Heart Rate Thresholds to Limit Activity in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients (Pacing): Comparison of Heart Rate Formulae and Measurements of the Heart Rate at the Lactic Acidosis Threshold during Cardiopulmonary Exercise Testing

C. (Linda) M. C. van Campen^1*, Peter C. Rowe², Frans C. Visser¹

¹Stichting Cardiozorg, Hoofddorp, NL, The Netherlands.
²Department of Paediatrics, Johns Hopkins University School of Medicine, Baltimore, USA.

DOI: 10.4236/ape.2020.102013                

Abstract

Introduction: Based on the hypothesis that oxidative metabolism is impaired in ME/CFS, a previous study recommended a pacing self-management strategy to prevent post-exertional malaise. This strategy involved a prescription to maintain a heart rate below the anaerobic threshold during physical activities. In the absence of lactate sampling or a cardiopulmonary exercise test (CPET), the pacing self-management formula defines 55% of the age-specific predicted maximal heart rate as the heart rate at the anaerobic threshold. Thus far there has been no empiric evidence to test this self-pacing method of predicting heart rate at anaerobic threshold. The aim of this study was to compare published formula-derived heart rates at the anaerobic threshold with the actual heart rate at the lactic acidosis threshold as determined by CPET. Methods and Results: Adults with ME/CFS who had undergone a symptom-limited CPET were eligible for this study (30 males, 60 females). We analysed males and females separately because of sex-based differences in peak oxygen consumption. From a review paper, formulae to calculate maximal predicted heart rate were used for healthy subjects. We compared the actual heart rate at the lactic acid threshold during CPET to the predicted heart rates determined by formulae. Using Bland-Altman plots, calculated bias: the mean difference between the actual CPET heart rate at the anaerobic threshold and the formula predicted heart rate across several formulae varied between -28 and 19 bpm in male ME/CFS patients. Even in formulae with a clinically acceptable bias, the limits of agreement (mean bias ± 2SD) were unacceptably high for all formulae. For female ME/CFS patients, bias varied between 6 and 23 bpm, but the limits of agreement were also unacceptably high for all formulae. Conclusion: Formulae generated in an attempt to help those with ME/CFS exercise below the anaerobic threshold do not reliably predict actual heart rates at the lactic acidosis threshold as measured by a cardiopulmonary exercise test. Formulae based on age-dependent predicted peak heart rate multiplied by 55% have a wide age-specific variability and therefore have a limited application in clinical practice.

Keywords

Chronic Fatigue Syndrome, Myalgic Encephalomyelitis, Cardiopulmonary Ex-ercise Testing, Peak Oxygen Consumption, Lactic Acidosis Threshold, Oxygen Consumption at the Anaerobic Threshold, Workload, Pacing, Heart Rate For-mulae

1. Introduction

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a serious and potentially disabling chronic disease ( Carruthers et al., 2011; Clayton, 2015; Fukuda et al., 1994; IOM, 2015). One of the main characteristics of patients with ME/CFS is a prolonged recovery and an increase in symptoms following mental or physical exercise, termed post-exertional malaise ( Jones et al., 2010; Paul et al., 1999). The duration of post-exertional malaise can vary between hours and months. The pathophysiology of post-exertional malaise is not exactly known but has been hypothesized to involve metabolic abnormalities, reduced efficiency of mitochondrial energy production, renin-angiotensin and sympathetic nervous system activation, central nervous system abnormalities, increased activity of antioxidant enzyme systems in muscle, alterations in the hypothalamic-pituitary-adrenal axis, altered immune responses, and an altered gut microbiomes with delayed clearance of blood bacteria following exercise ( Bains, 2008; Blundell et al., 2015; Bohne & Bohne, 2019; Brown et al., 2015; Cordero et al., 1996; Fulle et al., 2000; Fulle et al., 2007; Ghali et al., 2019; Jones et al., 2010; Josev et al., 2019; Lien et al., 2019; Light et al., 2012; McCully et al., 2003; McGregor et al., 2019; Nguyen et al., 2017; Nijs et al., 2014; Shukla et al., 2015; Siemionow et al., 2004; Sorensen et al., 2009; Van Den et al., 2007; Whistler et al., 2005; Wong et al., 1992). Other studies have provided conflicting data for some of the hypothesized underlying mechanisms ( Bouquet et al., 2019; Keech et al., 2016).

Based on the hypothesis that oxidative metabolism is impaired in ME/CFS, Davenport et al. recommended an exercise prescription that keeps the heart rate below the anaerobic threshold ( Davenport et al., 2010) as a method for preventing post-exertional malaise. As there is a direct relation between the heart rate and the anaerobic threshold, the heart rate at the anaerobic threshold was advocated to be used as a guide for physical activities, aiming to avoid ineffective anaerobic metabolism in ME/CFS patients. This strategy has been referred to as pacing self-management.

The gold standard for measuring the anaerobic threshold is an exercise test with periodic lactate sampling. However, this method is laborious, expensive, invasive. In ME/CFS patients the exercise test itself can lead to post-exertional malaise. It has been previously demonstrated in sedentary controls that the lactate threshold (as a reflection of the anaerobic threshold) was 59 (8)% and 67 (7)% of the peak oxygen consumption in females and males, respectively ( Demello et al., 1987), with heart rates at the anaerobic threshold being 69% of the peak heart rate in sedentary women and 77% of the peak heart rate in sedentary men. An alternative to determine the anaerobic threshold and the related heart rate is to use the lactic acidosis threshold as determined by the V-slope method during cardiopulmonary exercise testing (CPET) ( Beaver et al., 1986). A previous study has shown that the heart rate at the lactic acidosis threshold is highly reproducible between tests ( Aunola & Rusko, 1984).

