Introduction

Coronary heart disease (CHD) causes one in every 7 deaths in the United States and is the leading cause of death worldwide.^1,2 People with a history of CHD are at a greater risk of having recurrent non-fatal or fatal myocardial infarction (MI). Between 11% and 45% of people who have had a MI will either have a recurrent event or die of CHD within 5 years, with higher percentages in females, the elderly and African-Americans.¹ Secondary prevention of CHD aims to reduce the risk of recurring cardiovascular events in patients with established CHD. Secondary prevention strategies, such as cardiac rehabilitation programs, are emphasized in national and international guidelines as a Class I recommendation, and include management of modifiable risk factors through lifestyle modification and medication adherence. Traditional cardiac rehabilitation programs have been shown to reduce cardiovascular mortality by 26%,³ but are underutilized, with less than 30% of eligible patients participating in these programs.⁴ Some of the barriers to participation in such programs include competing work and personal commitments to attend the programs in person, long distance to travel, transport limitations, lack of parking, financial cost and lack of reimbursement.⁵ In the last two decades, with the growing availability of mobile technology devices, mobile health, or mHealth, has emerged as an alternative approach to deliver secondary prevention strategies to a large number of people at a low cost.⁶

mHealth in Secondary Prevention of CHD

mHealth is defined as the use of mobile and wireless technologies, including mobile phones, smartphones, tablets, wearable devices and smartwatches to support the achievement of health objectives.⁷ mHealth interventions can be used for a number of health needs, including patient education and monitoring, behavioral change support, and communication between patients and health professionals.⁸ This article reviews studies that have used mHealth interventions to improve the cardiovascular risk factor profiles of patients with CHD, through both lifestyle modification and medication adherence.

Lifestyle Modification

Lifestyle modification involves adherence to healthy behaviors, including smoking cessation, regular physical activity and a healthy diet. Some studies have investigated interventions using text messages to improve single or multiple healthy behaviors in patients with CHD and have shown promising results. The caveat, however, was that most studies were small, with sample sizes ranging from 64 to 171 patients, and had a short follow-up timeframe (3-6 months). Three studies evaluated the use of text messages, combined with access to a website or phone calls,^9-11 to improve physical activity and found significant improvements in the patients' self-reported physical activity, using the International Physical Activity Questionnaire (IPAQ). Maddison et al., however, did not find a significant improvement in peak oxygen uptake (PVO₂), a more objective measure of physical capacity during an exercise test.¹⁰ Another study found that using text messages and phone calls to encourage smoking cessation significantly improved the patients' quitting rates, demonstrated by reduced levels of cotinine in the patients' urine samples (smoking rate: intervention 53.3% vs. control 100%; p value not provided).¹² Three further studies used text messages to improve multiple healthy behaviors simultaneously. The TEXT ME (Tobacco, Exercise and Diet Messages) study by Chow et al. was the biggest study with 710 patients who were randomized to receive either usual care or usual care in addition to four text messages per week to motivate regular physical activity, a healthy diet and smoking cessation.¹³ The authors found a significant improvement in LDL cholesterol (mean difference -5 mg/dL, 95% CI -9 to 0, p = 0.04), systolic blood pressure (BP; mean difference -7.6 mmHg, 95% CI -9.8 to -5.4, p < 0.001), body mass index (BMI; mean difference -1.3 kg/m², 95% CI -1.6 to -0.9, p < 0.001), total physical activity (mean difference 345 METS-min/week, 95% CI 195 to 495, p < 0.001) and smoking rates (RR 0.61, 95% CI 0.48 to 0.76, p < 0.001) at 6 months. Another two studies investigated interventions using text-messages combined with access to a website and the use of a pedometer to improve diet, physical activity and smoking cessation. Frederix et al. and Pfaeffli Dale et al. randomized 140 and 123 patients, respectively, to receive either usual care, or the mHealth intervention, in addition to usual care.^14,15 At 6 months, neither study found a significant improvement in systolic BP, BMI or LDL cholesterol; however, Frederix et al. found a significant improvement in exercise capacity, measured by PVO₂ (PVO₂: intervention 24.46 mL/[min*kg], SD 7.57 vs. control 22.15 mL/[min*kg], SD 5.37, p < 0.001) and self-reported physical activity using the IPAQ (p = 0.01).

