Mobile Health Initiatives to Improve Outcomes in ... - Semantic Scholar

Curr Treat Options Cardio Med (2015) 17:59 DOI 10.1007/s11936-015-0417-7

Prevention (L Sperling and D Gaita, Section Editors)

Mobile Health Initiatives to Improve Outcomes in Primary Prevention of Cardiovascular Disease Bruno Urrea, MD2 Satish Misra, MD3 Timothy B. Plante, MD4 Heval M. Kelli, MD5 Sanjit Misra, BS6 Michael J. Blaha, MD MPH7 Seth S. Martin, MD MHS1,* Address *,1 Ciccarone Center for the Prevention of Heart Disease, Division of Cardiology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Carnegie 591, Baltimore, MD, 21287, USA Email: [email protected] 2 Ciccarone Center for the Prevention of Heart Disease, Division of Cardiology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Carnegie 568, Baltimore, MD, 21287, USA 3 Division of Cardiology, Johns Hopkins University School of Medicine, Carnegie 592, Baltimore, MD, 21287, USA 4 Division of General Internal Medicine, Johns Hopkins University School of Medicine, 2024 E Monument St, Suite 2-617, Baltimore, MD, 21287, USA 5 Emory Clinical Cardiovascular Research Institute, Emory University School of Medicine, 1462 Clifton Rd NE, Suite #513, Atlanta, GA, 30329, USA 6 Stanford Health Care, 300 Pasteur Dr, Stanford, CA, 94305, USA 7 Ciccarone Center for the Prevention of Heart Disease, Division of Cardiology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Blalock 524, Baltimore, MD, 21287, USA

* Springer Science+Business Media New York 2015

This article is part of the Topical Collection on Prevention Keywords Prevention I Cardiovascular disease I Risk factors I Mobile health technology

Opinion statement Cardiovascular disease affects more than a third of American adults and is the leading cause of mortality in the USA. Over the last 40 years, several behavioral and medical risk factors have been recognized as major contributors to cardiovascular disease. Effective

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management of many of these risk factors, particularly behavioral risk factors, remains challenging. With the growth of mobile health (mHealth) technology, a variety of novel strategies are now available to facilitate the delivery of interventions directed at reducing these risk factors. In this review, we discuss recent clinical studies and technologic innovations leveraging smartphone devices, social media, and wearable health tracking devices to facilitate behavioral interventions directed at three important and highly prevalent behavioral risk factors for cardiovascular disease: smoking, physical inactivity, and sub-optimal nutrition. We believe this technology has significant potential to provide low-cost, scalable, and individualized tools to improve management of these important cardiovascular disease risk factors.

Introduction Cardiovascular disease is the leading global cause of death accounting for 17.5 million deaths in 2012, a number that is expected to grow to 22.2 million by 2030 [1]. More than 80 million American adults suffer from 1 or more types of cardiovascular disease [2]. Conversely, it is also estimated that effective management of modifiable cardiovascular disease factors such as smoking, obesity, physical inactivity, unhealthy diets, and improved management of high blood pressure, lipids and diabetes could prevent up to 80 % of cardiovascular events [2]. Despite this knowledge, several behavioral risk factors remain prevalent [3–5]. While cigarette smoking has declined due to aggressive public health campaigns, it is estimated that nearly 20 % of adult Americans still smoke. Similarly, rates of overweight and obesity have steadily risen over the past several decades while rates of physical inactivity remain persistently high [4, 5]. Novel tools are required for more effective management of these behavioral factors, which have been consistently identified as critical areas for intervention. Use of increasingly ubiquitous mobile devices such as smartphones may offer one such tool. In 2011, there were more than five billion mobile phone subscribers

