Literature DB >> 34101730

Association between exercise habits and stroke, heart failure, and mortality in Korean patients with incident atrial fibrillation: A nationwide population-based cohort study.

Hyo-Jeong Ahn1, So-Ryoung Lee1, Eue-Keun Choi1,2, Kyung-Do Han3, Jin-Hyung Jung4, Jae-Hyun Lim1, Jun-Pil Yun1, Soonil Kwon1, Seil Oh1,2, Gregory Y H Lip2,5,6.   

Abstract

BACKGROUND: There is a paucity of information about cardiovascular outcomes related to exercise habit change after a new diagnosis of atrial fibrillation (AF). We investigated the association between exercise habits after a new AF diagnosis and ischemic stroke, heart failure (HF), and all-cause death. METHODS AND
FINDINGS: This is a nationwide population-based cohort study using data from the Korea National Health Insurance Service. A retrospective analysis was performed for 66,692 patients with newly diagnosed AF between 2010 and 2016 who underwent 2 serial health examinations within 2 years before and after their AF diagnosis. Individuals were divided into 4 categories according to performance of regular exercise, which was investigated by a self-reported questionnaire in each health examination, before and after their AF diagnosis: persistent non-exercisers (30.5%), new exercisers (17.8%), exercise dropouts (17.4%), and exercise maintainers (34.2%). The primary outcomes were incidence of ischemic stroke, HF, and all-cause death. Differences in baseline characteristics among groups were balanced considering demographics, comorbidities, medications, lifestyle behaviors, and income status. The risks of the outcomes were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting (IPTW) during a mean follow-up of 3.4 ± 2.0 years. The new exerciser and exercise maintainer groups were associated with a lower risk of HF compared to the persistent non-exerciser group: the hazard ratios (HRs) (95% CIs) were 0.95 (0.90-0.99) and 0.92 (0.88-0.96), respectively (p < 0.001). Also, performing exercise any time before or after AF diagnosis was associated with a lower risk of mortality compared to persistent non-exercising: the HR (95% CI) was 0.82 (0.73-0.91) for new exercisers, 0.83 (0.74-0.93) for exercise dropouts, and 0.61 (0.55-0.67) for exercise maintainers (p < 0.001). For ischemic stroke, the estimates of HRs were 10%-14% lower in patients of the exercise groups, yet differences were statistically insignificant (p = 0.057). Energy expenditure of 1,000-1,499 MET-min/wk (regular moderate exercise 170-240 min/wk) was consistently associated with a lower risk of each outcome based on a subgroup analysis of the new exerciser group. Study limitations include recall bias introduced due to the nature of the self-reported questionnaire and restricted external generalizability to other ethnic groups.
CONCLUSIONS: Initiating or continuing regular exercise after AF diagnosis was associated with lower risks of HF and mortality. The promotion of exercise might reduce the future risk of adverse outcomes in patients with AF.

Entities:  

Year:  2021        PMID： 34101730      PMCID： PMC8219164          DOI： 10.1371/journal.pmed.1003659

Source DB:  PubMed          Journal:  PLoS Med        ISSN： 1549-1277            Impact factor:   11.069


Introduction

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and the prevalence has been continuously growing over the past decades, potentially due to aging and an increase of risk factors predisposing to AF [1-3]. The clinical implication of AF is that it is associated with an increased risk of stroke, heart failure (HF), myocardial infarction (MI), and death [2,4]. As AF and its related healthcare burden are expected to surge in the coming years [5], this common condition necessitates a more integrated approach to management. Many studies have suggested risk factor modification be incorporated as the fourth pillar of AF management along with anticoagulation, rhythm control, and rate control [6-8]. Exercise of regular moderate intensity, as a part of risk factor management of AF, not only improves underlying conditions related to atherosclerotic cardiovascular disease (CVD) but has been established to provide benefit for AF-related outcomes, including symptoms, incidence, recurrence, burden of AF, and quality of life [9-12]. Though the link between exercise and AF-related outcomes has been supported by substantial evidence, benefits in cardiovascular morbidities led by exercise in AF patients are less likely to be offered. The majority of deaths in AF patients are cardiovascular in origin [13,14], thus emphasizing that optimal management of underlying heart disease should be a part of the holistic approach to AF care [15]. Given the paucity of information about cardiovascular outcomes related to exercise habit change after a new diagnosis of AF, we aimed to investigate the association between exercise habits after a new AF diagnosis and the risk of ischemic stroke, HF, and all-cause death.

Methods

Data source and study population

We defined a population-based cohort from the National Health Information Database (NHID), which incorporates all data from the National Health Insurance Service and covers the entire population of the Republic of Korea (hereafter denoted as Korea). NHID is only accessible online at designated analysis centers, with formal payment according to the period of browsing and analyzing the data and applying strict regulations regarding data release (https://nhiss.nhis.or.kr/). All insured adults are eligible for a biennially conducted general health examination, and examination reports—including demographic variables, self-questionnaire survey, clinical laboratory values, inpatient and outpatient usage, prescription records, income-based insurance contributions, and date of death—are available from NHID [16,17]. The institutional review board at Seoul National University Hospital (E-2001-111-1096) authorized this study. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). The statistical analysis plan was not prespecified. However, the analysis was performed with a priori–defined outcomes and covariates that were validated based on our previous publications before analysis [18,19]. We identified 523,174 patients newly diagnosed with AF between January 1, 2010, and December 31, 2016, and excluded patients with valvular AF or age under 20 years. Among the remaining patients, 132,741 patients who had data from 2 consecutive health examinations within 2 years before and after AF diagnosis were analyzed. Since we evaluated de novo incidence of ischemic stroke and HF after AF diagnosis, patients with prior recorded history of ischemic stroke or HF before the index date (health examination date after AF diagnosis) were excluded. Finally, 66,692 patients with AF were included in the analysis (Fig 1A).
Fig 1

Selection and categorization of the study population.

(A) Selection of study population from the National Health Insurance Service. (B) Categorization of study population according to the change of exercise status and overall scheme of study, including clinical outcomes. AF, atrial fibrillation; CHF, congestive heart failure.

Selection and categorization of the study population.

(A) Selection of study population from the National Health Insurance Service. (B) Categorization of study population according to the change of exercise status and overall scheme of study, including clinical outcomes. AF, atrial fibrillation; CHF, congestive heart failure.

Exercise evaluation

The individuals self-reported intensity and frequency of exercise via a questionnaire about lifestyle behaviors at the serial health examinations before and after AF diagnosis. The survey structure used for exercise evaluation was adopted and modified from the International Physical Activity Questionnaire (IPAQ), which was developed by the World Health Organization (WHO); both the reliability and validity of this short-form survey were reported in previous studies [20-23]. The questionnaire section on exercise was composed of 3 questions asking about the frequency (days per week) of light, moderate, and vigorous exercise during a recent week, i.e., the frequency of (i) light intensity exercise (e.g., walking slowly or sweeping carpets) for more than 30 min, (ii) moderate intensity exercise (e.g., brisk-pace walking, tennis doubles, or bicycling leisurely) for more than 30 min, and (iii) vigorous intensity exercise (e.g., running, climbing, fast cycling, or aerobics) for more than 20 min. Exercise “Yes” was defined as performing moderate intensity exercise for more than 30 min or vigorous intensity exercise for more than 20 min, at least once a week. Exercise “No” was defined as not engaging in any moderate or vigorous intensity exercise, regardless of any amount of light intensity exercise. By comparing categorization of exercise status at the health examinations before and after AF diagnosis, the study population was divided into 4 groups: persistent non-exercisers (No to No), new exercisers (No to Yes), exercise dropouts (Yes to No), and exercise maintainers (Yes to Yes). Fig 1B summarizes our study’s overall configuration and the selection of the population included in the analysis. To evaluate whether there exists an optimal exercise amount related to the best cardiovascular outcomes, we analyzed the hazards of cardiovascular morbidities according to stratified energy expenditure. This analysis was performed in the new exerciser (No to Yes) group to measure the influence of exercise introduction by isolating other healthy confounders that might contribute to the maintenance of exercise. Energy expenditure denotes the “minimum” amount of energy consumption calculated from a self-reported survey question that asks the frequency of each intensity of exercise as a unit of least duration. To calculate energy expenditure, we rated light, moderate, and vigorous intensity exercise as 2.9, 4.0, and 7.0 metabolic equivalents of task (METs), respectively [24]. The total energy expenditure level—the summation of multiplying 2.9, 4.0, and 7.0 METs by frequency of light, moderate, and vigorous exercise together with minimum duration—was stratified into <500, 500–999, 1,000–1,499, and ≥1,500 MET-min/wk in an explorative way.