For pacing purposes, without the use of lactate sampling or a CPET, formulae have been developed to approximate the heart rate at the anaerobic threshold. These formulae assume that the peak oxygen consumption is associated with the peak heart rate of an individual. The majority of these formulae are based on age ( Robergs, 2002). These age-dependent peak heart rate calculations are in general used for training purposes of athletes. For ME/CFS patient-pacing purposes the heart at the anaerobic threshold is set at 55% of the peak heart rate. A popular formula to determine the heart rate at the anaerobic threshold is the calculation: 220-age * 55%. This formula has been adopted in patient websites for pacing self-management such as http://www.cfsselfhelp.org/library/pacing-numbers-using-your-heart-rate-to-stay-inside-energy-envelope and https://www.healthrising.org/?s=pacing.

However, no empiric data are available on the reliability of the proposed formula for determination of the heart rate at the anaerobic threshold in ME/CFS patients. Robergs and Landwehr performed a large review of the studies of age-dependent peak heart rate formulae in general, and found that there are large errors inherent in the estimation of the maximum heart rate ( Robergs, 2002). Because it is based on these age-dependent peak heart rate formulae, the proposed pacing self-management formula for ME/CFS patients, is susceptible to similar methodologic errors. Therefore, the aim of this study was to compare the proposed formula-derived heart rate at the anaerobic threshold with the actual heart rate at the lactic acidosis threshold as determined by CPET.

2. Patients, Material and Methods

Eligible participants were male and female ME/CFS patients referred between 2012 and 2018 to the Stichting CardioZorg. This cardiology clinic specializes in diagnosing and treating adults with ME/CFS. The diagnosis of chronic fatigue syndrome (CFS) was made according to the Fukuda criteria ( Fukuda et al., 1994) and that of myalgic encephalomyelitis (ME) was made according to the international ME criteria ( Carruthers et al., 2011). In all patients alternative diagnoses which could explain the fatigue and other symptoms were ruled out. No important co-morbidities were present.

This study included the subset of patients in whom CPET was performed. The decision to perform CPET was made primarily to assess the degree of disability. Because of the female predominance in ME/CFS, we choose a 2:1 female/male ratio. Females and males were age-matched, due to the dependence of the age of subjects in the heart rate formulae. Patients were excluded when the lactic acidosis threshold of the CPET could not be accurately assessed. Males and females were analysed separately because of sex-specific differences in peak oxygen consumption, and because of possible sex-specific differences in the clinical phenotype of the disease ( Faro et al., 2016).

All patients give informed consent to analyze their data. The use of clinical data for descriptive studies was approved by the ethics committee of the Slotervaart Hospital, the Netherlands.

Cardiopulmonary exercise testing (CPET)

Patients underwent a symptom-limited exercise test on a cycle ergometer (Excalibur, Lode, Groningen, The Netherlands) according to a previously described protocol ( van Campen et al., 2020). A RAMP workload protocol was used varying between 10 - 30 Watt/min increases, depending on sex, age and expected exercise intolerance. Oxygen consumption (VO₂), carbon dioxide release (VCO₂) and oxygen saturation were continuously measured (Cortex, Procare, The Netherlands), and displayed on screen using Metasoft software (Cortex, Biophysic Gmbh, Germany). An ECG was continuously recorded and blood pressures were measured continuously using the Nexfin device (BMEYE, Amsterdam, The Netherlands) ( Martina et al., 2012). Cycle seat height was positioned to approximately 175˚ of knee extension. Expired gases were collected breath-by-breath through a two-way breathing valve, and analyzed using open circuit spirometry. The metabolic measurement system (Cortex, Biophysic Gmbh, Germany) was calibrated before each test with ambient air, standard gases of known concentrations and a 3-L calibration syringe. The lactic acidosis threshold (LAT) is an analog of anaerobic threshold, and was identified from expired gases using the V-Slope algorithm in the metabolic measurement system software. A trained investigator performed visual assessment and confirmation of the algorithm-derived LAT. Testing took place in a controlled environment with temperature range of 20˚C - 24˚C and 15% - 60% relative humidity. The test was supervised by an experienced cardiologist. Patients were encouraged by standard phrases each minute to perform maximally to the point of exhaustion. The mean of the VO₂ measurements of the last 15 seconds before ending the exercise (peak VO₂) was taken, and expressed as a percentage of the normal values of a population study: %peak VO₂ ( Glaser et al., 2010). We assessed the mean respiratory exchange ratio (RER; VCO₂/VO₂) of the last 15 seconds to determine the influence of this measure of maximal effort on the results. Immediately after the test the attending cardiologist noted the primary reason for terminating the exercise and judged whether motivation and efforts during exercise were optimal for the individual patient.

Formulae to calculate maximal predicted heart rate

From a review of published formulae to calculate maximal predicted heart rate, we selected those formulae used for healthy subjects. Formulae used in populations with diseases like hypertension, coronary heart disease, obesity, and mental retardation were excluded ( Robergs, 2002). To obtain the predicted heart rate at the lactic acidosis threshold using the pacing self-management formula of Davenport et al., the predicted maximal heart rate from each individual formula was multiplied by 0.55 ( Davenport et al., 2010). The patient data were used for all formulae to calculate maximal and anaerobic threshold. Those data were compared with the data resulting from the cardiopulmonary exercise test.

Table 1. Formulae for predicting maximal heart rate.

* and #: two different formulae for males and females described in the same paper.

