Curr Treat Options Cardio Med (2017) 19:60 DOI 10.1007/s11936-017-0560-4
Prevention (P Natarajan, Section Editor)
Digital Health Technologies to Promote Lifestyle Change and Adherence Numan Khan, MD Francoise A. Marvel, MD Jane Wang, BS Seth S. Martin, MD, MHS* Address * Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, 600 N Wolfe St, Carnegie 591, Baltimore, MD, 21287, USA Email:
[email protected]
* Springer Science+Business Media New York 2017
This article is part of the Topical Collection on Prevention Keywords Digital health I Mobile health I Health tech I Lifestyle change I Medication adherence I Cardiovascular disease
Opinion statement Cardiovascular disease is the leading cause of morbidity and mortality worldwide with an estimated 17.5 million deaths annually, or 31% of all global deaths, according to the World Health Organization. The majority of these deaths are preventable by addressing lifestyle modification (i.e., smoking cessation, diet, obesity, and physical inactivity) and promoting medication adherence. At present, initiatives to develop cost-effective modalities to support self-management, lifestyle modification, and medication adherence are a leading priority. Digital health has rapidly emerged as technology with the potential to address this gap in cardiovascular disease self-management and transform the way healthcare has been traditionally delivered. However, limited evidence exists about the type of technologies available and how they differ in functionality, effectiveness, and application. We aimed to review the most important and relevant recent studies addressing health technologies to promote lifestyle change and medication adherence including text messaging, applications (“apps”), and wearable devices. The current literature indicates that digital health technologies will likely play a prominent role in future cardiovascular disease management, risk reduction, and delivery of care in both resource-rich and resource-limited settings. However, there is limited large-scale evidence to support adoption of existing interventions. Further clinical research and healthcare policy change are needed to move the promise of new digital health technologies towards reality.
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Introduction Smartphones have revolutionized our society. According to the Pew Research Center, 72% of adults in the USA owned a smartphone in 2015 with the trend over the last few years indicating that this number will continue to grow. The technology is becoming so ubiquitous, in fact, that industry predicts 90% of the world’s population will own a smartphone by 2020 [1, 2]. This includes growth in emerging and developing nations after initially slower adoption compared to more advanced economies. Smartphones have rapidly integrated into many aspects of our lives, from our jobs to our relationships, and are increasingly being recognized as potentially useful tools in managing our health and lifestyle. The convergence of global technology companies such as Apple and Google, pharmaceutical companies, and consumers have driven the development of smartphones in healthcare. The emerging field known as mobile health (mHealth) was born out of mobile phone technology and has given rise to the more recent expansion of wearable devices. We can infer from the economic success of the digital health market that many of our patients are already incorporating mHealth into their lives [3]. In fact, the majority (62%) of smartphone owners in 2015 reported using their phone to retrieve information about a health condition, according to a Pew Research Center study [4]. Though benefits remain unproven, digital health technology has made it possible to connect our patients’
real-world health to the relatively brief snapshots seen in hospitals and clinics. Access to day-to-day patient heart rates, blood pressures, glucose readings, and activity levels, for example, could support a precision medicine model of patient care for clinical decision-making. One could also hypothesize that the ability to reinforce and modify care decisions via the same technology might result in increased adherence, decreased patient safety events, and improved outcomes. Yet, high-quality clinical studies are needed to formally assess whether day-today measurement improve patient care and outcomes. It is becoming increasingly attractive to leverage digital health technology with the fastest growing segment of new smartphone users being 955 years old [5], the advent of wearable devices, and increasing affordability of technology. However, there are still a number of barriers and outstanding questions to meaningful adoption including a lack of integration into existing workflow, concern for privacy and security, data overload, and currently limited clinical evidence. The American Heart Association released a scientific statement in 2015 urging an expansion of research efforts to evaluate the feasibility and efficacy of these technologies [6]. The present review aims to evaluate the current status of digital health technology in regard to what is available, what is known, and what further investigation is necessary with attention to lifestyle and adherence.