worldwide, and commercial wireless coverage reached over 85 % of the world’s population [6]. In the USA alone, more than 60 % of adults now own a smartphone. Among such persons, 62 % have used these devices to look up health-related information and 20 % have downloaded a health application [7]. Additionally, an estimated 69 % of American adults are tracking a health indicator [8]. These factors, coupled with the rapid development pace of the associated technology, have generated substantial interest in the use of mobile technology in healthcare (Fig. 1). Mobile health (mHealth) has been defined as medical and public health practice supported by mobile devices such as mobile phones, personal digital assistants, activity tracking devices, or any wireless device that could have access to internet [6]. mHealth interventions can employ a variety of platforms and communication tools such as smartphone applications, wearable health tracking devices, text messaging, social media, and more. While startup and early stage companies leveraging this type of technology have attracted billions of dollars in investment in recent years, highquality research evaluating the use of mHealth tools has only just begun.

Fig. 1. Applications released for smoking cessation, diet, and physical activity per month.

Curr Treat Options Cardio Med (2015) 17:59 The objective of this article is to review the latest research on the use of mHealth technology to deliver behavioral health interventions aimed at major cardiovascular disease risk factors, including smoking, physical inactivity, and unhealthy diets. For each risk factor, a

Page 3 of 12 59 review of studies utilizing short message services (SMS), smartphone applications, and social media-based interventions will be offered, as well as an overview of emerging technologies with potential applications to these risk factors.

Smoking cessation Smoking is a major cardiovascular risk factor, responsible for more than 480,000 annual premature deaths in the USA between 2005 and 2009. In 2010, it was the second leading risk factor for death in the USA, after dietary risks. Even though prevalence of smoking has declined, approximately 69 million Americans older than 12 years of age were still users of tobacco products in 2012 [2].

SMS-based interventions There are several SMS-based smoking cessation programs currently available, including the National Cancer Institute’s SmokefreeTXT program, the Text2Quit platform embedded in the Quit for Life program from the American Cancer Society and Alere Wellbeing, and the UK’s National Health Service Quitkit which includes the Txt2Stop. In one meta-analysis conducted by Spohr et al. in 2015, SMS-based interventions for smoking cessation increased the odds of successful cessation by 36 % when compared to control groups [9••]. A 2012 Cochrane review of clinical studies, which did not include a randomized trial involving Text2Quit, indicated that SMS-based interventions increased the long-term quit rates by 70 % when compared with control programs, using a definition of abstinence of smoking at 6 months. Yet, the authors cautioned that more research is needed on different types of mobile phone interventions and highlighted the importance of assessing the cost-effectiveness of smoking cessation interventions using mobile tools [10]. More recent studies have further supported the use of SMS-based interventions to support smoking cessation. One study, conducted by Abroms et al., evaluated the use of the Text2Quit platform in a randomized controlled trial [11••]. Participants were allocated to a group receiving Text2Quit intervention or self-help material, and smoking abstinence was biochemically confirmed. Participants using Text2Quit were two times more likely to quit smoking at 6 months post-enrollment when compared with the control group. SMS-based smoking cessation programs are complex interventions with numerous potential configurations. In the meta-analysis by Spohr et al., there was a trend towards greater efficacy for fixed schedule messaging in comparison to decreasing frequency messaging or variable frequency messaging. Studies utilizing a fixed schedule messaging strategy sent zero to three messages per day [9••]. In another study reporting patient feedback on an SMS intervention, the most helpful features were messaging directed towards the preparation to quit and suggestions for management of smoking urges. Study participants also suggested that more social connectivity may further improve the efficacy of these types of smoking cessation programs [12].

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The effectiveness of the SMS-based intervention is commonly attributed to the simplicity of text messages and familiarity of most participants with this type of communication system. It also allows researchers and physicians to provide real-time psychological support contributing to improvement of outcomes and optimization of health care resources [13, 14].