Statistical analysis

Data were analyzed using Graphpad 6.05. All continuous data were tested for normal distribution using the D’Agostino-Pearson omnibus normality test, and data are presented as the mean (SD) or as median (IQR), where appropriate. Bland-Altman plots were generated comparing the heart rate at the lactic acidosis threshold and the formula-generated heart rates at the anaerobic threshold. Bias and limits of agreement were calculated. The bias is computed as the mean value of the subtraction: the heart rate at the lactate acidosis threshold minus the formula-derived heart rate at the anaerobic threshold of all patients. Limits of agreement are calculated as the mean bias plus or minus two standard deviations. A clinically acceptable bias was predefined and set at plus or minus 5 bpm. Furthermore, linear regression analysis was performed on the mean heart rates of both methods vs the differences of both methods, to assess the presence of proportional bias. The proportional bias means that one method gives values that are higher (or lower) than those from the other by an amount that is proportional to the level of the measured variable ( Ludbrook, 1997). Differences in variance were assessed using the F test. A P value < 0.01 was considered significantly different.

3. Results

Table 2 shows the characteristics of the study participants (30 male ME/CFS patients and 60 female ME/CFS patients). Males were significantly taller and heavier than females. Disease duration did not differ significantly. Resting heart rate was significantly higher for women and peak systolic blood pressure significantly higher in men. Workload and VO₂ at the lactic acidosis threshold and at peak exercise were significantly higher in men than in women. In contrast, % predicted VO₂ at the lactic acidosis threshold and peak were not significantly different between men and women.

Figure 1 shows an example of the formula predicted heart rate at the anaerobic threshold vs the heart rate of the lactic acidosis threshold by CPET in patients. Figure 1(a) shows the heart rate at the anaerobic threshold using the prediction formula: 220-age ( Fox et al., 1971) * the multiplier 0.55 ( Davenport et al., 2010) for the 30 male ME/CFS patients compared with the actual heart rate at the lactic acidosis threshold of CPET. Figure 1(b) shows the anaerobic threshold heart rate data of the same prediction formula: (220-age) × 0.55 for the 60 female ME/CFS patients versus the heart rate at the lactic acidosis threshold. Both in males and females variance of the heart rate at the lactic acidosis threshold was larger than that of the formula (both p < 0.01)

Table 3(a) shows the results of the Bland-Altman analysis of the different heart rate prediction formulae of Table 1 versus the heart rate at the lactic acidosis threshold for male ME/CFS patients. Bias varied between −28 and 19 bpm, but even in formulae with a clinically acceptable bias, the limits of agreement are unacceptably high for all formulae (arbitrarily set at a bias of plus/minus 5 bpm). Table 3(b) shows the same analysis for female ME/CFS patients. Bias varied between 6 and 23 bpm, but also in women the limits of agreement were also unacceptably high for all formulae. An example of the Bland-Altman analysis (formula based on the maximal predicted heart rate of Lester et al. ( Lester et al., 1968) for men and women is shown in Figure 2(a) and Figure 2(b). Furthermore, a proportional bias analysis was performed ( Ludbrook, 1997). Table 3(a) and Table 3(b) show that all the regression lines of the mean of the prediction heart rate and the heart rate at the lactic acidosis threshold vs the differences between the two heart rates were highly significantly different from zero, indicating proportional bias. An example of this proportional bias is given in Figure 2(c) and Figure 2(d).

To further explore the differences between the predicted heart rate at the anaerobic threshold and the actual heart rate at the lactic acidosis threshold during CPET we plotted patient age versus the heart rate at the lactic acidosis threshold. This was done because age determines the calculated heart rate at the

Table 2. Baseline and cardiopulmonary exercise test data for male and female CFS patients.

BMI: body mass index; no: number; BP: blood pressure; BSA: body surface area; F: female; M: male; Perc: percent; RER: respiratory exchange ratio; VO₂: oxygen consumption; LAT: lactic acidosis threshold. Data are expressed as mean (SD) or median (25% - 75% IQR).

Figure 1. An example of the formula of the predicted heart rate at the anaerobic threshold vs the heart rate at the lactic acidosis threshold by CPET in ME/CFS patients. (a) shows the data in men using the prediction formula: (220-age) × 0.55 using the Fox et al. 1971 formula and the 0.55 multiplier from Davenport. (b) shows the data in females using the same prediction formula: (220-age) × 0.55. Both in males and females variance of the heart rate at the lactic acidosis threshold was larger than that of the formula (both P < 0.01). CPET: cardiopulmonary exercise test; AT: anaerobic threshold; LAT: lactic acidosis threshold.

(a)*: two different formulae for males and females described in the same paper. AT: anaerobic threshold; bpm: beats per minute; calc: calculated; HR: heart rate; LAT: lactic acidosis threshold; *proportional bias analysis significant: significance of the linear regression analysis of the mean heart rate of the two methods versus the difference between the two methods.
(b)* and #: two different formulae for males and females described in the same paper. For other abbreviations see Table 3(a).

(a)

Table 3. (a) Heart rates at the lactic acidosis threshold using different peak heart rate formula, bias and limits of agreement of formula vs the heart rate at the lactic acidosis threshold (Bland-Altman plots) in men; (b) Heart rates at the lactic acidosis threshold using different peak heart rate formula, bias and limits of agreement of formula vs the heart rate at the lactic acidosis threshold (Bland-Altman plots) in women.

anaerobic threshold. Figure 3 shows that age has a limited influence on the heart rate at the lactic acidosis threshold. The slopes of the regression line were non-significantly different from zero both in men and women and slopes were not significantly different between men and women. The actual heart rate at the lactic acidosis threshold for female ME/CFS patients ranged from 90 to 146 bpm and for male ME/CFS patients from 81 to 139 bpm.