Covariates, follow-up, and clinical outcomes

Demographics, comorbidities (hypertension, diabetes mellitus, dyslipidemia, MI, peripheral artery disease [PAD], chronic obstructive pulmonary disease [COPD], cancer, and chronic kidney disease [CKD]), CHA2DS2-VASc score, medications, laboratory results, anthropometric measurements, responses to the self-reported questionnaire conducted at the health examination after AF diagnosis (index date), and income-based insurance contributions were retrieved to analyze the baseline characteristics of individuals. To evaluate the relationship between exercise habit change and cardiovascular outcomes, we determined incident ischemic stroke and HF using International Classification of Diseases–10th Revision, Clinical Modification (ICD-10-CM) codes in inpatient and outpatient records. The date of death, and ICD-10-CM code recorded for the death, was obtained from NHID and used to investigate all-cause death. We defined a major cardiovascular death as a death from the most frequent 4 major specific diseases that belong to diseases of the circulatory system (I codes in the ICD-10 system), and that most often are the cause of death among Korean AF patients: cerebral infarction (I63), acute MI (I21), sequelae of cerebrovascular disease (I69), and HF (I50) [14]. The follow-up period was defined as the time from the index date to the first occurrence of ischemic stroke, HF, or death or December 31, 2017, whichever came first. Detailed definitions of comorbidities and study outcomes are provided in S1 Table [2,25,26].

Statistics

Continuous variables are reported as mean ± standard deviation, and categorical variables are reported as number (percentage). We performed inverse probability of treatment weighting (IPTW) using propensity scores derived from all the baseline covariates including demographics, comorbidities, anthropometric measures, lifestyle behaviors, medications, and income status to balance the weights across the 4 study groups. Maximum absolute standardized difference (ASD) < 0.1 (10%) was evaluated as a negligible difference in the baseline covariates between the study groups [27,28]. After IPTW, the weighted incidence rates of ischemic stroke, HF, and all-cause death were computed by dividing each weighted event number by the total follow-up duration and presented as per 1,000 person-years (PY). The risks of the outcomes were computed by weighted Cox proportional hazards models with IPTW. To compute the most optimal exercise amount for the best cardiovascular outcomes, we defined the persistent non-exerciser category (No to No) as the reference group and computed a multivariable-adjusted Cox proportional hazards model with 95% confidence intervals (CIs); model 1 was adjusted for age and sex, and model 2 was adjusted for body mass index (BMI), hypertension, diabetes mellitus, dyslipidemia, previous MI, PAD, COPD, cancer, CKD, CHA2DS2-VASc score, use of oral anticoagulation, use of antiplatelet agent, use of statin, smoking, heavy drinking, and income level in addition to the variables in model 1. All analyses were 2-tailed, and p-values < 0.05 were considered statistically significant. Data collection and statistical analysis were performed using SAS version 9.4 (SAS Institute, Cary, NC) from January 2020 to September 2020.

Subgroup and sensitivity analyses

We performed subgroup analyses of the primary outcomes according to sex, age (<65, 65 to <75, and ≥75 years), and CHA2DS2-VASc score (<3 and ≥3). The p-value for the interaction was estimated to assess the consistency of patterns in the main results among subgroups. To verify our results’ robustness, we performed sensitivity analyses by multivariable Cox proportional hazards models. Model 1 was adjusted for age and sex, and model 2 was adjusted for body measures, comorbidities, CHA2DS2-VASc score, medications, lifestyle behaviors, and income level in addition to the variables in model 1. Two analyses were performed in response to suggestions from peer review. We further evaluated the risk of the primary outcomes considering comorbidities or frailty using the Charlson Comorbidity Index (CCI), in addition to computing model 2 to adjust for confounding factors that might lead to a change in exercise status. The optimal amount of exercise was also analyzed in the exercise maintainer (Yes to Yes) group, to extend the finding of the most beneficial exercise level in the new exerciser (No to Yes) group.

Results

Baseline characteristics

Of the total 66,692 patients with AF, the mean age was 59.5 ± 12.4 years, and 42,410 (63.6%) were men. Comparison between the final study population and the excluded patients, most of whom were excluded due to unavailable health examination data within 2 years both before and after AF diagnosis, is summarized in S2 Table. Patients who received 0 or only 1 health examination were older, were more likely to be female, had more prevalent comorbidities, and had higher CHA2DS2-VASc scores than those with 2 health examinations. The final study population consisted of 4 categories: persistent non-exercisers (n = 20,354, 30.5%), new exercisers (n = 11,874, 17.8%), exercise dropouts (n = 11,630, 17.4%), and exercise maintainers (n = 22,834, 34.2%). The baseline characteristics of each group are shown in Table 1. New exerciser and exercise maintainer groups tended to be younger, male-predominant, and less affected with comorbidities (especially PAD, COPD, and CKD), and to have lower CHA2DS2-VASc scores and healthier lifestyle behaviors. We evaluated the differences and balances among multiple groups using the maximum ASDs of all baseline covariates, using a threshold of 0.1 to indicate imbalance. All of the covariates were well balanced after IPTW (ASDs ≤ 0.1). The multiple comparisons of covariates and the analysis of degrees of balance achievement between the 4 study groups were performed by calculating ASD (presented in S3 Table).
Table 1

Baseline characteristics of the total study population according to the change of exercise status.

