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Geriatr Gerontol Int 2014
ORIGINAL ARTICLE: EPIDEMIOLOGY, CLINICAL PRACTICE AND HEALTH
Association between the physical activity and heart rate corrected-QT interval in older adults Ryoma Michishita,1 Chika Fukae,1 Rikako Mihara,1 Masahiro Ikenaga,1 Kazuhiro Morimura,1 Noriko Takeda,2,3 Yosuke Yamada,4,5 Yasuki Higaki,1,5 Hiroaki Tanaka,1,5 Akira Kiyonaga1,5 and The Nakagawa Study Group 1
Laboratory of Exercise Physiology, Faculty of Health and Sports Science, Fukuoka University, 5Fukuoka University Institute for Physical Activity, Fukuoka, 2Japan Society for the Promotion of Science, 4Department of Nutritional Science, National Institute of Health and Nutrition, Tokyo, and 3Faculty of Sport Sciences, Waseda University, Saitama, Japan
Aim: Increased physical activity can reduce the incidence of cardiovascular disease and the mortality rate. In contrast, a prolonged heart rate corrected-QT (QTc) interval is associated with an increased risk of arrhythmias, sudden cardiac death and coronary artery disease. The present cross-sectional study was designed to clarify the association between the physical activity level and the QTc interval in older adults. Methods: The participants included 586 older adults (267 men and 319 women, age 71.2 ± 4.7 years) without a history of cardiovascular disease, who were taking cardioactive drugs. Electrocardiography was recorded with a standard resting 12-lead electrocardiograph, while the QTc interval was calculated according to Hodges’ formula. The physical activity level was assessed using a triaxial accelerometer. The participants were divided into four categories, which were defined equally quartile distributions of the QTc interval. Results: After adjusting for age, body mass index, waist circumference and the number of steps, the time spent in inactivity was higher and the time spent in light physical activity was significantly lower in the longest QTc interval group than in the shortest QTc interval group in both sexes (P < 0.05, respectively). However, there were no significant differences in the time spent in moderate and vigorous physical activities among the four groups in either sex. Conclusions: These results suggest that a decreased physical activity level, especially inactivity and light intensity physical activity, were associated with QTc interval in older adults. Geriatr Gerontol Int 2014; ••: ••–••. Keywords: older adults, inactivity time, light intensity physical activity, QTc interval.
Introduction It is well known that a decreased physical activity and aerobic capacity are associated with a significantly higher incidence of coronary artery disease (CAD).1–3 Conversely, habitual exercise can decrease the incidence of CAD.4 In contrast, a prolonged heart rate corrected-QT (QTc) interval, as measured with a standard electrocardiograph (ECG), is associated with an
Accepted for publication 6 July 2014. Correspondence: Mr Ryoma Michishita PhD, Laboratory of Exercise Physiology, Faculty of Health and Sports Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan. Email: [email protected]
© 2014 Japan Geriatrics Society
increased risk of arrhythmias, sudden cardiac death, CAD, left ventricular hypertrophy (LVH) and cardiac autonomic nervous system dysfunction.5–9 The QTc interval is longer in women than in men, and is influenced by age, sex hormones and electrolyte imbalance.6,10,11 Our previous cross-sectional study showed that a prolonged QTc interval correlated with a lower aerobic capacity (maximal oxygen uptake) and physical performance (muscle strength, balance and walking abilities) in postmenopausal overweight women and older adults.12,13 However, the association between physical activity and the QTc interval is still unknown, despite the observation that physical inactivity influences the development of CAD and arrhythmias as a result of reduced muscle strength and aerobic capacity.1,2,14 doi: 10.1111/ggi.12365
|
1
R Michishita et al.