4. Discussion

One of the main findings of this study was that the heart rate at the lactic acidosis threshold is minimally influenced by the age of the ME/CFS patients, whereas, by definition, the calculated heart rate at the anaerobic threshold is highly dependent on age. Thus, not only the formula 220-age, but also the other explored formulae in the present study are only of limited use for pacing strategies in ME/CFS patients.

One of the hallmarks of ME/CFS is a reduced capacity for physical and mental exercise ( Carruthers et al., 2003; IOM, 2015; Peterson et al., 1994), ranging from mild to very severe. The most severely affected ME/CFS patients are bedridden and need assistance from others for activities of daily living ( De Becker et al., 2000; Vanness et al., 2003). Post-exertional malaise with flare-up of symptoms after a mental or physical exercise, which exceeds the limited performance capacity of ME/CFS patients, is another serious problem for ME/CFS patients

CPET: cardiopulmonary exercise test; AT: anaerobic threshold; LAT: lactic acidosis threshold. In each panel, the distance between zero and the thick solid line is the bias of the formula-based HR measurement. The dashed lines represent 2SD from that mean. In an ideal test, the bias would be close to zero. For Panels (c) and (d), an ideal test would have a non-significant slope.

Figure2. Shows the comparison of the heart rate at the lactic acidosis threshold measured at CPET (method (a)) and the heart rate at the anaerobic threshold using the formula by Lester (method (b)) ( Lester et al., 1968). Panels (a) and (b) show Bland-Altman plots for male (panel (a)) and female (panel (b)) ME/CFS patients. Panels (c) and (d) show the proportional bias regression analysis for male (panel (c)) and female (panel (d)) ME/CFS patients.

Figure 3. Correlation of heart rate at the lactic acidosis threshold as measured during CPET and age in both males (blue dots) and females (red dots) ME/CFS patients. The slopes of the regression line of age versus the heart rate at the lactic acidosis threshold were not significantly different from zero and slopes between men and women were not significantly different. The dotted lines are 95% prediction intervals.

because the increase in symptoms further limits daily activities ( Davenport et al., 2011; Vanness et al., 2010). The importance of post-exertional malaise is exemplified in the diagnostic criteria of ME, as without the presence of post exertional malaise, the diagnosis ME cannot be made ( Carruthers et al., 2011).

To reduce post-exertional malaise in ME/CFS patients strategies, called “pacing” have been developed. Nijs et al. ( Nijs et al., 2008) suggested pacing to encourage patients to balance activities and rest to ensure no deterioration of symptoms. This strategy should include realistic goals and monitoring of exercise and effects on energy balance. Also, fluctuations in symptom severity and delayed recovery from exercise due to post-exertional malaise should be taken into account. Activities should be varied with pauses in duration related to the duration of performed activities.

Based on the observation that oxidative metabolism may be impaired in ME/CFS, Davenport et al. recommended, to avoid post-exertional malaise, exercise prescription below the anaerobic threshold ( Davenport et al., 2010). As there is a direct relation between the heart rate and the anaerobic threshold ( Aunola & Rusko, 1984), the heart rate at the anaerobic threshold was advocated to be used as a guide for physical activities, aiming to avoid ineffective anaerobic metabolism in ME/CFS patients. Although intuitively the use of this heart rate at the anaerobic threshold as an upper limit of performed activity may be correct, there are no studies available demonstrating the effectiveness of this approach. Indirect evidence may come from graded exercise therapy studies. Assuming that during graded exercise therapy the training involves frequent activities above the anaerobic threshold, patients reported deterioration in physical functioning ( Davenport et al., 2010) after graded exercise therapy. Also, a study of Black et al. showed an increase in activity of ME/CFS patients with exercise therapy early in the study, but a large decline in activity duration at the end of the trial ( Black & McCully, 2005).

There are shortcomings in using the formula 220-age for the prediction of the maximum heart rate. Roberg et al. stated: “Despite the acceptance of this formula, research spanning more than two decades reveals the large error inherent in the estimation of HRmax”. Furthermore: “A brief review of alternate HRmax prediction formula reveals that the majority of age-based univariate prediction equations also have large prediction errors” ( Robergs, 2002). The second statement suggests that there may be accurate formula to predict age dependent peak heart rates. Furthermore, the choice of assigning the heart rate at the anaerobic threshold at 55% of the peak heart rate has also not been validated in the ME/CFS population.

Therefore, the aim of this study was to compare predicted heart rates at the anaerobic threshold, using different published peak heart rate formula and applying the Davenport et al. 55% multiplier for ME/CFS patients, with the actual heart rate at the lactic acidosis threshold as determined by cardiopulmonary exercise stress testing. The main finding of this study was that all formulae used to calculate predicted heart rate at the anaerobic threshold showed a large variation in bias, but more importantly, a clinically unacceptable range in the limits of agreement. The large range in limits of agreement was due to the presence of proportional bias, meaning that one method gives values that are higher (or lower) than those from the other by an amount that is proportional to the level of the measured variable ( Ludbrook, 1997). The difference between formulae and the measurement of the heart rate at the lactic acidosis threshold is shown in Figure 3. The heart rate at the lactic acidosis threshold is minimally influenced by the age of the ME/CFS patients, whereas, by definition, the calculated heart rate at the anaerobic threshold is highly dependent on age. Thus, not only the formula 220-age, but also the other explored formulas in the present study are not clinically useful for pacing strategies in ME/CFS patients. The usefulness of the formula in other diseases than ME/CFS need to be studied in future, as some might be potentially detrimental if followed rigidly.

