Digital health is an emerging field that involves the use of information technology for a range of health-related applications—from general wellness to medical devices [1]. A wide range of digital health technologies across the spectrum of mobile health, wearable devices, telehealth, and personalized medicine solutions is being explored. Digital health interventions (DHIs) are digital tools that help modify an individual’s health behavior through direct interaction. These approaches can potentially improve the quality, accessibility, and affordability of health care worldwide [2]. Moreover, due to the availability and convenience of technology access and its immense potential for development, DHIs successfully implemented in a clinical practice can have the capability of driving patients’ health behavioral change in response to real-time tracking of intervention progress.

The COVID-19 pandemic has accelerated the adoption of digital health technologies on a global scale, with large programs already deployed in clinical practice [3,4]. With rapid digitalization of health care coupled with recent efforts to reimburse DHIs (eg, Digitale Gesundheitsanwendungen, also translated as digital health applications in Germany, the mHealthBELGIUM platform in Belgium, National Health Service Scotland) and the emergence of new digital interaction modalities, for example, Metaverse [5], as potential interfaces for DHIs in the future [6], guidance on how to best improve an individual’s health with DHIs is required, including effective patient engagement strategies.

In this viewpoint study, we aimed to identify, in broad strokes, how DHIs could be potentially integrated into future health care. We focus on patient engagement as the critical component to effectively change the behavior of the patient via DHIs [7]. As summarized in a review by Barello et al [8] on eHealth, patient engagement encompasses adherence to treatments and medications and thus directly impacts all relevant outcomes of health interventions [9]. Among others, treatment adherence has been the most recently discussed as a key explanation for the observed reduction in cardiovascular mortality in the polypill trials [10]. By targeting patient engagement, it is possible to reshape behavioral intentions, motivations, and attitudes, as well as provide direct and continuous feedback on the effect of the changed behavior, which can further reinforce and sustain appropriate change [11,12]. Despite its significant role in digital health tool efficiency, patient engagement in the context of “last-foot delivery” of DHIs has not yet been explored to its full potential.

Patient engagement is highly interlinked with communication [13] and is a natural extension of the standard clinical patient education [14]. During a DHI, communication represents a dynamic process, which can begin passively (eg, hearing about the intervention through peers or doctors or advertisements), followed by more active interactions during initial and continued usage [15]. According to an integrated conceptual framework for health marketing communications [12], the communication potential and goals vary through different stages of patient interaction with the DHI (Figure 1). Given the variety and adaptability of communication channels [16], deploying the right communication channels and their dynamic adaptation throughout the intervention period can be leveraged as an effective means to build and sustain patient engagement [12]. This consideration precedes the introduction of other epiphenomena of communication, such as gamification [17].

First, patients need to form the motivation for using DHIs by means of prior information and involvement [12]. As such, it is crucial to raise awareness about the health issue and DHIs through physical channels such as health care providers and traditional mass media and virtual channels such as social media. As each patient’s awareness about DHIs is increased, communication channels can evolve to become more personalized and engage patients more actively. During this stage, promoting the motivation to change and forming behavioral intentions and new positive attitudes are essential [12]. Increasing autonomous motivation has been shown to lead to positive health behavioral changes [18], and simultaneous use of multiple communication channels has the potential to employ a variety of motivation and behavior change techniques [19]. Eventually, as patients progress through the DHI process with high motivation and engagement, the goals of communication channels should shift toward initiating and maintaining changed behavior through less effortful thinking. At this stage, continued cue to action through moderate reminders and the use of peripheral cues such as recommendations from doctors and discussion with other patients can be beneficial [12]. Different communication channels can serve different roles and have varying effectiveness on engagement (see Dahl et al [20] for the list of omnichannel touchpoints and perceived effectiveness). Table 1 explores the potential application of communication channels and probable patient adoption considerations as inferred from existing literature on implementing these channels into DHIs. Of note, given the fast-paced nature of technology development, inventories of communication channels will continue to develop as new channels are established and older channels are phased out. With a diverse list of communication possibilities, patient engagement will remain dynamic as informed by the users’ changing communication preferences, the stage of their DHI journey, and the information collected through the longitudinal intervention monitoring.

For optimal personalization and seamless integration, the DHI needs to adapt to the user communication preferences, which vary with demographics, psychographics, and the specific health condition [35,36]. Broadly, young adults prefer solutions that blend in with their current usage—accessible through existing hardware and software, have a familiar syntax (eg, use of emoticons), and are aligned with their existing habits [37]. Older adults, often with limited digital skills, also prefer familiar or easy-to-use communication channels for DHIs [35,36]. Despite the stated importance of a seamless and personalized experience in health care across physical and virtual channels [38], many of the current DHIs are unable to integrate seamlessly with each other and the rest of the health care system to personalize and improve engagement. The lack of integration of DHIs in the patient journey can exacerbate the existing health care fragmentation, which is particularly evident in the gaps in the continuity of care between the hospital and outpatient treatments. The effect of this discontinuity in user experience, among others, shows in the attrition rates with digital health apps. In one of many examples, the usage analysis of 57 mental health apps showed that the percentage of users who opened the app dropped from 69.4% to 3.9% within the first 15 days [39], potentially rendering them ineffective. This rapid disengagement is a particularly detrimental issue for DHIs that target chronic diseases, where continuity of care is crucial.