TotalBefore IPTWPost IPTW
Persistentnon-exerciserNew exerciserExercisedrop-outsExercise maintainerMaximum ASDPersistentnon-exerciserNew exerciserExercisedrop-outsExercise maintainerMaximum ASD
No. of participants (%)6669220354(30.52)11874(17.80)11630(17.44)22834(34.24)20446.1(30.66)11870.9(17.80)11635.8(17.45)22739.0(34.10)
Age59.50±12.4062.32±12.2659.28±12.3760.49±12.1956.67±11.940.4159.22±12.9159.53±12.4259.47±12.5559.24±11.900.02
    < 65 years41216(61.80)10605(52.10)7448(62.73)6787(58.36)16376(71.72)12760.9(62.41)7342.0(61.85)7147.2(61.42)14195.3(62.43)
    65 to < 75 years18565(27.84)6508(31.97)3270(27.54)3539(30.43)5248(22.98)5446.1(26.64)3295.4(27.76)3299.8(28.36)6549.5(28.80)
    ≥ 75 years6911(10.36)3241(15.92)1156(9.74)1304(11.21)1210(5.30)2239.0(10.95)1233.5(10.39)1188.8(10.22)1994.2(8.77)
Sex　0.50　0.00
    Male42410(63.59)10676(52.45)7299(61.47)7167(61.63)17268(75.62)13052.1(63.84)7554.5(63.64)7401.6(63.61)14496.9(63.75)
    Female24282(36.41)9678(47.55)4575(38.53)4463(38.37)5566(24.38)7393.9(36.16)4316.4(36.36)4234.2(36.39)8242.1(36.25)
Comorbidities　　
    Hypertension41466(62.18)13008(63.91)7309(61.55)7286(62.65)13863(60.71)0.0712612.9(61.69)7380.8(62.18)7236.1(62.19)14029.6(61.70)0.01
    Diabetes mellitus12887(19.32)4252(20.89)2264(19.07)2349(20.20)4022(17.61)0.083939.7(19.27)2295.3(19.34)2241.8(19.27)4365.0(19.20)0.00
    Dyslipidemia7537(11.30)2361(11.60)1326(11.17)1352(11.63)2498(10.94)0.022320.4(11.35)1341.5(11.30)1317.2(11.32)2567.6(11.29)0.00
    Previous MI2221(3.33)745(3.66)376(3.17)387(3.33)713(3.12)0.03672.9(3.29)395.6(3.33)385.4(3.31)745.5(3.28)0.00
    PAD12053(18.07)4164(20.46)2171(18.28)2289(19.68)3429(15.02)0.143655.4(17.88)2145.1(18.07)2095.3(18.01)4053.4(17.83)0.01
    COPD10640(15.95)3836(18.85)1942(16.36)1919(16.50)2943(12.89)0.163272.1(16.00)1895.7(15.97)1852.3(15.92)3633.8(15.98)0.00
    Cancer4197(6.29)1260(6.19)835(7.03)752(6.47)1350(5.91)0.051294.7(6.33)746.1(6.29)725.0(6.23)1439.1(6.33)0.00
    CKD (GFR ≤ 60)6749(10.12)2535(12.45)1113(9.37)1207(10.38)1894(8.29)0.142035.8(9.96)1203.0(10.13)1174.5(10.09)2256.5(9.92)0.01
CHA2DS2-VASc score　0.53　0.02
    < 347027(70.51)12289(60.38)8431(71.00)7903(67.95)18404(80.60)14408.8(70.47)8392.7(70.70)8213.9(70.59)16219.1(71.33)
    ≥ 319665(29.49)8065(39.62)3443(29.00)3727(32.05)4430(19.40)6037.2(29.53)3478.2(29.30)3421.9(29.41)6519.9(28.67)
Medications　　
    OAC11771(17.65)3544(17.41)2092(17.62)2115(18.19)4020(17.61)0.023567.6(17.45)2100.4(17.69)2045.2(17.58)3966.8(17.44)0.01
        Warfarin9016(13.52)2605(12.80)1631(13.74)1590(13.67)3190(13.97)0.032744.2(13.42)1609.5(13.56)1566.8(13.46)3036.1(13.35)0.01
        NOAC3374(5.06)1148(5.64)567(4.78)646(5.55)1013(4.44)0.061020.9(4.99)600.4(5.06)588.1(5.05)1121.9(4.93)0.01
    Aspirin13263(19.89)4212(20.69)2248(18.93)2339(20.11)4464(19.55)0.044010.9(19.62)2363.9(19.91)2306.2(19.82)4457.5(19.60)0.01
    P2Y12 inhibitor2834(4.25)955(4.69)489(4.12)498(4.28)892(3.91)0.04865.6(4.23)503.1(4.24)491.2(4.22)942.9(4.15)0.00
    Statin9865(14.79)3240(15.92)1730(14.57)1712(14.72)3183(13.94)0.063013.6(14.74)1759.4(14.82)1718.9(14.77)3359.8(14.78)0.00
Laboratory findings (2nd exam)　　
    BMI (kg/m2)24.48±3.2524.35±3.4324.49±3.2524.48±3.2324.60±3.080.0824.47±3.4324.48±3.2324.48±3.2324.45±3.080.01
    Waist circumference (cm)84.10±8.9883.81±9.2983.93±9.0484.23±9.0184.38±8.630.0684.35±9.3084.08±8.9884.26±9.0283.67±8.730.08
    Systolic BP (mmHg)125.09±15.01125.78±15.57124.99±15.12125.13±15.06124.49±14.360.09124.92±15.22125.18±15.10124.93±14.98125.01±14.660.02
    Diastolic BP (mmHg)77.19±10.0977.14±10.2477.05±10.0577.11±10.1077.34±9.960.0377.10±10.1977.15±10.0577.12±10.0877.17±9.980.01
    Fasting glucose (mmol/L)5.75±1.425.77±1.505.73±1.415.77±1.435.73±1.360.035.75±1.505.74±1.425.75±1.425.74±1.370.01
    Total cholesterol (mg/dL)186.13±39.94186.09±39.72186.31±43.06186.07±41.35186.10±37.650.01186.13±39.47186.16±43.26186.04±41.10186.21±38.010.00
    eGFR (ml/min/1.73m2)83.47±28.3182.78±27.7184.23±30.5983.42±29.4983.72±26.950.0584.57±27.2983.81±30.2283.88±30.0782.73±26.760.07
Alcohol consumption　　
    Heavy drinker4665(6.99)1293(6.35)770(6.48)781(6.72)1821(7.97)0.061410.2(6.90)831.3(7.00)812.7(6.98)1575.5(6.93)0.00
Smoking　0.38　0.00
    Never smoker38014(57.00)13353(65.60)6855(57.73)7052(60.64)10754(47.10)11628.3(56.87)6764.7(56.99)6628.3(56.97)12941.2(56.91)
    Ex-smoker18229(27.33)3936(19.34)3125(26.32)2806(24.13)8362(36.62)4757.3(23.27)3166.6(26.68)3015.0(25.91)6931.9(30.48)
    Current smoker10449(15.67)3065(15.06)1894(15.95)1772(15.24)3718(16.28)4060.5(19.86)1939.6(16.34)1992.4(17.12)2865.9(12.60)
Low income *10817(16.22)3736(18.36)1943(16.36)1950(16.77)3188(13.96)0.123311.1(16.19)1926.2(16.23)1882.3(16.18)3658.0(16.09)0.00

Data are presented as means ± standard deviation or No. (Percentages).

Percentages may not total 100 because of rounding.

Abbreviation: IPTW, inverse probability of treatment weighting; ASD, absolute standardized difference; MI, myocardial infarction; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; OAC, oral anticoagulant; BMI, body mass index; BP, blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate.

* Low income denotes income in the lowest 20% among the entire Korean population; individuals with low income are supported by the medical aid program.

Data are presented as means ± standard deviation or No. (Percentages). Percentages may not total 100 because of rounding. Abbreviation: IPTW, inverse probability of treatment weighting; ASD, absolute standardized difference; MI, myocardial infarction; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CKD, chronic kidney disease; OAC, oral anticoagulant; BMI, body mass index; BP, blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate. * Low income denotes income in the lowest 20% among the entire Korean population; individuals with low income are supported by the medical aid program. The mean period from the health examination before AF diagnosis (first examination) to the date of AF diagnosis was 1.1 ± 0.5 years, and from AF diagnosis to the health examination after AF diagnosis (second examination) was 0.9 ± 0.5 years. Overall, the time between the 2 consecutive biennial health examinations was 2.0 ± 0.5 years.

Change in exercise status and ischemic stroke, HF, and all-cause death

Mean follow-up durations for ischemic stroke, HF, and all-cause death were 3.4 ± 2.0, 3.0 ± 2.0, and 3.4 ± 1.9 years, respectively. Table 2 provides the weighted event numbers, weighted incidence rates (per 1,000 PY), and weighted hazard ratios (HRs) of ischemic stroke, HF, and all-cause death after IPTW.
Table 2

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to change of exercise status.

Outcome and exercise groupNumber of individualsNumber of events*IR* (per 1,000 PY)HR* (95% CI) or p-value
Ischemic stroke
Persistent non-exercisers20,354633.229.131 (Ref.)
New exercisers11,874331.358.250.90 (0.79–1.03)
Exercise dropouts11,630323.738.150.89 (0.78–1.02)
Exercise maintainers22,834597.227.870.86 (0.77–0.96)
p = 0.057
Heart failure
Persistent non-exercisers20,3544,132.3266.511 (Ref.)
New exercisers11,8742,279.6162.950.95 (0.90–0.99)
Exercise dropouts11,6302,368.3766.531 (0.95–1.05)
Exercise maintainers22,8344,201.3361.320.92 (0.88–0.96)
p < 0.001
All-cause death
Persistent non-exercisers20,354981.2013.871 (Ref.)
New exercisers11,874462.9611.320.82 (0.73–0.91)
Exercise dropouts11,630465.0411.490.83 (0.74–0.93)
Exercise maintainers22,834651.078.420.61 (0.55–0.67)
p < 0.001

p-Values were evaluated by the likelihood ratio test.

HR, hazard ratio; IR, incidence rate; PY, person-years.

*Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting (IPTW). The HRs were computed by weighted Cox proportional hazards models with IPTW.

p-Values were evaluated by the likelihood ratio test. HR, hazard ratio; IR, incidence rate; PY, person-years. *Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting (IPTW). The HRs were computed by weighted Cox proportional hazards models with IPTW. Performing exercise any time before or after AF diagnosis was related to a lower risk of mortality compared to those without exercise: the HR (95% CI) was 0.82 (0.73–0.91) for new exercisers, 0.83 (0.74–0.93) for exercise dropouts and 0.61 (0.55–0.67) for exercise maintainers (p < 0.001). Initiating and maintaining exercise after AF diagnosis was associated with a lower risk of HF compared to the persistent non-exerciser group: the HR (95% CI) was 0.95 (0.90–0.99) for the new exerciser group and 0.92 (0.88–0.96) for the exercise maintainer group (p < 0.001). However, the exercise dropouts exhibited a similar risk of HF as the persistent non-exerciser group. For ischemic stroke, participating in exercise was associated with 10%–14% lower risk compared to the persistent non-exerciser group, but the precision in the estimates of HRs was statistically insignificant: the HR (95% CI) was 0.90 (0.79–1.03) for the new exerciser group and 0.86 (0.77–0.96) for the exercise maintainer group (p = 0.057). The weighted incidence rates and risks of cardiovascular outcomes according to the change of exercise status are illustrated in Fig 2.
Fig 2

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status.

The bars denote weighted incidence rates, the dots denote hazard ratios, and the whiskers denote 95% confidence intervals computed by weighted Cox proportional hazards models with inverse probability of treatment weighting.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status.