Potential limitations have to be mentioned. This was a retrospective study that selected patients who had undergone cardiopulmonary exercise stress testing, mainly to establish the degree of disability for social security claims. This may have led to inclusion bias. On the other hand, baseline characteristics of the present study like age ( Keller et al., 2014; Snell et al., 2013), workload ( Keller et al., 2014; Nelson et al., 2019) and oxygen consumption at the lactic acidosis threshold ( Van Campen, 2020; Vermeulen & Vermeulen van Eck, 2014) and at peak exercise ( Keller et al., 2014; Van Campen, 2020) (Table 1), are consistent with other studies. Second, the heart rate at the lactic acidosis threshold, as determined during the cardiopulmonary exercise test, has been used as the standard for the heart rate at the anaerobic threshold. The exact point where CO₂ output starts to rise disproportionally, relative to the oxygen consumption, may be difficult to assess because of irregular breathing, suboptimal plotting or a poor ventilatory response ( Wasserman et al., 2012). In that case the value of the oxygen consumption and related heart rate at that point may be different from the true values. Although a trained investigator performed visual assessment and confirmation of the algorithm-derived lactic acidosis threshold, we cannot exclude the possibility of erroneous heart rate values.

5. Conclusion

Calculated heart rates at the anaerobic threshold, based on age-dependent peak heart formula times a fixed percentage of 55%, with the aim to limit ME/CFS patients to perform exercise below the anaerobic threshold, do not reliably predict heart rates at the lactic acidosis threshold as measured by a cardiopulmonary exercise test and should be used with caution in clinical practice.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

