For years now, cardiovascular disease (CVD) has remained a significant persistent global threat, resulting in approximately 18 million deaths a year and whose impact reaches more than 422 million people. In Europe, the diseases account for more than 1.9 million deaths a year, emerging as one of the single most common causes of death (Buys et al. 2016, p. 1). Clearly, these statistics represent a call for interventions that are not only effective but one that could also scale up for population-wide use. At the moment, there is a range of traditional medical, public health, and patient-oriented interventions targeted for both the prevention and management of these diseases. However, a multitude of challenges, including accessibility, affordability, psychological barriers, and the modes of the delivery, see to it that only a small population gains and small risk reductions are achieved. According to Cajita et al. (2021, p. 1), an intervention that would transform the management and prevention of these diseases must be flexible and accessible to reach out to massive populations worldwide (Cajita, 2021). The ubiquity and functionality of digital health technologies make them the most suitable interventions for this goal.
A convergence of digital technologies, including smartphones, wearable devices, defibrillators, machine learning algorithms, and pedometers, hold greater capacities in not only managing CVD symptoms but also the risk factors attributed to the diseases. Whitelaw et al. (2021, p. 62) identify a set of six applications related to the above-named technologies. For the most basic application, mobile phones are key to functions such as patients’ self-monitoring and self-management. On the other hand, technological tools such as wearable devices are transforming the clinician’s ability to monitor patients remotely besides providing evidence-based support for their decision-making practices at the point of care. The digital platforms are also a platform for virtual care, social networking, and educational modules targeted for both patients and clinicians. Finally, the digital platforms form a basis for research by facilitating activities ranging from randomization to CVD-related data collection (p. 62). All these are just but a few pointers of digital technology’s potential to revolutionize CVD healthcare delivery.
Mobile health (mHealth) technology, for instance, is revolutionizing preventative interventions targeting CVDs. According to Spring et al. (2018), the world’s population manifest impactful behavioral risk factors that merit targeting as a primary mean of managing CVDs. For example, the researchers report that an average adult manifests at least two chronic disease risk behaviors, while 25% of adults worldwide exhibit three or more risk behaviors. The two commonly reported behaviors are unhealthy diet and lack of physical activity, but the list goes on to include other factors such as smoking, alcohol intake, sleeping patterns, and psycho-social factors such as stress. In particular, hardly 15% of the US adult population achieves five servings of daily fruits and vegetables; the median intake of these servings is usually half of the above-said population (Lee-Kwan et al. 2017, p. 1245). Furthermore, only 29% of the population adheres to dietary guidelines to consume below 10% calories from saturated fats, while more than 50% exceed two hours of watching television a day (p. 1245). Such behavior patterns are all indications of a heightened risk of cardiovascular diseases implying that any form of effort that could result in a slight magnitude of behavior change will report a significant impact in managing CVDs.
Mobile health (mHealth) approaches have proved the capacity to optimize the above-indicated efforts considering that they mark one of the best portable and flexible tools able to register these health-based information and support follow-up and feedback activities. Precisely, research literature reports that mHealth is a more effective tool in managing weight loss and dietary behaviors than the traditional approaches. Of the mHealth tools, Lugones-Sanchez et al. (2022) identify smartphones as the best-positioned approach to manage the most effective and beneficial outcomes of weight and dietary management in the short term. Nonetheless, Schoeppe et al. (2016) acknowledge that, on average, mobile phone interventions only manage modest improvements in lifestyle-related behaviors, an indicator that the intervention needs to be complemented with other tools (Schoeppe, 2016).
In this regard, wearable devices have in recent years garnered much attention when it comes to managing sedentary lifestyles and other related risk behaviors. For example, Richardson et al. (2008, p. 3) report that there has been an extensive exploration of pedometer-based interventions for the purpose of CVD primary prevention but the constant evolution of these tools calls for time-to-time inclusion of their emerging versions. On the other hand, Brickwood et al. (2019) believe that activity tracker wrist bands, also referred to as smart bands, have passed all tests of validity and reliability in measuring and influencing decisions in physical activity-related outcomes. These tools take a record of an individual’s activity outcomes such as the distance walked, steps, and intensity in return, helping the wider population accomplish key of the given standards for physical activities. Furthermore, the tools are not just limited to primary preventative measures as they are as well used among the population already with chronic diseases as a means of managing the symptoms of these diseases (Islam & Maddison 2019, p. 1). Nonetheless, research literature acknowledges that wearable devices alone would not be sufficient in managing expected behavior changes, and hence a beneficial strategy would be to employ multi-component intervention including most of the above-discussed technologies.
For the case of CVD management, remote cardiovascular monitoring is earning recognition as the transformative way forwards. Thanks to remote cardiovascular monitoring, collecting patient data is now straightforward. By using a variety of stand-alone technologies, mostly an array of wearables, clinicians can now access a range of patients’ symptoms, including blood pressure, body weight, respiratory rate, pulse rate, and regularity, sleep quality, activity, oxygen saturation, and heart sounds (Cowie & Lam 2021, p. 457). All this information, together with other advances that are being made in point-of-care testing, have now augmented the capacity to effectively manage patients in out-of-hospital settings. Furthermore, patients with heart failure or a history of sudden cardiac death can now benefit from cardiac implantable electronic devices such as defibrillators which make it possible for continuous access to a stream of information on physiological variables so as to avoid emergency outcomes (Cowie & Lam 2021, p. 457). On the other hand, advances in both traditional and machine learning algorithms are now being employed in the detection of those at risk of clinically important events for in-time diagnosis (Krittanawong et al. 2021, p. 76). And with a combination of the two, clinicians can now speed up decisions on outcomes such as patient adherence to medication, early clinical face-to-face reviews, and the need to modify drug therapies. Ultimately, whether for prevention or management goals, digital health technology has seen a significant improvement in personalized medical approaches and increased patient involvement towards the fulfillment of the promise of quality CVD-based healthcare.
Nonetheless, the concept of technology in CVD prevention and management is not all rosy. For instance, according to Whitelaw et al. (2021, p. 62), there is still very limited evidence of the long-term efficacy of these technologies. On the other hand, Fraser et al. (2022, p. 6) believe that the application of these technologies faces a range of barriers, including accessibility given cases of internet disparities, privacy concerns, patient safety, ease of use, data quality, and robustness. Regardless, the case of availability of efficacy evidence still promises a future of the successful application of these interventions because the little available research evidence, whether systematic reviews or randomized controlled trials already g (TheUniversityofManchester, 2022)ive massive support for these interventions. For instance, a review by Cajita et al. (2021, p. 21) reports that almost all of the already reviewed randomized control trials establish a strong correlation between technology and reduction of CVD risk factors and successful management of their symptoms. For the case of barriers, successful navigation will depend on systematic structures put in place to manage the acquisition of these technologies. The case of the United Kingdom and Europe at large as well looks promising given the measures already in place. For instance, at the moment, the UK’s digital policy and strategy have prioritized goals such as developing world-class digital infrastructure and providing everyone with access to digital skills (Black 2017). Furthermore, the University of Manchester (2022, p. 44) reports that the 2018 implementation of the General Data Protection Regulation is at the core of sustaining cyber security.
Ultimately, technology marks one of the interventions that hold the promise of reversing the already surging statistics in CVDs. Whether in managing behavioral change as means of prevention or collecting information for symptoms management, technology shows capacity for optimal performance. And while there exists a set of related challenges and barriers, the right strategy holds the potential of revolutionizing the CVD care system towards a future of better health outcomes.
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