of lung function is actually due to COVID-19. Furthermore, it is important to highlight that the sample of this study was restricted to a specific profile of participants with SAH, who presented mild COVID-19 without the need for hospitalization, with an average age of 55 years and a higher prevalence of females (78 .8%), which may limit the extrapolation of results to other groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to our observations, we can conclude that there is no change in the ANS or HR in patients with hypertension who recovered from COVID-19, but there is a worsening of lung function, with the presence mainly of restrictive disorder that can negatively contribute to CPET performance. Therefore, the importance of a comprehensive approach in the post-infection evaluation of hypertensive patients is highlighted.\u003c/p\u003e \u003cp\u003eThese findings provide valuable insights for health professionals and researchers, such as the clinical importance of evaluating the pulmonary component in these individuals after mild COVID-19, with pre-existing conditions, such as SAH and instituting Cardiorespiratory Rehabilitation programs. We suggest that new studies be carried out evaluating patients recovered from COVID-19 in different populations, including ANS dysfunction as a post-infection sequelae.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of conflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this article.\u003c/p\u003e\u003ch2\u003eFunding source\u003c/h2\u003e \u003cp\u003eThis work was supported by the Coordination for the Improvement of Higher Education Personnel \u0026ndash; Brazil (CAPES) \u0026ndash; under grant number 001.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B (2022) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. 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Dispon\u0026iacute;vel em: https://pubmed.ncbi.nlm.nih.gov/12416949/\u003c/li\u003e\n\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":"COVID-19, SARS-CoV-2, arterial hypertension, autonomic nervous system disease, functional capacity","lastPublishedDoi":"10.21203/rs.3.rs-6363534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6363534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eLong-term impact of COVID-19 on cardiac and pulmonary autonomic function in patients with systemic arterial hypertension (SAH) was evaluated in a cross-sectional study of 52 individuals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants were allocated to two groups based on COVID-19 history. They were underwent heart rate variability on the 24-hour Holter, lung function was assessed by spirometry and functional capacity (CF) was assessed by the cardiopulmonary exercise test.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWas revealed worsened lung function in COVID-19-recovered SAH patients, indicated by lower forced expiratory volume in the first second (FEV1) rates [2.3 (1.9\u0026ndash;2.6) vs. 2.5 (2.2\u0026ndash;3.0), p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)] and FEV1/[81.8 (77.5\u0026ndash;83.9) \u003cem\u003evs\u003c/em\u003e. 84.6 (80.8\u0026ndash;87.7) p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)], with 30% showing restrictive disorder. However, no significant differences were found in cardiac autonomic control. A positive and moderate association between VO\u003csub\u003e2peak\u003c/sub\u003e and FEV1 in COVID-19-recovered SAH patients was noted (r\u0026thinsp;=\u0026thinsp;0.50 p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between VO\u003csub\u003e2peak\u003c/sub\u003e and the 0V% index was noted a negative and moderate association (r =-0,55 p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFindings suggest mild COVID-19 in SAH patients may not cause significant long-term HRV changes. However, there is worsening of lung function, with the presence mainly of restrictive disorder in 30% of cases.\u003c/p\u003e","manuscriptTitle":"Long-term impact of COVID-19 on cardiac and pulmonary autonomic function in hypertensive individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:35:20","doi":"10.21203/rs.3.rs-6363534/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":"dba09c43-8d0d-4e46-aeb1-373d91f4c70f","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T13:13:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:35:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6363534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6363534","identity":"rs-6363534","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}