Text messaging Basic mobile phones with short-text messaging service (SMS) capability have been widely used long before smartphones existed. The majority of early mHealth studies utilized text messaging as the primary intervention to change behavior. In multiple clinical trials, text messaging has shown efficacy for improving outcomes, such as physical activity, smoking cessation, and medication adherence [7•, 8•, 9•, 10, 11]. Within the mActive randomized clinical trial, we evaluated the ability of an automated and personalized text messaging system to increase physical activity [7•]. The text messages were customized to an individual’s schedule as well as their real-time level of physical activity, among other personal factors such as name and favorite athlete (e.g., “Jon, you are on track to have a VERY ACTIVE day! Outstanding! We might as well call you Lebron James!”). We recruited 48 patients followed at the Johns Hopkins Ciccarone Center for the Prevention of Heart Disease. The patient population included some with cardiovascular
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disease risk factors such as diabetes and some with known coronary heart disease, thus targeting both primary and secondary prevention. Using a sequential randomization design, we evaluated two core interventions: activity tracking and text messaging. Regarding the activity tracking component, we found that physical activity outcomes were similar when participants could access the activity tracker data in real-time (unblinded) as compared with those who could not (blinded to real-time activity data but wore the same tracker). Yet, we witnessed a 25% increase in physical activity (~1 mile per day increase) when we delivered automated personalized text messages to patients. Our results support text messaging as a lifestyle modifier and the need for such motivational drivers in addition to simple self-monitoring with devices. The main limitations of the mActive trial include its short-term duration and limited size and scope. A larger randomized clinical trial with 710 patients conducted in Sydney, Australia called the Tobacco, Exercise, and Diet Messages (TEXT ME) trial also evaluated text messaging in cardiac patients [8•]. The investigators targeted improvement in multiple cardiovascular diseases risk factors such as smoking, hypertension, and low physical activity levels. Patients with known coronary artery disease were randomized to either a group receiving usual care or one receiving usual care and text messages that provided guidance, encouraging reminders, and support to alter lifestyle behaviors. For example, a participant with the risk factor of smoking would be sent text messages that gave advice or motivation to quit. There were improvements in multiple cardiovascular risk factors, including LDL-C, and about 30% in the intervention group versus 10% in the control group achieved guideline recommended risk factor control. Remaining questions include whether the effects can be sustained for longer than 6 months and whether they can be extended to other populations. A well-designed trial utilizing a text messaging intervention for smoking cessation was published in 2016, providing favorable short-term results that were comparable to conventional smoking cessation interventions. The Nicotine Exit (NEXit) trial [9•] implemented a program where the study group received 157 text messages based on components of effective smoking cessation interventions for 12 weeks. The control group received just one text every 2 weeks thanking them for participating in the study. Smoking abstinence with 8-week prolonged abstinence (primary outcome) was reported by 203 (25.9%) in the intervention group and 105 (14.6%) in the control group; and 4-week complete cessation of 161 (20.6%) and 102 (14.2%) participants, respectively, a mean of 3.9 months after the quit date. Future studies will need to assess the long-term durability of the smoking cessation. Additionally, cost analyses in the future would be useful to examine how this approach could supplement traditional smoking cessation interventions to reduce smoking rates. Recent studies have also sought to determine the effect of text messaging on medication adherence in chronic disease. The findings of this line of work are summarized in a recent meta-analysis of 16 such randomized clinical trials [10]. Some limitations of the analyzed trials include short-term data, lack of reporting of clinical outcomes from changes in medication adherence, and use of self-report to assess medication adherence. Cognizant of these limitations, the authors of the meta-analysis reported that text messaging may improve adherence from an assumed 50% baseline to 68%. Interestingly, studies have credited certain text messaging characteristics such as personalization or two-
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Curr Treat Options Cardio Med (2017) 19:60 way communication with impacting study outcomes. However, this metaanalysis found that no particular text messaging characteristic showed significant benefit over another. Personalization and two-way communication are broad strategies that may be more effectively executed in certain cases. Furthermore, future studies will be challenged with the task of delineating which characteristics of text messaging influence acceptability and efficacy according to the patient population and study setting. Another meta-analysis performed by our group examined the effect of mHealth interventions on medication adherence specifically in cardiovascular disease [11]. Ten randomized clinical trials met inclusion criteria, and all but two utilized text messaging alone or in conjunction with other tools. The included trials were heterogeneous in their methods, designs, and outcome measurements, but the primary outcome of medication adherence in all studies was positively impacted by the digital health intervention. The magnitude of effect was not large in all studies, and in one study was similar to a telehealth comparator. Greater harmonization of study methods moving forward will allow for stronger data synthesis in the future. Additionally, limitations of trials including duration, self-report, and lack of hard clinical outcomes, are similar to those stated above and provide opportunities for future investigation.
