Consistency between the Standardized Methods of Exercise Prescription and the Target Heart Rate for the Cardiopulmonary Exercise Test in Individuals with Metabolic Syndrome

Consistency between the Standardized Methods of Exercise Prescription and the Target Heart Rate for the Cardiopulmonary Exercise Test in Individuals with Metabolic Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Consistency between the Standardized Methods of Exercise Prescription and the Target Heart Rate for the Cardiopulmonary Exercise Test in Individuals with Metabolic Syndrome Liu Ruojiang, Qin Jinmei, Xue Weizhen, Wang Feng, Zhu Huihui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4002742/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose This study aimed to compare differences and consistencies between the target heart rate (THR) for aerobic exercise based on the standardized physiological maximum value percentage and the first-ventilatory-threshold heart rate (HR VT1 ). The maximum physiological value percentage with the highest consistency with HR VT1 , combined with the maximum heart rate (HR max ) prediction equation, can guide safe and effective exercise in patients with metabolic syndrome (MetS) in practical applications. Methods Three HR max prediction equations were used to calculate the THR as 35%, 45% and 50% heart rate reserve (HRR) and 55%, 60% and 65% HR max, and the results were compared for correlation and consistency with HR VT1 . HR VT1 was measured through a cardiopulmonary exercise test (CPET) that complied with current guidelines and laboratory standards. Results According to the Fox equation, the difference in HR VT1 and 35% HRR was not statistically significant (P = 0.060). Bland‒Altman analysis indicated that the mean difference in HR VT1 and 35% HRR was − 0.350, which was close to the 0 line, and 95.83% of the patients (46/48) had values within the 95% limits of agreement. The absolute maximum difference within the limits of agreement was 7.90, the interclass correlation coefficient (ICC) was 0.862 (P < 0.001), and the 95% confidence interval was 0.767–0.920, indicating high consistency and good accuracy. Conclusion Among Chinese MetS patients, 35% HRR determined via the standardized HRR method and the acquired CPET VT1 had high consistency. Especially when applying the Fox equation, the exercise intensity of MetS patients can be best kept within the moderate-intensity domain near VT1. Health sciences/Cardiology Health sciences/Health care Health sciences/Health occupations aerobic exercise maximum physiological value first ventilatory threshold Bland‒Altman analysis interclass correlation coefficient Figures Figure 1 Figure 2 1. Introduction Metabolic syndrome (MetS) is a noncommunicable chronic disease that is an umbrella term for high-risk conditions including diabetes and cardiovascular disease (CVD). CVD is currently the main cause of death and low quality of life among the elderly population worldwide, so it is important to study MetS prevention and treatment to reduce CVD progression risk 1,2 . The pathogenic mechanism of MetS is complicated, and individuals develop high-risk factors for CVD in various ways, including genetic and epigenetic factors and poor lifestyle habits, such as a sedentary lifestyle, a lack of physical activity, overeating, an imbalanced diet and overweight, obesity and ectopic fat accumulation 3 . For MetS patients, exercise prescription features specific interventions and can evidently improve protective factors against CVD, such as cardiorespiratory fitness and increased muscle mass. Aerobic exercise is the foundation for the treatment and recovery of MetS patients 4–7 . The benefits and various factors of exercise prescription are closely related to MetS treatment. Since exercise intensity plays a significant role in the practical application of exercise prescriptions, a setting that is objective and safe and can ensure appropriate benefits is a popular topic of current research 8 . There is significant heterogeneity in the post-training response to aerobic exercise between individuals, which is caused by factors such as biology and methodology. The methods used to determine exercise intensity are modifiable and important parts of methodology 9 . Two related studies in 2020 10,11 reported poor agreement between the traditional standardized method based on the maximum physiological value parameters (constant percentage) and the exercise intensity domain in which the body carries out metabolism during practical exercise. The research methods cannot control the metabolic stimulation well. The standardized method based on the maximum physiological value percentage may lead different individuals to exercise under the same effort level and put them in different exercise intensity domains. For different individuals, the first ventilatory threshold (VT1) may occur in different exercise intensity domains, which cannot be ignored in exercise prescriptions that are centered on exercise intensity. When individuals are in different exercise intensity domains, they experience different metabolic pressures from exercise, as well as different benefits and risks. The optimal exercise intensity must be strictly controlled to achieve the expected effects to reduce and reverse the impact of various chronic diseases and maintain health. In fact, as early as 2010, Scharhag-Rosenberger et al. 12 had already questioned traditional methods for determining exercise intensity. Peter Hofmann et al. 13 pointed out that the method to determine the physiological threshold is rarely mentioned in the relevant cardiac rehabilitation guidelines, and the target heart rate (THR) for prescribed exercise in individuals with disease and apparently healthy individuals should not be estimated only by relevant parameters of the maximum physiological value. In 2013, the European Association for Cardiovascular Prevention and Rehabilitation (EACPR), American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) and Canadian Association of Cardiac Rehabilitation (CACR) released a joint position statement in which a method for prescribing exercise and exercise intensity based on a physiological metabolic threshold was introduced to maximally improve the benefits of aerobic exercise in patients undergoing cardiac rehabilitation 14 . In the years following the position statement, a series of intervention experiments were conducted, which found that, compared with standardized exercise intensity prescriptions, physiological metabolic threshold-based exercise intensity prescriptions yielded a 100% response rate to maximum oxygen uptake in sedentary people 15–19 and performed better at improving MetS severity 20 . In 2022, the European Association of Preventive Cardiology (EAPC) released a position statement that set VT1 based on a cardiopulmonary exercise test (CPET) as the gold standard for aerobic exercise intensity 21 . MetS patients themselves are at high risk for CVD and include recovered individuals who have previously experienced cardiovascular events. Therefore, maintaining intensity exercise around VT1 can ensure appropriate exercise benefits and prevent the heart and other metabolic indices from being negatively impacted by aerobic exercise 22 . In addition, by mainly using CPETs to evaluate VT1, the second ventilatory threshold (VT2) and peak oxygen uptake, a system based on the physiological metabolic threshold classifies exercise intensity into four levels: “light to moderate intensity”, “moderate to high intensity”, “high to severe intensity”, and “severe to extreme intensity”. The intensity range around VT1 is regarded as “moderate intensity” 23,24 . Although there are unique training benefits to exercise intensity above VT1 and near VT2, exercise prescriptions are usually progressively advanced, and VT1 can serve as a good initial intensity control for the individual's actual exercise intensity domains. Despite existing problems, standardized prescription methods combined with the maximum heart rate prediction equations are still irreplaceable due to their convenience and affordability, and a proper standardized exercise intensity prescription method must be established for MetS patients. Therefore, this study aimed to compare differences and consistencies between the THR for aerobic exercise based on the standardized physiological maximum value and the HR VT1 measured via the CPET, in order to determine a safe, effective and simple way of instructing MetS patients in grassroots recovery institutions and community hospitals where CPET statistics are not available for performing aerobic exercise. 