Smartphone application interventions There are currently hundreds of smartphone applications available for cigarette smoking cessation that uses a variety of different strategies to support cessation efforts. In some cases, smartphone applications are adjunctive tools to traditional smoking cessation programs, aiding in the behavioral change process to set a quit date and giving participants the option to view and track progress. They are also used to provide support through tips on how to cope with cravings and entertaining activities to distract participants when help is required to control smoking urges. Some applications use social interaction resources in which participants can share personal experiences and effectively create virtual support groups [3, 15]. However, in a survey of applications conducted in 2012, Abroms et al. found overall poor agreement with clinical practice guidelines [16••]. Since then, however, there has been considerable effort invested into the development of more guideline based smartphone applications for smoking cessation. The National Cancer Institute has released QuitGuide and QuitStart, applications based on practice guidelines intended for adults and teens, respectively. The Department of Veteran Affairs has similarly released the Stay Quit Coach application. Smartphone applications may be effective in supporting smoking cessation. In one recent randomized clinical trial, Bricker et al. evaluated the use of the SmartQuit application in comparison with the National Cancer Institute’s QuitGuide application. SmartQuit utilizes a behavior change model called acceptance and commitment therapy (“ACT”). This trial recruited 196 participants and found that participants using SmartQuit opened the application two times more than the QuitGuide group, and quit rates at 2 months for participants using SmartQuit were 13 % (95 % confidence interval (CI), 6–22 %) vs. 8 % (95 % CI, 2–20 %) from QuitGuide [17••]. Other studies have suggested opportunities to improve smartphone applications for smoking cessation. Bindhim et al. delivered a survey to users of Quit Advisor, an application developed by the same group. They found that, among the 602 respondents, 77 % of users were ready to quit in the next 30 days, 71 % had not contacted healthcare professionals regarding quitting, and 75 % had made a quitting attempt lasting at least 24 h using an app [18]. Efficacy of smartphone applications could potentially be improved by tailoring application design to these characteristics; for example, by placing greater emphasis on engagement with healthcare professionals. Other studies have suggested that offering applications in conjunction with adaptive messaging may increase abstinence from smoking when compared to interventions using smartphone applications alone [14].

Social media interventions The use of social media and web-based interventions is promising; however, many tobacco cessation programs are not taking advantage of this platform to either promote or enhance their programs [19].


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One smartphone application developed by a group at the University of British Columbia and University of Waterloo, Crush the Crave, integrates an open, moderated Facebook group into its design. In an analysis of the use of this social media component, nearly 40 % of the user-generated posts shared personal experiences related to cessation attempts. Almost all user-generated posts were in response to a post by a group moderator. However, it is important to note that there are little data on the additive benefit of these types of social media interactions [20].

Emerging tools Additionally, there are a number of early stage and commercial efforts using digital health technology to support smoking cessation. A trend likely to impact smoking cessation efforts is the bundling of pharmaceuticals with digital health tools. For example, the Propeller Health platform, which monitors inhaler use, has recently received FDA approval for use with a variety of asthma and chronic obstructive pulmonary disease medications [21]. Another medication compliance platform using ingestible sensors, developed by Proteus Digital Health, was also recently cleared by the FDA for use in adherence monitoring [22]. These types of tracking tools have interesting, though as yet untested, applications in supporting smoking cessation, such as through monitoring of adherence to medications or nicotine replacement. Finally, a National Institutes of Health (NIH)-supported effort called the Mobile Sensor Data-to-Knowledge program, based at the University of Memphis, is exploring the use of a variety of sensor technologies to detect smoking relapse. Currently, in early stages after having received a grant from the NIH, the effort aims to develop a “big data” approach integrating data streams from a number of sensors to deliver just-in-time interventions to prevent smoking relapse [23].

Physical inactivity Physical inactivity is a modifiable risk factor for cardiovascular disease [2]. Current physical activity guidelines suggest at least 150 min of moderate exercise per week [24]. However, less than half of adults meet these targets [2]. Digital health technology offers several opportunities for increasing leisure time physical activity. Advantages include automated activity tracking and the opportunity to deliver ongoing, adaptive interventions based on this data to encourage patients to increase their physical activity [25, 26].