Apps While text messaging has been accessible for quite some time in varying degrees and forms, the advent of the smartphone widely introduced applications (“apps”) to consumers. The app market has proliferated since and smartphone users utilize apps for innumerable purposes. Apps aiming to promote lifestyle changes and assist users in managing multiple aspects of their health are leading the consumer market. According to research2guidance, a mobile app consultancy company, by 2018 half of the more than 3.4 billion smartphone and tablet users will have downloaded mHealth apps [12]. The United States Food & Drug Administration (FDA) regulates a small subset of apps that may pose potential risk to the consumer. Apps subject to FDA’s regulatory enforcement include those that “are intended to be used as an accessory to a regulated medical device, or transform a mobile platform into a regulated medical device.” This excludes the majority of mobile health applications that are available. Apps that are considered lower risk to consumers, even those that serve as medical devices, are also outside of FDA regulation [13–15]. An example of an mHealth app that is FDA-regulated is AliveCor, which uses algorithms to give users instant feedback on their electrocardiogram tracing. This has largely left the responsibility of determining accuracy, efficacy, and safety up to the app developers, app distributors, and consumers. Some academic investigators have taken on the challenging task of investigating the claims and effects of some of these lifestyle and adherence apps under the realm of mHealth. For example, apps that have been studied critically by medical professionals are ones used for their potential diagnostic capabilities, such as detecting acute coronary syndrome [16], arrhythmias [17], or blood pressure [18•], thus fall outside the realm of this discussion of lifestyle and adherence. One relevant randomized clinical trial recently reported the effects of an interactive smartphone app on drug adherence and lifestyle changes in patients
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who had suffered a myocardial infarction [19•]. It was a multicenter trial conducted in Sweden randomizing 91 patients to the active group and 83 to the control group. The active group received an interactive app featuring modules with four main domains: extended drug adherence e-diary, exercise, weight, and smoking. This group could also register data regarding their blood pressure, low-density lipoprotein cholesterol, and blood glucose levels. The app provided participants with individualized feedback based on their performance with regard to the four domains previously listed, such as medication adherence. The control group received a simplified drug adherence e-diary without any of the interactive features. Ultimately, 162 patients were included in the analysis, and the active group was found to have significantly improved drug adherence (primary outcome) at 6 months. This study provides further evidence for the potential of digital health to improve medication adherence and lifestyle, now with an app-based approach rather than text messages. The MyHeart Counts Cardiovascular Health Study [20] aimed to determine the feasibility of using smartphone apps to obtain health data from users, including physical activity, fitness, and sleep. Participants were asked to log their physical activity, complete health questionnaires, and perform a 6-min walk test. Among 48,968 individuals (predominantly young men) consenting to participate, 81.7% uploaded some amount of physical activity data and answered questions in the health questionnaire, 41.5% completed 4 of 7 days of motion data collection, 9.3% completed all 7 days, and 10.2% completed the 6-min walk test. While the study showed a degree of feasibility of utilizing mobile apps to collect health data, at the same time, it draws attention to the need for ways to include more diverse participants who engage in providing complete data. Another limitation with mHealth apps is the lack of experimental data to validate individual digital health apps. Some healthcare professionals have embraced this, aiming to provide guidance based on a variety of factors such as specific app features or prior user feedback. A review of the most popular health and fitness apps was recently published by Dr. John P. Higgins, assessing user ratings, uniqueness, reliability, ability to grow and innovate, ability to sync with wearable devices, cost, and the number of users logging into the app via Facebook [21]. Dr. Higgins categorized apps by their particular utility such as weight loss, exercise tracking, or sleep, and then further analyzed patient scenarios where certain apps may be more relevant and useful. The ongoing utility of such a guide for physicians and patients is likely limited given the dynamic and ever-changing landscape of digital health. Optimal forums are probably web-based platforms, like iMedicalApps, where physician authors provide the most up-to-date review of medical apps [22]. More patients are using digital health tools to manage their health. Although the clinical impact of these tools is to be determined, physicians have a responsibility to provide guidance for responsible usage.
