Accuracy of Heart Rate Measurement Using Smartphones During Treadmill Exercise in Male Patients With Ischemic Heart Disease
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective To evaluate the accuracy of a smartphone application measuring heart rates (HRs), during an exercise and discussed clinical potential of the smartphone application for cardiac rehabilitation exercise programs.Methods Patients with heart disease (14 with myocardial infarction, 2 with angina pectoris) were recruited. Exercise protocol was comprised of a resting stage, Bruce stage II, Bruce stage III, and a recovery stage. To measure HR, subjects held smartphone in their hands and put the tip of their index finger on the built-in camera for 1 minute at each exercise stage such as resting stage, Bruce stage II, Bruce stage III, and recovery stage. The smartphones recorded photoplethysmography signal and HR was calculated every heart beat. HR data obtained from the smartphone during the exercise protocol was compared with the HR data obtained from a Holter electrocardiography monitor (control).Results In each exercise protocol stage (resting stage, Bruce stage II, Bruce stage III, and the recovery stage), the HR averages obtained from a Holter monitor were 76.40±12.73, 113.09±14.52, 115.64±15.15, and 81.53±13.08 bpm, respectively. The simultaneously measured HR averages obtained from a smartphone were 76.41±12.82, 112.38±15.06, 115.83±15.36, and 81.53±13 bpm, respectively. The intraclass correlation coefficient (95% confidence interval) was 1.00 (1.00–1.00), 0.99 (0.98–0.99), 0.94 (0.83–0.98), and 1.00 (0.99–1.00) in resting stage, Bruce stage II, Bruce stage III, and recovery stage, respectively. There was no statistically significant difference between the HRs measured by either device at each stage (p>0.05).Conclusion The accuracy of measured HR from a smartphone was almost overlapped with the measurement from the Holter monitor in resting stage and recovery stage. However, we observed that the measurement error increased as the exercise intensity increased.
Abstract
Objective To evaluate the accuracy of a smartphone application measuring heart rates (HRs), during an exercise and discussed clinical potential of the smartphone application for cardiac rehabilitation exercise programs.Methods Patients with heart disease (14 with myocardial infarction, 2 with angina pectoris) were recruited. Exercise protocol was comprised of a resting stage, Bruce stage II, Bruce stage III, and a recovery stage. To measure HR, subjects held smartphone in their hands and put the tip of their index finger on the built-in camera for 1 minute at each exercise stage such as resting stage, Bruce stage II, Bruce stage III, and recovery stage. The smartphones recorded photoplethysmography signal and HR was calculated every heart beat. HR data obtained from the smartphone during the exercise protocol was compared with the HR data obtained from a Holter electrocardiography monitor (control).Results In each exercise protocol stage (resting stage, Bruce stage II, Bruce stage III, and the recovery stage), the HR averages obtained from a Holter monitor were 76.40±12.73, 113.09±14.52, 115.64±15.15, and 81.53±13.08 bpm, respectively. The simultaneously measured HR averages obtained from a smartphone were 76.41±12.82, 112.38±15.06, 115.83±15.36, and 81.53±13 bpm, respectively. The intraclass correlation coefficient (95% confidence interval) was 1.00 (1.00–1.00), 0.99 (0.98–0.99), 0.94 (0.83–0.98), and 1.00 (0.99–1.00) in resting stage, Bruce stage II, Bruce stage III, and recovery stage, respectively. There was no statistically significant difference between the HRs measured by either device at each stage (p>0.05).Conclusion The accuracy of measured HR from a smartphone was almost overlapped with the measurement from the Holter monitor in resting stage and recovery stage. However, we observed that the measurement error increased as the exercise intensity increased.
참고문헌 (21)
- World Health Organization, "Prevention of cardiovascular disease: guidelines for assessment and management of cardiovascular risk", World Health Organization, ~ (2007)
- "Annual report on the cause of death statistics in 2014" (2015)[URL]
- Smith SC Jr, "AHA", ACCF secondary prevention and risk reduction therapy for patients with coronary and other atherosclerotic vascular disease:2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation, 2011, pp.124, 2458~2473
- Witt BJ, "Cardiac rehabilitation after myocardial infarction in the community", J Am Coll Cardiol, 44, pp.988~996 (2004)
- Suaya JA, "Use of cardiac rehabilitation by Medicare beneficiaries after myocardial infarction or coronary bypass surgery", Circulation, 116, pp.1653~1662 (2007)
- Pfaeffli Dale L, "Acceptability of a mobile health exercise-based cardiac rehabilitation intervention: a randomized trial", J Cardiopulm Rehabil Prev, 35, pp.312~319 (2015)
- Leijdekkers P, "Personal heart monitoring and rehabilitation system using smart phones", Proceedings of 2006 International Conference on Mobile Business, pp.29~ (2006)
- Worringham C, "Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation", PLoS One, 6, pp.e14669~ (2011)
- Strisland F, "ESUMS: a mobile system for continuous home monitoring of rehabilitation patients", Conf Proc IEEE Eng Med Biol Soc 2013, pp.4670~4673 (2013)
- 이영화, "Impact of Home-Based Exercise Training with Wireless Monitoring on Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention", Journal of Korean Medical Science, 28(4), pp.564~568 (2013), 대한의학회
- American College of Sports Medicine, "ACSM’s guidelines for exercise testing and prescription" (2013), Lippincott Williams & Wilkins
- Scully CG, "Physiological parameter monitoring from optical recordings with a mobile phone", IEEE Trans Biomed Eng, 59, pp.303~306 (2012)
- Lee J, "Atrial fibrillation detection using an iPhone 4S", IEEE Trans Biomed Eng, 60, pp.203~206 (2013)
- Lee J, "Time-varying autoregressive modelbased multiple modes particle filtering algorithm for respiratory rate extraction from pulse oximeter", IEEE Trans Biomed Eng, 58, pp.790~794 (2011)
- Lee J, "An autoregressive model-based particle filtering algorithms for extraction of respiratory rates as high as 90 breaths per minute from pulse oximeter", IEEE Trans Biomed Eng, 57, pp.2158~2167 (2010)
- Wallen MP, "Accuracy of Heart Rate Watches: Implications for Weight Management", PLoS One, 11, pp.e0154420~ (2016)
- Crouter SE, "Accuracy of polar S410 heart rate monitor to estimate energy cost of exercise", Med Sci Sports Exerc, 36, pp.1433~1439 (2004)
- Kwan G, "Cardiac rehabilitation 2012: advancing the field through emerging science", Circulation, 125, pp.e369~373 (2012)
- Dunlay SM, "Koepsell E, et al. Barriers to participation in cardiac rehabilitation", Am Heart J, 158, pp.852~859 (2009)
- Jolly K, "Home-based compared with hospital-based cardiac rehabilitation in a multi-ethnic population: costeffectiveness and patient adherence. The Birmingham Rehabilitation Uptake Maximisation Study (BRUM)", Health Technol Assess, 11, pp.1~118 (2007)
- Terbizan DJ, "Validity of seven commercially available heart rate monitors", Meas Phys Educ Exerc Sci, 6, pp.243~247 (2002)