2. Materials and Methods 2.1. Materials 48 MetS patients from Taiyuan Central Hospital, including 32 males and 16 females, were enrolled from November 2022 to June 2023. Patients ranged in age from 24 to 64 years (44.31 ± 10.62 years), body mass index (30.69 ± 4.06 kg/m 2 ), abdominal perimeter (104.48 ± 12.06 cm) and body fat percentage (33.38 ± 5.84%). Diagnostic criteria: The MetS diagnostic criteria released by the Chinese Diabetes Society were adopted in this research 25 . Patients who had 3 or more of the following 5 symptoms met the diagnostic criteria: ① abdominal obesity (central obesity): an abdominal perimeter equal to or higher than 90 cm in males and equal to or higher than 85 cm in females; ② hyperglycemia: a fasting blood glucose level equal to or higher than 6.1 mmol/L, a blood glucose level equal to or higher than 7.8 mmol/L two hours after glucose loading or a diabetes diagnosis; ③ hypertension: blood pressure equal to or higher than 130/85 mmHg or hypertension diagnosis and treatment; ④ a fasting triglyceride level equal to or higher than 1.70 mmol/L; and ⑤ fasting high-density lipoprotein cholesterol lower than 1.04 mmol/L. The inclusion criteria were as follows: ① patients aged 24 to 65 years with a sedentary lifestyle; ② patients with no use of beta blockers or other drugs that could impact their heart rate or exercise tolerance; and ③ patients who signed an informed consent form. The exclusion criteria were as follows: absolute exercise contraindications, including the acute stage of various diseases, lower limb fracture, organ system failure, tumor, the acute phase of myocardial infarction, cardiac insufficiency, myocarditis, or chronic lung disease. The shedding and rejection criteria were as follows: ① poor compliance; ② a CPET peak value respiratory exchange ratio (RER) lower than 1.10; and ③ unable to sustain 60 r/min with power cycling by following the metronome during the CPET. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This research was reviewed and approved by the Taiyuan Central Hospital Ethics Committee (No. 2022026). 2.2. Testing Scheme Patients who participated in the study signed an informed consent form and underwent medical body composition analysis (770, InBody, Seoul, Korea), abdominal perimeter measurements, and blood draws to screen indices such as fasting blood glucose and blood lipid levels at the Taiyuan Central Hospital one day before the start of testing. Those who met the inclusion criteria and did not fit any of the exclusion criteria were informed by phone to participate in the testing. On the testing day, the premeasured weight and other data of the patients were input into the CPET operation system (CPX-770, HeartGym, Beijing, China). Gas and volume calibration was conducted according to the manufacturer’s instructions, and the environmental temperature was maintained between 19 and 21°C. Before the formal test, patients sat on the chair silently for 5 minutes, with their back against the chair, their feet on the floor, and their arms supported at heart level. An automatic upper arm sphygmomanometer (M5 professional, Omron, Mannheim, Germany) was used to measure the resting heart rate and blood pressure 8 . Within the doctor ward, a 12-lead electrocardiogram (ECG) system (EC-12S, Labtech, Debrecen, Hungary) was connected to continuously monitor the ECG and blood pressure during the exercise test period. Patients rested during power cycling (Ergoselect 100, Ergoline, Germany) for 3 minutes and performed a 3-minute zero-load warm-up at 60 r/min. According to sex and age, proper incremental power (8–30 W/min) was chosen to ensure that the patients reached symptom-limited peak exercise within 6–10 minutes. Then, the patients could rest and recover for 5–10 minutes, after which the test ended. All patients were encouraged to reach their maximum effort level. All participants were limited because of muscular fatigue. 2.3. Determination of the Testing Data 2.3.1 Determination of VT1 VT1 was jointly determined by three verified methods 24 in the operation system. The three methods include the V-slope method (the comparatively increased slope inflection point of VCO 2 in comparison with VO 2 during the test), the VE/VO 2 method, and the end-expiratory O 2 pressure method. All the data were evaluated by two professional cardiac rehabilitation physicians, and the corresponding heart rate of VT1 determined in the system was HR VT1 . 2.3.2. Standardized Heart Rate Reserve (HRR) Method and Maximum Heart Rate (HR max ) Method The American College of Sports Medicine (THR= (maximum heart rate – resting heart rate) ×exercise intensity percentage + resting heart rate) was used 8 , and the Fox equation (HR max = 220 – age), Miller equation (HR max = 200 − 0.48×age) and Tanaka equation (HR max = 208 − 0.7×age) were applied to calculate the HR max . The THR of 35% HRR, 45% HRR, and 50% HRR and 55% HR max , 60% HR max , and 65% HR max were calculated separately. 2.4. Statistical Analysis The data were processed with SPSS 27.0 (IBM, Armonk, NY, USA), and the SPSSAU data analysis platform ( https://spssau.com/ ) was used to construct diagrams. All the data are presented as . The Kolmogorov‒Smirnov test for normality was performed, and the significance level α was equal to 0.05. Four statistical methods were applied to evaluate the differences and consistency between the two exercise intensity prescription methods. Pearson correlation analysis and the paired-sample t test were used for preliminary testing of correlations and differences. The Bland‒Altman test and the interclass correlation coefficient (ICC) were used for further consistency testing to ensure that the results had the highest reliability and validity. The standardized data for each sample were acquired from the same CPET. Therefore, the ICCs in this research were absolutely consistent, the model selected was a 2-way random-effects model. 3. Results 3.1. Normality Test The aerobic exercise THR data obtained from MetS patients using standardized methods and three maximum heart rate prediction equations, as well as HR VT1 data collected during the CPET, all showed a normal distribution (P>0.05). The results of the Kolmogorov‒Smirnov test are presented in Table 1. 3.2. Comparison of the Standardized Methods THR and HR VT1 The THR determined by the standardized %HRR method (r>0.8, P<0.001) and HR VT1 were strongly correlated. The THR obtained by combining 35% HRR with any one of the three HR max equations was not significantly different from HR VT1 . That is, the difference between 35% HRR (113.56±8.20) calculated with the Fox equation and HR VT1 (113.21±9.16) was not statistically significant (P=0.600), the 35% HRR (114.72±6.80) calculated with the Miller equation was similar HR VT1 (P=0.051), and the 35% HRR (114.12±7.28) calculated with the Tanaka equation was similar to HR VT1 (P =0.200) (Table 2). The THR determined by the standardized %HR max method (0.8>r>0.6, P<0.001) and HR VT1 were relatively highly correlated. Only the THR obtained by combining 65% HR max with the two HR max equations was not significantly different from HR VT1 . The 65% HR max (114.20±6.90) calculated with the Fox equation was not significantly different from HR VT1 (P=0.326). The 65% HR max (115.04±4.83) calculated with the Tanaka equation was not significantly different from HR VT1 (P=0.075) (Table 3). 3.3. Bland‒Altman Consistency Testing According to the standardized reserve heart rate method, the mean difference between the 35% HRR determined by the Fox equation and HR VT1 was -0.350, which was close to the 0th line (Table 4); 95.83% of the patients (46/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 7.90 (Figure 1A). The mean difference in the test results for 35% HRR according to the Miller equation and HR VT1 was -1.509, which was relatively close to the 0th line (Table 4); 95.83% of the patients (46/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 9.82 (Figure 1B). The mean difference in the test results for 35% HRR according to the Tanaka equation and HR VT1 was -0.913, which was quite close to the 0th line (Table 4); 95.83% (46/48) of the points were within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 8.85 (Figure 1C). Therefore, among the three HR max prediction equations, the 35% HRR determined by the Fox equation was highly consistent with HR VT1 , the 35% HRR determined by the Tanaka equation was fairly consistent with HR VT1 , and the 35% HRR determined by the Miller equation was the least consistent with HR VT1 . Compared to the other two equations, the Miller equation for 35% HRR does not meet the conditions for practical application even though it is not significantly different from HR VT1 . According to the standardized maximum heart rate method, the mean difference in the test results for the 65% HR max determined by the Fox equation and HR VT1 was -0.989, close to the 0th line (Table 5); 91.67% of the patients (44/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 7.80 (Figure 2A). The mean difference in the test results for 65% of the HR max determined by the Miller equation and HR VT1 was -2.97, which was relatively close to the 0th line (Table 5); 93.75% of the patients (45/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 11.23 (Figure 2B). The mean difference in the test results for 65% of the HR max values determined by the Tanaka equation and HR VT1 was -1.829, which was relatively close to the 0th line (Table 5); 91.67% of the patients (44/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 9.36 (Figure 2C). Therefore, among the three HR max predictions, the 65% HR max determined by the Fox equation was highly consistent with HR VT1 , the 65% HR max determined by the Tanaka equation was less consistent with HR VT1 , and the 65% HR max determined by the Miller equation was the least consistent with HR VT1 . 