Wearable devices As mHealth has advanced exponentially with smartphones, so has the development of cooperative paired devices and sensors (e.g., health bands, smart watches, and heart rate tracking devices). This newer realm of digital health
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Curr Treat Options Cardio Med (2017) 19:60 technology should be evaluated for its accuracy and impact on clinical outcomes. Traditional pedometers have been around for many years, but the data for their efficacy alone remains unclear. A systematic review published a decade ago reported that pedometers significantly increased physical activity as measured by step count by 26.9% over baseline [23]. A major limitation of this review, however, was that most of the studies could not reliably differentiate between differences in step count secondary to wear time versus true changes in physical activity. Similar to much of the data in this field, the randomized clinical trials and observational studies included in that review only looked at short-term effects with a mean intervention duration of 18 weeks. A more recent Cochrane review of workplace pedometer interventions published in 2013 determined that there was limited and poor quality data on the use of these devices to determine effectiveness of increasing physical activity and changing health outcomes [24]. Problems with many of the studies that were reviewed included high risk of bias due to lack of blinding, selfreported outcome measurement, incomplete data secondary to attrition, and unpublished protocols increasing likelihood of selective reporting. The recently published Innovative Approaches to Diet, Exercise, and Activity (IDEA) trial conducted at the University of Pittsburgh reported that young adults using an activity tracker compared to the control group actually regained more weight at the 24-month mark [25]. The randomized clinical trial of 471 participants suggests that wearable activity trackers may not provide a supplemental benefit over standard behavioral interventions. It is worth considering, however, that the accuracy of the BodyMedia data was not validated and the wearable technology was not initiated at the onset of the intervention which may have limited adoption and use. In fact, participants only wore the device for 4 h a day and approximately half of the participants reported that wearing the device was uncomfortable. Given these issues, further trials are needed. The Trial of Economic Incentives to Promote Physical Activity (TRIPPA) conducted in Singapore used a more popular wearable activity-tracking device, the Fitbit Zip, in conjunction with incentives [26•]. Employees from 13 organizations were randomized to a control group without the tracker or incentives, a group with the Fitbit Zip device without incentives, a group with the device plus charity incentives, and a group with the device plus cash incentives. Incentives were tied to weekly steps. The primary outcome evaluated was moderate-to-vigorous physical activity (MVPA) in bout min per week as measured by an accelerometer. At 6 months, compared to the control group, the Fitbit Zip group without incentives showed increased physical activity though this was not statistically significant, the device plus charity incentives group significantly increased physical activity, and the device plus cash incentives group significantly increased physical activity. Interestingly, at 12-month follow-up, 6 months after the end of the trial, the Fitbit Zip group without incentives showed significantly higher physical activity compared to control, as did the charity incentives group, and no significant difference remained between the cash incentive and control groups. The benefit seen in groups at 12 months was partially driven by a decrease in activity of the control group. No changes in health outcomes were seen at 6 or 12 months. The decline seen in the group with cash incentives after the incentives were removed suggests that monetary incentives may reduce the development of intrinsic
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motivation leading to less durable behavioral modification [27]. This trial also faced the challenge of contamination, as employees who were randomized to different groups interacted with one another in the workplace. The Stepathlon Cardiovascular Health Study sought to determine the efficacy of using a low-tech pedometer and website to increase physical activity across 64 countries with 69,219 participants. This was not a randomized trial, nor was there a comparator control group; instead, it was conducted as a preand post-event comparison [28•]. The large-scale global study had inherent limitations such as reliance on self-reported data and a large attrition rate (nearly half). Overall, significant improvements were seen in exercise days/week and sitting duration which were both associated with modest but significant reductions in weight. Notably, the analysis of the data does not attribute changes to any particular aspect of the