[1]	Aunola, S., & Rusko, H. (1984). Reproducibility of Aerobic and Anaerobic Thresholds in 20-50 Year Old Men. European Journal of Applied Physiology and Occupational Physiology, 53, 260-266. https://doi.org/10.1007/BF00776600
[2]	Bains, W. (2008). Treating Chronic Fatigue States as a Disease of the Regulation of Energy Metabolism. Medical Hypotheses, 71, 481-488. https://doi.org/10.1016/j.mehy.2008.02.022
[3]	Beaver, W. L., Wasserman, K., & Whipp, B. J. (1986). A New Method for Detecting Anaerobic Threshold by Gas Exchange. Journal of Applied Physiology, 60, 2020-2027. https://doi.org/10.1152/jappl.1986.60.6.2020
[4]	Black, C. D., & McCully, K. K. (2005). Time Course of Exercise Induced Alterations in Daily Activity in Chronic Fatigue Syndrome. DynMed, 4, 10.
[5]	Blundell, S., Ray, K. K., Buckland, M., & White, P. D. (2015). Chronic Fatigue Syndrome and Circulating Cytokines: A Systematic Review. Brain, Behavior, and Immunity, 50, 186-195. https://doi.org/10.1016/j.bbi.2015.07.004
[6]	Bohne, V. J. B., & Bohne, O. (2019). Suggested Pathology of Systemic Exertion Intolerance Disease: Impairment of the E3 Subunit or Crossover of Swinging ARMs of the E2 Subunit of the Pyruvate Dehydrogenase Complex Decreases Regeneration of Cofactor Dihydrolipoic Acid of the E2 Subunit. Medical Hypotheses, 130, Article ID: 109260. https://doi.org/10.1016/j.mehy.2019.109260
[7]	Bouquet, J. et al. (2019). Whole Blood Human Transcriptome and Virome Analysis of ME/CFS Patients Experiencing Post-Exertional Malaise Following Cardiopulmonary Exercise Testing. PLoS ONE, 14, e0212193. https://doi.org/10.1371/journal.pone.0212193
[8]	Brown, A. E., Jones, D. E., Walker, M., & Newton, J. L. (2015). Abnormalities of AMPK Activation and Glucose Uptake in Cultured Skeletal Muscle Cells from Individuals with Chronic Fatigue Syndrome. PLoS ONE, 10, e0122982. https://doi.org/10.1371/journal.pone.0122982
[9]	Bruce, R. A., Fisher, L. D., Cooper, M. N., & Gey, G. O. (1974). Separation of Effects of Cardiovascular Disease and Age on Ventricular Function with Maximal Exercise. American Journal of Cardiology, 34, 757-763. https://doi.org/10.1016/0002-9149(74)90692-4
[10]	Carruthers, B. M. et al. (2011). Myalgic Encephalomyelitis, International Consensus Criteria. Journal of Internal Medicine, 270, 327-338. https://doi.org/10.1111/j.1365-2796.2011.02428.x
[11]	Carruthers, B. M., De Merileir, K. L., Peterson, D. L., Klimas, N. G., Lerner, A. M., Bested, A. C., Flor-Henry, P., Joshi, P., Powles, A. C. P., Sherkey, J. A., & van de Sande, M. I. (2003). Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, Clinical Working Case Definition, Diagnostic and Treatment Protocols. Journal of Chronic Fatigue Syndrome, 11, 7-116. https://doi.org/10.1300/J092v11n01_02
[12]	Clayton, E. W. (2015). Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: An IOM Report on Redefining an Illness. Journal of the American Medical Association, 313, 1101-1102. https://doi.org/10.1001/jama.2015.1346
[13]	Cordero, D. L., Sisto, S. A., Tapp, W. N., LaManca, J. J., Pareja, J.G., & Natelson, B. H. (1996). Decreased Vagal Power during Treadmill Walking in Patients with Chronic Fatigue Syndrome. Clinical Autonomic Research, 6, 329-333. https://doi.org/10.1001/jama.2015.1346
[14]	Davenport, T. E., Stevens, S. R., Baroni, K., Van, N. M., & Snell, C. R. (2011). Diagnostic Accuracy of Symptoms Characterising Chronic Fatigue Syndrome. Disability and Rehabilitation, 33, 1768-1775. https://doi.org/10.3109/09638288.2010.546936
[15]	Davenport, T. E., Stevens, S. R., VanNess, M. J., Snell, C. R., & Little, T. (2010). Conceptual Model for Physical Therapist Management of Chronic Fatigue Syndrome/Myalgic Encephalomyelitis. Physical Therapy, 90, 602-614. https://doi.org/10.2522/ptj.20090047
[16]	De Becker, P., Roeykens, J., Reynders, M., McGregor, N., & De Meirleir, K. (2000). Exercise Capacity in Chronic Fatigue Syndrome. Archives of Internal Medicine, 160, 3270-3277. https://doi.org/10.1001/archinte.160.21.3270
[17]	Demello, J. J., Cureton, K. J., Boineau, R. E., & Singh, M. M. (1987). Ratings of Perceived Exertion at the Lactate Threshold in Trained and Untrained Men and Women. Medicine & Science in Sports & Exercise, 19, 354-362. https://doi.org/10.1249/00005768-198708000-00006
[18]	Faro, M., Saez-Francas, N., Castro-Marrero, J., Aliste, L., Fernandez de Sevilla, T., & Alegre, J. (2016). Gender Differences in Chronic Fatigue Syndrome. Reumatología Clínica, 12, 72-77. https://doi.org/10.1016/j.reuma.2015.05.007
[19]	Fernhall, B., McCubbin, J. A., Pitetti, K. H., Rintala, P., Rimmer, J. H., Millar, A. L., & De Silva, A. (2001). Prediction of Maximal Heart Rate in Individuals with Mental Retardation. Medicine & Science in Sports & Exercise, 33, 1655-1660. https://doi.org/10.1097/00005768-200110000-00007
[20]	Fox, S. M., Naughton, J. P., & Haskell, W. L. (1971). Physical Activity and the Prevention of Coronary Heart Disease. Annals of Clinical Research, 3, 404-432.
[21]	Froelicher, V. F., & Jonathan, M. (2000). Exercise and the Heart (4th ed.). Philadelphia, PA: WB Saunders Company.
[22]	Fukuda, K., Straus, S. E., Hickie, I., Sharpe, M. C., Dobbins, J. G., & Komaroff, A. (1994). The Chronic Fatigue Syndrome: A Comprehensive Approach to Its Definition and Study. International Chronic Fatigue Syndrome Study Group. Annals of Internal Medicine, 121, 953-959. https://doi.org/10.7326/0003-4819-121-12-199412150-00009
[23]	Fulle, S. et al. (2000). Specific Oxidative Alterations in Vastus Lateralis Muscle of Patients with the Diagnosis of Chronic Fatigue Syndrome. Free Radical Biology and Medicine, 29, 1252-1259. https://doi.org/10.1016/S0891-5849(00)00419-6