We hypothesized that a reduction of physical activity might be a sensitive factor predicting a prolonged QTc interval. The present study was designed to clarify the association between the physical activity levels and the QTc interval in older adults. In this study, we focused on older adults, because increases in the sympathetic nervous system activity, heart rate and blood pressure during exercise are all associated with aging.15
Methods Participants The present study enrolled 586 independent older adults (267 men, 319 women, age 71.2 ± 4.7 years, body mass index [BMI] 23.1 ± 2.9 kg/m2, waist circumference 86.3 ± 8.8 cm, resting systolic/diastolic blood pressure 139.2 ± 16.9/85.0 ± 9.0 mmHg, resting heart rate 63.2 ± 10.1 beats/min, R-R interval 959.1 ± 161.5 msec, QT interval 411.2 ± 31.6 msec, QTc interval 416.3 ± 21.0 msec) who participated in our Sarcopenia and Dementia Prevention Program (The Nakagawa Study). Those taking cardioactive drugs, such as β-blockers and ∂-blockers, or patients with a history of cerebrovascular disease, CAD, LVH, cardiac valve disease, receiving hormone replacement therapy, signs of dementia or diabetic complication (diabetic neuropathy, diabetic nephropathy and diabetic retinopathy) or an electrolyte disturbance, bundle branch block, intraventricular conduction disturbance, abnormal Q wave and abnormal ST-T waves, were excluded from the study. All patients gave informed consent for participation after agreeing with the purpose, methods and significance of the study. The present study was approved by the ethics committee of Fukuoka University (No. 11-04-01).
Resting ECG, blood pressure and anthropometric measurements An ECG was recorded with a standard resting 12-lead ECG (FCP-7401; Fukuda Denshi, Tokyo, Japan) at a paper speed of 25 mm/s after more than 2 min of rest. The QTc interval was calculated according to Hodges’ formula16 (QTc interval = QT interval + 1.75 × [heart rate − 60]). In this ECG, the QT interval measurement was 4 msec of the sampling interval, and was evaluated from the average wave of both extremity and chest leads using the differential threshold methods. Blood pressure was measured in the right arm, with the participant sitting in a chair, after more than 5 min of rest, and was expressed as an average of duplicate measurements. Height and body weight were measured, while BMI was calculated as the ratio of the bodyweight (kg) to the height squared (m2). The waist circumference was measured at the level of the umbilicus. 2
|
Physical activity level measurement The physical activity level was assessed using a triaxial accelerometer (Actimarker EW4800; Panasonic Electric Works, Osaka, Japan), which has been shown to be a valid method for determining the total energy expenditure or energy expenditure associated with doubly labeled water.17 An accelerometer was worn by each participant on a belt at the waist level for 10 days, except when sleeping or bathing. After the measurements were taken, the device was retrieved and the data were downloaded to a personal computer. To minimize any potential influence of wearing the device on the physical activity level and to assess the typical physical activity levels, data from the first and last days were discarded. In addition, only the data from at least 7 days on which the accelerometer was worn for at least days with ≥300 step counts and ≥10 h of over 2 metabolic equivalents (METs) per day were used for the analysis. The acceleration in the anterior-posterior (x), mediolateral (y) and vertical (z) axes were calculated using a sensor with a sample rate of 20 Hz over a range from 0 to 2 × g. The apparatus stores the standard deviation of the vector norm of the composite acceleration (Km) in three dimensions each minute as follows:
Km =
2 2 2 ⎛ n ⎞ ⎛ n ⎞ ⎫⎪⎤ 1 ⎡⎢⎛ n 2 n 2 n 2 ⎞ 1 ⎧⎪⎛ n ⎞ ∑ x + ∑ y + ∑ z − ⎨ ∑ x + ⎜⎝ ∑ y⎟⎠ + ⎜⎝ ∑ z⎟⎠ ⎬⎥⎥ n − 1 ⎢⎜⎝ k =1 k k =1 k k =1 k ⎟⎠ n ⎪⎜⎝ k =1 ⎟⎠ ⎪⎭⎦ k =1 k =1 ⎩ ⎣
where n is the number of data for 1 min, and ∑ x , ∑ y and ∑ z are the sums of the accelerations in each axis for 1 min (n = 1200). This accelerometer can evaluate the daily number of steps and time spent in physical activity. A previous validation study noted that the Km was strongly and significantly correlated (r2 = 0.86) with the oxygen uptake while carrying out seven levels of walking to running speed and seven types of daily housework, such as carrying out self-care while standing, changing clothes, cooking, simulating eating supper, washing dishes, doing laundry and using a vacuum cleaner.18 In this accelerometer, the METs intensity levels of physical activity were calculated by a simple linear regression of Km. Based on the frequency and magnitude of accelerations, the total time spent in inactivity (, Q3 >, Q2 >, Q1 in both older men and women (Q4 > Q3 > Q2 > Q1, P < 0.05, respectively). In the Q4 group, a prolonged QTc interval (men >440 msec and women >460 msec) was observed 33 of 67 participants (49.3%) and 13 of 80 participants (16.3%). The age was also significantly higher in the same order in men. The QT interval and R-R interval were significantly shorter and heart rate was higher in the order of Q4 >, Q3 >, Q2 >, Q1 in both older men and women (Q4 > Q3 > Q2 > Q1, P < 0.05, respectively). There were no significant differences in cigarette smoking habit, BMI, waist circumference, and systolic and diastolic blood pressure among the four groups in both older men and women. In the simple regression analysis, the QTc interval was positively correlated with age in men (r = 0.201, P < 0.01). However, there was no significant relationship between the QTc interval and age in women (r = 0.034, P = 0.545; Fig. 1). The time spent in inactivity was higher, and the number of steps and time spent in light physical activity were significantly lower in the longest QTc interval group (Q4) than in the shortest QTc interval group (Q1) in both sexes (P < 0.05, respectively). There were no significant differences in the moderate and vigorous physical activity levels among the four groups in either men or women. Figures 2 and 3 show the differences in the time spent in inactive, light, moderate and vigorous intensity physical activities for the four QTc interval quartiles after adjusting for age, BMI, waist circumference, and the number of steps in older men and women. In this ANCOVA, the independent variable was four levels of QTc interval (Q1 to Q4), and the dependent variables were the time spent in inactive, light, moderate and vigorous intensity physical activities. The age, BMI, waist circumference and number of steps were entered for adjusted factors, because age, BMI and waist circumference potentially influence both QTc interval and physical activity levels. After adjusting for age, BMI, waist circumference and the number of steps, the time
All (n = 267)
Results
Table 1 Differences in the participants’ characteristics and physical activity levels for the four corrected-QT intervals in older men
interval (Q1 to Q4). The intermultiple group relationships were determined using a one-way repeated measures analysis of variance (ANOVA) and the Tukey– Kramer method. The inter-multiple group relationships after adjusting for age, BMI, waist circumference and the number of steps were determined using an analysis of covariance (ANCOVA). Pearson’s simple regression analysis was carried out in order to determine the associations among continuous variables. A P-value
Geriatr Gerontol Int 2014
ORIGINAL ARTICLE: EPIDEMIOLOGY, CLINICAL PRACTICE AND HEALTH
Association between the physical activity and heart rate corrected-QT interval in older adults Ryoma Michishita,1 Chika Fukae,1 Rikako Mihara,1 Masahiro Ikenaga,1 Kazuhiro Morimura,1 Noriko Takeda,2,3 Yosuke Yamada,4,5 Yasuki Higaki,1,5 Hiroaki Tanaka,1,5 Akira Kiyonaga1,5 and The Nakagawa Study Group 1
Laboratory of Exercise Physiology, Faculty of Health and Sports Science, Fukuoka University, 5Fukuoka University Institute for Physical Activity, Fukuoka, 2Japan Society for the Promotion of Science, 4Department of Nutritional Science, National Institute of Health and Nutrition, Tokyo, and 3Faculty of Sport Sciences, Waseda University, Saitama, Japan
Aim: Increased physical activity can reduce the incidence of cardiovascular disease and the mortality rate. In contrast, a prolonged heart rate corrected-QT (QTc) interval is associated with an increased risk of arrhythmias, sudden cardiac death and coronary artery disease. The present cross-sectional study was designed to clarify the association between the physical activity level and the QTc interval in older adults. Methods: The participants included 586 older adults (267 men and 319 women, age 71.2 ± 4.7 years) without a history of cardiovascular disease, who were taking cardioactive drugs. Electrocardiography was recorded with a standard resting 12-lead electrocardiograph, while the QTc interval was calculated according to Hodges’ formula. The physical activity level was assessed using a triaxial accelerometer. The participants were divided into four categories, which were defined equally quartile distributions of the QTc interval. Results: After adjusting for age, body mass index, waist circumference and the number of steps, the time spent in inactivity was higher and the time spent in light physical activity was significantly lower in the longest QTc interval group than in the shortest QTc interval group in both sexes (P < 0.05, respectively). However, there were no significant differences in the time spent in moderate and vigorous physical activities among the four groups in either sex. Conclusions: These results suggest that a decreased physical activity level, especially inactivity and light intensity physical activity, were associated with QTc interval in older adults. Geriatr Gerontol Int 2014; ••: ••–••. Keywords: older adults, inactivity time, light intensity physical activity, QTc interval.