Web-based interventions and social media A 2012 Cochrane review found consistent evidence that telephone and webbased interventions can generate a moderate improvement in physical activity at 1 year (SMD 0.20; 95 % CI 0.11 to 0.28). Of note, only three of the 11 included studies utilized pedometers as part of the intervention. In this review, they found that the most effective interventions utilized individually tailored approaches and provided interim feedback to participants [27]. In a more recent randomized study evaluating the efficacy of a web-based educational intervention, participants in the intervention group received access

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Curr Treat Options Cardio Med (2015) 17:59 to a web-based application (“web app”) providing educational material as well as diet and physical activity tracking functionality. Use of the web-based application led to a mean increase of 63 min in self-reported physical activity per week versus a 30-min reduction in the control group [28]. Another study randomized approximately 300 middle-aged Australian men to either a web-based dietary and activity intervention or print educational materials alone. The intervention in this case consisted of online educational material and a series of diet and activity challenges. This study did not find significant differences in self-reported activity levels between the web-based intervention group and the print-material only group; both groups did have significant improvements in self-reported activity levels [29]. Specific evaluation of the use of social media for promotion of physical activity is more limited. Consequently, this represents an opportunity for further research.

Smartphone applications Recent generations of smartphones have included sensors capable of tracking a range of motions. For example, the latest generation of the Apple iPhone includes an accelerometer, compass, gyroscope, and barometer, as along with a dedicated co-processor chip for continuous measurement of data from these sensors [30]. A study conducted by Glynn et al. evaluated the use of native smartphone sensors and an associated smartphone application to increase physical activity. In this study, 90 participants were recruited and given Accupedo, a commercially available smartphone application for step tracking. The intervention group received information on how to use the application to achieve physical activity goals while the control group just received the app and a daily step goal. This intervention resulted in an adjusted difference in mean improvement of 1,029 steps in the intervention group compared to the control group (p=0.009) over the 8-week study period [31••]. A follow-up qualitative study assessing the experiences of a subset of study participants revealed additional insights into perceived benefits. In particular, participants described a sequential process of behavior change involving awareness of current activity levels, goal setting, feedback from step tracking, rewards from observing incremental improvement, control over goal achievement strategies, confidence development, and ownership over their activity goal. They also identified a cascade effect in which participants’ reported that individuals within their social networks observed the participants’ use of the application and began to use it themselves. One important usability issue identified was device battery life, which did lead to significant study drop out [32]. With the addition of these embedded sensors within smartphones, hundreds of applications have become available for step tracking. Comparisons of these applications with validated pedometers have been encouraging, suggesting that appropriately designed and tested applications can provide accurate data [33, 34•]. However, the content of these applications does present an opportunity for improvement. In a study conducted by Conroy et al., investigators characterized the behavior change techniques used by top selling


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physical activity applications on the Android and iOS application markets. Two hundred applications were evaluated. These applications included a mean of 4 behavior change techniques each, with a total of 26 behavior change techniques identified. The most common techniques were instructions to perform a behavior, modeling/demonstration of a behavior, and feedback on performance. In particular, they noted that one of the most well-established techniques for helping individuals move from intention to enacting a desired behavior, action planning, was rare among the evaluated applications [35•].

Pedometers and wearables There are a wide variety of wearable activity tracking devices available that are capable of collecting varying types of activity and physiologic data. While these devices are popular among consumers and frequently recommended by clinicians, there are very little data on their effectiveness in improving activity levels [36••]. Others have described the difficulty in generating a technology usage habit before achieving behavioral changes and highlighted the importance of device accuracy and participant adherence over time [37•]. Research projects using wearable technology that have integrated behavioral change-based text messages in their interventions have resulted in considerable increments in physical activity in adults. This is the case of an intervention where participants who were unblinded to physical activity tracking and receiving feedback through automated and tailored text messages increased their daily step count by 2,334 steps (25 %) compared with those unblinded to physical activity alone. Total activity time and aerobic time also increased in the text receiving group by 21 and 13 min/day, respectively, when compared to the blinded and unblinded—no text groups [38••]. In another study by Compernolle et al., participants in the intervention arm were given information on how to increase steps, a digital pedometer with 7 days memory, and were granted access to a tailored web-based step advisor. A recommended 10,000 daily step goal was used. The intervention resulted in an increase of 1,056 daily steps in the intervention group compared with a reduction of 256 daily steps in the control group using only a blinded pedometer [39].