Another four studies have investigated the role of mobile phones in telemonitoring interventions, where patients were provided devices to regularly measure clinical parameters and a smartphone to transmit the data to researchers. Two of these studies were small feasibility studies, with only six and 25 patients, that concluded that the interventions were feasible.^16,17 Another study by Varnfield et al. randomized 120 patients to receive a traditional cardiac rehabilitation (CR) program or a smartphone-based CR program. As part of the smartphone-based CR, the patients received a smartphone, a pedometer, a BP monitor and a weight scale, and were instructed to enter health information on the smartphone daily, including BP, weight, caloric and nutritional information and smoking patterns. In addition, patients received motivational and educational materials via text messages, and audio and video files that were pre-installed into the smartphone. The authors found that the smartphone-based CR program increased program uptake, adherence and completion rates compared to traditional CR at 6 weeks.¹⁸ However, there were no significant differences in clinical parameters, self-reported dietary patterns and functional capacity, which was evaluated using the 6-minute walk test. The biggest study evaluating the effects of a telemonitoring intervention on cardiovascular risk factors was conducted by Blasco et al. The authors randomized 203 patients to receive either usual care or a telemonitoring intervention in addition to usual care, in which the patients were required to send information on BP daily, weight weekly and glucose and lipid levels monthly via a mobile phone.¹⁹ A cardiologist accessed the information and provided feedback to the patients via text messages. The results showed a significant improvement in the patients' cardiovascular risk profiles with 69.6% of the intervention patients achieving combined treatment goals, including smoking cessation, LDL-cholesterol <100 mg/dL, BP <140/90 mmHg and HbA1c <7%, compared to 50.5% of control patients (RR 1.4, 95% CI 1.1 to 1.7, p = 0.010).

Medication Adherence

mHealth interventions can also be used to improve medication adherence by providing regular reminders to reduce forgetfulness, which is an established, unintentional reason for non-adherence. A recent meta-analysis of 16 trials in adults with chronic disease found that text messaging improved medication adherence (OR 2.11, 95% CI 1.52 – 2.93, p < 0.001).²⁰ Table 1 describes the studies that investigated mHealth interventions to promote medication adherence in a population with CHD, in which five studies used text messages, one used a smartphone app and one study compared these two types of mHealth interventions to phone calls. The findings are encouraging and showed improvements in medication adherence; however, the majority of the studies assessed self-reported adherence, which is subject to bias. In addition, most of these studies were small, with a short-term follow-up.

Another growing area of research is the use of wearable devices and smartwatches as a tool in CHD prevention. One potential use of these novel devices is physical activity monitoring and tracking. In a study by Vogel et al., 25 CHD patients participating in a cardiac rehabilitation program were randomized to use a smart wearable device (Polar Loop) to monitor and track their physical activity or to usual care.^21,22 At 12 weeks, patients in the wearable device group achieved a significantly higher maximum power (p < 0.001) and relative performance (p < 0.001) in a maximum capacity cardiac exercise test. Recent studies are now evaluating smartwatches as a tool to analyze heart rhythm. The Apple Watch has been recently shown to be effective in differentiating atrial fibrillation (AF) from normal sinus rhythm. This distinction was shown to be accurately done by analyzing the rhythm using photoplethysmography or the single lead rhythm strip accessory KardiaBand.^23,24 In addition to being useful tools for AF screening and stroke prevention, new smartwatches, such as the iBeat,²⁵ may be important tools in prevention of fatal acute coronary events in the near future. Furthermore, smartwatches will probably have a role in blood pressure control when wearable devices, such as the Omron Project Zero 2.0 Concept wireless wrist blood pressure monitor,²⁶ are available in the market. However, at this stage, there is still a lack of research evaluating the impact of these new wearable devices in health outcomes.

Overall, there is limited, yet promising, data that mHealth interventions are effective in promoting lifestyle modification and improving medication adherence in patients with CHD. Further evidence from robust, large randomized clinical trials is needed to confirm these positive results and provide evidence of sustained effects. mHealth remains an emerging area of research with at least another 10 trials registered at ClinicalTrials.gov evaluating either text-messaging or smartphone apps for secondary prevention of CHD.

Table 1: Studies Evaluating mHealth Interventions to Improve Medication Adherence in Patients with Coronary Heart Disease