3.4. ICC Reliability Testing Like the Bland‒Altman test, the ICC test showed that among the three standardized HRRs, only 35% HRR combined with the three HR max equations showed high consistency with HR VT1 (113.21±9.16), and among the three equations, the 35% HRR calculated with the Fox equation exhibited the highest consistency with HR VT1 (Table 6). The ICC for 35% HRR (113.56±8.20) determined by the Fox equation and HR VT1 was 0.862 (P<0.001), and the 95% confidence interval was 0.767-0.920, indicating high consistency and good accuracy. The ICC for 35% HRR (114.72±6.80) determined by the Miller equation and HR VT1 was 0.780 (P<0.001), and the 95% confidence interval was 0.636-0.871, indicating relatively high consistency and comparatively good accuracy. The ICC for 35% HRR (114.12±7.28) determined by the Tanaka equation and HR VT1 was 0.825 (P<0.001), and the 95% confidence interval was 0.709-0.898, indicating high consistency and good accuracy (Table 6). Among the three standardized HR max percentages, only the 65% HR max calculated with the Fox and Tanaka equations showed moderate consistency with HR VT1 (113.21±9.16) (Table 7). The ICC for the 65% HR max (114.20±6.90) determined by the Fox equation and HR VT1 was 0.639 (95% confidence interval 0.437-0.780) (P<0.001), with moderate consistency and normal accuracy. The ICC for the 65% HR max (115.04±4.83) determined by the Tanaka equation and HR VT1 was 0.537 (95% confidence interval 0.304-0.710) (P<0.001), with moderate consistency and normal accuracy (Table 7). 4. Discussion The results of the differential analysis preliminarily showed that the THR obtained by the standardized methods of 45% and 50% HRR and 55% and 60% HR max with the three maximum heart rate prediction equations were significantly different from HR VT1 (113.21 ± 9.16) measured by CPET (p < 0.001). All the consistency test results showed that 35% HRR, regardless of which HR max prediction equations were combined, was highly consistent with HR VT1 . Although the results of the Bland‒Altman test indicated good consistency between 65% HR max calculated with the Fox equation and HR VT1 . ICC analysis showed that the exercise intensity of 65% HR max calculated with the Fox equation had moderate consistency with HR VT1 determined by CPET, with poor reliability and low credibility for practical application. In summary, we recommend using the exercise intensity of 35% HRR as a substitute for the intensity near VT1 when prescribing exercise for MetS patients. In the exercise intensity domain, which is divided based on the maximum physiological value percentage, 35% HRR is in the light intensity zone. The previous moderate intensity zone of the 40–59 HRR% 8 or the 40–69 HRR% 26 , which is based on the maximum physiological value percentage, will make the actual exercise intensity for MetS patients higher than VT1. In fact, the guidelines for exercise testing and exercise prescription by the American College of Sports Medicine recommend that unhealthy people carry out exercise at 30%~39% HRR or VO 2 R 8 , which is basically the same as the results in this research. The widely promoted and applied Fox equation and Tanaka Eq. 2 7 were used in this research. Because the average body fat percentage of the participants in this study was higher than 30%, the Miller equation, which is recommended for overweight people with sedentary lifestyles, was also tested 28 . Many more improved equations are suitable for different individuals 8 , but an HR max prediction equation that is suitable for MetS patients has yet to be developed. The purpose of this study was not to involve the development of the HR max prediction equations. The three HR max prediction equations were used only to test the corresponding standardized exercise intensity in the VT1 exercise intensity domain in MetS patients. Compared with the other two HR max prediction equations, the 35% HRR (113.56 ± 8.20) calculated by the Fox equation showed the highest consistency with HR VT1, which might be due to the average age of the participants in this study of 40–50 years 29 . Additionally, the HR max predicted by the Fox equation decreases quickly with increasing age, so it can be underestimated in healthy individuals 30 , but it might just meet the situation in which the HR max of MetS patients is generally decreased. Due to pathological factors, MetS patients have a higher resting heart than healthy individuals 31 . With the progression of the disease, most MetS patients tend to encounter problems, including ventricular diastolic dysfunction and structural changes 32 , impaired cardiac autonomic nervous function, increased sympathetic nerve activity and decreased heart rate variability 33 . In addition to their overall cardiorespiratory endurance level, which is lower than that of normal individuals 34,35 , MetS patients also face problems such as a sedentary lifestyle, a lack of physical activity, decreased skeletal muscle mass, decreased mitochondrial function 36 , an earlier-onset anaerobic threshold and a low lactate clearance rate 37 . All of these factors lead to lower exercise tolerance in MetS patients than in healthy people who regularly exercise. In the CPET, MetS patients stop exercising when their heart rate is much lower than the projected maximum heart rate. All the abovementioned factors contribute to the uncertainty of the exercise HR in MetS patients. The objective exercise intensity domain for different MetS patients varies significantly when different exercise intensity prescription methods are applied. 4.1 Limitations The limitations of this research include the small sample and wide age range. Future studies should validate our findings by expanding the sample size with a detailed differentiation by age group and sex. Due to the limited sample size, we did not conduct any subgroup analysis with MetS patients as the reference. We excluded MetS patients who were using drugs that could impact their heart rate, which may limit the applicability of the results. In addition, no equations for predicting the HR max of the MetS population were developed in this study, and only the Fox equation was used as a substitute. 4.2 Perspective The first ventilatory threshold is the baseline for moderate-intensity continuous exercise, and most exercise prescriptions require a gradual increase in exercise intensity and volume in practical applications. Our study provides a way to approximate the VT1 in the CPET in patients with metabolic syndrome using traditional exercise intensity determination methods. Without CPET equipment and related technical operators, MetS patients can start effective and safe endurance training in this exercise intensity domain based on our results. The consistency between the exercise intensity determined by 35% HRR calculated by the Fox equation and the exercise intensity determined by VT1 in the practical application of exercise prescriptions for improving cardiovascular risk factors and cardiovascular fitness in MetS patients needs to be verified. By verifying the consistency of the two methods for determining exercise intensity in practical applications, it may be possible to further refine the percentage of HRR corresponding to VT1 suitable for MetS patients. In addition, an HR max prediction equation suitable for adult MetS patients needs to be developed, and given the specificity of the MetS clinical cohort, individuals using β-blockers should be developed separately from those who do not 38 . 5. Conclusion Although the exercise intensity prescription method based on the maximum physiological value percentage has been gradually replaced by a method based on the threshold in the clinical rehabilitation medicine field, it is still necessary to apply 35% HRR standardized method combined with the Fox equation in instructing MetS patients to exercise due to the absence of CPET equipment and relevant technicians and the limited popularity and acceptance of this method by the general public. Declarations Author Contribution LRJ designed the research, collected the data and wrote the paper. QJM participated in the research proposition, method construction and data analysis revision. WF was involved in the data collection. XWZ and ZHH helped to enroll people in the study and to revise the data analysis. All the authors have read and approved the final version of the paper and agreed to the presentation sequence. Acknowledgments This research was sponsored by the 2022 China National Regional Medical Center Science and Technology Innovation Plan Project (project number: 202242). Liu Ruojiang received graduate student scholarship support from North University of China. The authors express appreciation to all the participants in this research and are grateful to Guo Jinhong and Zhao Ya for their assistance in the data collection. Additional Information The authors state that they have no conflicts of interest. Data Availability Statement The datasets generated during the current study are available from the corresponding author on reasonable request. References Lemieux, I. & Després, J.-P. Vol. 12 3501 (MDPI, 2020). Fahed, G. et al. Metabolic syndrome: Updates on pathophysiology and management in 2021. International Journal of Molecular Sciences 23 , 786 (2022). Wang, H. H., Lee, D. K., Liu, M., Portincasa, P. & Wang, D. Q.-H. 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Metabolic Syndrome and Related Disorders 20 , 414–420 (2022). Kim, B. et al. Cardiorespiratory fitness is strongly linked to metabolic syndrome among physical fitness components: a retrospective cross-sectional study. Journal of Physiological Anthropology 39 , 1–9 (2020). Richter-Stretton, G. L., Fenning, A. S. & Vella, R. K. Skeletal muscle–A bystander or influencer of metabolic syndrome? Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 , 867–875 (2020). Jones, T. E. et al. Plasma lactate as a marker of metabolic health: Implications of elevated lactate for impairment of aerobic metabolism in the metabolic syndrome. Surgery 166 , 861–866 (2019). Godlasky, E. et al. Effects of β-blockers on maximal heart rate prediction equations in a cardiac population. Journal of Cardiopulmonary Rehabilitation and Prevention 38 , 111–117 (2018). Tables Table 1 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Jun, 2024 Reviews received at journal 13 May, 2024 Reviews received at journal 09 May, 2024 Reviewers agreed at journal 27 Apr, 2024 Reviewers agreed at journal 26 Apr, 2024 Reviewers invited by journal 17 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Editor invited by journal 15 Mar, 2024 Submission checks completed at journal 15 Mar, 2024 First submitted to journal 01 Mar, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4002742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280870883,"identity":"f55587db-7d5e-4207-9eaf-c710fb29dbdd","order_by":0,"name":"Liu Ruojiang","email":"","orcid":"","institution":"North University of China","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Ruojiang","suffix":""},{"id":280870884,"identity":"370e738b-4507-4ce0-9e80-26a951384649","order_by":1,"name":"Qin Jinmei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLACxgZmBjb29oMPEipqCKvmgWnh4zmTbPDgzDEStMhJJJhJPmxhJqzFXiLHTPLnDuvENoaEtIrEBjYG/vbuBPy2ALVISJ5JN2ZjOHjsRuIOGQaJM2c3ENZi2HZYjo2xIe1G4hk2BgOJXCK0JLYd5mFjZjArSGxjJlLLQZAtbAxmDMRpOfOs2LIR5BcenmSJhDPHeAj6hb09eeNNUIjNn//84McfFTVy/O29+LUwCGSYSKBYi185CPAff/yBsKpRMApGwSgY0QAAPKNGg/hldD0AAAAASUVORK5CYII=","orcid":"","institution":"Peking University First Hospital Taiyuan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qin","middleName":"","lastName":"Jinmei","suffix":""},{"id":280870885,"identity":"198e8311-7cb8-448b-bedf-c334e54c034c","order_by":2,"name":"Xue Weizhen","email":"","orcid":"","institution":"Peking University First Hospital Taiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Weizhen","suffix":""},{"id":280870887,"identity":"b6a9e505-fc9f-4b34-84d4-e848f3d50eb1","order_by":3,"name":"Wang Feng","email":"","orcid":"","institution":"Peking University First Hospital Taiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Feng","suffix":""},{"id":280870888,"identity":"f53025ab-16f5-413d-88d0-338107039c43","order_by":4,"name":"Zhu Huihui","email":"","orcid":"","institution":"Peking University First Hospital Taiyuan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Huihui","suffix":""}],"badges":[],"createdAt":"2024-03-01 09:25:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4002742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4002742/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03084-7","type":"published","date":"2025-05-27T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53014434,"identity":"dbb1dd6f-61e9-4ed2-a3e3-9a15ab680fff","added_by":"auto","created_at":"2024-03-19 15:52:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32396,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bland‒Altman consistency test between HR\u003csub\u003eVT1\u003c/sub\u003e and 35% HRR\u003c/p\u003e\n\u003cp\u003eAbbreviations: HRR=heart rate reserve; HR\u003csub\u003eVT1\u003c/sub\u003e=Heart Rate of VT1.\u003c/p\u003e\n\u003cp\u003ea. Data calculated with the Fox equation; b. data calculated with the Miller equation; c. data calculated with the Tanaka equation.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4002742/v1/a4946860b69bcf5b989a4ad5.png"},{"id":53014433,"identity":"7eb5c6f5-abde-4f40-ae2f-fbe6687c1443","added_by":"auto","created_at":"2024-03-19 15:52:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32392,"visible":true,"origin":"","legend":"\u003cp\u003eThe Bland‒Altman consistency test between HR\u003csub\u003eVT1\u003c/sub\u003e and 65% HRmax.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HRmax=Maximum heart rate; HR\u003csub\u003eVT1\u003c/sub\u003e=Heart Rate of VT1.\u003c/p\u003e\n\u003cp\u003ea. Data calculated with the Fox equation; b. data calculated with the Miller equation; c. data calculated with the Tanaka equation.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4002742/v1/dc837b1aa6284ba5cabd63a1.png"},{"id":83782990,"identity":"070634d5-1266-458b-89b9-36f904ea5343","added_by":"auto","created_at":"2025-06-02 16:09:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":807799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4002742/v1/d6ec580d-a9c4-4a31-9e00-d2ba87e7543e.pdf"},{"id":53014428,"identity":"75ad0dcf-4254-4eae-b5cd-828ceb90e47c","added_by":"auto","created_at":"2024-03-19 15:52:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32820,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4002742/v1/03333a8a8d989953da58555a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Consistency between the Standardized Methods of Exercise Prescription and the Target Heart Rate for the Cardiopulmonary Exercise Test in Individuals with Metabolic Syndrome","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is a noncommunicable chronic disease that is an umbrella term for high-risk conditions including diabetes and cardiovascular disease (CVD). CVD is currently the main cause of death and low quality of life among the elderly population worldwide, so it is important to study MetS prevention and treatment to reduce CVD progression risk \u003csup\u003e1,2\u003c/sup\u003e. The pathogenic mechanism of MetS is complicated, and individuals develop high-risk factors for CVD in various ways, including genetic and epigenetic factors and poor lifestyle habits, such as a sedentary lifestyle, a lack of physical activity, overeating, an imbalanced diet and overweight, obesity and ectopic fat accumulation\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor MetS patients, exercise prescription features specific interventions and can evidently improve protective factors against CVD, such as cardiorespiratory fitness and increased muscle mass. Aerobic exercise is the foundation for the treatment and recovery of MetS patients\u003csup\u003e4\u0026ndash;7\u003c/sup\u003e. The benefits and various factors of exercise prescription are closely related to MetS treatment. Since exercise intensity plays a significant role in the practical application of exercise prescriptions, a setting that is objective and safe and can ensure appropriate benefits is a popular topic of current research\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere is significant heterogeneity in the post-training response to aerobic exercise between individuals, which is caused by factors such as biology and methodology. The methods used to determine exercise intensity are modifiable and important parts of methodology \u003csup\u003e9\u003c/sup\u003e. Two related studies in 2020 \u003csup\u003e10,11\u003c/sup\u003e reported poor agreement between the traditional standardized method based on the maximum physiological value parameters (constant percentage) and the exercise intensity domain in which the body carries out metabolism during practical exercise. The research methods cannot control the metabolic stimulation well. The standardized method based on the maximum physiological value percentage may lead different individuals to exercise under the same effort level and put them in different exercise intensity domains. For different individuals, the first ventilatory threshold (VT1) may occur in different exercise intensity domains, which cannot be ignored in exercise prescriptions that are centered on exercise intensity. When individuals are in different exercise intensity domains, they experience different metabolic pressures from exercise, as well as different benefits and risks. The optimal exercise intensity must be strictly controlled to achieve the expected effects to reduce and reverse the impact of various chronic diseases and maintain health.\u003c/p\u003e \u003cp\u003eIn fact, as early as 2010, Scharhag-Rosenberger et al.\u003csup\u003e12\u003c/sup\u003e had already questioned traditional methods for determining exercise intensity. Peter Hofmann et al.\u003csup\u003e13\u003c/sup\u003e pointed out that the method to determine the physiological threshold is rarely mentioned in the relevant cardiac rehabilitation guidelines, and the target heart rate (THR) for prescribed exercise in individuals with disease and apparently healthy individuals should not be estimated only by relevant parameters of the maximum physiological value. In 2013, the European Association for Cardiovascular Prevention and Rehabilitation (EACPR), American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) and Canadian Association of Cardiac Rehabilitation (CACR) released a joint position statement in which a method for prescribing exercise and exercise intensity based on a physiological metabolic threshold was introduced to maximally improve the benefits of aerobic exercise in patients undergoing cardiac rehabilitation\u003csup\u003e14\u003c/sup\u003e. In the years following the position statement, a series of intervention experiments were conducted, which found that, compared with standardized exercise intensity prescriptions, physiological metabolic threshold-based exercise intensity prescriptions yielded a 100% response rate to maximum oxygen uptake in sedentary people \u003csup\u003e15\u0026ndash;19\u003c/sup\u003e and performed better at improving MetS severity\u003csup\u003e20\u003c/sup\u003e. In 2022, the European Association of Preventive Cardiology (EAPC) released a position statement that set VT1 based on a cardiopulmonary exercise test (CPET) as the gold standard for aerobic exercise intensity\u003csup\u003e21\u003c/sup\u003e. MetS patients themselves are at high risk for CVD and include recovered individuals who have previously experienced cardiovascular events. Therefore, maintaining intensity exercise around VT1 can ensure appropriate exercise benefits and prevent the heart and other metabolic indices from being negatively impacted by aerobic exercise\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, by mainly using CPETs to evaluate VT1, the second ventilatory threshold (VT2) and peak oxygen uptake, a system based on the physiological metabolic threshold classifies exercise intensity into four levels: \u0026ldquo;light to moderate intensity\u0026rdquo;, \u0026ldquo;moderate to high intensity\u0026rdquo;, \u0026ldquo;high to severe intensity\u0026rdquo;, and \u0026ldquo;severe to extreme intensity\u0026rdquo;. The intensity range around VT1 is regarded as \u0026ldquo;moderate intensity\u0026rdquo; \u003csup\u003e23,24\u003c/sup\u003e. Although there are unique training benefits to exercise intensity above VT1 and near VT2, exercise prescriptions are usually progressively advanced, and VT1 can serve as a good initial intensity control for the individual's actual exercise intensity domains.