Vigorous physical activity is associated with lower death rates in patients with stable coronary artery disease

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Vigorous physical activity one or two times a week is associated with the lowest death rates in patients with stable coronary artery disease, reports a study published today in the European Journal of Preventive Cardiology, a journal of the European Society of Cardiology (ESC).

The findings come ahead of World Heart Day on 29 September, which highlights that cardiovascular disease is the world’s number one killer, causing 17.9 million deaths a year.

Study author Dr. Simone Biscaglia of the University of Ferrara, Italy said:

“The optimum level of exercise is achievable in almost all patients with stable coronary artery disease worldwide – but was only reached in one-third of participants in our study.

Walking once or twice a week at a pace that causes shortness of breath or a raised heart rate is all that’s needed.”

It is common knowledge that exercise is healthy for heart patients – but how often and at what intensity is controversial.

This analysis of the CLARIFY registry examined these questions.

In addition, since most heart patients do little exercise, the study examined the most important determinants of physical activity.

CLARIFY enrolled 32,370 outpatients with stable coronary artery disease from 45 countries in Africa, Asia, Australia, Europe, the Middle East, and North, Central, and South America who were followed up for five years. Patients were divided into four groups according to self-reported physical activity at the inclusion visit:

  1. sedentary (16.1 percent)
  2. only light physical activity most weeks (51.4 percent)
  3. vigorous physical activity once or twice per week (16.8 percent)
  4. vigorous physical activity three or more times per week (15.7 percent)

The primary outcome was the composite of cardiovascular death, myocardial infarction, and stroke.

Compared to the light activity group, patients who did vigorous exercise one or two times per week had the lowest risk of the primary outcome, with a hazard ratio (HR) of 0.82. More frequent activity did not lead to further benefit.

Sedentary patients had the highest risk of the primary outcome (HR 1.32).

Patients in the group performing vigorous exercise one or two times per week also had the lowest risks of all-cause death, cardiovascular death, and stroke – 19 percent, 21 percent, and 26 percent lower, respectively, compared to the light activity group.

“Stable coronary artery disease patients should avoid being sedentary,” said Dr. Biscaglia. “The goal should be to exercise every week, not to achieve the highest possible frequency, since this is unlikely to be sustainable and does not translate into better outcomes.”

Women were less likely to be physically active than men. Dr. Biscaglia said: “In previous studies we found that women were less often referred to cardiac rehabilitation programs and had less intensive lifestyle counseling.

Clinicians should provide the same exercise advice and referrals to women as they do for men, which should result in equal levels of physical activity.”

Other predictors of low physical activity were comorbidities such as peripheral artery disease, diabetes, and previous myocardial infarction or stroke.

“Patients with these conditions may worry that exercise could worsen their symptoms,” said Dr. Biscaglia.

“But our study shows that they get the same benefit as others with stable coronary artery disease so these conditions should not deter them from being physically active.”

The rate of myocardial infarction during follow-up was comparable between the four physical activity groups.

“This can reassure patients with stable coronary artery disease—fear of having a heart attack should not be a barrier to physical activity,” said Dr. Biscaglia. “More research is needed to discover what drives the reduction in cardiovascular death seen with exercise – partly this is due to fewer fatal strokes, but the other mechanisms are currently unknown.”


Physical activity is associated with lower risks of all cause mortality, cardiovascular disease, and certain cancers.123 

However, much of the epidemiology arises from observational studies assessing physical activity at a single point in time (at baseline), on subsequent mortality and chronic disease outcomes.

From 1975 to 2016, over 90% of these epidemiological investigations on physical activity and mortality have used a single assessment of physical activity at baseline.4 

Relating mortality risks to baseline physical activity levels does not account for within-person variation over the long term, potentially diluting the epidemiological associations.

As physical activity behaviours are complex and vary over the life course,5 assessing within-person trajectories of physical activity over time would better characterise the association between physical activity and mortality.

Fewer studies have assessed physical activity trajectories over time and subsequent risks of mortality.67891011 

Some of these investigations have only included small samples of older adults, in either men or women.

Importantly, most studies were limited by crude categorisations of physical activity patterns, without exposure calibration against objective measures with established validity.

Many studies also do not adequately account for concurrent changes in other lifestyle risk factors—such as overall diet quality and body mass index—which might potentially confound the association between physical activity and mortality.