Author, year, country, duration	Number of participants	Interventions	Findings
Fang27 2016 China 6 months	n = 280 Intervention 1 = 95 Intervention 2 = 92 Control = 93	Intervention 1 – Medication reminders and education via SMS Intervention 2 – Medication reminders via SMS + educational materials via a mobile messaging app Control – Monthly phone calls	Patients in the intervention groups 1 and 2 achieved better adherence to lipid-lowering drugs, measured by self-report using the 4-item MMAS (scores not provided, p < 0.001)
Khonsari28 2014 Malaysia 2 months	n = 62 Intervention = 31 Control = 31	Intervention – Daily SMS reminders for medication-taking Control – Usual care	Proportions of high-adherent patients assessed by self-report using the 8-item MMAS: Intervention 64.5% vs Control 12.9% (p < 0.001)
Mertens29 2016 Germany 3 months	n = 24 (cross-over design)	Intervention – Medication Plan app on an Apple iPad Control – Paper dairy	Mean adherence score assessed by self-report using the A14-scale Intervention 53.96 (SD 2.01) vs Control 52.60 (SD 2.49) (p = 0.02)
Pandey30 2017 Canada 12 months	n = 34 Intervention = 17 Control = 17	Intervention – Daily SMS reminders for medication-taking Control – Usual care	Mean PDC assessed by self-report using a logbook: Intervention 94% vs Control 80% (Mean difference 14%, 95% CI 7% – 21%, p < 0.001)
Park31 2014 United States 1 month	n = 90 Intervention 1 = 30 Intervention 2 = 30 Control = 30	Intervention 1 – SMS reminders twice a day + educational SMS 3-times a week Intervention 2 – Educational SMS 3-times a week Control – Usual care	Percentage of doses taken assessed by MEMS for: 1- Anti-platelets – Intervention 1 93.7% vs. Intervention 2 95.8% vs Control 79.1%, (p 0.03), and 2 – Statins – Intervention 1 92.4% vs Intervention 2 90.1% vs. Control 83.3%, (p 0.28)
Pfaeffli Dale14 2015 New Zealand 6 months	n = 123 Intervention = 61 Control = 62	Intervention – Daily SMS for first 12 weeks and 5 SMS per week for last 12 weeks + website access + pedometer Control – Usual care	Mean medication adherence score assessed by self-report using the 8-item MMAS: Intervention 7.3 (SD 0.9) vs. Control 6.8 (SD 1.2) (Mean difference 0.58, 95% CI 0.19 – 0.97, p 0.004)
Quilici32 2013 France 1 month	n = 521 Intervention = 262 Control = 259	Intervention – Daily SMS reminders for medication-taking Control – Usual care	Proportion of patients non-adherent to aspirin, measured by platelet testing: Intervention 5.2% vs Control 11.2% (p = 0.01)