[24]	Fulle, S., Pietrangelo, T., Mancinelli, R., Saggini, R., & Fano, G. (2007). Specific Correlations between Muscle Oxidative Stress and Chronic Fatigue Syndrome: A Working Hypothesis. Journal of Muscle Research and Cell Motility, 28, 355-362. https://doi.org/10.1007/s10974-008-9128-y
[25]	Ghali, A. et al. (2019). Elevated Blood Lactate in Resting Conditions Correlate with Post-Exertional Malaise Severity in Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Scientific Reports, 9, 18817. https://doi.org/10.1038/s41598-019-55473-4
[26]	Glaser, S. et al. (2010). Influence of Age, Sex, Body Size, Smoking, and Beta Blockade on Key Gas Exchange Exercise Parameters in an Adult Population. European Journal of Cardiovascular Prevention & Rehabilitation, 17, 469-476. https://doi.org/10.1097/HJR.0b013e328336a124
[27]	Graettinger, W. F., Smith, D. H., Neutel, J. M., Myers, J., Froelicher, V. F., & Weber, M. A. (1995). Relationship of Left Ventricular Structure to Maximal Heart Rate during Exercise. Chest, 107, 341-345. https://doi.org/10.1378/chest.107.2.341
[28]	Hossack, K. F., & Bruce, R. A. (1982). Maximal Cardiac Function in Sedentary Normal Men and Women: Comparison of Age-Related Changes. Journal of Applied Physiology: Respiratory, Environmental and Exercise Physiology, 53, 799-804. https://doi.org/10.1152/jappl.1982.53.4.799
[29]	Inbar, O., Oren, A., Scheinowitz, M., Rotstein, A., Dlin, R., & Casaburi, R. (1994). Normal Cardiopulmonary Responses during Incremental Exercise in 20- to 70-yr-Old Men. Medicine & Science in Sports & Exercise, 26, 538-546. https://doi.org/10.1249/00005768-199405000-00003
[30]	IOM (2015). Beyond Mayalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. Washington DC: The National Academies Press.
[31]	Jones, D. E., Hollingsworth, K. G., Taylor, R., Blamire, A. M., & Newton, J. L. (2010). Abnormalities in pH Handling by Peripheral Muscle and Potential Regulation by the Autonomic Nervous System in Chronic Fatigue Syndrome. Journal of Internal Medicine, 267, 394-401. https://doi.org/10.1111/j.1365-2796.2009.02160.x
[32]	Jones, N. L., Makrides, L., Hitchcock, C., Chypchar, T., & McCartney, N. (1985). Normal Standards for an Incremental Progressive Cycle Ergometer Test. The American Review of Respiratory Disease, 131, 700-708.
[33]	Josev, E. K., Malpas, C. B., Seal, M. L., Scheinberg, A., Lubitz, L., Rowe, K., & Knight, S.J. (2019). Resting-State Functional Connectivity, Cognition, and Fatigue in Response to Cognitive Exertion: A Novel Study in Adolescents with Chronic Fatigue Syndrome. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-019-00119-2
[34]	Keech, A., Vollmer-Conna, U., Barry, B. K., & Lloyd, A.R. (2016). Gene Expression in Response to Exercise in Patients with Chronic Fatigue Syndrome: A Pilot Study. Frontiers in Physiology, 7, 421. https://doi.org/10.3389/fphys.2016.00421
[35]	Keller, B.A., Pryor, J. L., & Giloteaux, L. (2014). Inability of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Patients to Reproduce VO (2) Peak Indicates Functional Impairment. Journal of Translational Medicine, 12, 104. https://doi.org/10.1186/1479-5876-12-104
[36]	Lester, M., Sheffield, L. T., Trammell, P., & Reeves, T. J. (1968). The Effect of Age and Athletic Training on the Maximal Heart Rate during Muscular Exercise. American Heart Journal, 76, 370-376. https://doi.org/10.1016/0002-8703(68)90233-0
[37]	Lien, K. et al. (2019). Abnormal Blood Lactate Accumulation during Repeated Exercise Testing in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Physiological Reports, 7, e14138. https://doi.org/10.14814/phy2.14138
[38]	Light, A. R., Bateman, L., Jo, D., Hughen, R. W., Vanhaitsma, T. A., White, A. T., & Light, K. C. (2012). Gene Expression Alterations at Baseline and Following Moderate Exercise in Patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. Journal of Internal Medicine, 271, 64-81. https://doi.org/10.1111/j.1365-2796.2011.02405.x
[39]	Ludbrook, J. (1997). Comparing Methods of Measurements. Clinical and Experimental Pharmacology and Physiology, 24, 193-203. https://doi.org/10.1111/j.1440-1681.1997.tb01807.x
[40]	Martina, J. R. et al. (2012). Noninvasive Continuous Arterial Blood Pressure Monitoring with Nexfin (R). Anesthesiology, 116, 1092-1103. https://doi.org/10.1097/ALN.0b013e31824f94ed
[41]	McCully, K. K., Smith, S., Rajaei, S., Leigh Jr., J. S., & Natelson, B. H. (2003). Blood Flow and Muscle Metabolism in Chronic Fatigue Syndrome. Clinical Science, 104, 641-647. https://doi.org/10.1042/CS20020279
[42]	McGregor, N. R., Armstrong, C. W., Lewis, D. P., & Gooley, P. R. (2019). Post-Exertional Malaise Is Associated with Hypermetabolism, Hypoacetylation and Purine Metabolism Deregulation in ME/CFS Cases. Diagnostics (Basel), 9, E70. https://doi.org/10.3390/diagnostics9030070
[43]	Nelson, M. J., Buckley, J. D., Thomson, R. L., Clark, D., Kwiatek, R., & Davison, K. (2019). Diagnostic Sensitivity of 2-Day Cardiopulmonary Exercise Testing in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Journal of Translational Medicine, 17, 80. https://doi.org/10.1186/s12967-019-1836-0
[44]	Nguyen, C. B. et al. (2017). Whole Blood Gene Expression in Adolescent Chronic Fatigue Syndrome: An Exploratory Cross-Sectional Study Suggesting Altered B Cell Differentiation and Survival. Journal of Translational Medicine, 15, 102. https://doi.org/10.1186/s12967-017-1201-0
[45]	Nijs, J., Nees, A., Paul, L., De Kooning, M., Ickmans, K., Meeus, M., & Van Oosterwijck, J. (2014). Altered Immune Response to Exercise in Patients with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis: A Systematic Literature Review Exercise. Immunology Review, 20, 94-116.