The bars denote weighted incidence rates, the dots denote hazard ratios, and the whiskers denote 95% confidence intervals computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. Since the effect size of lowered estimates of HRs in all-cause death was larger than that of ischemic stroke and HF, we investigated whether the lower risk of all-cause death was indirectly attributable to the unadjusted confounders such as comorbidities or frailty, rather than possible beneficial effects of exercise. The risk of death from major cardiovascular events (major cardiovascular death) was lower in the exercise maintainers (HR 0.66, 95% CI 0.49–0.90, p = 0.040) compared to the persistent non-exercisers. Both the new exercisers (HR 0.74, 95% CI 0.52–1.06) and the exercise dropouts (HR 0.71, 95% CI 0.49–1.02) tended to show a lower HR without statistical significance, which might be due to low event numbers (S4 Table). However, a lower risk of major cardiovascular death in the exercise maintainer group and lower estimates of HRs in the new exerciser and the exercise dropout groups were consistent with the positive risk benefits in all-cause death (p = 0.040).

The relationship between energy expenditure and the risk of cardiovascular outcomes

Multivariable-adjusted Cox proportional hazards analysis of ischemic stroke, HF, and all-cause death in the new exerciser group (No to Yes) according to the stratified amount of exercise is presented in S5 Table. Exercising 1,000–1,499 MET-min/wk (1,208.1 ± 159.7 MET-min/wk) was consistently associated with a lower risk of adverse cardiovascular outcomes (adjusted HR 0.77, 95% CI 0.60–0.99, p = 0.229, for ischemic stroke; adjusted HR 0.90, 95% CI 0.82–0.99, p = 0.166, for HF) and all-cause death (adjusted HR 0.68, 95% CI 0.54–0.84, p = 0.002) compared to the persistent non-exerciser group. Lower risks of ischemic stroke, HF, and all-cause death were correlated with energy expenditure increment, but the associations became attenuated in the group with exercise amount at or above 1,500 MET-min/wk (Fig 3).
Fig 3

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death in the new exerciser group (No to Yes) according to energy expenditure (MET-min/wk).

The bars denote weighted incidence rates, the dots denote hazard ratios, and the whiskers denote 95% confidence intervals computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. MET, metabolic equivalent of task.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death in the new exerciser group (No to Yes) according to energy expenditure (MET-min/wk).

The bars denote weighted incidence rates, the dots denote hazard ratios, and the whiskers denote 95% confidence intervals computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. MET, metabolic equivalent of task. Subgroup analysis was performed by sex, age, and CHA2DS2-VASc score (S6–S8 Tables). For ischemic stroke, there were no significant statistical interactions for sex, age, and CHA2DS2-VASc score. The incidence of HF showed an interaction with age, potentially due to an augmented lower risk of HF in the exercise maintainer group among those aged 75 years and older (p for interaction = 0.019). The risk of all-cause death presented an interaction with sex that might be explained by greater benefits of exercise in male patients (p for interaction = 0.048). Sensitivity analyses validating the risks of primary outcomes according to the change of exercise habits estimated by multivariable-adjusted Cox proportional hazards models are presented in S9 Table. The risks of ischemic stroke, HF, and all-cause death adjusted by model 2 and CCI were consistent with those in the main analysis. Indeed, S1 Fig shows various adjustment models and corresponding HRs, showing consistent outcomes irrespective of the variables incorporated in the adjustments. The HRs were only different in subtle ways between the models. Another sensitivity analysis verifying whether there exists a dose–response relationship between energy expenditure and the risk of cardiovascular outcomes was performed in the exercise maintainer group (Yes to Yes) and is presented in S10 Table. The levels of energy expenditure showing the lowest estimates of HRs were all different across ischemic stroke, HF, and all-cause death, unlike the consistent level in the new exerciser group (No to Yes).

Discussion

In this nationwide population-based cohort study, our principal findings are as follows. First, initiation and maintenance of exercise after AF diagnosis were associated with a significantly lower risk for HF and all-cause death. Interestingly, exercise dropout was associated with a lower risk of all-cause death as well. Second, the estimates of HRs of ischemic stroke were lower in AF patients with any lifetime experience of exercise before and after AF diagnosis, though the association was statistically insignificant. Third, performing exercise at an energy expenditure level of 1,000–1,499 MET-min/wk (moderate intensity exercise 170–240 min/wk or vigorous intensity exercise 140–210 min/wk) was consistently associated with lower risks of ischemic stroke, HF, and all-cause death in the new exerciser group. This estimated range of energy expenditure is in accord with the previously recommended volume of exercise in AF patients [11], yet slightly higher than the minimum leisure-time physical activity suggested for the general population [29]. AF is associated with an increased risk of death and other cardiovascular events [4], which confers a major healthcare burden. A recent cohort study in men demonstrated the inverse association between fitness and mortality [30,31], and the same relationship was reproduced in patients with AF. For example, in the HUNT3 study, physical activity levels in AF patients showed an inverse correlation with risks of all-cause and CVD mortality, as well as the risk of CVD morbidity [32]. Also, the EORP-AF registry reported a lower risk of all-cause death in AF patients engaging in regular exercise at 1-year follow-up and showed an inverse association of cardiovascular outcomes with exercise levels [33]. In addition to there being a mortality benefit of anticoagulation therapy in AF patients [34], our results provide evidence of a mortality benefit of exercise in AF patients as a part of a holistic approach to AF care [15]. Moreover, the benefit of exercise with respect to all-cause death was robust even after evaluating the association by another statistical method and adjusting the model, including as many covariates as possible. Also, the risk benefit for major cardiovascular death, using the 4 most frequent major CVDs explaining deaths of AF patients in Korea [13,14,35-37], presented a pattern consistent with that of all-cause death; thus, the lowered risk of all-cause death might come from the positive effects of exercise. HF is closely linked to AF by sharing common predisposing conditions, and the coexistence of AF and HF is associated with adverse prognosis [38]. Newly diagnosed AF increases the risk of HF, and individuals with concomitant AF and HF are at a greater risk of all-cause mortality and have higher hospital admission rates [39-41]. Exercise is known to have a protective effect in preventing HF and is beneficial to patients with already established HF as well [42]. Also, moderate and high exercise levels are associated with a lower risk of HF regardless of BMI in the general population [43]. Possible mechanisms contributing to the favorable result regarding long-term HF risk include the reduction of subclinical myocardial damage and abnormal left ventricle remodeling [44,45]. Our study extends the previous findings of exercise in HF patients to patients with AF, whereby those with regular moderate exercise after AF diagnosis showed 5% to 8% lower risk of HF development. Interestingly, the individuals 75 years and older showed even greater benefits of exercise for the risk of HF, suggesting that assessment of daily physical activities and recommendations on exercise are still important for elderly individuals with newly diagnosed AF. On the other hand, we found that the association between exercise and the risk of stroke was not statistically significant. Increased physical activity lowers stroke risk in the general population [46], but this association was conflicting in patients with AF among previous publications. The HUNT3 study showed an insignificantly lower risk of stroke among patients with AF fulfilling physical activity guidelines, consistent with our study [32]. Meanwhile, another study of cross-country skiers demonstrated that endurance exercise in AF patients is associated with a lower risk of stroke [47]. Moreover, an inverse association between cardiorespiratory fitness and the risk of stroke in patients with AF also has been reported [48]. The relatively low event rate of ischemic stroke compared to HF and all-cause death in our study might attribute to the lack of statistical power; thus, further research needs to address the association between exercise and the risk of ischemic stroke. The amount of exercise and the risk of AF show a J-shaped relationship; for example, endurance exercise is known to increase the risk of AF [49]. However, no current data provide the specific intensity and amount of exercise that could be recommended to AF patients to benefit cardiovascular outcomes. We not only showed an association between exercise habit change and the clinical outcomes (ischemic stroke, HF, and mortality) in AF patients, we tried to suggest an appropriate amount of exercise. We found that exercising 1,000–1,499 MET-min/wk was associated with lower event rates for stroke, HF, and mortality in the new exerciser group. This level of energy expenditure corresponds to performing moderate intensity exercise 170–240 min/wk and is comparable to the amount recommended to adults aged 18–64 years with or without chronic conditions: 150–300 min/wk of moderate intensity aerobic physical activity, for substantial health benefits according to WHO guidelines on physical activity and sedentary behavior [50]. Interestingly, the association appeared to be attenuated in the range of exercise ≥1,500 MET-min/wk rather than persistently showing a direct inverse correlation. The mechanisms for intensive exercise to increase the risk of AF are diverse, including changes in autonomic activation [51,52], atrial dilatation [53,54], fibrosis, and inflammation [55,56]. Similar physiological changes would explain the attenuated risk benefit in AF patients with exercise level ≥ 1,500 MET-min/wk across cardiovascular outcomes, and particularly in all-cause death. However, considering the wide 95% CIs in patients with ≥1500 MET-min/wk, the small patient numbers in this group might be underpowered. Besides, the optimal amount of exercise in the exercise maintainer group showed different energy levels having the lowest estimates of HRs for each cardiovascular outcome. We assume that accumulated and prolonged exercise would have influenced the clinical outcomes with different magnitudes of impacts. Taking the evidence together, this exercise dose–response relationship should be interpreted cautiously due to several limitations, including the explorative stratification of exercise and the lack of statistical power. Our quantitative analysis could give a clue for future prospective studies investigating the optimal intensity and amount of exercise in arrhythmia patients. Exercise has been widely documented to modify potential contributors to AF, such as obesity, hypertension, and diabetes, by improving weight, glucose and lipid control, and endothelial function, and lowering resting heart rate and blood pressure [57-59]. Not only can risk factors for AF but also cardiac structure and function be modified by exercise. For example, Malmo et al. [11] found that, in patients with AF, the exercise group was associated with improved left atrial and ventricular function indices. These mechanisms might contribute to the noticeably lower risk of HF and all-cause mortality (and possibly a lower HR for ischemic stroke) in our study in the new exerciser and exercise maintainer groups compared to the persistent non-exerciser group. Also, arrhythmogenic risk factors and cardiac alterations through exercise might exert continued advantageous effects even in the exercise dropout group, resulting in lower mortality. Of note, we categorized the study population by the change of exercise status after diagnosis of AF; thus, the link between exercise and cardiovascular outcomes could be analyzed according to new exerciser or exercise maintainer status, which could differentiate the benefit of exercise per se from an accumulated gain of exercise and other healthy confounders that enable maintenance of exercise. A recent large prospective study quantifying daily physical activity among individuals using electronic wearable devices found that individuals with AF engage in significantly less average daily physical activity than those without AF [60]. As we have demonstrated, the influence of exercise mitigated the risks of HF, all-cause death, and probably ischemic stroke in patients with AF, and thus, exercise should be an essential component of nonpharmacological management of AF. Indeed, exercise is part of the lifestyle recommendations in the ABC (Atrial fibrillation Better Care) pathway of holistic or integrated AF management and has been shown in the prospective mAFA-II randomized trial to improve overall outcomes [61]. The ABC pathway refers to the following: “A” for avoid stroke/anticoagulation, “B” for better symptom management, and “C” for cardiovascular and comorbidity management, including lifestyle changes [15].