\u003c/p\u003e \u003cp\u003eDespite existing problems, standardized prescription methods combined with the maximum heart rate prediction equations are still irreplaceable due to their convenience and affordability, and a proper standardized exercise intensity prescription method must be established for MetS patients.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to compare differences and consistencies between the THR for aerobic exercise based on the standardized physiological maximum value and the HR\u003csub\u003eVT1\u003c/sub\u003e measured via the CPET, in order to determine a safe, effective and simple way of instructing MetS patients in grassroots recovery institutions and community hospitals where CPET statistics are not available for performing aerobic exercise.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003e48 MetS patients from Taiyuan Central Hospital, including 32 males and 16 females, were enrolled from November 2022 to June 2023. Patients ranged in age from 24 to 64 years (44.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.62 years), body mass index (30.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06 kg/m\u003csup\u003e2\u003c/sup\u003e), abdominal perimeter (104.48\u0026thinsp;\u0026plusmn;\u0026thinsp;12.06 cm) and body fat percentage (33.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84%).\u003c/p\u003e \u003cp\u003eDiagnostic criteria: The MetS diagnostic criteria released by the Chinese Diabetes Society were adopted in this research\u003csup\u003e25\u003c/sup\u003e. Patients who had 3 or more of the following 5 symptoms met the diagnostic criteria: ① abdominal obesity (central obesity): an abdominal perimeter equal to or higher than 90 cm in males and equal to or higher than 85 cm in females; ② hyperglycemia: a fasting blood glucose level equal to or higher than 6.1 mmol/L, a blood glucose level equal to or higher than 7.8 mmol/L two hours after glucose loading or a diabetes diagnosis; ③ hypertension: blood pressure equal to or higher than 130/85 mmHg or hypertension diagnosis and treatment; ④ a fasting triglyceride level equal to or higher than 1.70 mmol/L; and ⑤ fasting high-density lipoprotein cholesterol lower than 1.04 mmol/L.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: ① patients aged 24 to 65 years with a sedentary lifestyle; ② patients with no use of beta blockers or other drugs that could impact their heart rate or exercise tolerance; and ③ patients who signed an informed consent form.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: absolute exercise contraindications, including the acute stage of various diseases, lower limb fracture, organ system failure, tumor, the acute phase of myocardial infarction, cardiac insufficiency, myocarditis, or chronic lung disease.\u003c/p\u003e \u003cp\u003eThe shedding and rejection criteria were as follows: ① poor compliance; ② a CPET peak value respiratory exchange ratio (RER) lower than 1.10; and ③ unable to sustain 60 r/min with power cycling by following the metronome during the CPET.\u003c/p\u003e \u003cp\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This research was reviewed and approved by the Taiyuan Central Hospital Ethics Committee (No. 2022026).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Testing Scheme\u003c/h2\u003e \u003cp\u003e Patients who participated in the study signed an informed consent form and underwent medical body composition analysis (770, InBody, Seoul, Korea), abdominal perimeter measurements, and blood draws to screen indices such as fasting blood glucose and blood lipid levels at the Taiyuan Central Hospital one day before the start of testing. Those who met the inclusion criteria and did not fit any of the exclusion criteria were informed by phone to participate in the testing.\u003c/p\u003e \u003cp\u003eOn the testing day, the premeasured weight and other data of the patients were input into the CPET operation system (CPX-770, HeartGym, Beijing, China). Gas and volume calibration was conducted according to the manufacturer\u0026rsquo;s instructions, and the environmental temperature was maintained between 19 and 21\u0026deg;C. Before the formal test, patients sat on the chair silently for 5 minutes, with their back against the chair, their feet on the floor, and their arms supported at heart level. An automatic upper arm sphygmomanometer (M5 professional, Omron, Mannheim, Germany) was used to measure the resting heart rate and blood pressure\u003csup\u003e8\u003c/sup\u003e. Within the doctor ward, a 12-lead electrocardiogram (ECG) system (EC-12S, Labtech, Debrecen, Hungary) was connected to continuously monitor the ECG and blood pressure during the exercise test period. Patients rested during power cycling (Ergoselect 100, Ergoline, Germany) for 3 minutes and performed a 3-minute zero-load warm-up at 60 r/min. According to sex and age, proper incremental power (8\u0026ndash;30 W/min) was chosen to ensure that the patients reached symptom-limited peak exercise within 6\u0026ndash;10 minutes. Then, the patients could rest and recover for 5\u0026ndash;10 minutes, after which the test ended. All patients were encouraged to reach their maximum effort level. All participants were limited because of muscular fatigue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Determination of the Testing Data\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Determination of VT1\u003c/h2\u003e \u003cp\u003eVT1 was jointly determined by three verified methods \u003csup\u003e24\u003c/sup\u003e in the operation system. The three methods include the V-slope method (the comparatively increased slope inflection point of VCO\u003csub\u003e2\u003c/sub\u003e in comparison with VO\u003csub\u003e2\u003c/sub\u003e during the test), the VE/VO\u003csub\u003e2\u003c/sub\u003e method, and the end-expiratory O\u003csub\u003e2\u003c/sub\u003e pressure method. All the data were evaluated by two professional cardiac rehabilitation physicians, and the corresponding heart rate of VT1 determined in the system was HR\u003csub\u003eVT1\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Standardized Heart Rate Reserve (HRR) Method and Maximum Heart Rate (HR\u003csub\u003emax\u003c/sub\u003e) Method\u003c/h2\u003e \u003cp\u003eThe American College of Sports Medicine (THR= (maximum heart rate \u0026ndash; resting heart rate) \u0026times;exercise intensity percentage\u0026thinsp;+\u0026thinsp;resting heart rate) was used\u003csup\u003e8\u003c/sup\u003e, and the Fox equation (HR\u003csub\u003emax\u003c/sub\u003e = 220 \u0026ndash; age), Miller equation (HR\u003csub\u003emax\u003c/sub\u003e = 200\u0026thinsp;\u0026minus;\u0026thinsp;0.48\u0026times;age) and Tanaka equation (HR\u003csub\u003emax\u003c/sub\u003e = 208\u0026thinsp;\u0026minus;\u0026thinsp;0.7\u0026times;age) were applied to calculate the HR\u003csub\u003emax\u003c/sub\u003e. The THR of 35% HRR, 45% HRR, and 50% HRR and 55% HR\u003csub\u003emax\u003c/sub\u003e, 60% HR\u003csub\u003emax\u003c/sub\u003e, and 65% HR\u003csub\u003emax\u003c/sub\u003e were calculated separately.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe data were processed with SPSS 27.0 (IBM, Armonk, NY, USA), and the SPSSAU data analysis platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://spssau.com/\u003c/span\u003e\u003cspan address=\"https://spssau.