low-tech intervention. While the effects of the intervention and its components are not conclusive, this study provides a framework for scaling mHealth interventions. Higher-end consumer wearables with sleek designs and multiple functions contrast with the simple device used in the Stepathlon study. Such wearable devices often report energy expenditure and heart rates. These features may be adding to the popularity of wearable devices, making the investigation of their validity even more critical. For example, there currently is little evidence to support the accuracy of these devices in detecting energy expenditure. One small study with 19 participants recently sought to investigate this feature of 12 different devices (eight popular among consumers, four validated for research) and found widely differing absolute values among them with significant variation from gold standard measurements [29]. Further investigation regarding accuracy of energy expenditure derived from wearables is required. Heart rate monitoring is another novel key feature of wearable devices and is highly relevant for healthcare providers given the clinical significance of these data. A study recently conducted at the Cleveland Clinic recruited 50 healthy adults and evaluated the accuracy of four wrist-worn monitors: Fitbit Charge HR, Apple Watch, Mio Alpha, and Basis Peak [30•]. These wrist-worn devices were compared to standard electrocardiographic limb leads and a Polar H7 chest strap monitor. Measurements were recorded at rest and during treadmill exercise. Overall, accuracy of the wrist-worn monitors was best at rest and decreased with exercise. Of the four tested devices, the Apple Watch and Mio Fuse both had the best accuracy with concordance correlation coefficients of 0.91. The Fitbit Charge HR and Basis Peak had concordance correlation coefficients of 0.84 and 0.83, respectively. None of the wrist-worn devices were as accurate as the chest strap monitor, which had a concordance correlation coefficient of 0.99. Heart rate monitor accuracy has been cause for concern among consumers; for example, Fitbit is engaged in a lawsuit related to concern about its heart rate data.
Where we stand and future steps Current best evidence, as discussed in detail above and summarized in Table 1, suggests that digital health has potential to improve lifestyle and medication adherence. If such effects are significant enough and durable enough then
SMS & mobile app Primary outcome: physical activity
SMS Primary outcomes: self-reported prolonged abstinence and smoking cessation Mobile app Primary outcomes: medication adherence Wearable Primary outcomes: weight loss
Wearable Primary outcomes: moderate-tovigorous physical activity (MVPA) bout min per week
Wearable Primary outcomes: physical activity, sitting, and weight
Martin et al. [7•] USA RCT; N 48 mean age 58 ± 8 follow-up: 4 W
Müssener et al. [9•] Sweden RCT; N 783 mean age: 25.5 follow-up: 12 W
Johnston et al. [19•] Sweden RCT; N 174 mean age: 58 follow-up: 6 M
Jakicic et al. [25] USA RCT; N 471 mean age: 30.9 follow-up: 24 M
Finkelstein et al. [26•] Singapore RCT; N 800 mean age: 35 follow-up: 6 M, 12 M
Ganesan et al. [28•] Asia, Europe, Africa, USA, South America, Australia, and New Zealand Prospective cohort study mean age: 36 ± 9 years 100 day event 69,219 pre event 36,652 post event
Modality SMS Primary outcomes: LDL-c, BP, BMI, physical activity, smoking
Chow et al. [8•] Australia RCT; N 710 mean age 58 ± 9.2 follow-up: 6 M
Study
Participants were organized in worksite-based teams, issued pedometers, and encouraged to increase daily steps and physical activity as part of the team-based race
G1: Fitbit Zip activity tracker plus charity incentives G2: Fitbit Zip activity tracker plus cash incentives. G3: Fitbit Zip activity tracker 6 months CT: no device
Following Stepathlon completion, participants recorded improved step counts (+3519 steps/day; 95% confidence interval [CI]: 3484 to 3553 steps/day; p G 0.0001), exercise days (+0.89 days; 95% CI: 0.87 to 0.92 days; p G 0.0001), sitting duration (−0.74 h; 95% CI: −0.78 to −0.71 h; p G 0.0001), and weight (−1.45 kg; 95% CI: −1.53 to −1.38 kg; p G 0.0001)
At 6 months, the cash group had significant more MVPA (p = 0.0024), as did the charity group (p = 0.0310); and no significant difference in FitBit alone. 6 month post-intervention follow-up period—FitBit and charity group had significant more MVPA (p = 0.0001; p = 0.0013), but no difference in cash group (p = 0.1363)
At 24 months, both groups had significant improvements in body composition, fitness, physical activity, and diet; however, weight loss was significantly less (by 2.4 kg) in the wearable intervention group
At 6 months, greater patient-registered drug adherence was achieved in the interactive app vs the control group (nonadherence score: 16.6 vs 22.8 [P = .025])
8 week prolonged abstinence reported 203 (25.9%) in intervention and 105 (14.6%) in control 4-week cessation of 161 (20.6%) and 102 (14.2%) respectively a mean of 3.9 months s/p quit date.
