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Volume 13 No. 02
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Accepted Papers





Scientific Investigations

Exercise, Occupational Activity, and Risk of Sleep Apnea: A Cross-Sectional Study

Roberto P. da Silva, PhD1; Denis Martinez, MD, PhD1,2,3,4; Martina M. Pedroso, MSc1; Camila G. Righi, PhD1; Emerson F. Martins, PhD1; Leticia M.T. Silva, MD4; Maria do Carmo S. Lenz, MD, PhD3; Cintia Z. Fiori, PhD1
1Graduate Program in Cardiology and Cardiovascular Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; 2Cardiology Unit, Hospital de Clinicas de Porto Alegre (HCPA - UFRGS), Porto Alegre, RS, Brazil; 3Sleep Clinic Porto Alegre, RS, Brazil; 4Graduate Program in Medicine, HCPA - UFRGS, Porto Alegre, RS, Brazil

ABSTRACT

Study Objectives:

To investigate whether structured exercise and occupational activity are associated with obstructive sleep apnea (OSA) severity.

Methods:

The International Physical Activity Questionnaire was answered by 5,453 individuals who underwent full-night polysomnography. Participants were classified as exercisers or non-exercisers and also as occupationally active or non-active. The apnea-hypopnea index (AHI), minimum oxygen saturation (SaO2min), and time with saturation below 90% (TB90%) during polysomnography were used as indicators of OSA severity.

Results:

The sample included mostly men (59%), non-exercisers (56%), and occupationally non-active individuals (75%). Mean age (± standard deviation) was 44 ± 14 years, and mean body mass index was 29.9 ± 7.3 kg/m2. Non-exercisers had higher AHI (median 14, 25–75% interquartile range 4–34) than exercisers (8 [2–24]), lower SaO2min (83 ± 9 vs. 86 ± 8%), and longer TB90% (2 [0–18] vs. 0 [0–7] minutes), with p < 0.001 for all comparisons. AHI was higher in active (16 [6–34]) vs. non-active occupations (10 [3–27]; p < 0.001). Multinomial logistic regression with control for age, sex, overweight, obesity, and occupational activity showed that structured exercise was significantly associated with a 23% lower odds ratio for moderate OSA and 34% lower odds ratio for severe OSA. Active occupation was not associated with OSA.

Conclusions:

Structured physical exercise is associated with lower odds for OSA, independently of confounders. Occupational activity does not seem to replace the effects of regular exercise. Compensatory behaviors may be involved in these diverging outcomes. Our results warrant further research about the effect of occupational activity on OSA severity.

Citation:

da Silva RP, Martinez D, Pedroso MM, Righi CG, Martins EF, Silva LM, Lenz MD, Fiori CZ. Exercise, occupational activity, and risk of sleep apnea: a cross-sectional study. J Clin Sleep Med. 2017;13(2):197–204.


INTRODUCTION

Obstructive sleep apnea (OSA), an important public health problem, has been associated with increased risk of hypertension, stroke, cardiovascular events, metabolic disease, cancer, and all-cause mortality.13 The prevalence of OSA (defined as apnea-hypopnea index [AHI] ≥ 5) has increased over time in different populations, with recent reports showing a prevalence as high as 50% in females aged 20–70 years.4 In a large city in South America, the prevalence of OSA (defined as AHI ≥ 5 plus daytime sleepiness) in the general population has been reported to be higher than 30%.5,6

OSA is characterized by excessive daytime sleepiness and fatigue,7 with decreased energy and motivation during the day.8,9 Insufficient motivation, a common barrier to exercise participation, may therefore be secondary to OSA10,11: sleep deprivation may limit the ability of individuals with sleep apnea to perform intense exercise. A previous study has shown that one night without sleep caused participants to self-select a slower stride when walking or running. Participants in that study also reported a higher subjective perception of exertion during physical activity.12 However, the relationship between OSA severity and exercise remains uncertain.13

BRIEF SUMMARY

Current Knowledge/Study Rationale: Programmed physical exercise is associated with less severe OSA. The influence of occupational activity on OSA severity is not completely understood.