This is important, as some studies have shown that associations between physical activity and weight gain are weak or inconsistent, suggesting that being overweight or obese might instead predict physical inactivity rather than the reverse.1213 

Previous investigations have also not quantified the population impact of different physical activity trajectories over time on mortality. We examined associations of baseline and long term trajectories of within-person changes in physical activity on all cause, cardiovascular disease, and cancer mortality in a population based cohort study and quantified the number of preventable deaths from the observed physical activity trajectories.

Methods

Study population

The data for this investigation were from the European Prospective Investigation into Cancer and Nutrition-Norfolk (EPIC-Norfolk) study, comprising a baseline assessment and three follow-up assessments. The EPIC-Norfolk study is a population based cohort study of 25 639 men and women aged 40 to 79, resident in Norfolk, UK, and recruited between 1993 to 1997 from community general practices as previously described.14

After the baseline clinic assessment (1993 to 1997), the first follow-up (postal questionnaire) was conducted between 1995 and 1997 at a mean of 1.7 (SD 0.1) years after baseline, the second follow-up (clinic visit) took place 3.6 (0.7) years after baseline, and the third follow-up (postal questionnaire) was initiated 7.6 (0.9) years after the baseline clinic visit. All participants with repeated measures of physical activity (at least baseline and final follow-up assessments) were included, resulting in an analytical sample of 14 599 men and women.

Assessment of physical activity

Habitual physical activity was assessed with a validated questionnaire, with a reference time frame of the past year.1516 

The first question inquired about occupational physical activity, classified as five categories: unemployed, sedentary (eg, desk job), standing (eg, shop assistant, security guard), physical work (eg, plumber, nurse), and heavy manual work (eg, construction worker, bricklayer).

The second open ended question asked about time spent (hours/week) on cycling, recreational activities, sports, or physical exercise, separately for winter and summer.

The validity of this instrument has previously been examined in an independent validation study, by using individually-calibrated combined movement and heart rate monitoring as the criterion method; physical activity energy expenditure (PAEE) increased through each of four ordinal categories of self reported physical activity comprising both occupational and leisure time physical activity.15 

In this study, we disaggregated the index of total physical activity into its original two variables that were domain specific and conducted a calibration to PAEE using the validation dataset, in which the exact same instrument had been used (n=1747, omitting one study centre that had used a different instrument).

Specifically, quasi-continuous and marginalised values of PAEE in units of kJ/kg/day were derived from three levels of occupational activity (unemployed or sedentary occupation; standing occupation; and physical or heavy manual occupation) and four levels of leisure time physical activity (none; 0.1 to 3.5 hours; 3.6 to 7 hours; and >7 hours per week). This regression procedure allows the domain specific levels of occupational and leisure time physical activity to have independent PAEE coefficients, while assigning a value of 0 kJ/kg/day to individuals with a sedentary (or no) occupation and reporting no leisure time physical activity (LTPA). The resulting calibration equation was: PAEE (kJ/kg/day) =0 (sedentary or no job) + 5.61 (standing job) + 7.63 (manual job) + 0 (no LTPA) + 3.59 (LTPA of 0.1 to 3.5 hours per week) + 7.17 (LTPA of 3.6 to 7 hours per week) + 11.26 (LTPA >7 hours per week).

Assessment of covariates

Information about participants’ lifestyle and clinical risk factors were obtained at both clinic visits, carried out by trained nurses at baseline and 3.6 years later. Information collected during clinic visits included: age; height; weight; blood pressure; habitual diet; alcohol intake (units consumed per week); smoking status (never, former, and current smokers); physical activity; social class (unemployed, non-skilled workers, semiskilled workers, skilled workers, managers, and professionals); education level (none, General Certificate of Education (GCE) Ordinary Level, GCE Advanced Level, bachelor’s degree, and above); and medical history of heart disease, stroke, cancer, diabetes, fractures (wrist, vertebral, and hip), asthma, and other chronic respiratory conditions (bronchitis and emphysema). Additionally, updated information on heart disease, stroke, and cancer up to the final physical activity assessment (third follow-up) were also collected by using data from hospital episode statistics. This is a database containing details of all admissions, including emergency department attendances and outpatient appointments at National Health Service hospitals in England. Non-fasting blood samples were collected and refrigerated at 4°C until transported within a week of sampling to be assayed for serum triglycerides, total cholesterol, and high density lipoprotein cholesterol by using standard enzymatic techniques. We derived low density lipoprotein cholesterol by using the Friedewald equation.17