References

1. Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation 2017;135:e146-603.
2. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1151-1210.
3. Anderson L, Oldridge N, Thompson DR, et al. Exercise-based cardiac rehabilitation for coronary heart disease: Cochrane systematic review and meta-analysis. J Am Coll Cardiol 2016;67:1-12.
4. Menezes AR, Lavie CJ, Milani RV, Forman DE, King M, Williams MA. Cardiac rehabilitation in the United States. Prog Cardiovasc Dis 2014;56:522-9.
5. Neubeck L, Freedman SB, Clark AM, Briffa T, Bauman A, Redfern J. Participating in cardiac rehabilitation: a systematic review and meta-synthesis of qualitative data. Eur J Prev Cardiol 2012;19:494-503.
6. Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS One 2017;12:e0170581.
7. mHealth: New horizons for health through mobile technologies: second global survey on eHealth. Switzerland: World Health Organization; 2011. Available at: http://www.who.int/goe/publications/goe_mhealth_web.pdf. Accessed 20 October 2017.
8. Santo K, Chalmers J, Chow CK, et al. m-Health in coronary disease preventive care. J Cardiol Therapy 2015;2:459-64.
9. Antypas K, Wangberg SC. An internet- and mobile-based tailored intervention to enhance maintenance of physical activity after cardiac rehabilitation: short-term results of a randomized controlled trial. J Med Internet Res 2014;16:e77.
10. Maddison R, Pfaeffli L, Whittaker R, et al. A mobile phone intervention increases physical activity in people with cardiovascular disease: results from the HEART randomized controlled trial. Eur J Prev Cardiol 2015;22:701-9.
11. Alsaleh E, Windle R, Blake H. Behavioural intervention to increase physical activity in adults with coronary heart disease in Jordan. BMC Public Health 2016;16:643.
12. Park AH, Lee SJ, Oh SJ. The effects of a smoking cessation programme on health-promoting lifestyles and smoking cessation in smokers who had undergone percutaneous coronary intervention. Int J Nurs Pract 2015;21:107-17.
13. Chow CK, Redfern J, Hillis GS, et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial. JAMA 2015;314:1255-63.
14. Pfaeffli Dale L, Whittaker R, Jiang Y, Stewart R, Rolleston A, Maddison R. Text message and internet support for coronary heart disease self-management: results from the Text4Heart randomized controlled trial. J Med Internet Res 2015;17:e237.
15. Frederix I, Hansen D, Coninx K, et al. Medium-term effectiveness of a comprehensive internet-based and patient-specific telerehabilitation program with text messaging support for cardiac patients: randomized controlled trial. J Med Internet Res 2015;17:e185.
16. Worringham C, Rojek A, Stewart I. Development and feasibility of a smartphone, ECG and GPS based system for remotely monitoring exercise in cardiac rehabilitation. PLoS One 2011;6:e14669.
17. Ammenwerth E, Woess S, Baumgartner C, et al. Evaluation of an integrated telemonitoring surveillance system in patients with coronary heart disease. Methods Inf Med 2015;54:388-97.
18. Varnfield M, Karunanithi M, Lee CK, et al. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial. Heart 2014;100:1770-9.
19. Blasco A, Carmona M, Fernandez-Lozano I, et al. Evaluation of a telemedicine service for the secondary prevention of coronary artery disease. J Cardiopulm Rehabil Prev 2012;32:25-31.
20. Thakkar J, Kurup R, Laba TL, et al. Mobile technology text messaging for medication adherence in chronic disease: a meta-analysis. JAMA Intern Med 2016;176:340-9.
21. Polar Loop Wearable Device. Available from: https://www.polar.com/en/products/lifestyle/loop. Accessed 6 April 2018.
22. Vogel J, Auinger A, Riedl R, Kindermann H, Helfert M, Ocenasek H. Digitally enhanced recovery: investigating the use of digital self-tracking for monitoring leisure time physical activity of cardiovascualr disease (CVD) patietns undergoing cardiac rehabilitation. PLoS One 2017;12:e0186261.
23. Sanchez J, Ballinger B, Olgin J, et al. Detecting atrial fibrillation using a smart watch - the mRhythm study. Heart Rhythm Scientific Sessions; 2017; Chicago. Available at: http://www.abstractsonline.com/pp8/#!/4227/presentation/11303. Accessed 06 April 2018.
24. Bumgarner J, Lambert C, Cantillon D, et al. Assessing the accuracy of an automated atrial fibrillation detection algorithm using novel smartwatch technology among patients presenting for elective cardioversion. American College of Cardiology 67th Annual Scientific Session; 2018; Orlando. Available at: http://www.abstractsonline.com/pp8/#!/4496/presentation/41378. Accessed 6 April 2018.
25. iBeat. Available at: https://www.ibeat.com/. Accessed 6 April 2018.
26. Omron Project Zero 2.0 Concept - Wireless Wrist Blood Pressure Monitor. Available at: https://omronhealthcare.com/generation-zero-old/. Accessed 6 April 2018.
27. Fang R, Li X. Electronic messaging support service programs improve adherence to lipid-lowering therapy among outpatients with coronary artery disease: an exploratory randomised control study. J Clin Nurs 2016;25:664-71.
28. Kohnsari S, Subramanian P, Chinna K, Latif LA, Ling LW, Gholami O. Effect of a reminder system using an automated short message service on medication adherence following acute coronary syndrome. Eur J Cardiovasc Nurs 2015;14:170-9.
29. Mertens A, Brandl C, Miron-Shatz T, et al. A mobile application improves therapy-adherence rates in elderly patients undergoing rehabilitation: a crossover design study comparing documentation via iPad with paper-based control. Medicine 2016;95:e4446.
30. Pandey A, Krumme A, Patel T, Choudhry N. The impact of text messaging on medication adherence and exercise among postmyocardial infarction patients: randomized controlled pilot trial. JMIR Mhealth Uhealth 2017;5:e110.
31. Park LG, Howie Esquivel J, Chung ML, Dracup K. A text messaging intervention to promote medication adherence for patients with coronary heart disease: a randomized controlled trial. Patient Educ Couns 2014;94:261-8.
32. Quilici J, Fugon L, Beguin S, et al. Effect of motivational mobile phone short message service on aspirin adherence after coronary stenting for acute coronary syndrome. Int J Cardiol 2013;168:568-9.

Keywords: Primary Prevention, Secondary Prevention, Text Messaging, Social Media, Cotinine, Tobacco Use, Body Mass Index, Cholesterol, LDL, Medication Adherence, Cardiac Rehabilitation, Glycated Hemoglobin A, Smoking Cessation, Risk Factors, Feasibility Studies, Blood Pressure, Glucose, Follow-Up Studies, Actigraphy, Cardiovascular Diseases, Cell Phone, Health Behavior, Telemedicine, Coronary Disease, Myocardial Infarction, Exercise, Chronic Disease

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