Limitations

Several limitations are evident in our study. First, the self-reported nature of the health examination survey asking about exercise frequency would be influenced by recall bias. Second, the minimum energy expenditure estimated from the 3 questions is limited in its ability to reflect the actual amount of exact exercise level; it would not be possible to precisely quantify exercise (e.g., using pedometers) in this population-based study. Due to limited data about exact exercise volume from the questionnaire, the relationship between endurance exercise and cardiovascular outcomes could not be evaluated. Third, unavailable confounding factors might not be fully adjusted for. Fourth, the type and burden of AF were not available; thus, the contribution of these factors to the outcomes could not be analyzed. Fifth, potential change in exercise status from the index date to the end of follow-up might introduce bias in the associations. Sixth, the study was conducted in an Asian population; therefore, physiological and cultural differences in exercise behavior or biological response to exercise might limit the external generalizability to other ethnic groups. Seventh, the inclusion of only patients who had 2 consecutive health examinations before and after AF diagnosis would cause some selection bias. Our results would have limited evidence in those who could not receive health screening due to underlying diseases; thus, the results should be interpreted and applied cautiously to the general AF population. Lastly, this was not a randomized controlled study, and causality and the direct mechanistic link between exercise and cardiovascular outcomes cannot be determined. However, there may be an ethical issue with study designs restricting exercise (or stopping any exercise), which would make it difficult to include as part of a randomized controlled study. Instead, our data from this large population-based research provide potential evidence for the beneficial effects of exercise on clinical outcomes as part of holistic AF management.

Conclusion

In this nationwide population-based cohort study, initiating or continuing regular exercise after AF diagnosis was associated with lower risks of HF and mortality. Regular moderate exercise 170–240 min/wk might be an optimal amount for deriving maximal cardiovascular benefits in patients who initiate exercise after AF diagnosis, but further prospective research needs to delineate the specific optimal energy expenditure. Timely encouragement of exercise to overcome misguided fears of arrhythmogenic and sequential adverse outcomes in AF patients would be warranted. (DOC) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status calculated from the various multivariable-adjusted Cox proportional hazards models.

CI, confidence interval. Model 1 adjusted for age and sex. Model 2 adjusted for body mass index (BMI), smoking, heavy drinking, and low income in addition to the variables in model 1. Model 3 adjusted for diabetes mellitus, hypertension, dyslipidemia, and previous myocardial infarction (MI) in addition to the variables in model 2. Model 4 adjusted for peripheral artery disease (PAD), chronic obstructive pulmonary disease (COPD), cancer, and chronic kidney disease (CKD) in addition to the variables in model 3. Model 5 adjusted for CHA2DS2-VASc score in addition to the variables in model 4. Model 6 adjusted for use of oral anticoagulation (OAC), use of antiplatelet agent, and use of statin in addition to the variables in model 5. p-Values were evaluated by the likelihood ratio test. The dots denote hazard ratios, and the whiskers denote 95% confidence intervals computed by multivariable Cox proportional hazards models. (DOCX) Click here for additional data file.

Definition of covariates and outcomes.

CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; N/A, not applicable; PAD, peripheral artery disease. *Combination: 1 = ICD-10-CM code and medication; 2 = number of diagnosis; and 3 = diagnostic tests or treatment. (DOCX) Click here for additional data file.

Baseline characteristics of newly diagnosed AF patients according to the number of health examinations before and after their AF diagnosis.

Data are presented as mean ± standard deviation or number (percentage). Percentages may not total 100 because of rounding. AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; DOAC, direct oral anticoagulant; HF, heart failure; MI, myocardial infarction; PAD, peripheral artery disease. *Low income denotes income in the lowest 20% among the entire Korean population; individuals with low income are supported by the medical aid program. (DOCX) Click here for additional data file.

The multiple comparisons between the study groups presented as absolute standardized differences.

ASD, absolute standardized difference; BMI, body mass index; BP, blood pressure; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MI, myocardial infarction; OAC, oral anticoagulant; PAD, peripheral artery disease. *Group A denotes persistent non-exercisers, group B denotes new exercisers, group C denotes exercise dropouts, and group D denotes exercise maintainers. #Low income denotes income in the lowest 20% among the entire Korean population; individuals with low income are supported by the medical aid program. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for major cardiovascular death according to the change of exercise status.

CI, confidence interval; HR, hazard ratio; IR, incidence rate; PY, person-years. *Major cardiovascular deaths are defined as deaths due to cerebral infarction (I63), acute myocardial infarction (I21), sequelae of cerebrovascular disease (I69), and heart failure (I50). Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting. The HRs were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death in the new exerciser group (No to Yes) according to the energy expenditure (MET-min/wk).

Exercise dose presented as mean ± SD. CI, confidence interval; HR, hazard ratio; IR, incidence rate; MET, metabolic equivalent of task; PY, person-years. Model 1 adjusted for age and sex. Model 2 adjusted for age, sex, body mass index (BMI), hypertension, diabetes mellitus, dyslipidemia, previous myocardial infarction (MI), peripheral artery disease (PAD), chronic obstructive pulmonary disease (COPD), cancer, chronic kidney disease (CKD), CHA2DS2-VASc score, use of oral anticoagulation (OAC), use of antiplatelet agent, use of statin, smoking, heavy drinking, and low income. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status and sex.

CI, confidence interval; HR, hazard ratio; IR, incidence rate; PY, person-years. Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting. The HRs were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status and age.

CI, confidence interval; HR, hazard ratio; IR, incidence rate; PY, person-years. Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting. The HRs were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status and CHA2DS2-VASc score.

CI, confidence interval; HR, hazard ratio; IR, incidence rate; PY, person-years. Weighted event numbers and weighted IRs were computed after inverse probability of treatment weighting. The HRs were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death according to the change of exercise status calculated from the multivariable-adjusted Cox proportional hazards model.

CI, confidence interval; HR, hazard ratio; IR, incidence rate; PY, person-years. Model 1 adjusted for age and sex. Model 2 adjusted for age, sex, body mass index (BMI), hypertension, diabetes mellitus, dyslipidemia, previous myocardial infarction (MI), peripheral artery disease (PAD), chronic obstructive pulmonary disease (COPD), cancer, chronic kidney disease (CKD), CHA2DS2-VASc score, use of oral anticoagulation (OAC), use of antiplatelet agent, use of statin, smoking, heavy drinking, and low income. Model 3 adjusted for age, sex, BMI, hypertension, diabetes mellitus, dyslipidemia, previous MI, PAD, COPD, cancer, CKD, CHA2DS2-VASc score, use of OAC, use of antiplatelet agent, use of statin, smoking, heavy drinking, low income, and Charlson Comorbidity Index (CCI). p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file.