We assessed habitual dietary intake during the previous year by using validated 130 item food-frequency questionnaires administered at baseline and at the second clinic visit. The validity of this food-frequency questionnaire for major foods and nutrients was previously assessed against 16 day weighed diet records, 24 hour recall, and selected biomarkers in a subsample of this cohort.1819 

We created a comprehensive diet quality score for each participant, separately for baseline and at follow-up, incorporating eight dietary components known to influence health and the risk of chronic disease.20 The composite diet quality score included: wholegrains, refined grains, sweetened confectionery and beverages, fish, red and processed meat, fruit and vegetables, sodium, and the ratio of unsaturated to saturated fatty acids from dietary intakes. We created tertiles for each dietary component and then scored these as −1, 0, or 1, with the directionality depending on whether the food or nutrient was associated with health risks or benefits.20 

Scores from the eight dietary components were summed into an overall diet quality score which ranged from −8 to 8, with higher values representing a healthier dietary pattern. We also collected updated information on body weight and height from the two postal assessments (first and third follow-up).

Mortality ascertainment

All participants were followed-up for mortality by the Office of National Statistics until the most recent censor date of 31 March 2016. Causes of death were confirmed by death certificates which were coded by nosologists according to ICD-9 (international classification of diseases, ninth revision) and ICD-10 (international classification of diseases, 10th revision). We defined cancer mortality and cardiovascular disease mortality by using codes ICD-9 140-208 or ICD-10 C00-C97 and ICD-9 400-438 or ICD-10 I10-I79, respectively.

Statistical analysis

We used Cox proportional hazards regression models to derive hazard ratios and 95% confidence intervals. Individuals contributed person time from the date of the last physical activity assessment (third follow-up) until the date of death or censoring. We used all available assessments of physical activity to better represent long term habitual physical activity and used linear regression against elapsed time to derive an overall physical activity trajectory (ΔPAEE) for each individual. We used the resulting coefficient of the calibrated ΔPAEE values in kJ/kg/day/year, together with baseline PAEE, as mutually-adjusted exposure variables in the Cox regression models.

We created categories reflecting approximate tertiles of both baseline PAEE and ΔPAEE to investigate joint effects of baseline and long term trajectories of physical activity. We defined the categories of baseline PAEE as: low (PAEE=0 kJ/kg/day), medium (0<PAEE<8.4 kJ/kg/day), and high (PAEE≥8.4 kJ/kg/day). We defined the categories of ΔPAEE over time as: decreasers (ΔPAEE≤−0.20 kJ/kg/day/year), maintainers (−0.20<ΔPAEE<0.20 kJ/kg/day/year), and increasers (ΔPAEE≥0.20 kJ/kg/day/year). We then created joint exposure categories by cross-classifying the three baseline by the three trajectory categories, resulting in eight categories.

The reference group was individuals with consistently low physical activity (by definition, there would be no exposure category comprising individuals declining from no baseline physical activity). We estimated the potential number of preventable deaths at the population level in each joint exposure category, using the absolute difference in adjusted mortality rates between the reference group (consistently inactive) and each joint exposure category, multiplied by the person years observed in the corresponding joint exposure category. We derived adjusted mortality rates by using multivariable exponential regression, with covariates used in the most comprehensively adjusted analytical model.

In model 1 we adjusted for: general demographics (age, sex, socioeconomic status, education level, and smoking status), dietary factors (total energy intake, overall diet quality, alcohol consumption), and medical history (asthma, chronic respiratory conditions, bone fractures, diabetes, heart disease, stroke, and cancer). Age, energy and alcohol intake, and diet quality were continuous variables.

In model 2 we accounted for changes in the above covariates by further inclusion of updated variables at the second clinic visit (3.6 years later), as well as updated status of cardiovascular disease and cancer from hospital episode statistics up until the final physical activity assessment.

In model 3 we further accounted for changes in body mass index by including continuous values of body mass index at baseline and at the final physical activity assessment. In model 4 we accounted for changes in blood pressure and lipids by further including continuous values of systolic and diastolic blood pressure, serum triglycerides, low density lipoprotein cholesterol, and high density lipoprotein cholesterol at baseline and at the second clinic visit.