Study Impact: The lack of association between OSA and occupational activity suggests the potential importance of programed physical exercise in OSA management.

In addition, although a sedentary lifestyle has been suggested as a risk factor for OSA, the evidence regarding this relationship is scarce,14 and the issue is further complicated by the impact of occupational activity. A study with 2,340 individuals with OSA reported decreasing AHI with increasing work activity.15 In non-obese men, AHI has been strongly linked to the time spent sitting during the day.16

The aim of the present study was to investigate the levels of structured exercise and occupational activity in persons undergoing polysomnography to investigate sleep disorders. We hypothesized that physical inactivity was associated with more severe OSA, independently of confounders.

METHODS

This cross-sectional prospective study included males and females aged 18 years or older referred to a university sleep laboratory to investigate sleep disorders. Participants were enrolled between March 2013 and August 2015, and all consented to the anonymous use of their data.

Individuals with a minimum of 4 hours of sleep recorded on polysomnography were included. On the night of the study, participants were weighed and had their neck and waist circumference measured by a trained technician. Also, before the polysomnography, they answered a standard questionnaire including a visual-analogue fatigue scale (0–10), the STOP questionnaire,17 and the following question: “Have you been feeling down in the past week?” Possible answers ranged from “not at all” (score of 0) to “very much” (score of 4).

Physical Activity

Physical activity was assessed by an abridged version of International Physical Activity Questionnaire (IPAQ) for young and middle-aged adults, validated for Brazilian Portuguese.18 The IPAQ was proposed by an international consensus group with representatives of 25 countries, including Brazil, under the auspices of the World Health Organization (WHO).19 The IPAQ is a self-completed questionnaire with 8 questions that estimate the weekly time spent in different types of at least moderate physical activity and physical inactivity (sitting position). Based on the answers to the IPAQ, patients were classified as non-exercisers or exercisers.

Individuals reporting at least 10 minutes of moderate or high intensity physical activity were classified as regularly active. Exercise was classified regarding type as endurance training, resistance training, or combined training as, for instance, in sports practice. Intensity was classified as moderate or vigorous. Moderate activity was defined as that which made the participant breathe somewhat harder than normal. Vigorous activity was defined as that demanding hard effort which made the respondent breathe much harder than normal. Frequency was recorded in days per week, duration of exercise in minutes per day, and time sitting in hours per day. Participants who did not meet the criterion of ≥ 10 minutes of moderate or high intensity physical activity at least once a week were classified as non-exercisers. Incomplete or inconsistent questionnaires were excluded.

Occupational Activity

We employed the Physical Demands – Strength Rating from the Dictionary of Occupational Titles20 to describe the strength requirements of each job. The rating is based on the worker's involvement in standing, walking, sitting, lifting, carrying, pushing, and pulling. By analogy with the IPAQ, in which only moderate and vigorous exercise is considered to classify a person as physically active, participants performing sedentary (S) or light work (L) were classified as non-active. Those performing medium (M), heavy (H), and very heavy (V) work were classified as active (Table S1 in the supplemental material).

Polysomnography

All subjects underwent polysomnography as previously described.21 In brief, patients arrived at the sleep laboratory around 21:00. The recording lasted from around 23:00 to 07:00. Electroencephalogram (C4-A1, F4-A1, and O2-A1), right and left electro-oculogram (A1), submental electromyogram, and electrocardiogram (D1 or modified V4) were recorded. The recordings were made at temperatures between 22°C and 26°C. Airflow was measured by a nasal cannula connected to a pressure transducer (Ultima PT2 Dual, Braebon, Kanata, Canada). Respiratory effort was assessed by respiratory inductance plethysmography (QRIP, Braebon, Kanata, Canada), and oxygen saturation by pulse oximetry (XPOD, Nonin, Plymouth, MN, USA).