Introduction It is well known that a decreased physical activity and aerobic capacity are associated with a significantly higher incidence of coronary artery disease (CAD).1–3 Conversely, habitual exercise can decrease the incidence of CAD.4 In contrast, a prolonged heart rate corrected-QT (QTc) interval, as measured with a standard electrocardiograph (ECG), is associated with an
Accepted for publication 6 July 2014. Correspondence: Mr Ryoma Michishita PhD, Laboratory of Exercise Physiology, Faculty of Health and Sports Science, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka 814-0180, Japan. Email: [email protected]
© 2014 Japan Geriatrics Society
increased risk of arrhythmias, sudden cardiac death, CAD, left ventricular hypertrophy (LVH) and cardiac autonomic nervous system dysfunction.5–9 The QTc interval is longer in women than in men, and is influenced by age, sex hormones and electrolyte imbalance.6,10,11 Our previous cross-sectional study showed that a prolonged QTc interval correlated with a lower aerobic capacity (maximal oxygen uptake) and physical performance (muscle strength, balance and walking abilities) in postmenopausal overweight women and older adults.12,13 However, the association between physical activity and the QTc interval is still unknown, despite the observation that physical inactivity influences the development of CAD and arrhythmias as a result of reduced muscle strength and aerobic capacity.1,2,14 doi: 10.1111/ggi.12365
|
1
R Michishita et al.
We hypothesized that a reduction of physical activity might be a sensitive factor predicting a prolonged QTc interval. The present study was designed to clarify the association between the physical activity levels and the QTc interval in older adults. In this study, we focused on older adults, because increases in the sympathetic nervous system activity, heart rate and blood pressure during exercise are all associated with aging.15
Methods Participants The present study enrolled 586 independent older adults (267 men, 319 women, age 71.2 ± 4.7 years, body mass index [BMI] 23.1 ± 2.9 kg/m2, waist circumference 86.3 ± 8.8 cm, resting systolic/diastolic blood pressure 139.2 ± 16.9/85.0 ± 9.0 mmHg, resting heart rate 63.2 ± 10.1 beats/min, R-R interval 959.1 ± 161.5 msec, QT interval 411.2 ± 31.6 msec, QTc interval 416.3 ± 21.0 msec) who participated in our Sarcopenia and Dementia Prevention Program (The Nakagawa Study). Those taking cardioactive drugs, such as β-blockers and ∂-blockers, or patients with a history of cerebrovascular disease, CAD, LVH, cardiac valve disease, receiving hormone replacement therapy, signs of dementia or diabetic complication (diabetic neuropathy, diabetic nephropathy and diabetic retinopathy) or an electrolyte disturbance, bundle branch block, intraventricular conduction disturbance, abnormal Q wave and abnormal ST-T waves, were excluded from the study. All patients gave informed consent for participation after agreeing with the purpose, methods and significance of the study. The present study was approved by the ethics committee of Fukuoka University (No. 11-04-01).