Encouraged by its success in retail [40], we identified omnichannel engagement (OCE) as a highly promising and untapped approach for DHI patient engagement. OCE is a strategy to integrate and interconnect multiple communication channels in a synchronized operating model, which leverages data and digital tools to deliver a seamless, consistent, and personalized experience for the user [41]. The key features of OCE are (1) integrated and interconnected communication using multiple available channels, (2) synchronized/coordinated activity, (3) personalized and consistent experience for the user, and (4) continuous improvement via data and digital tools.

The correct management of the following 6 enablers is crucial for achieving a successful OCE: people, platform, service and content, channels, legal framework, and data and measurement (Table 2). Several consumer technology companies have maximized their profits by executing long-term omnichannel strategies based on a thorough management of these enablers. The consumer technology omnichannel approach is therefore being considered the state-of-the-art and a reference for other industries [42]. Regarding the patient digital behavior, studies show that individuals who seek and are capable of utilizing eHealth information are also more open to use health services provided digitally [43]. Accordingly, health care companies and public sector utilize new ways of doing marketing, where medical science liaisons—services for patients and health care professionals—use increasingly more digital means besides the traditional in-person communication channel [44]. For example, social media posts on Twitter and Facebook have been used by the largest hospital chain in Turkey [45] as well as by the Portuguese national health service to improve health literacy on disease prevention and well-being together with other nondigital channels [46]. The electronic word-of-mouth–based strategy on social media for pharma marketing on over-the-counter medications has also been explored [47]. It is not surprising that the health care market has been targeted in recent years by the consumer technology companies familiar with OCE, which have introduced fitness apps, wearables, and activity trackers [48].

We stipulate that OCE can play a significant role as an integral element of DHIs and not only as an adjacent marketing stream. We envision a patient journey where DHIs are a crucial part of a stand-alone or auxiliary treatment and behavioral change and leverage OCE to boost its efficiency. In our vision, DHIs are distributed into several services (eg, dedicated disease service, tracking service), which are integrated with various communication channels as well as wearables/sensors and the digital presence of the patient. The information flow is bidirectional—the patient receives the digital intervention through OCE and provides information for the DHI operations. All available relevant data, for example, medical data, health data, connections, digital preferences, and data for health can be centrally collected and integrated to form a digital twin [49,50]. Digital twin is a digital representation of the individual whose data it is based on. It can offer insights about that individual, which, in turn, facilitate the design and monitoring of an intervention. The digital twin in our vision may not need to encompass all available data but merely may need access to multiple sources such as the personal health record to gain sufficient insights to be actionable. For example, to understand the phenotypic response and to recommend an optimal pharmacological dosage with sufficient safety and accuracy, only 3-6 data pairs may be necessary [51,52]. Artificial intelligence could prove crucial in managing and extracting relevant features from the multidimensional, noisy, and incomplete data as well as in following up with a variety of appropriate adaptations and actions [53,54]. It will be required to find the right balance of complexity and size of the data model versus the quality and rapid generation of actionable insights, according to the general premise of efficient use of artificial intelligence in health care [55].

OCE serves the purpose of linking the patient to a network of health services through various integrated communication channels revolving around the patient’s health-seeking experience. In already existing examples, instant messaging, be it stand-alone (eg, WhatsApp) or platforms linked to social media such (eg, Facebook Messenger), can be used for triaging in-person and web-based consultations [56]. Wearables enable long-term symptom management, and emails and teleconsultations provide a seamless and convenient method for follow-ups and communication of clinical data. These services can be combined with the aligned nudges for lifestyle and nutritional choices delivered according to the patient’s digital presence, for example, specific food suggestions when shopping online.

OCE also creates opportunities for obtaining additional insights. An analysis of the interconnection between communication channels may allow for analysis of the network of communication and support, which can be a powerful ally for behavioral change. In 1 example, obesity is known to spread through social ties through a diffusion of unhealthy behaviors [57], including physical inactivity [58]. Accordingly, social connections become informal channels of information with real impact on health decisions for self and others, as observed in parenting [59]. Indeed, rapid diffusion of information has been demonstrated within social networks [60]. Such insights could lead to broader understanding of one’s behavior motivators and their environment to launch a precise DHI and form beneficial habits.