Sleep scoring followed the 2012 American Academy of Sleep Medicine manual.22 Apnea was defined as a drop in airflow ≥ 90% of baseline for ≥ 10 s; hypopnea was defined as a drop in airflow ≥ 50% of baseline for ≥ 10 s, accompanied by ≥ 3% arterial oxygen desaturation or arousal. Respiratory effort related arousal (RERA) was identified by flattening of the shape of the inspiratory nasal pressure waveform lasting ≥ 10 s followed by an arousal ≥ 3 s with return of the flow tracing to its normal shape. AHI was calculated by dividing the number of apneas and hypopneas by hours of sleep. Apnea severity was classified according to: non-OSA (AHI < 5 events/h), mild OSA (AHI 5–14), moderate OSA (AHI 15–29), and severe OSA (AHI ≥ 30). Snoring was rated from 0 to 10 in an arbitrary scale by technicians during the night. This rating was reconciled with the all-night tracing of snoring by the reviewing physician. Patients diagnosed with respiratory disorders other than OSA were excluded.

Statistical Analysis

Data were analyzed using SPSS v.18 (SPSS Inc., Chicago, IL, USA). Continuous variables with normal distribution were presented as mean ± standard deviation. Data with non-normal distribution according to the Kolmogorov-Smirnov test were expressed as median and interquartile range (25% to 75%). Differences between groups were calculated using Mann-Whitney U or Student t tests for independent samples. The χ2 test was used to compare proportions. Analysis of variance (ANOVA) with Bonferroni post hoc test was used to test the differences between OSA categories. Multivariate linear regression was used to test the association of AHI, minimum oxygen saturation (SaO2 min), and time with saturation below 90% (TB90%) with regressors physical exercise (non-exerciser, exerciser), age, gender, body mass index (BMI) (normal weight, overweight, and obesity), and occupational activity (non-active, active). Natural logarithm transformation of AHI (lnAHI), SaO2min, and TB90% was employed to ensure near-normal distribution. Multinomial logistic regression was employed to quantify the association between OSA severity and structured exercise, controlling for the above mentioned confounders. A mediation model was used to test whether the correlation between obesity and OSA severity was affected by the inclusion of physical exercise as a mediator variable. Sobel's test was employed to assess the significance of the mediation effect. Given the many comparisons performed and the large sample size, only results with a probability of alpha error < 1% were considered significant.

RESULTS

We reviewed 5,984 sleep studies. The following were excluded: 231 duplicate studies, 78 with less than 4 hours of recorded sleep, 77 studies from patients younger than 18 years of age, and 145 with incomplete questionnaire data. Thus, 5,453 participants were included in the final sample. The distribution of patients according to OSA category was as follows: non-OSA, 1,691 participants (31%); mild OSA, 1,325 participants (24%); moderate OSA, 1,026 participants (19%); and severe OSA, 1,411 participants (26%).

The characteristics of participants according to exercise and occupation status are presented in Table 1. Exercisers differed from non-exercisers regarding BMI, prevalence of obesity, smoking, waist and neck circumference, systolic and diastolic blood pressure, resting heart rate, and symptoms of tiredness and sleepiness. The active occupation group included a higher number of obese participants and 15% fewer exercisers as compared to the non-active occupation group. Also, mean age was higher in the active vs. non-active group. The prevalence of comorbidities was similar in exercisers, non-exercisers, active, and non-active workers.

Characteristics of the population according to exercise and occupational activity status.

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Table 1

Characteristics of the population according to exercise and occupational activity status.

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As shown in Table 2, median duration of exercise practice was 36 (10–120) months, while mean duration (± SD) was 99 ± 135 months, more than twice the median. This asymmetry is explained at least in part by 557 cases with less than 1 year of practice. Exercisers differed from non-exercisers regarding most anthropometric and cardiovascular variables. Exclusion of cases with less than 1 year of practice did not change the overall significance of the association between exercise and AHI, SaO2min, and TB90%. For exercisers, the mean weekly exercise frequency was 3.1 ± 1.4 days per week, with a mean of 73 ± 35 minutes per exercise session. Ninety-five respondents did not inform at what period of the day they exercised, and 4 respondents did not inform the workout intensity.

Characteristics of exercise behavior according severity of sleep apnea.

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Table 2

Characteristics of exercise behavior according severity of sleep apnea.