Participants receiving SMS increased their step counts by 2534 compared to tracking participants and 3376 compared to blinded controls (p G 0.001 for both). Tracking alone did not result in significantly higher step counts
Participants receiving SMS had significantly lower LDL-c compared to the control group (79 vs. 84 mg/dL; p = 0.04). Systolic BP, BMI, smoking, and physical activity were better in the experimental group than control
Results
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Arm-band wearable device and accompanying web interface to monitor diet and physical activity CT: self-monitoring of diet and physical activity using a website
Interactive app with drug adherence e-diary, exercise, weight, and smoking. 6 months CT: app with simplified drug adherence e-diary 6 months
157 SMS on effective smoking cessation. CT: 1 text every 2 weeks thanking them for participation in study
G1: unblinded smartphone step tracking—4w G2: unblinded smartphone step tracking 2w with automated coaching SMS 2w CON: blinded tracking 4w
4 health coaching, motivational SMS per week + usual care CT: usual care
Intervention
Table 1. Digital health studies for lifestyle and medication adherence
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digital health strategies should also improve hard cardiovascular outcomes. To this point, a recent systematic review included 51 studies examining any element of digital health in the prevention of cardiovascular disease outcomes and modification of cardiovascular disease risk factors, including telemedicine, webbased strategies, email, mobile phones, mobile apps, text messaging, and monitoring sensors [31]. The pooled analysis of digital health interventions suggested a 40% relative risk reduction in cardiovascular disease. However, despite such encouraging data, the approach to digital health interventions is highly heterogeneous in nature, and there is a need for greater harmonization to support large-scale high-quality evidence that could justify adoption in clinical practice guidelines and daily practice. Moreover, there is a need for attention to any possible safety concerns such as sleep disruption or anxiety. Digital health technology research remains in an early stage both in terms of the data and the methodology for how it is conducted. As development of digital health hardware and software is often being driven by non-clinicians, this may pose a unique challenge to integration into clinical workflow, unless clinicians are involved early on. Moreover, clinicians are increasingly burdened by time-consuming use of electronic health records and data overload. Therefore, there may be limited capacity to process take on additional responsibilities and process additional data related to mHealth tools. The successful integration of digital technology into health improvement and maintenance strategies will require scientific rigor and novel approaches in many cases.
Conclusion Digital health is a unique field of medicine in that tech companies and consumers have thus far driven its success. Much of the necessary research will need to be conducted on technologies that are already widely utilized. To some extent, this may pre-emptively address concerns for usability, demand, practicality, adaptability, and integration into existing models [32]. Active engagement from the medical community provides the opportunity for guidance on clinical evaluation and responsible use. With many new mHealth interventions, particularly apps, bypassing the FDA, academic medical centers and medical organizations must provide broader guidance [33]. There is potential in digital health technologies to improve the quality and implementation of our medical recommendations. From text messaging to improve medication adherence to advanced interventions such as the development of digital cardiac rehab programs [34] to increase participation, the possible applications are innumerable. Each approach will come with new challenges and require further investigation to determine costs, feasibility, and value in improving care [35].
Compliance with Ethical Standards Conflict of Interest Numan Khan and Jane Wang report no conflicts. Francoise A. Marvel has received research support from Apple.
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Seth S. Martin has received research support from Apple. Dr. Martin has also received research support from the PJ Schafer Cardiovascular Research Fund, American Heart Association, Aetna Foundation, CASCADE FH, and Google. Dr. Martin declares being a co-inventor on a pending patent filed by Johns Hopkins University for the novel method of low-density lipoprotein cholesterol estimation. He has served as a consultant to Abbott Nutrition, Pressed Juicery, Quest Diagnostics, Sanofi/Regeneron, Amgen, and the Pew Research Center. Human and Animal Rights and Informed Consent This article does not contain studies with human or animal subjects performed by any of the authors.
References and Recommended Reading Papers of particular interest, published recently, have been highlighted as: • Of importance 1.