Resting ECG, blood pressure and anthropometric measurements An ECG was recorded with a standard resting 12-lead ECG (FCP-7401; Fukuda Denshi, Tokyo, Japan) at a paper speed of 25 mm/s after more than 2 min of rest. The QTc interval was calculated according to Hodges’ formula16 (QTc interval = QT interval + 1.75 × [heart rate − 60]). In this ECG, the QT interval measurement was 4 msec of the sampling interval, and was evaluated from the average wave of both extremity and chest leads using the differential threshold methods. Blood pressure was measured in the right arm, with the participant sitting in a chair, after more than 5 min of rest, and was expressed as an average of duplicate measurements. Height and body weight were measured, while BMI was calculated as the ratio of the bodyweight (kg) to the height squared (m2). The waist circumference was measured at the level of the umbilicus. 2
|
Physical activity level measurement The physical activity level was assessed using a triaxial accelerometer (Actimarker EW4800; Panasonic Electric Works, Osaka, Japan), which has been shown to be a valid method for determining the total energy expenditure or energy expenditure associated with doubly labeled water.17 An accelerometer was worn by each participant on a belt at the waist level for 10 days, except when sleeping or bathing. After the measurements were taken, the device was retrieved and the data were downloaded to a personal computer. To minimize any potential influence of wearing the device on the physical activity level and to assess the typical physical activity levels, data from the first and last days were discarded. In addition, only the data from at least 7 days on which the accelerometer was worn for at least days with ≥300 step counts and ≥10 h of over 2 metabolic equivalents (METs) per day were used for the analysis. The acceleration in the anterior-posterior (x), mediolateral (y) and vertical (z) axes were calculated using a sensor with a sample rate of 20 Hz over a range from 0 to 2 × g. The apparatus stores the standard deviation of the vector norm of the composite acceleration (Km) in three dimensions each minute as follows:
Km =
2 2 2 ⎛ n ⎞ ⎛ n ⎞ ⎫⎪⎤ 1 ⎡⎢⎛ n 2 n 2 n 2 ⎞ 1 ⎧⎪⎛ n ⎞ ∑ x + ∑ y + ∑ z − ⎨ ∑ x + ⎜⎝ ∑ y⎟⎠ + ⎜⎝ ∑ z⎟⎠ ⎬⎥⎥ n − 1 ⎢⎜⎝ k =1 k k =1 k k =1 k ⎟⎠ n ⎪⎜⎝ k =1 ⎟⎠ ⎪⎭⎦ k =1 k =1 ⎩ ⎣
where n is the number of data for 1 min, and ∑ x , ∑ y and ∑ z are the sums of the accelerations in each axis for 1 min (n = 1200). This accelerometer can evaluate the daily number of steps and time spent in physical activity. A previous validation study noted that the Km was strongly and significantly correlated (r2 = 0.86) with the oxygen uptake while carrying out seven levels of walking to running speed and seven types of daily housework, such as carrying out self-care while standing, changing clothes, cooking, simulating eating supper, washing dishes, doing laundry and using a vacuum cleaner.18 In this accelerometer, the METs intensity levels of physical activity were calculated by a simple linear regression of Km. Based on the frequency and magnitude of accelerations, the total time spent in inactivity (, Q3 >, Q2 >, Q1 in both older men and women (Q4 > Q3 > Q2 > Q1, P < 0.05, respectively). In the Q4 group, a prolonged QTc interval (men >440 msec and women >460 msec) was observed 33 of 67 participants (49.3%) and 13 of 80 participants (16.3%). The age was also significantly higher in the same order in men. The QT interval and R-R interval were significantly shorter and heart rate was higher in the order of Q4 >, Q3 >, Q2 >, Q1 in both older men and women (Q4 > Q3 > Q2 > Q1, P < 0.05, respectively). There were no significant differences in cigarette smoking habit, BMI, waist circumference, and systolic and diastolic blood pressure among the four groups in both older men and women. In the simple regression analysis, the QTc interval was positively correlated with age in men (r = 0.201, P < 0.01). However, there was no significant relationship between the QTc interval and age in women (r = 0.034, P = 0.545; Fig. 1). The time spent in inactivity was higher, and the number of steps and time spent in light physical activity were significantly lower in the longest QTc interval group (Q4) than in the shortest QTc interval group (Q1) in both sexes (P < 0.05, respectively). There were no significant differences in the moderate and vigorous physical activity levels among the four groups in either men or women. Figures 2 and 3 show the differences in the time spent in inactive, light, moderate and vigorous intensity physical activities for the four QTc interval quartiles after adjusting for age, BMI, waist circumference, and the number of steps in older men and women. In this ANCOVA, the independent variable was four levels of QTc interval (Q1 to Q4), and the dependent variables were the time spent in inactive, light, moderate and vigorous intensity physical activities. The age, BMI, waist circumference and number of steps were entered for adjusted factors, because age, BMI and waist circumference potentially influence both QTc interval and physical activity levels. After adjusting for age, BMI, waist circumference and the number of steps, the time
All (n = 267)
Results
Table 1 Differences in the participants’ characteristics and physical activity levels for the four corrected-QT intervals in older men
interval (Q1 to Q4). The intermultiple group relationships were determined using a one-way repeated measures analysis of variance (ANOVA) and the Tukey– Kramer method. The inter-multiple group relationships after adjusting for age, BMI, waist circumference and the number of steps were determined using an analysis of covariance (ANCOVA). Pearson’s simple regression analysis was carried out in order to determine the associations among continuous variables. A P-value