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The AHI of exercisers (16 ± 19 events/h) and non-exercisers (23 ± 25 events/h; effect size [Cohen's d]: 0.32) is presented in Table 3 along with polysomnographic data. Significant differences were observed between exercisers and non-exercisers, and also between different exercise intensities and different periods of the day for the practice of exercise in all markers of sleep apnea severity (AHI, minimum saturation, time with saturation below 90%, snoring, and Epworth Sleepiness Scale). Evening exercisers slept longer during the polysomnography and had higher sleep efficiency.

Polysomnographic characteristics according to exercise status, period of the day, and intensity.

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Table 3

Polysomnographic characteristics according to exercise status, period of the day, and intensity.

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Table 4 compares the exercise habits and polysomnographic findings of occupational activity groups. Older age and higher BMI were associated with higher AHI and poorer sleep quality in active workers. The inclusion of exercise as a variable in a mediation model modified the relationship between BMI and AHI. After introducing exercise into the model, the beta for BMI was reduced from 0.418 to 0.406. This 3% change was highly signifi-cant in the Sobel test (p < 0.001).

Exercise behavior and polysomnographic findings according to occupational activity status.

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Table 4

Exercise behavior and polysomnographic findings according to occupational activity status.

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Natural logarithmic transformation did not normalize the distribution of OSA severity indicators. In multivariate linear regression models to test the association between OSA and 3 markers of apnea severity (natural log of AHI, SaO2min, and TB90%), exercise was significantly associated with reduced OSA severity after control for confounders age, sex, BMI, and occupational activity. Although highly significant, the beta for exercise was small in the following 3 models: (1) ln AHI as dependent variable (R2 = 0.31; beta for exercise = −0.062; p < 0.001; (2) time with saturation below 90% as dependent variable (R2 = 0.15; beta for exercise = 0.05; p < 0.001); and (3) minimum saturation as dependent variable (R2 = 0.32; beta for physical exercise = 0.04; p < 0.001).

Multinomial logistic regression was used to estimate the odds ratio for mild, moderate, and severe OSA, using non-OSA as reference. This model included male sex, age, overweight and obesity (vs. normal BMI), active occupation, and exercise, as regressors. Exercise was significantly associated with a 23% (99% confidence interval [CI] 0.52–0.84) lower odds ratio for moderate OSA, and with a 34% (99%CI 0.52–0.84) lower odds ratio for severe OSA (Figure 1).

Odds ratio associated with degrees of sleep apnea severity in a multinomial regression model.

Data from 1,325 mild, 1,026 moderate, and 1,411 severe obstructive sleep apnea (OSA) cases are compared to 1,691 non-OSA cases, the reference category. The scale is logarithmic and the error bars represent 99% confidence interval. The association with regular exercise practice is adjusted for male sex, age in 10-year intervals, overweight and obesity (with normal BMI category as reference), and active occupation. While the usual OSA confounders, sex, age, and BMI, are associated with higher odds ratio of OSA than the reference categories, structured exercise is associated with lower odds ratio than the non-exerciser condition. Active occupation is not significant in this model. This suggests that occupational activity per se does not replace structured exercise in the association with OSA severity (see discussion).

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Figure 1

Odds ratio associated with degrees of sleep apnea severity in a multinomial regression model.

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DISCUSSION

This study with a large sample of individuals with polysomnographic diagnosis of OSA confirmed an expected association of sedentariness with OSA severity; however, contrary to our expectations, no association was observed between non-active occupation and OSA. Although these findings require further confirmation, some hypotheses can be proposed.