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messaging on risk factor modification in patients with coronary heart disease. Jama [Internet]. 2015;314:1255. Available from: http://jama.jamanetwork.com/article. aspx?doi=10.1001/jama.2015.10945. The randomized Tobacco, Exercise, and Diet Messages (TEXT ME) trial included 710 adults in Australia. It showed that, compared with the participants who received usual care only, those who received usual care plus four motivational texts weekly for 6 months had significant improvement in lowdensity lipoprotein cholesterol (LDL-C), the primary end point, as well as decreases in body-mass index (BMI) and smoking status and an increase in physical activity. 9.• Müssener U, Bendtsen M, Karlsson N, White IR, McCambridge J, Bendtsen P. Effectiveness of short message service text-based smoking cessation intervention among university students: a randomized clinical trial. JAMA Intern Med [Internet]. 2016;176:321–8. Available from: http://www.ncbi. nlm.nih.gov/pubmed/26903176. The Nicotine Exit (NEXit) trial implemented a text messaging intervention for smoking cessation and included 1502 adults in its analysis. Eight-week prolonged abstinence from smoking and 4-week complete cessation as a result of the intervention were significant and comparable to current standard smoking cessation techniques. 10. Thakkar J, Kurup R, Laba T-L, Santo K, Thiagalingam A, Rodgers A, et al. Mobile telephone text messaging for medication adherence in chronic disease. JAMA Intern Med [Internet]. 2016;176:340. Available from: http:// archinte.jamanetwork.com/article.aspx?doi=10.1001/ jamainternmed.2015.7667 11. Gandapur Y, Kianoush S, Kelli HM, Misra S, Urrea B, Blaha MJ, et al. The role of mHealth for improving medication adherence in patients with cardiovascular disease: a systematic review. Eur Heart J Qual Care Clin Outcomes [Internet]. 2016;qcw018. Available from: http://ehjqcco.oxfordjournals.org/lookup/doi/10. 1093/ehjqcco/qcw018. 12. Ralf-Gordon Jahns. 500m people will be using healthcare mobile applications in 2015 [Internet]. research2guidance. 2015. Available from: http://
Curr Treat Options Cardio Med (2017) 19:60 research2guidance.com/500m-people-will-be-usinghealthcare-mobile-applications-in-2015-2/. 13. Examples of mobile apps for which the FDA will exercise enforcement discretion [Internet]. U.S. Food Drug Adm. Available from: http://www.fda.gov/ MedicalDevices/DigitalHealth/ MobileMedicalApplications/ucm368744.htm. 14. FDA. Mobile medical applications. U.S. Deapartment Heal. Hum. Serv. Food Drug Adm. [Internet]. 2015;44. Available from: http://www.fda.gov/downloads/ MedicalDevices/DeviceRegulationandGuidance/ GuidanceDocuments/UCM263366.pdf. 15. U.S. Food and Drug Administration. Draft guidance medical device data systems, medical image storage devices, and medical image communications devices draft guidance for industry and food and drug administration staff. 2014;1–9. Available from: http://www. fda.gov/downloads/MedicalDevices/ DeviceRegulationandGuidance/GuidanceDocuments/ UCM401996.pdf. 16. R.K. R, A. K, S.P. K. Symptom-based smartphone app for detecting acute coronary syndrome: A diagnostic accuracy study [Internet]. J Am Coll Cardiol. 2016. p. 632. Available from: http://ovidsp.ovid.com/ovidweb. cgi?T=JS&PAGE=reference&D=emed13&NEWS= N&AN=72242135. 17. Sardana M, Saczynski J, Esa N, Floyd K, Chon K, Chong JW, et al. Performance and usability of a novel smartphone application for atrial fibrillation detection in an ambulatory population referred for cardiac monitoring. J Am Coll Cardiol [Internet]. American College of Cardiology Foundation; 2016;67:844. Available from: http://linkinghub.elsevier.com/ retrieve/pii/S0735109716308452. 18.• Plante T, Urrea B, MacFarlane Z, Blumenthal R, Miller E III, Appel L, et al. Validation of the instant blood pressure smartphone app. JAMA Intern Med. 2016;176:E1–2. This study is important because it is one of the few that attempts to critically evaluate the claims of a popular smartphone app. 19.