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct diagrams. All the data are presented as \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e. The Kolmogorov‒Smirnov test for normality was performed, and the significance level α was equal to 0.05. Four statistical methods were applied to evaluate the differences and consistency between the two exercise intensity prescription methods. Pearson correlation analysis and the paired-sample t test were used for preliminary testing of correlations and differences. The Bland‒Altman test and the interclass correlation coefficient (ICC) were used for further consistency testing to ensure that the results had the highest reliability and validity. The standardized data for each sample were acquired from the same CPET. Therefore, the ICCs in this research were absolutely consistent, the model selected was a 2-way random-effects model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Normality Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aerobic exercise THR data obtained from MetS patients using standardized methods and three maximum heart rate prediction equations, as well as HR\u003csub\u003eVT1\u003c/sub\u003e data collected during the CPET, all showed a normal distribution (P\u0026gt;0.05). The results of the Kolmogorov‒Smirnov test are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Comparison of the Standardized Methods THR and HR\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eVT1\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eThe THR determined by the standardized\u0026nbsp;%HRR method (r\u0026gt;0.8, P\u0026lt;0.001) and HR\u003csub\u003eVT1\u003c/sub\u003e were strongly correlated. The THR obtained by combining 35% HRR with any one of the three HR\u003csub\u003emax\u003c/sub\u003e equations was not significantly different from HR\u003csub\u003eVT1\u003c/sub\u003e. That is, the difference between 35% HRR (113.56\u0026plusmn;8.20) calculated with the Fox\u0026nbsp;equation and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003e(113.21\u0026plusmn;9.16)\u003csub\u003e\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;not statistically significant (P=0.600), the 35% HRR (114.72\u0026plusmn;6.80) calculated with the Miller\u0026nbsp;equation was\u0026nbsp;similar HR\u003csub\u003eVT1\u003c/sub\u003e (P=0.051), and the 35% HRR (114.12\u0026plusmn;7.28) calculated with the Tanaka equation was similar to HR\u003csub\u003eVT1\u003c/sub\u003e (P =0.200) (Table 2).\u003c/p\u003e\n\u003cp\u003eThe THR determined by the standardized\u0026nbsp;%HR\u003csub\u003emax\u0026nbsp;\u003c/sub\u003emethod (0.8\u0026gt;r\u0026gt;0.6, P\u0026lt;0.001) and HR\u003csub\u003eVT1\u003c/sub\u003e were relatively highly correlated. Only the THR obtained by combining 65% HR\u003csub\u003emax\u003c/sub\u003e with the two HR\u003csub\u003emax\u003c/sub\u003e equations was not significantly different from HR\u003csub\u003eVT1\u003c/sub\u003e. The\u0026nbsp;65% HR\u003csub\u003emax\u0026nbsp;\u003c/sub\u003e(114.20\u0026plusmn;6.90) calculated with the Fox\u0026nbsp;equation was\u0026nbsp;not significantly different from HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003e(P=0.326). The\u0026nbsp;65% HR\u003csub\u003emax\u003c/sub\u003e (115.04\u0026plusmn;4.83) calculated with the Tanaka equation was not significantly different from HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003e(P=0.075) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Bland‒Altman Consistency Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the standardized reserve heart rate method, the mean difference between the 35% HRR determined by the Fox equation and HR\u003csub\u003eVT1\u003c/sub\u003e was -0.350,\u0026nbsp;which was\u0026nbsp;close to\u0026nbsp;the\u0026nbsp;0th\u0026nbsp;line (Table 4);\u0026nbsp;95.83% of the patients (46/48) had values\u0026nbsp;within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement\u0026nbsp;was\u0026nbsp;7.90 (Figure\u0026nbsp;1A). The mean\u0026nbsp;difference\u0026nbsp;in\u0026nbsp;the\u0026nbsp;test\u0026nbsp;results for\u0026nbsp;35% HRR\u0026nbsp;according to\u0026nbsp;the Miller\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u003c/sub\u003e was\u0026nbsp;-1.509, which was relatively close to the 0th line (Table 4); 95.83% of the patients (46/48)\u0026nbsp;had values\u0026nbsp;within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 9.82 (Figure\u0026nbsp;1B). The mean difference\u0026nbsp;in\u0026nbsp;the\u0026nbsp;test\u0026nbsp;results for\u0026nbsp;35% HRR\u0026nbsp;according to\u0026nbsp;the\u0026nbsp;Tanaka\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;-0.913, which was quite close to the 0th line (Table 4); 95.83% (46/48)\u0026nbsp;of the points were\u0026nbsp;within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement\u0026nbsp;was\u0026nbsp;8.85 (Figure\u0026nbsp;1C).\u003c/p\u003e\n\u003cp\u003eTherefore, among the three HR\u003csub\u003emax\u003c/sub\u003e prediction equations, the 35% HRR determined by\u0026nbsp;the Fox\u0026nbsp;equation\u0026nbsp;was highly consistent with HR\u003csub\u003eVT1\u003c/sub\u003e, the 35% HRR determined by\u0026nbsp;the Tanaka\u0026nbsp;equation\u0026nbsp;was fairly consistent with HR\u003csub\u003eVT1\u003c/sub\u003e, and the 35% HRR determined by\u0026nbsp;the\u0026nbsp;Miller\u0026nbsp;equation\u0026nbsp;was the least consistent with HR\u003csub\u003eVT1\u003c/sub\u003e. Compared to the other two equations, the Miller equation for 35% HRR does not meet the conditions for practical application even though it is not significantly different from HR\u003csub\u003eVT1\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to\u0026nbsp;the standardized maximum heart rate method, the mean difference\u0026nbsp;in\u0026nbsp;the test results for the\u0026nbsp;65% HR\u003csub\u003emax\u0026nbsp;\u003c/sub\u003edetermined by\u0026nbsp;the Fox\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;-0.989, close to the 0th line (Table 5); 91.67% of the patients (44/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 7.80 (Figure\u0026nbsp;2A). The mean difference\u0026nbsp;in\u0026nbsp;the test results for\u0026nbsp;65% of the\u0026nbsp;HR\u003csub\u003emax\u0026nbsp;\u003c/sub\u003edetermined by the\u0026nbsp;Miller\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;-2.97, which was relatively close to the 0th line (Table 5); 93.75% of the patients (45/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 11.23 (Figure\u0026nbsp;2B). The mean difference\u0026nbsp;in\u0026nbsp;the test results for\u0026nbsp;65% of the HR\u003csub\u003emax\u0026nbsp;\u003c/sub\u003evalues\u003csub\u003e\u0026nbsp;\u003c/sub\u003edetermined by\u0026nbsp;the Tanaka\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;-1.829, which was relatively close to the 0th line (Table 5); 91.67% of the patients (44/48) had values within the 95% limits of agreement, and the absolute value of the maximum difference within the limits of agreement was 9.36 (Figure\u0026nbsp;2C).\u003c/p\u003e\n\u003cp\u003eTherefore, among the three HR\u003csub\u003emax\u003c/sub\u003e predictions, the 65% HR\u003csub\u003emax\u003c/sub\u003e determined by the Fox equation was highly consistent with HR\u003csub\u003eVT1\u003c/sub\u003e, the 65% HR\u003csub\u003emax\u003c/sub\u003e determined by the Tanaka equation was less consistent with HR\u003csub\u003eVT1\u003c/sub\u003e, and the 65% HR\u003csub\u003emax\u003c/sub\u003e determined by the Miller equation was the least consistent with HR\u003csub\u003eVT1\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. ICC Reliability Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike the Bland‒Altman test, the ICC test showed that among the three standardized HRRs, only 35% HRR combined with the three HR\u003csub\u003emax\u003c/sub\u003e equations showed high consistency with HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003e(113.21\u0026plusmn;9.16), and among the three equations, the 35% HRR calculated with the Fox equation exhibited the highest consistency with HR\u003csub\u003eVT1\u003c/sub\u003e (Table 6). The ICC for\u0026nbsp;35% HRR (113.56\u0026plusmn;8.20) determined by\u0026nbsp;the\u0026nbsp;Fox\u0026nbsp;equation and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;0.862 (P\u0026lt;0.001),\u0026nbsp;and the 95% confidence interval\u0026nbsp;was\u0026nbsp;0.767-0.920, indicating high consistency and\u0026nbsp;good accuracy. The ICC for\u0026nbsp;35% HRR (114.72\u0026plusmn;6.80) determined by\u0026nbsp;the\u0026nbsp;Miller\u0026nbsp;equation and HR\u003csub\u003eVT1\u003c/sub\u003e was 0.780 (P\u0026lt;0.001),\u0026nbsp;and the 95% confidence interval\u0026nbsp;was\u0026nbsp;0.636-0.871, indicating relatively high consistency and comparatively good accuracy. The ICC for 35% HRR (114.12\u0026plusmn;7.28) determined by the\u0026nbsp;Tanaka equation and HR\u003csub\u003eVT1\u003c/sub\u003e was 0.825 (P\u0026lt;0.001),\u0026nbsp;and the 95% confidence interval\u0026nbsp;was\u0026nbsp;0.709-0.898, indicating high consistency and good accuracy (Table 6).\u003c/p\u003e\n\u003cp\u003eAmong the three standardized HR\u003csub\u003emax\u003c/sub\u003e percentages, only\u0026nbsp;the 65% HR\u003csub\u003emax\u003c/sub\u003e calculated with the Fox\u0026nbsp;and Tanaka\u0026nbsp;equations\u0026nbsp;showed moderate consistency with HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003e(113.21\u0026plusmn;9.16)\u0026nbsp;(Table 7). The ICC for the\u0026nbsp;65% HR\u003csub\u003emax\u003c/sub\u003e (114.20\u0026plusmn;6.90) determined by the\u0026nbsp;Fox\u0026nbsp;equation\u0026nbsp;and\u0026nbsp;HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas\u0026nbsp;0.639 (95% confidence interval\u0026nbsp;0.437-0.780) (P\u0026lt;0.001), with moderate consistency and normal accuracy. The ICC for the 65% HR\u003csub\u003emax\u003c/sub\u003e (115.04\u0026plusmn;4.83) determined by the Tanaka equation and HR\u003csub\u003eVT1\u0026nbsp;\u003c/sub\u003ewas 0.537 (95% confidence interval 0.304-0.710) (P\u0026lt;0.001), with moderate consistency and normal accuracy (Table 7).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results of the differential analysis preliminarily showed that the THR obtained by the standardized methods of 45% and 50% HRR and 55% and 60% HR\u003csub\u003emax\u003c/sub\u003e with the three maximum heart rate prediction equations were significantly different from HR\u003csub\u003eVT1\u003c/sub\u003e (113.