We used height and weight measurements from the baseline and second clinic visit to calibrate self reported height and weight provided by the postal questionnaires. Self reported values were multiplied by the ratio of mean clinically-measured values and self-reported values.

We imputed missing values of covariates at follow-up by using regression on their baseline values. A complete case analysis was conducted as a sensitivity analysis. Reverse causation owing to undiagnosed disease was mitigated by excluding participants who died within one year of the final physical activity assessment (beginning of follow-up for mortality) in all analyses.

Predefined subgroups were age, sex, clinically-defined cut points of body mass index, and history of cardiovascular disease and cancer. We performed additional sensitivity analyses by excluding individuals with any period-prevalent chronic diseases (heart disease, stroke, and cancer) up to the final physical activity assessment, as well as excluding deaths occurring within two years of the final physical activity assessment. All analyses were performed by using Stata SE version 14.2.

Patient and public involvement

Patients and members of the public were not formally involved in the design, analysis or interpretation of this study. Nonetheless, the research question in this article is of broad public health interest. The results of this study will be disseminated to study participants and the general public through the study websites, participant engagement events, seminars, and conferences.

Study population

Among 14 599 participants with a mean baseline age of 58.0 (SD 8.8), followed for a median of 12.5 (interquartile range 11.9-13.2) years after the final physical activity assessment, there were 3148 deaths (950 from cardiovascular disease and 1091 from cancer) during 171 277 person years of follow-up. Table 1 shows the study population characteristics at the four assessment time points. On average, dietary factors such as total energy intake, alcohol consumption, and overall diet quality were similar at baseline and at the second clinic visit. The prevalence of diabetes, cardiovascular disease, cancer, and respiratory conditions increased over time. From baseline to the final follow-up assessment, mean body mass index increased from 26.1 kg/m2 to 26.7 kg/m2, and mean PAEE declined by 17% from 5.9 kJ/kg/day to 4.9 kJ/kg/day. The Pearson correlation coefficients were r=0.57 between PAEE at baseline and 1.7 years later; and r=0.45 between PAEE at baseline and 7.6 years later (final physical activity assessment).

Assessment of physical activity

Habitual physical activity was assessed with a validated questionnaire, with a reference time frame of the past year.1516 The first question inquired about occupational physical activity, classified as five categories: unemployed, sedentary (eg, desk job), standing (eg, shop assistant, security guard), physical work (eg, plumber, nurse), and heavy manual work (eg, construction worker, bricklayer). The second open ended question asked about time spent (hours/week) on cycling, recreational activities, sports, or physical exercise, separately for winter and summer.

The validity of this instrument has previously been examined in an independent validation study, by using individually-calibrated combined movement and heart rate monitoring as the criterion method; physical activity energy expenditure (PAEE) increased through each of four ordinal categories of self reported physical activity comprising both occupational and leisure time physical activity.15

 In this study, we disaggregated the index of total physical activity into its original two variables that were domain specific and conducted a calibration to PAEE using the validation dataset, in which the exact same instrument had been used (n=1747, omitting one study centre that had used a different instrument).

Specifically, quasi-continuous and marginalised values of PAEE in units of kJ/kg/day were derived from three levels of occupational activity (unemployed or sedentary occupation; standing occupation; and physical or heavy manual occupation) and four levels of leisure time physical activity (none; 0.1 to 3.5 hours; 3.6 to 7 hours; and >7 hours per week). This regression procedure allows the domain specific levels of occupational and leisure time physical activity to have independent PAEE coefficients, while assigning a value of 0 kJ/kg/day to individuals with a sedentary (or no) occupation and reporting no leisure time physical activity (LTPA). The resulting calibration equation was: PAEE (kJ/kg/day) =0 (sedentary or no job) + 5.61 (standing job) + 7.63 (manual job) + 0 (no LTPA) + 3.59 (LTPA of 0.1 to 3.5 hours per week) + 7.17 (LTPA of 3.6 to 7 hours per week) + 11.26 (LTPA >7 hours per week).


More information: Simone Biscaglia et al. Relationship between physical activity and long-term outcomes in patients with stable coronary artery disease, European Journal of Preventive Cardiology (2019). DOI: 10.1177/2047487319871217

Journal information: European Journal of Preventive Cardiology
Provided by European Society of Cardiology

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