Hazard ratios with 95% confidence intervals for ischemic stroke, heart failure, and all-cause death for exercise maintainer group (Yes to Yes) according to the energy expenditure (MET-min/wk).

CI, confidence interval; HR, hazard ratio; IR, incidence rate; MET, metabolic equivalent of task; PY, person-years. Model 1 adjusted for age and sex. Model 2 adjusted for age, sex, body mass index (BMI), hypertension, diabetes mellitus, dyslipidemia, previous myocardial infarction (MI), peripheral artery disease (PAD), chronic obstructive pulmonary disease (COPD), cancer, chronic kidney disease (CKD), CHA2DS2-VASc score, use of oral anticoagulation (OAC), use of antiplatelet agent, use of statin, smoking, heavy drinking, and low income. p-Values were evaluated by the likelihood ratio test. (DOCX) Click here for additional data file. 14 Jul 2020 Dear Dr Choi, Thank you for submitting your manuscript entitled "Exercise habits and the risk of stroke, heart failure, and mortality in patients with incident atrial fibrillation: a nationwide population-based cohort study" for consideration by PLOS Medicine. Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review. However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire. Please re-submit your manuscript within two working days, i.e. by . Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. 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When completing the checklist, please use section and paragraph numbers, rather than page numbers. ----------------------------------------------------------- Comments from the reviewers: Reviewer #1: The study by Ahn and coworkers provides "big data" from nationwide population based cohort study on the impact of exercise on clinical sequelae of newly diagnosed atrial fibrillation. They retrieved data from more than 66'000 South Korean patients how had their first diagnosis of atrial fibrillation between two consecutive biennially health care examinations from the National Health Insurance data. The categorized these patients according to their exercise behavior before and after the diagnosis into 4 groups. The primary endpoint was the incidence of stroke, heart failure and overall mortality. Furthermore, they strived to identify the optimal amount of exercise per week in order to decrease the clinical endpoints in patients who newly started to exercise. The paper is very well written and the analysis is based on an impressive number of patients. The results are relevant since they underline the importance of initiating or maintaining regular exercise in order to decrease the incidence of heart failure and overall mortality. Furthermore, it provides some guidance as to the weekly duration of moderate exercise in order to derive the maximum benefit. Specific comments: It needs to be mentioned in the limitation section that these results apply to an Asian population which shows some physiological as well as cultural differences. The amount of women who do not exercise at all appears to be much higher than in a Western European population. One of the biggest drawbacks is certainly that we do not have any information on the type and burden of AF. ----------------------------------------------------------- Reviewer #2: This is a statistical review of manuscript PMEDICINE-D-20-03103_R1. The manuscript is well-written, well-structured and I must say that Figure 1 is really good because it is easy to understand and it captures the whole study design. I have one question for the authors, two suggestions and a few minor points. Question: 1/ "Lower risks of ischemic stroke, HF, and all-cause death were correlated with energy expenditure increment, but with a trend towards increased risks at ≥1500 MET min/week group". In eTable2, I've noticed that the sample size for the group >= 1500 is the smallest by far. Is there any biological plausibility for the U-shape for the HR curve in Figure 3 ? The CIs overlap so much that I am not sure one can conclude and mention in the abstract that 1000-1499 is the "best" amount of exercise. What are your thoughts? I would suggest to perhaps tone down the conclusions with respect to this analysis. If anything, I see a downward trend. Suggestions: 1/ the HRs that you report in the abstract are adjusted HRs. I think that this fact could be mentioned, e.g adjusted hazard ratio (HR) 0.94 2/ we performed reexamination of outcomes within subgroups of sex, age, and CHA2DS2-VASc score. I think it would be helpful to provide more details on the model. Was the model the same as Model 2, with an interaction term between exercise category and sex, age or the score? Minor points: I've noticed two sentences where I think one word was missing (see [xxx]) 1/ The amount of energy expenditure [that] maximizes cardiovascular benefits appears to be 2/ Since we evaluated de novo incidence of ischemic stroke and HF after AF diagnosis, patients [with] recorded prior history of ischemic stroke and HF before the index date (health examination date after AF diagnosis) were excluded ----------------------------------------------------------- Reviewer #3: The study by Ahn et al sought to determine the influence of exercise after atrial fibrillation (AF) diagnosis on risk of stroke, heart failure and all-cause death. The topic and aim are highly relevant given that specific exercise guidelines are lacking for AF patients and there is a paucity of data on cardiovascular outcomes after exercise in established AF. Particularly novel is the use of data on exercise habits before and after first AF diagnosis. The design and results strengthen the idea that exercise is causally related to outcomes, in a relatively short term, and therefore might have a place in secondary prevention and cardiac rehabilitation of these patients. Other strengths of the study are a large population sample, a high event rate and seemingly valid and relevant endpoints. The paper is also well-written and structured. I have only some minor comments that may improve the manuscript: * Stroke risk is obviously a major issue in AF and exercise may play a role in risk reduction. Unfortunately, this study, similar to the cited study from the HUNT cohort, was not able to provide convincing evidence that exercise adoption or maintenance would greatly reduce the risk of stroke. Still I think the statement that `exercise was associated lower risk of HF, but not stroke` (abstract, conclusion etc.) is too strong and may even be misleading when read briefly and out of context (as many do). The main effect estimates (HR) for stroke in exercisers actually seem in line with, or even stronger, than those for HF although precision obviously is lower. Whether the lack of precision is due to power (events) or other confounders/effect modifiers could be discussed. Also, a certain amount of exercise in new exercisers were even "significant" (Fig 3/eTable 2). I would consider re-wording these conclusions in a neutral language, including line 219 ("impact..not as strong") given that this may be the take-home-message for many readers. Keeping the left x-axis in Fig. 2 consistent across outcomes would also help on correct interpretation. * Similarly, at p. 9, line 176, it is stated that the highest exercise volumes had a "trend towards increased risks", although the main effect were even lower than the reference, even significantly for deaths. Although I understand the statement refer to the J-shape (or reverse J-shape?) and a comparison to the second highest category, I suggest change to "attenuated risk reduction" or similar. * PA was reported ~1 year before and after AF diagnosis and that is a strength of the study, but might also be a limitation. I.e. a potential change in PA status from examination to end of follow up might bias the associations and is not mentioned in limitations. In line with that, I`m surprised about the number of "new exercisers", compared to drop-outs after being diagnosed with this exercise-limiting disease (by the way, I would change the word "drop-out" just for the reasons above, as well as "any lifetime experience" at line 188). Can you just comment? Are there any exercise recommendations/rehabilitation standards after AF diagnosis in Korea? * The secondary analyses on energy expenditure is interesting and provides novel information for future guidelines. But why were only new exercisers analysed? Would there be differences in maintainers? I think "adopters" or "new exercisers" could be added to the sentence at lines 189-90 since this conclusion about volumes is only valid for previously inactive patients` exercise a year after diagnosis. * Some spelling and grammar should be corrected, i.e. line 44, 253. Line 273-5; Hard to understand the sentence, do you mean RCT`s is not possible or ethical in this population? * Figure 1 seems to be duplicated ----------------------------------------------------------- Reviewer #4: Hyo-Jeong et al present data obtained from the National Health Information Database from the Republic of Korea. Specifically, they assess the impact of self-reported exercise habits before an initial diagnosis of atrial fibrillation and the impact of a change in exercise habits after that diagnosis using no exercise pre and post diagnosis (persistent non-exerciser) as the baseline risk group for comparison. Outcomes assessed included stroke, CHF and all-cause mortality at follow-up. Additionally, their data allowed an evaluation of the minimum amount of energy expenditure ranges (MET-min/week) in the new exerciser group across the primary outcomes of stroke, CHF and all-cause mortality. This is an interesting study from a large data-set that does contribute to our rapidly growing knowledge set on lifestyle and atrial fibrillation. The authors appropriately note the limitations of self-reported exercise habits which is a common limitation in many such studies. Overall, the study is well written but I have a few comments that should be addressed. * Line 65: "piled up" is more of a vernacular phrase and should be deleted or reworded * Line 65: "direct clinical consequences are less likely offered" is vague and I am unclear as to what you intended to state with this phrase * Line 66 "… limited… " is vague and unclear. There are many exercise recommendations for patients with atrial fibrillation. * Lines 176-177:".., but with a trend towards increased risks…" I am not proponent of using and stating there were non-significant trends especially in the context of exercise at the end range such as your >1500 MET-min/week group. This is non-significant and should be left as such. In reviewing your eTable 2, all-cause death in this group in model 1 and model 2 still have HR <1 and better than no/minimal exercise. The curve appears to flatten saying that it trends toward increasing is a bold and potentially incorrect statement. * Line 187: Again there is mention of "trend" * Lines 232-233: I would caution and recommend not stating "appeared to be J-shape". The curve certainly flattens and this has been shown in previous studies. However, stating a true J-curve exists is difficult given the generally lower events in the end range group with widening of the error bars. * Lines 235-237: "Similar mechanisms would explain … in patients with AF." See above discussion/comments. * Lines 244-245: Again there is a mention of trend (see above) ----------------------------------------------------------- Reviewer #5: Thanks for giving me the opportunity to review this interesting manuscript titled 'Exercise habits and the risk of stroke, heart failure, and mortality in patients with incident atrial fibrillation: a nationwide population-based cohort study'. 1.Severe selection bias is a big concern. From a big pool of 523174 patients, they end up to a set of 66692 patients for analysis. It is not known how different are these patients from those patients in the original pool. If the authors can provide some comparison, it will be helpful. 