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16) measured by CPET (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All the consistency test results showed that 35% HRR, regardless of which HR\u003csub\u003emax\u003c/sub\u003e prediction equations were combined, was highly consistent with HR\u003csub\u003eVT1\u003c/sub\u003e. Although the results of the Bland‒Altman test indicated good consistency between 65% HR\u003csub\u003emax\u003c/sub\u003e calculated with the Fox equation and HR\u003csub\u003eVT1\u003c/sub\u003e. ICC analysis showed that the exercise intensity of 65% HR\u003csub\u003emax\u003c/sub\u003e calculated with the Fox equation had moderate consistency with HR\u003csub\u003eVT1\u003c/sub\u003e determined by CPET, with poor reliability and low credibility for practical application. In summary, we recommend using the exercise intensity of 35% HRR as a substitute for the intensity near VT1 when prescribing exercise for MetS patients.\u003c/p\u003e \u003cp\u003eIn the exercise intensity domain, which is divided based on the maximum physiological value percentage, 35% HRR is in the light intensity zone. The previous moderate intensity zone of the 40\u0026ndash;59 HRR%\u003csup\u003e8\u003c/sup\u003e or the 40\u0026ndash;69 HRR%\u003csup\u003e26\u003c/sup\u003e, which is based on the maximum physiological value percentage, will make the actual exercise intensity for MetS patients higher than VT1. In fact, the guidelines for exercise testing and exercise prescription by the American College of Sports Medicine recommend that unhealthy people carry out exercise at 30%~39% HRR or VO\u003csub\u003e2\u003c/sub\u003eR\u003csup\u003e8\u003c/sup\u003e, which is basically the same as the results in this research.\u003c/p\u003e \u003cp\u003eThe widely promoted and applied Fox equation and Tanaka Eq.\u0026nbsp;2\u003csup\u003e7\u003c/sup\u003e were used in this research. Because the average body fat percentage of the participants in this study was higher than 30%, the Miller equation, which is recommended for overweight people with sedentary lifestyles, was also tested\u003csup\u003e28\u003c/sup\u003e. Many more improved equations are suitable for different individuals\u003csup\u003e8\u003c/sup\u003e, but an HR\u003csub\u003emax\u003c/sub\u003e prediction equation that is suitable for MetS patients has yet to be developed. The purpose of this study was not to involve the development of the HR\u003csub\u003emax\u003c/sub\u003e prediction equations. The three HR\u003csub\u003emax\u003c/sub\u003e prediction equations were used only to test the corresponding standardized exercise intensity in the VT1 exercise intensity domain in MetS patients. Compared with the other two HR\u003csub\u003emax\u003c/sub\u003e prediction equations, the 35% HRR (113.56\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20) calculated by the Fox equation showed the highest consistency with HR\u003csub\u003eVT1,\u003c/sub\u003e which might be due to the average age of the participants in this study of 40\u0026ndash;50 years\u003csup\u003e29\u003c/sup\u003e. Additionally, the HR\u003csub\u003emax\u003c/sub\u003e predicted by the Fox equation decreases quickly with increasing age, so it can be underestimated in healthy individuals\u003csup\u003e30\u003c/sup\u003e, but it might just meet the situation in which the HR\u003csub\u003emax\u003c/sub\u003e of MetS patients is generally decreased.\u003c/p\u003e \u003cp\u003eDue to pathological factors, MetS patients have a higher resting heart than healthy individuals\u003csup\u003e31\u003c/sup\u003e. With the progression of the disease, most MetS patients tend to encounter problems, including ventricular diastolic dysfunction and structural changes\u003csup\u003e32\u003c/sup\u003e, impaired cardiac autonomic nervous function, increased sympathetic nerve activity and decreased heart rate variability\u003csup\u003e33\u003c/sup\u003e. In addition to their overall cardiorespiratory endurance level, which is lower than that of normal individuals\u003csup\u003e34,35\u003c/sup\u003e, MetS patients also face problems such as a sedentary lifestyle, a lack of physical activity, decreased skeletal muscle mass, decreased mitochondrial function\u003csup\u003e36\u003c/sup\u003e, an earlier-onset anaerobic threshold and a low lactate clearance rate\u003csup\u003e37\u003c/sup\u003e. All of these factors lead to lower exercise tolerance in MetS patients than in healthy people who regularly exercise. In the CPET, MetS patients stop exercising when their heart rate is much lower than the projected maximum heart rate. All the abovementioned factors contribute to the uncertainty of the exercise HR in MetS patients. The objective exercise intensity domain for different MetS patients varies significantly when different exercise intensity prescription methods are applied.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eThe limitations of this research include the small sample and wide age range. Future studies should validate our findings by expanding the sample size with a detailed differentiation by age group and sex. Due to the limited sample size, we did not conduct any subgroup analysis with MetS patients as the reference. We excluded MetS patients who were using drugs that could impact their heart rate, which may limit the applicability of the results. In addition, no equations for predicting the HR\u003csub\u003emax\u003c/sub\u003e of the MetS population were developed in this study, and only the Fox equation was used as a substitute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Perspective\u003c/h2\u003e \u003cp\u003eThe first ventilatory threshold is the baseline for moderate-intensity continuous exercise, and most exercise prescriptions require a gradual increase in exercise intensity and volume in practical applications. Our study provides a way to approximate the VT1 in the CPET in patients with metabolic syndrome using traditional exercise intensity determination methods. Without CPET equipment and related technical operators, MetS patients can start effective and safe endurance training in this exercise intensity domain based on our results. The consistency between the exercise intensity determined by 35% HRR calculated by the Fox equation and the exercise intensity determined by VT1 in the practical application of exercise prescriptions for improving cardiovascular risk factors and cardiovascular fitness in MetS patients needs to be verified. By verifying the consistency of the two methods for determining exercise intensity in practical applications, it may be possible to further refine the percentage of HRR corresponding to VT1 suitable for MetS patients. In addition, an HR\u003csub\u003emax\u003c/sub\u003e prediction equation suitable for adult MetS patients needs to be developed, and given the specificity of the MetS clinical cohort, individuals using β-blockers should be developed separately from those who do not\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAlthough the exercise intensity prescription method based on the maximum physiological value percentage has been gradually replaced by a method based on the threshold in the clinical rehabilitation medicine field, it is still necessary to apply 35% HRR standardized method combined with the Fox equation in instructing MetS patients to exercise due to the absence of CPET equipment and relevant technicians and the limited popularity and acceptance of this method by the general public.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLRJ designed the research, collected the data and wrote the paper. QJM participated in the research proposition, method construction and data analysis revision. WF was involved in the data collection. XWZ and ZHH helped to enroll people in the study and to revise the data analysis. All the authors have read and approved the final version of the paper and agreed to the presentation sequence.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e This research was sponsored by the 2022 China National Regional Medical Center Science and Technology Innovation Plan Project (project number: 202242). Liu Ruojiang received graduate student scholarship support from North University of China. The authors express appreciation to all the participants in this research and are grateful to Guo Jinhong and Zhao Ya for their assistance in the data collection.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no conflicts of\u0026nbsp;interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLemieux, I. \u0026amp; Despr\u0026eacute;s, J.-P. 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Changes in metabolic syndrome severity following individualized versus standardized exercise prescription: A feasibility study. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 2594 (2018).\u003c/li\u003e\n \u003cli\u003eHansen, D. \u003cem\u003eet al.\u003c/em\u003e Exercise intensity assessment and prescription in cardiovascular rehabilitation and beyond: why and how: a position statement from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology. \u003cem\u003eEuropean journal of preventive cardiology\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 230\u0026ndash;245 (2022).\u003c/li\u003e\n \u003cli\u003eTirandi, A., Carbone, F., Montecucco, F. \u0026amp; Liberale, L. The role of metabolic syndrome in sudden cardiac death risk: Recent evidence and future directions. \u003cem\u003eEuropean Journal of Clinical Investigation\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, e13693 (2022).\u003c/li\u003e\n \u003cli\u003eMacIntosh, B. R., Murias, J. M., Keir, D. A. \u0026amp; Weir, J. M. What is moderate to vigorous exercise intensity? \u003cem\u003eFrontiers in Physiology\u003c/em\u003e, 1481 (2021).