[46]	Nijs, J., Paul, L., & Wallman, K. (2008). Chronic Fatigue Syndrome, an Approach Combining Self-Management with Graded Exercise to Avoid Exacerbations. Journal of Rehabilitation Medicine, 40, 241-247. https://doi.org/10.2340/16501977-0185
[47]	Paul, L., Wood, L., Behan, W. M., & Maclaren, W. M. (1999). Demonstration of Delayed Recovery from Fatiguing Exercise in Chronic Fatigue Syndrome. European Journal of Neurology, 6, 63-69. https://doi.org/10.1046/j.1468-1331.1999.610063.x
[48]	Peterson, P. K., Sirr, S. A., Grammith, F. C., Schenck, C. H., Pheley, A. M., Hu, S., & Chao, C. C. (1994). Effects of Mild Exercise on Cytokines and Cerebral Blood Flow in Chronic Fatigue Syndrome Patients. Clinical and Diagnostic Laboratory Immunology, 1, 222-226. https://doi.org/10.1128/CDLI.1.2.222-226.1994
[49]	Ricard, R. M., Leger, L., & Massicotte, D. (1990). Validity of the “220-Age Formula” to Predict Maximal Heart Rate. Medicine & Science in Sports & Exercise, 2, 575. https://doi.org/10.1249/00005768-199004000-00574
[50]	Robergs, R. A. L. R. (2002). The Surprising History of the "HRmax=220-Age"Equation. Journal of Exercise Physiology Online, 5, 1-10.
[51]	Rodeheffer, R. J., Gerstenblith, G., Becker, L. C., Fleg, J. L., Weisfeldt, M. L., & Lakatta, E. G. (1984). Exercise Cardiac Output Is Maintained with Advancing Age in Healthy Human Subjects, Cardiac Dilatation and Increased Stroke Volume Compensate for a Diminished Heart Rate. Circulation, 69, 203-213. https://doi.org/10.1161/01.CIR.69.2.203
[52]	Schiller, B. C., Casas, Y. G., Desouza, C. A., & Seals, D. R. (2001). Maximal Aerobic Capacity across Age in Healthy Hispanic and Caucasian Women. The Journal of Applied Physiology, 91, 1048-1054. https://doi.org/10.1152/jappl.2001.91.3.1048
[53]	Sheffield, L. T., Maloof, J. A., Sawyer, J. A., & Roitman, D. (1978). Maximal Heart Rate and Treadmill Performance of Healthy Women in Relation to Age. Circulation, 57, 79-84. https://doi.org/10.1161/01.CIR.57.1.79
[54]	Shukla, S. K. et al. (2015). Changes in Gut and Plasma Microbiome Following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). PLoS ONE, 10, e0145453. https://doi.org/10.1371/journal.pone.0145453
[55]	Siemionow, V., Fang, Y., Calabrese, L., Sahgal, V., & Yue, G. H. (2004). Altered Central Nervous System Signal during Motor Performance in Chronic Fatigue Syndrome. Clinical Neurophysiology, 115, 2372-2381. https://doi.org/10.1016/j.clinph.2004.05.012
[56]	Snell, C. R., Stevens, S. R., Davenport, T. E., & Van Ness, J. M. (2013). Discriminative Validity of Metabolic and Workload Measurements to Identify Individuals with Chronic Fatigue Syndrome. Physical Therapy, 93, 1484-1492. https://doi.org/10.2522/ptj.20110368
[57]	Sorensen, B., Jones, J. F., Vernon, S. D., & Rajeevan, M. S. (2009). Transcriptional Control of Complement Activation in an Exercise Model of Chronic Fatigue Syndrome. Molecular Medicine, 15, 34-42. https://doi.org/10.2119/molmed.2008.00098
[58]	Tanaka, H., Monahan, K. D., & Seals, D. R. (2001). Age-Predicted Maximal Heart Rate Revisited. Journal of the American College of Cardiology, 37, 153-156. https://doi.org/10.1016/S0735-1097(00)01054-8
[59]	van Campen, C. M. C., Rowe, P. C., & Visser, F. C. (2020). Validity of 2-Day Cardiopulmonary Exercise Testing in Male Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Advances in Physical Education, 10, 68-80. https://doi.org/10.4236/ape.2020.101007
[60]	Van Campen, F. C. (2020). Validity of 2-Day Cardiopulmonary Exercise Testing in Male Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Advances in Physical Education, 10, 68-80. https://doi.org/10.4236/ape.2020.101007
[61]	Van Den, E. F., Moorkens, G., Van, H. B., Cosyns, P., & Claes, S. J. (2007). Hypothalamic-Pituitary-Adrenal Axis Function in Chronic Fatigue Syndrome. Neuropsychobiology, 55, 112-120. https://doi.org/10.1159/000104468
[62]	Vanness, J. M., Snell, C. R., Strayer, D. R., Dempsey, L., & Stevens, S. R. (2003). Subclassifying Chronic Fatigue Syndrome through Exercise Testing. Medicine & Science in Sports & Exercise, 35, 908-913. https://doi.org/10.1249/01.MSS.0000069510.58763.E8
[63]	Vanness, J. M., Stevens, S. R., Bateman, L., Stiles, T. L., & Snell, C. R. (2010). Postexertional Malaise in Women with Chronic Fatigue Syndrome. Journal of Women’s Health, 19, 239-244. https://doi.org/10.1089/jwh.2009.1507
[64]	Vermeulen, R. C., & Vermeulen van Eck, I. W. (2014). Decreased Oxygen Extraction during Cardiopulmonary Exercise Test in Patients with Chronic Fatigue Syndrome. Journal of Translational Medicine, 12, 20. https://doi.org/10.1186/1479-5876-12-20
[65]	Wasserman, K., Hansen, J. E., Sue, D. Y., Stringer, W. W., Sietsema, K. E., Sun, X. G., & Whipp, B. J. (2012). Principles of Exercise Testing and Interpretation (5th ed.). Philadelphia, PA: Lippincott Williams and Wilkins.
[66]	Whaley, M. H., Kaminsky, L. A., Dwyer, G. B., Getchell, L. H., & Norton, J. A. (1992). Predictors of over- and Underachievement of Age-Predicted Maximal Heart Rate. Medicine & Science in Sports & Exercise, 24, 1173-1179. https://doi.org/10.1249/00005768-199210000-00017
[67]	Whistler, T., Jones, J. F., Unger, E. R., & Vernon, S. D. (2005). Exercise Responsive Genes Measured in Peripheral Blood of Women with Chronic Fatigue Syndrome and Matched Control Subjects. BMC Physiology, 5, 5. https://doi.org/10.1186/1472-6793-5-5
[68]	Wong, R. et al. (1992). Skeletal Muscle Metabolism in the Chronic Fatigue Syndrome. In Vivo Assessment by 31P Nuclear Magnetic Resonance Spectroscopy. Chest, 102, 1716-1722. https://doi.org/10.1378/chest.102.6.1716