2.The commonest cause of death in patients with AF are non-cardiac death, heart failure, and stroke. In this paper, exercise status is not significantly associated with ischemic stroke, and marginally significant associated with heart failure, but very significantly associated with all-cause death. Please explain whether this is due to unadjusted confounders, which means the changes of exercise status is due to other co-morbidities, or frailty of the patients. 3.The intensity and frequency of exercise is self-reported. Whether the questionnaire is validated and how accuracy is the data collected? 4.All the definition of confounders and outcomes are not clearly defined. It would be good to clarify them. 5.Figure 1 and figure 1 should be combined, as the information of efigure 1 is also important. 6.efigure 1, the flow chart, all the excluded patients and reason for exclusion should be listed. I guess 523174-19148 patients did not get consecutive health examination? ----------------------------------------------------------- Any attachments provided with reviews can be seen via the following link: [LINK] 21 Oct 2020 Submitted filename: [Exercise_CVD_AF] Response to review_final.docx Click here for additional data file. 5 Feb 2021 Dear Dr. Choi, Thank you very much for submitting your manuscript "Exercise habits and the risk of stroke, heart failure, and mortality in patients with incident atrial fibrillation: a nationwide population-based cohort study" (PMEDICINE-D-20-03103R2) for consideration at PLOS Medicine. Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below: [LINK] In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers. Thank you for addressing the previously stated issues. Before we proceed, please address the following issues: • Please mention country in title and abstract. • PLOS defines the “minimal data set” to consist of the data set used to reach the conclusions drawn in the manuscript with related metadata and methods, and any additional data required to replicate the reported study findings in their entirety. Authors do not need to submit their entire data set, or the raw data collected during an investigation. Please submit the following data: 1) The values behind the means, standard deviations and other measures reported; 2) The values used to build graphs; and 3) The points extracted from images for analysis. • Please bullet and trim the author summary - preferably use the active voice in 1-2 points ("We investigated ..." etc). • Abstract Methods and Findings: 1. Please include the study design, population (summary of demographic details for participants), and setting. 2. Please quantify the main results (please present both 95% CIs and p values). 3. Please include the important variables that are adjusted for in the analyses. 4. Please include the actual amounts and/or absolute risk(s) of relevant outcomes (including NNT or NNH where appropriate), not correlation coefficients 5. Final sentence of the "Methods and findings" subsection of abstract should begin "Study limitations include ..." or similar. • In the Abstract conclusion, please mention specific implications for policy or clinical care substantiated by the results. Please revise Conclusion, first line (line 50) and instead write, “Initiation or continuing regular exercise after AF was associated with a lower risk of HF and mortality among patients.” • In Methods and Results please update the following: 1. Early in methods section of main text, please state that there was no protocol or prespecified analysis plan 2. Please provide the actual numbers of events for the outcomes, not just summary statistics or ORs. 3. Please provide p values in addition to 95% Cis 4. When a p value is given, please specify the statistical test used to determine it. 5. Changes in the analysis made in response to peer review comments should be identified as such in the Methods section of the paper, with rationale. 6. Please ensure p<0.0001 is revised to p<0.001 • The following issues have the potential to introduce bias in your study. Please address the following in your analyses or explaining how you addressed potential bias: 1. Reporting bias is a threat to internal validity. Please discuss the potential for systematic differences in responses between those who experienced and did not experience the outcomes of interest. 2. In your statistical analyses, please use hierarchical/ multilevel models given that nationwide data is likely clustered at various regional levels. The potential clustering of data (e.g., among patients from the same locality or hospital) would result in spurious effect estimates and standard errors. 3. Please employ more robust methods to address unobserved confounding e.g., use propensity score matching or instrumental variables. 4. Please clearly describe how you selected adjustment variables in model 2; most of the included variables are potentially correlated. Directed acyclic graphs are instrumental in making models as parsimonious as possible. 5. Please state whether you did post-hoc corrections for multiple comparisons in your analyses • Under the “Subgroup and additional analysis” section, 2nd paragraph (line 249), the term "trend" is used to refer to a nonsignificant P value. The term trend should be used only when the test for trend has been conducted. Please revise accordingly. • Please replace "subject" with participant, patient, individual, or person. • Discussion: Line 294: Please change "statistically insignificant" to "not statistically significant" or similar. • Discussion: Line 355: Please change "western populations" to "populations of high-income countries" or similar. • Table 2, eTable 4,5,6,7,8, and 9: Please provide p values in addition to the 95% CIs reported. • Please indicate in the figure caption the meaning of the bars and whiskers in Figure 2 & 3 • In the Reference section, please delete the following statement from reference 42 (line) and paste it under the appropriate COI section: “www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.” In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript. In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org. We expect to receive your revised manuscript by Feb 26 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns. ***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.*** We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests. Please use the following link to submit the revised manuscript: https://www.editorialmanager.com/pmedicine/ Your article can be found in the "Submissions Needing Revision" folder. To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods. Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it. We look forward to receiving your revised manuscript. Sincerely, Dr Raffaella Bosurgi, executive editor Medicine PLOS Medicine plosmedicine.org ----------------------------------------------------------- Requests from the editors: Comments from the reviewers: Reviewer #2: Thank you for addressing my previous comments. I do not have any further comments. Best of luck. Reviewer #3: All my comments and concerns are taken into account in this manuscript that I think is very much improved. I think the consistency across y-axis A, B, C in Fig. 2 very much helped the interpretation and would consider to do the same with Fig. 3 although I leave the decision to the authors or editors. Reviewer #5: My comments are all well addressed. I have no further comments. Any attachments provided with reviews can be seen via the following link: [LINK] 23 Mar 2021 Submitted filename: [Exercise_CVD_AF] Response to review_0321.docx Click here for additional data file. 22 Apr 2021 Dear Dr. Choi, Thank you very much for re-submitting your manuscript "Exercise habits and the risk of stroke, heart failure, and mortality in patients with incident atrial fibrillation: a Korean nationwide population-based cohort study" (PMEDICINE-D-20-03103R3) for review by PLOS Medicine. I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal. The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript: [LINK] ***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.*** In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns. We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it. To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org. If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org. We look forward to receiving the revised manuscript by Apr 29 2021 11:59PM. Sincerely, Beryne Odeny, Associate Editor PLOS Medicine plosmedicine.org ------------------------------------------------------------ Requests from Editors: • Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative. It should begin with main concept if possible. "risk" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design in the subtitle (ie, after a colon). For example, “Exercise habits, stroke, heart failure, and mortality in patients with incident atrial Fibrillation in Korea: a population-based retrospective cohort study” • Please trim your author summary bullet points • Please remove NNT values from your results as recommended by reviewer #5. These are not applicable • The Data Availability Statement (DAS) requires revision. For each data source used in your study: a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number). b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data. c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author). • In your methods, please indicate how you accounted for multiple comparisons in your analyses. Did you use the Bonferroni correction or similar? • Please avoid using causal language such as “risk of”, “impact of”, “effect of” etc, and use “associated with” instead, in keeping with the study design of this work. Comments from Reviewers: Reviewer #2: Dear authors, thank you for addressing the comments from previous reviews. I do not have additional comments at this stage. Reviewer #5: This R3 manuscript is generally well written. The authors endeavored to address the reviewer's comments and they should be commended for having done a good job. However, I still have 2 major concerns with the manuscript: 1. This is an observational study, which only association can be derived. NNT suggests a causal effect. The authors stated they added the NNTs as the peer reviewer required, but I strongly discourage adding such an interpretation to mislead the readers. 2. The authors concluded "Regular moderate exercise 170-240 min/week was associated with the maximal benefit on cardiovascular outcomes in patients who initiate exercise after AF diagnosis". I do not agree such an interpretation. from an eyeball impression, the confidence interval of HR for different exercise groups are largely overlapped. Interaction has not ever been tested. It is also not believable 170-240min/week moderate exercise is the best recommendation to AF patients. Moreover, as the data of exercise is self-reported and only roughly classified into different categories, it is not reasonable to evaluate these effect quantitatively. Any attachments provided with reviews can be seen via the following link: [LINK] 29 Apr 2021 Submitted filename: [Exercise_CVD_AF] Response to review_0429.docx Click here for additional data file. 18 May 2021 Dear Dr Choi, On behalf of my colleagues and the Academic Editor, Kazem Rahimi, I am pleased to inform you that we have agreed to publish your manuscript "Association between exercise habits and stroke, heart failure, and mortality in Korean patients with incident atrial fibrillation: a nationwide population-based cohort study" (PMEDICINE-D-20-03103R4) in PLOS Medicine. Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes. In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. PRESS We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf. We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/. To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. Sincerely, Beryne Odeny Associate Editor PLOS Medicine
  59 in total