The influence of physical (in)activity on OSA severity has been investigated in previous studies, many of which had methodological limitations. Beitler et al.23 have demonstrated a strong relationship between higher AHI and impaired exercise capacity. However, the study population was subdivided in smaller subgroups, possibly reducing the study's power. Awad et al.24 describe an association between exercise and reduced incidence of mild and moderate sleep-disordered breathing at follow-up. Also, in the same study, reducing the exercise frequency over time was associated with worsening of sleep-disordered breathing. Simpson et al.15 compared 1,931 controls vs. 2,340 individuals referred to a sleep clinic for suspected OSA. The control sample did not undergo polysomnography for screening of moderate-severe OSA; however, control participants did not have symptoms compatible with OSA, resulting in a low probability of AHI > 15, estimated in another study by the same group as 8% in the general population.25 The results of the study by Simpson et al.15 are not directly comparable with our findings because of differences in the instruments used and in the method of classification of active or sedentary professions. Those authors describe an association between occupational activity and OSA, which we were unable to reproduce. Also limiting comparison is the fact that the results of Simpson et al. are stratified by sex, while we included sex as a variable in the multivariate model. Additionally, they use different reference groups for each comparison, while we used the sedentary group as reference in all analyses.

Using the non-exerciser group as reference for comparisons is justified by the fact that this information is probably more accurate than reported exercise practice. Physical fitness is difficult to assess through questionnaires. Compared to objective assessments, such as peak oxygen uptake or actimetry, the performance of any questionnaire is poor.26 A binary approach comparing sedentariness versus regular exercise, as done in our study, may reliably detect physical status through one single question. It is unlikely that a fit person will report a sedentary lifestyle, while the opposite might be expected due to the value attributed to fitness in our society. Thus, this binary approach may enhance the reliability of the present results. One finding that supports the reliability of our questionnaire is that even the participants who had begun exercising most recently and also the mildest exercisers were more fit in terms of a significantly lower heart rate than those classified as sedentary. The significantly lower heart rate of the exerciser group also confirms adherence to exercise. It has been reported that half of the beginners in exercise programs abandon the practice in the first month.27 The IPAQ instructions, however, explicitly refer to exercise in the last week.

Regarding the intensity of occupational activity, our results differ from those reported by Simpson et al.15 Again, this difference is explained at least in part by the binary approach we employed. Simpson et al. used medium intensity work as reference among four categories—if instead they had used the sedentary group as reference, light occupational activity would entail higher risk for moderate-severe OSA in men and women. The lack of a significant association between work activity and OSA severity in our study is probably explained by the older age, higher BMI, and lower rate of exercisers in the active worker group, offsetting any putative beneficial effect of non-structured physical activity. Without adjustment for sex, age, and BMI, active work is a risk factor for OSA. A randomized trial introducing structured exercise for heavy workers with OSA would be the best approach to determine the actual role of combined activity—occupational plus structured exercise—in OSA severity.

All models in this study were controlled for sex and age, which were significantly associated with all severity categories. The number one OSA risk factor is obesity, which increases by 22 times the odds of severe OSA. Because the non-exerciser group has 21% more obese participants than the exerciser group (Table 1), we attempted to quantify the relative importance of exercise/sedentariness vs. obesity using mediation analysis. Mediation analysis tests to what extent the effect of an independent variable on the dependent variable is reduced by inclusion of another independent variable in the model. The 3% change observed suggests that the effect of sedentariness on OSA severity is not fully derived from its association with obesity. It is thus possible to speculate that absence of regular exercise has a direct action on OSA severity, independent of obesity. This direct action may be linked to another putative mediator of the exercise-OSA relationship, that is, fluid retention in the legs.16 The concept of rostral fluid displacement from the legs into the peripharyngeal area during recumbence as cause of OSA has been investigated.28 A linear correlation of ∼0.6 between number of hours sitting and overnight change in leg fluid volume has been observed in non-obese males. Additionally, change in leg fluid volume shows a quadratic correlation of ∼0.8 with AHI, suggesting that “a sedentary way of life may predispose to OSA.”16

Another possible mediator would be improved pharyngeal muscle tone resulting from the practice of regular exercise. Hypothetically, a widespread tonic effect29 would lead to improvement of the pharyngeal abductor muscles function and to better airway patency during sleep.

It is conceivable that exercisers and active workers represent a healthier population as compared to their sedentary counterparts. Table 1, however, shows that both exercisers and non-exercisers, as well as active and non-active workers, display an almost identical prevalence of comorbidities. Hence, the differences encountered in terms of OSA prevalence between the groups is not attributable to differences in prevalence of comorbid medical conditions reported by the participants.