Emerging technology Technology to measure and promote physical activity is developing at a rapid pace. One area which has received substantial interest is clothing embedded with sensor technology. Companies like Zephyr Technology, Athos, and Hexoskin offer a range of sensor-embedded clothing capturing data on heart rate, ECG, respiration, body temperature, and even local electromyography. While these technologies are currently primarily marketed to high performance athletes, exercise enthusiasts, the military, and emergency responders, there is considerable interest in studying these technologies for medical applications like remote cardiac rehabilitation [40•]. Others are applying a variety of social media strategies to encourage exercise and general physical activity. For example, Pact Health users commit to specific diet and exercise goals which are then tracked through their smartphone application and connected devices. Users commit to paying a preset amount of

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Curr Treat Options Cardio Med (2015) 17:59 money if they do not meet their goals, money which is then divided among users who did meet their goal [41].

Diet An unhealthy diet is a well-recognized contributor to cardiovascular disease, contributing to risk factors such as high blood pressure, dyslipidemia, and insulin resistance. Poor nutrition is linked to an estimated $33 billion in medical expenses every year [2]. Mobile technology offers an opportunity to provide ongoing education as well as support for improved dietary choices at the moment in which individuals are making food decisions. Data on the use of digital health tools to improve diet are limited. As outlined in the recent American Heart Association (AHA) Scientific Statement on mHealth, studies that include dietary interventions typically do so within the context of complex, multi-modal interventions such physical activity promotion or one-on-one support from a counsellor [36••]. The 2013 obesity treatment guidelines found moderate strength evidence for the effectiveness of internet or mobile technology-assisted weight loss programs that included a trained interventionist providing personalized guidance [42]. However, as noted in the AHA Scientific Statement, mHealth interventions tested within clinical studies are often not commercially available while many commercially available tools, such as smartphone applications, have not been systematically evaluated [36••]. Nonetheless, there are innumerable diet-related smartphone applications and connected health devices currently available. One of the most popular diet tracking applications, MyFitnessPal, has been installed more than ten million times on Android smartphones alone according to Google Play metrics [43]. Another popular smartphone diet tracking application, LoseIt, reports over three million monthly active users [44]. These types of applications offer a variety of functionalities including caloric tracking facilitated by automated food identification through barcode scanning and extensive linkage with nutritional databases for specific nutritional data on millions of food items. The AHA Scientific Statement identifies several features that healthcare practitioners should seek when recommending a mHealth intervention in support of weight loss including calorie-controlled healthy eating, specific goal setting, self-monitoring, personalized feedback, and social support [36••]. Many commercially available mHealth tools incorporate many if not all of these features. For example, both of the above-described applications allow users to set individualized caloric intake goals and form peer groups to facilitate goal sharing and social support. However, clinical studies evaluating the efficacy of these tools or conformance to accepted behavior change models are limited.