• Johnston N, Bodegard J, Jerström S, Åkesson J, Brorsson H, Alfredsson J, et al. Effects of interactive patient smartphone support app on drug adherence and lifestyle changes in myocardial infarction patients: a randomized study. Am Heart J. 2016;178:85–94. This trial conducted in Sweden found that utilizing an interactive app significantly improved drug adherence (primary outcome) at six months and showed a trend of improvement in some of the secondary outcomes such as smoking cessation, physical activity, and quality of life. 20. McConnell MV, Shcherbina A, Pavlovic A, Homburger JR, Goldfeder RL, Waggot D, et al. Feasibility of obtaining measures of lifestyle From a Smartphone App: The MyHeart Counts Cardiovascular Health Study. JAMA Cardiol. 2017;2:67–76. 21. Higgins JP. Smartphone applications for patients’ health and fitness. Am J Med [Internet]. 2015;129:11–
Page 11 of 12 60 9. Available from: http://www.sciencedirect.com/ science/article/pii/S0002934315005379 22. Misra, Satish, Greg Von Portz, Iltifat Husain, Douglas Maurer, Melissa Murfin, Brian Chau and David Tseng. IMedicalApps-Reviews of Medical Apps & Healthcare Technology [Internet]. [cited 2016 Nov 20]. Available from: http://www.imedicalapps.com/. 23. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA [Internet]. 2007;298:2296–304. Available from: http://www.ncbi.nlm.nih.gov/pubmed/ 18029834 24. Freak-Poli RLA, Cumpston M, Peeters A, Clemes SA. Workplace pedometer interventions for increasing physical activity. Cochrane Database Syst rev. 2013;4 25. Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D, et al. Effect of wearable technology combined with a lifestyle intervention on long-term weight loss. JAMA [Internet]. 2016;316:1161. Available from: http://jama.jamanetwork.com/article.aspx?doi=10. 1001/jama.2016.12858 26.• Finkelstein EA, Haaland BA, Bilger M, Sahasranaman A, Sloan RA, Nang EEK, et al. Effectiveness of activity trackers with and without incentives to increase physical activity (TRIPPA): a randomised controlled trial. Lancet Diabetes Endocrinol [Internet]. Elsevier Ltd; 2013;0:219–29. Available from: doi:10.1016/S22138587(16)30284-4. TRIPPA implemented an intervention using activity trackers to increase physical activity but introduced the element of external incentive. While significant increases in physical activity were seen in the cash incentive group, these results were shortlived, which highlights the importance of intrinsic motivation. 27. Monroe CM. Valuable steps ahead: promoting physical activity with wearables and incentives. LANCET Diabetes Endocrinol [Internet]. Elsevier Ltd; 2016;8587:30284. Available from: doi:10.1016/ S2213-8587(16)30264-9. 28.• Ganesan AN, Louise J, Horsfall M, Bilsborough SA, Hendriks J, McGavigan AD, et al. International mobilehealth intervention on physical activity, sitting, and weight: the Stepathlon cardiovascular health study. J Am Coll Cardiol. 2016;67:2453–63. The Stepathlon Study implemented an international (64 countries), mass-participation (69,219 subjects), and low-cost mHealth activity-tracker intervention with results that were reproducible annually for three consecutive years. Significant improvements were found in step count, exercise days, sitting duration, and weight. 29. Murakami H, Kawakami R, Nakae S, Nakata Y, Ishikawa-Takata K, Tanaka S, et al. Accuracy of wearable devices for estimating total energy expenditure. JAMA Intern. Med. [Internet]. 2016;176:E1–2. Available from: http://archinte.jamanetwork.com/article. aspx?doi=10.1001/jamainternmed.2016.0152 30.• Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol. [Internet]. 2016;313:625–6.
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Available from: http://jamanetwork.com/journals/ jamacardiology/article-abstract/2566167. This study was important as it sets a precedent for the academic community to critically evaluate and validate wearable devices that can impact patient outcomes. 31. Widmer RJ, Collins NM, Collins CS, West CP, Lerman LO, Lerman A. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc [Internet]. 2015;90:469–80. Available from: https://www.scopus. com/inward/record.uri?eid=2-s2.084926389726&partnerID=40&md5= 1206a327c626d417dfe4ccb20285f872 32. Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med [Internet]. Elsevier; 2016;51:843–51. Available from: doi:10.1016/j. amepre.2016.06.008.
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