1.  Effects of short-term exercise training on symptoms and quality of life in patients with chronic atrial fibrillation.

Authors:  Finn Hegbom; Knut Stavem; Svein Sire; Magnus Heldal; Otto M Orning; Knut Gjesdal
Journal:  Int J Cardiol       Date:  2006-06-30       Impact factor: 4.164

2.  Physical Activity, Obesity, and Subclinical Myocardial Damage.

Authors:  Roberta Florido; Chiadi E Ndumele; Lucia Kwak; Yuanjie Pang; Kunihiro Matsushita; Jennifer A Schrack; Mariana Lazo; Vijay Nambi; Roger S Blumenthal; Aaron R Folsom; Josef Coresh; Christie M Ballantyne; Elizabeth Selvin
Journal:  JACC Heart Fail       Date:  2017-05       Impact factor: 12.035

3.  The ABC pathway: an integrated approach to improve AF management.

Authors:  Gregory Y H Lip
Journal:  Nat Rev Cardiol       Date:  2017-09-29       Impact factor: 32.419

4.  C-reactive protein elevation in patients with atrial arrhythmias: inflammatory mechanisms and persistence of atrial fibrillation.

Authors:  M K Chung; D O Martin; D Sprecher; O Wazni; A Kanderian; C A Carnes; J A Bauer; P J Tchou; M J Niebauer; A Natale; D R Van Wagoner
Journal:  Circulation       Date:  2001-12-11       Impact factor: 29.690

5.  Causes of death in Japanese patients with atrial fibrillation: The Fushimi Atrial Fibrillation Registry.

Authors:  Yoshimori An; Hisashi Ogawa; Yugo Yamashita; Mitsuru Ishii; Moritake Iguchi; Nobutoyo Masunaga; Masahiro Esato; Hikari Tsuji; Hiromichi Wada; Koji Hasegawa; Mitsuru Abe; Gregory Y H Lip; Masaharu Akao
Journal:  Eur Heart J Qual Care Clin Outcomes       Date:  2019-01-01

6.  The interplay between atrial fibrillation and heart failure on long-term mortality and length of stay: Insights from the, United Kingdom ACALM registry.

Authors:  Oliver J Ziff; Paul R Carter; John McGowan; Hardeep Uppal; Suresh Chandran; Stuart Russell; Kevin R Bainey; Rahul Potluri
Journal:  Int J Cardiol       Date:  2018-02-01       Impact factor: 4.164

Review 7.  Lifestyle modifications for treatment of atrial fibrillation.

Authors:  Melissa E Middeldorp; Jonathan Ariyaratnam; Dennis Lau; Prashanthan Sanders
Journal:  Heart       Date:  2019-11-11       Impact factor: 5.994

8.  Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea.

Authors:  Sang Cheol Seong; Yeon-Yong Kim; Young-Ho Khang; Jong Heon Park; Hee-Jin Kang; Heeyoung Lee; Cheol-Ho Do; Jong-Sun Song; Ji Hyon Bang; Seongjun Ha; Eun-Joo Lee; Soon Ae Shin
Journal:  Int J Epidemiol       Date:  2017-06-01       Impact factor: 7.196

9.  All-cause mortality in 272,186 patients hospitalized with incident atrial fibrillation 1995-2008: a Swedish nationwide long-term case-control study.

Authors:  Tommy Andersson; Anders Magnuson; Ing-Liss Bryngelsson; Ole Frøbert; Karin M Henriksson; Nils Edvardsson; Dritan Poçi
Journal:  Eur Heart J       Date:  2013-01-14       Impact factor: 29.983

10.  Mortality reduction with physical activity in patients with and without cardiovascular disease.

Authors:  Sang-Woo Jeong; Sun-Hwa Kim; Si-Hyuck Kang; Hee-Jun Kim; Chang-Hwan Yoon; Tae-Jin Youn; In-Ho Chae
Journal:  Eur Heart J       Date:  2019-11-14       Impact factor: 29.983
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  8 in total

1.  Effects of exercise initiation and smoking cessation after new-onset type 2 diabetes mellitus on risk of mortality and cardiovascular outcomes.

Authors:  Mee Kyoung Kim; Kyungdo Han; Bongsung Kim; Jinyoung Kim; Hyuk-Sang Kwon
Journal:  Sci Rep       Date:  2022-06-23       Impact factor: 4.996

2.  Application of Inverse-Probability-of-Treatment Weighting to Estimate the Effect of Daytime Sleepiness in Patients with Obstructive Sleep Apnea.

Authors:  François Bettega; Clémence Leyrat; Renaud Tamisier; Monique Mendelson; Yves Grillet; Marc Sapène; Maria R Bonsignore; Jean Louis Pépin; Michael W Kattan; Sébastien Bailly
Journal:  Ann Am Thorac Soc       Date:  2022-09

3.  Association between exercise and risk of cardiovascular diseases in patients with non-cystic fibrosis bronchiectasis.

Authors:  Hayoung Choi; Sang Hyuk Kim; Kyungdo Han; Tai Sun Park; Dong Won Park; Ji-Yong Moon; Sang-Heon Kim; Tae-Hyung Kim; Jang Won Sohn; Ho Joo Yoon; Hyun Lee
Journal:  Respir Res       Date:  2022-10-18

Review 4.  Atrial Fibrillation Specific Exercise Rehabilitation: Are We There Yet?

Authors:  Benjamin J R Buckley; Signe S Risom; Maxime Boidin; Gregory Y H Lip; Dick H J Thijssen
Journal:  J Pers Med       Date:  2022-04-10

5.  2021 Focused Update Consensus Guidelines of the Asia Pacific Heart Rhythm Society on Stroke Prevention in Atrial Fibrillation: Executive Summary.

Authors:  Tze-Fan Chao; Boyoung Joung; Yoshihide Takahashi; Toon Wei Lim; Eue-Keun Choi; Yi-Hsin Chan; Yutao Guo; Charn Sriratanasathavorn; Seil Oh; Ken Okumura; Gregory Y H Lip
Journal:  Thromb Haemost       Date:  2021-11-13       Impact factor: 5.249

6.  Association of Physical Activity Level With Risk of Dementia in a Nationwide Cohort in Korea.

Authors:  Minjae Yoon; Pil-Sung Yang; Moo-Nyun Jin; Hee Tae Yu; Tae-Hoon Kim; Eunsun Jang; Jae-Sun Uhm; Hui-Nam Pak; Moon-Hyoung Lee; Boyoung Joung
Journal:  JAMA Netw Open       Date:  2021-12-01

Review 7.  Current Trends of Big Data Research Using the Korean National Health Information Database.

Authors:  Mee Kyoung Kim; Kyungdo Han; Seung-Hwan Lee
Journal:  Diabetes Metab J       Date:  2022-07-27       Impact factor: 5.893

8.  Low Risk of Dementia in Patients With Newly Diagnosed Atrial Fibrillation and a Clustering of Healthy Lifestyle Behaviors: A Nationwide Population-Based Cohort Study.

Authors:  Sang-Hyeon Park; So-Ryoung Lee; Eue-Keun Choi; HuiJin Lee; Jaewook Chung; JungMin Choi; Minju Han; Hyo-Jeong Ahn; Soonil Kwon; Seung-Woo Lee; Kyung-Do Han; Seil Oh; Gregory Y H Lip
Journal:  J Am Heart Assoc       Date:  2022-03-24       Impact factor: 6.106
  8 in total

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