\u003c/li\u003e\n \u003cli\u003eBinder, R. K. \u003cem\u003eet al.\u003c/em\u003e Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing. \u003cem\u003eEuropean Journal of Preventive Cardiology\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 726\u0026ndash;734 (2008).\u003c/li\u003e\n \u003cli\u003eAssociation, D. B. o. C. M. Guideline for the prevention and treatment of type 2 diabetes mellitus in China. \u003cem\u003eChinese Journal of Practical Internal Medicine\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, 757\u0026ndash;784, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19538/j.nk2021090106\u003c/span\u003e\u003c/span\u003e (2021).\u003c/li\u003e\n \u003cli\u003eBudts, W. \u003cem\u003eet al.\u003c/em\u003e Recommendations for participation in competitive sport in adolescent and adult athletes with congenital heart disease (CHD): position statement of the Sports Cardiology \u0026amp; Exercise Section of the European Association of Preventive Cardiology (EAPC), the European Society of Cardiology (ESC) Working Group on Adult Congenital Heart Disease and the Sports Cardiology, Physical Activity and Prevention Working Group of the Association for European Paediatric and Congenital Cardiology (AEPC). \u003cem\u003eEuropean heart journal\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 4191\u0026ndash;4199 (2020).\u003c/li\u003e\n \u003cli\u003eTanaka, H., Monahan, K. D. \u0026amp; Seals, D. R. Age-predicted maximal heart rate revisited. \u003cem\u003eJournal of the american college of cardiology\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 153\u0026ndash;156 (2001).\u003c/li\u003e\n \u003cli\u003eMiller, W. C., Wallace, J. P. \u0026amp; Eggert, K. E. Predicting max HR and the HR-VO2 relationship for exercise prescription in obesity. \u003cem\u003eMedicine and science in sports and exercise\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1077\u0026ndash;1081 (1993).\u003c/li\u003e\n \u003cli\u003eFranckowiak, S. C., Dobrosielski, D. A., Reilley, S. M., Walston, J. D. \u0026amp; Andersen, R. E. Maximal heart rate prediction in adults that are overweight or obese. \u003cem\u003eJournal of strength and conditioning research/National Strength \u0026amp; Conditioning Association\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1407 (2011).\u003c/li\u003e\n \u003cli\u003eGellish, R. L. \u003cem\u003eet al.\u003c/em\u003e Longitudinal modeling of the relationship between age and maximal heart rate. \u003cem\u003eMedicine and science in sports and exercise\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 822\u0026ndash;829 (2007).\u003c/li\u003e\n \u003cli\u003eLiu, X. \u003cem\u003eet al.\u003c/em\u003e Resting heart rate and risk of metabolic syndrome in adults: a dose\u0026ndash;response meta-analysis of observational studies. \u003cem\u003eActa diabetologica\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 223\u0026ndash;235 (2017).\u003c/li\u003e\n \u003cli\u003eAijaz, B. \u003cem\u003eet al.\u003c/em\u003e in \u003cem\u003eMayo Clinic Proceedings.\u003c/em\u003e 1350\u0026ndash;1357 (Elsevier).\u003c/li\u003e\n \u003cli\u003eStuckey, M. I., Tulppo, M. P., Kiviniemi, A. M. \u0026amp; Petrella, R. J. Heart rate variability and the metabolic syndrome: a systematic review of the literature. \u003cem\u003eDiabetes/metabolism research and reviews\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 784\u0026ndash;793 (2014).\u003c/li\u003e\n \u003cli\u003eRodriguez, J. C. \u003cem\u003eet al.\u003c/em\u003e Cardiopulmonary Exercise Responses in Individuals with Metabolic Syndrome: The Ball State Adult Fitness Longitudinal Lifestyle Study. \u003cem\u003eMetabolic Syndrome and Related Disorders\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 414\u0026ndash;420 (2022).\u003c/li\u003e\n \u003cli\u003eKim, B. \u003cem\u003eet al.\u003c/em\u003e Cardiorespiratory fitness is strongly linked to metabolic syndrome among physical fitness components: a retrospective cross-sectional study. \u003cem\u003eJournal of Physiological Anthropology\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1\u0026ndash;9 (2020).\u003c/li\u003e\n \u003cli\u003eRichter-Stretton, G. L., Fenning, A. S. \u0026amp; Vella, R. K. Skeletal muscle\u0026ndash;A bystander or influencer of metabolic syndrome? \u003cem\u003eDiabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 867\u0026ndash;875 (2020).\u003c/li\u003e\n \u003cli\u003eJones, T. E. \u003cem\u003eet al.\u003c/em\u003e Plasma lactate as a marker of metabolic health: Implications of elevated lactate for impairment of aerobic metabolism in the metabolic syndrome. \u003cem\u003eSurgery\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e, 861\u0026ndash;866 (2019).\u003c/li\u003e\n \u003cli\u003eGodlasky, E. \u003cem\u003eet al.\u003c/em\u003e Effects of \u0026beta;-blockers on maximal heart rate prediction equations in a cardiac population. \u003cem\u003eJournal of Cardiopulmonary Rehabilitation and Prevention\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 111\u0026ndash;117 (2018).\u003c/li\u003e\n \n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"aerobic exercise, maximum physiological value, first ventilatory threshold, Bland‒Altman analysis, interclass correlation coefficient","lastPublishedDoi":"10.21203/rs.3.rs-4002742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4002742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to compare differences and consistencies between the target heart rate (THR) for aerobic exercise based on the standardized physiological maximum value percentage and the first-ventilatory-threshold heart rate (HR\u003csub\u003eVT1\u003c/sub\u003e). The maximum physiological value percentage with the highest consistency with HR\u003csub\u003eVT1\u003c/sub\u003e, combined with the maximum heart rate (HR\u003csub\u003emax\u003c/sub\u003e) prediction equation, can guide safe and effective exercise in patients with metabolic syndrome (MetS) in practical applications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThree HR\u003csub\u003emax\u003c/sub\u003e prediction equations were used to calculate the THR as 35%, 45% and 50% heart rate reserve (HRR) and 55%, 60% and 65% HR\u003csub\u003emax,\u003c/sub\u003e and the results were compared for correlation and consistency with HR\u003csub\u003eVT1\u003c/sub\u003e. HR\u003csub\u003eVT1\u003c/sub\u003e was measured through a cardiopulmonary exercise test (CPET) that complied with current guidelines and laboratory standards.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the Fox equation, the difference in HR\u003csub\u003eVT1\u003c/sub\u003e and 35% HRR was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.060). Bland‒Altman analysis indicated that the mean difference in HR\u003csub\u003eVT1\u003c/sub\u003e and 35% HRR was \u0026minus;\u0026thinsp;0.350, which was close to the 0 line, and 95.83% of the patients (46/48) had values within the 95% limits of agreement. The absolute maximum difference within the limits of agreement was 7.90, the interclass correlation coefficient (ICC) was 0.862 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the 95% confidence interval was 0.767\u0026ndash;0.920, indicating high consistency and good accuracy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAmong Chinese MetS patients, 35% HRR determined via the standardized HRR method and the acquired CPET VT1 had high consistency. Especially when applying the Fox equation, the exercise intensity of MetS patients can be best kept within the moderate-intensity domain near VT1.\u003c/p\u003e","manuscriptTitle":"Consistency between the Standardized Methods of Exercise Prescription and the Target Heart Rate for the Cardiopulmonary Exercise Test in Individuals with Metabolic Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 15:51:56","doi":"10.21203/rs.3.rs-4002742/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-04T06:38:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T23:22:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-09T18:25:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b44ae6fe-ed7d-4d8c-8f2a-d1934154b541","date":"2024-04-27T09:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5e87eeb4-c7ff-4a25-8fc5-0a47825e1a75","date":"2024-04-26T11:43:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-17T17:37:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T08:03:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-15T11:32:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-15T11:30:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-01T09:20:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fef66a58-328a-47ee-b178-ebdbb8072af9","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29574447,"name":"Health sciences/Cardiology"},{"id":29574448,"name":"Health sciences/Health care"},{"id":29574449,"name":"Health sciences/Health occupations"}],"tags":[],"updatedAt":"2025-06-02T16:03:36+00:00","versionOfRecord":{"articleIdentity":"rs-4002742","link":"https://doi.org/10.1038/s41598-025-03084-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-27 15:57:54","publishedOnDateReadable":"May 27th, 2025"},"versionCreatedAt":"2024-03-19 15:51:56","video":"","vorDoi":"10.1038/s41598-025-03084-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03084-7","workflowStages":[]},"version":"v1","identity":"rs-4002742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4002742","identity":"rs-4002742","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}