One of the main findings of this study, besides the association of structured exercise with lower odds of moderate-severe OSA, is that occupational activity is not associated with any degree of OSA severity. There may be several possible explanations for this unexpected finding. First, the age and BMI of active workers was significantly higher than those of non-active workers. Second, the percentage of active workers practicing regular exercise was lower than that of non-active workers, with lower frequency of vigorous exercise and half the duration of exercise (Table 4). The data in Table 4 also show that active occupation is associated with higher AHI and greater impact of OSA, as indicated by differences in stage N3, SaO2min, and TB90%. Finally, the lower exercise rate among active workers could be explained by the compensatory behavior observed after intense activity. Compensatory behavior is seen after one single exercise session30 or in the long term.31 Increasing exercise intensity has been observed to produce only half of predicted weight loss, indicating that subjects reduce their energy expenditure in a compensatory way.32 In addition to behavioral changes, physical activity may induce metabolic compensation,33 which could also influence BMI.

The present study has close to 100% power to identify significance in the moderate-severe OSA categories after controlling for usual confounders, at 1% significance level (Figure 1). The large sample size may have produced significance for comparisons and estimates that are clinically meaningless. Cohen recommends 0.1 as the minimum effect size of clinical significance for group differences.34 Effect sizes ∼0.3 were observed in our results only for ESS score and polysomnographic markers of OSA severity, namely, AHI, SaO2min, TB90%, and snoring (Table 3). This is relevant for data interpretation, since these were also our dependent variables (Table S2 in the supplemental material).

Limitations

This study has limitations that must be addressed. First of all, the study's cross-sectional design does not allow inferences about causality. Although it seems plausible that lack of physical activity leads to the development of obesity, and that obesity in turn contributes to the onset of OSA, additional studies are required to confirm this hypothesis. Indeed, the opposite hypothesis is also likely—that is, that severe OSA impairs physical activity and thus causes obesity. Further evidence to support either possibility must be collected in long-term longitudinal studies. Since exercisers and non-exercisers display an almost identical prevalence of comorbidities, the medical conditions mentioned cannot be implicated in the difference in OSA prevalence observed in the present study.

The use of questionnaire data to assess physical activity is another limitation. In a systematic review of 85 versions of physical activity questionnaires,35 the quality of the studies assessing the measurement properties of the questionnaires was considered poor. Only a few questionnaires had sufficient construct validity and reliability, but even these would need further validation. In the present study, the limitations of the instrument may have been overcome by the large number of participants. In terms of moderate exercise, the population with sleep complaints did not differ largely from other samples classified using the IPAQ short form. People from Australia36 reported exercising moderately 64 minutes per session, while our patients reported 69 minutes of exercise (Cohen's d = 0.09).

A strength of the present study was the use of full-night polysomnographic data. Also, the sample size yielded close to 0% chance of beta error. The choice of low probability of alpha error (1%) also adds internal validity to the study. Thus, the present findings are hypothesis-generating and add to the current knowledge on the relationship between OSA and physical activity.

In conclusion, structured exercise is associated with lower odds for OSA, independently of confounders. Occupational activity does not seem to produce the effects observed for regular exercise. Future studies should focus on the role of exercise in the treatment of OSA.

DISCLOSURE STATEMENT

This was not an industry supported study. Funding for this project was provided by the institutional Research Incentive Fund (FIPE-HCPA). Denis Martinez and Maria do Carmo S. Lenz are co-owners of a sleep clinic. The other authors have indicated no financial conflicts of interest.

ABBREVIATIONS

AHI

apnea-hypopnea index

ANOVA

analysis of variance

BMI

body mass index

IPAQ

International Physical Activity Questionnaire

OSA

obstructive sleep apnea

SaO2min

minimum oxygen saturation

TB90%

time with oxygen saturation below 90%

ACKNOWLEDGMENTS

The authors acknowledge the contribution of statistician Luciano Santos Pinto Guimarães, from the Graduate Research Group at Hospital de Clínicas de Porto Alegre (GPPG-HCPA).

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Supplemental Material

Supplemental Material

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