Smartphone interventions One common use of smartphone applications is to assist traditional commercially available weight loss programs. These programs, as noted above, are typically multi-modal, addressing both diet and physical activity. In general, the mHealth components of these programs have not been well studied. One recent study involving 40 patients evaluated a mHealth-based multi-modal weight loss program called SmartLoss. In this study, patients randomized to receive


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SmartLoss received a smartphone application that facilitated diet tracking, weight tracking through a linked weight scale, and personalized ongoing feedback for the duration of the study; of note, both automated, adaptive feedback and trained counselor feedback were provided. The comparison group received educational materials via text message and email. In this primarily female cohort, significantly greater weight loss was achieved in the intervention group (9.4 % of baseline weight vs. −0.6 %, pG0.001) [45]. Others have evaluated strategies that are entirely patient driven. In one small study evaluating the LoseIt smartphone application in comparison to paper diet tracking, an improvement in overall quality of diet was found though no difference in weight loss was found at the end of the 8-week study period [46•]. Another opportunity for improving patient food choices is to improve knowledge of the nutritional content. Prior studies have shown that consumers often have difficulty understanding the nutritional labels of food and that this information can be misleading [47]. Foodswitch is a smartphone application that uses barcode scanning technology to give users at a glance easy to read nutritional information by using an existing food database. This commercially available application has been downloaded by 400,000 unique users. A feature of this application is that it uses guidance from government agencies in Australia and the UK to implement a three-tiered “traffic light” rating for each food. This system is used to provide healthier alternatives at the moment the user scans each food item [48]. Another smartphone application, Eatery, uses crowd-sourced feedback to inform dietary choices. Users take pictures using their smartphone of meals which are then shared for feedback from other users. A study comparing overall assessments of nutritional value through this crowd-sourced mechanism with detailed assessment by expert raters found a strong correlation [49]. While these smartphone applications offer interesting adjunctive tools for dietary modification, it is important to note that their impact on clinical outcomes like weight loss has not been well evaluated.

Emerging technology A limitation of many interventions targeted at dietary modification is that diet tracking can be a tedious and time-consuming process. There are several groups that are developing technology designed to make diet tracking less time-intensive. One approach is through the automation of diet tracking. One group has described automated food detection software that can determine the content of a plate of food and provide estimated nutritional information [50]. In their description of internal testing, they found reasonable accuracy with a limited food sample set, and work is ongoing to improve the accuracy and scalability of this platform. Another currently available service avoids diet logging altogether. BagIQ, a commercially available platform, uses passively collected data from customer loyalty programs at grocery stores to assess an individual’s dietary choices. This data is then used to provide an overall quality score as well as personalized feedback on dietary modification such as specific items that an individual purchased for which healthier alternatives exist. Finally, in addition to diet tracking, there are emerging novel approaches for providing diet counselling. One recently launched commercial platform, Rise,

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offers individualized nutrition counselling for subscribers. Users take pictures of their meals and snacks with their smartphone and receive feedback on from a nutritionist or dietician on ways to improve their diet; users can also use a builtin messaging functionality to ask questions or get advice on diet choices.

Conclusion While many mHealth tools, such as smartphone applications, are already widely available commercially, evidence on effectiveness remains limited. Here, we have reviewed the small but growing body of evidence evaluating mHealth technology as well as novel emerging technology for three important behavioral cardiovascular risk factors: smoking, physical inactivity, and poor nutrition. In some areas, such as smoking cessation and physical activity, there is promising data on the use of adaptive SMS-based interventions, smartphone applications, and wearable health trackers for risk factor modification. More research is needed, however, to evaluate these technologies in larger, more diverse populations. Additionally, greater attention to the impact of specific components of studied interventions will be important to guide both the development of more effective mHealth tools as well as selection of appropriate resources by clinicians and patients. The rapid growth of the mHealth market as well as the pressing need for more effective tools for management of these important risk factors demands more initiatives to evaluate the content, effectiveness, and safety of mHealth tools.

Compliance with Ethical Standards Conflict of Interest Bruno Urrea, Satish Misra, Timothy B. Plante, and Heval M. Kelli each declare no potential conflicts of interest. Sanjit Misra is a partner at iMedical Apps. Michael J. Blaha reports grants from NIH/NHLBI, FDA, AHA, and Aetna Foundation and personal fees from Pfizer, Luitpold Pharmaceuticals, and ACC. Seth S. Martin reports grants from AHA and Aetna Foundation and personal fees from ACC. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

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