Nonmodifiable factors are generally immutable and/or recalcitrant to change, which include sociodemographic (eg, age, race/ethnicity, sex) and psychosocial and behavioral (socioeconomic status [SES]) factors that individuals have little to no control over. Traditionally, adherence to primary prevention and management of chronic health condition solutions mistakenly target nonmodifiable factors to alter and increase adherence behaviors. Such an assumption would yield flawed assertions such as your race or sex determines your level of adherence to therapies and healthy behaviors. This should not be confused with acknowledging the likelihood that someone’s race or sex may be an important factor in their adherence behaviors. The distinguishing factor between the two approaches is that the former uses nonmodifiable factors to develop interventions that target subgroups, whereas the latter uses nonmodifiable factors to tailor interventions for subgroups of individuals that share common lived experiences (eg, neighborhood). For example, instead of targeting blacks, we can tailor interventions for low-income blacks living in midsized urban neighborhoods.

We acknowledge that there are rare times when nonmodifiable factors are the root cause of nonadherence behaviors, such as race-specific adverse medication side effects and suboptimal response to medications by certain racial/ethnic groups (eg, ACE inhibitors). Despite this, we argue that the fundamental premise of interventions aimed at increasing adherence behaviors is to modify factors that facilitate or impede adherence and to do this exclusively with nonmodifiable factors (eg, race, family history, and SES) is impossible. Instead, the process of tailoring interventions around nonmodifiable factors, whereby contents and activities of an intervention are geared toward cohorts of people classified by nonmodifiable factors (eg, race, family history, or SES) is a better strategy for obtaining adherence. Tailored solutions should use nonmodifiable factors to develop heterogeneous profiles of individuals to determine appropriate and congruent strategies to effect positive behavior change and maintain adherence to such behaviors.

Compared with nonmodifiable determinants of adherence, modifiable determinants can be altered to increase adherence to primary prevention and management of chronic health conditions strategies. Some notable modifiable determinants of adherence include social support, motivation, emotional status, stress, health literacy, forgetfulness, health system, patient-provider communication, cost of health services, and health coverage and insurance.

Develop a detailed idiographic profile represented as a meta-cognitive/mind map (Figure 2, idiographic profile and care journey of chronic disease) using an individual’s behavioral (behaviors that impact health and adherence to healthy lifestyle and wellbeing, such as sleep, physical activity, diet, and stress), demographic (eg, age, sex, and race/ethnicity), psychosocial (neighborhood and household context), clinical (any medical diagnosis or risk of disease), and cultural (attitudes, beliefs, and cognitions about health and wellness) data. Idiographic profiles consist of modifiable and nonmodifiable determinants of the 6 As of the chronic disease care journey: awareness, avoidance, access, assessment, acceptance, and adherence. The goal of a personalized approach is to target modifiable determinants (such as attitudes, behaviors, beliefs, and cognitions) to increase the likelihood of awareness, avoidance, access, assessment, acceptance, and adherence behaviors.

Develop a generic health care-continuum journey map to achieve adherence for a specific health condition via focus groups from multiple stakeholders. The care journey map should identify critical bottlenecks and decision points (moments where participants are likely to relapse or adhere) in achieving treatment adherence as well as potential modifiable factors (intentions, behaviors, and motivations) for each bottleneck and decision point. As key modifiable factors for each bottleneck and decision point are idiosyncratic, developers should capture as much data on a wide range of potential modifiable factors before intervention deployment.

Develop and train a prediction algorithm that can identify critical decision points (vulnerable and opportune times to adhere) based on the individual’s idiographic profile and care continuum journey map. This is best achieved by 1 of 2 strategies: (1) an a priori approach (profiling the individual via baseline data collection and determining if their idiographic profile fits a previously validated profile) and (2) a posteriori approach (developing an idiographic profile via baseline data or a phase-in stage where the individual is observed without being exposed to any intervention). To validate decision points, it is important to capture the input of patients in real time via responses to queries from ecological momentary assessments to maximize the timing, frequency, and duration of the intervention [83,84].

After validation of decision points and bottle necks, the care journey must undergo further refinement. Beyond the initial stages of intervention exposure, the idiographic profile of each individual must be constantly updated to reflect any changes in their profile. To do so, we will capture in real time, participants’ responses to ecological momentary assessments to maximize timing, frequency, and duration of intervention exposure over a prolonged period. These data will be consistently ingested, stored, and analyzed through cloud computing. Insights obtained from cloud computing algorithms will trigger the delivery of adaptive and personalized behavioral interventions to sustain healthy behaviors. In summary, the proposed personalized models will amalgamate patient-level factors with real-time changes to create dynamic idiographic profiles, enabling the delivery of just-in-time messages that are responsive to real-time context.

Despite the proliferation of mHealth solutions and the high uptake and short-term use of such solutions, prolonged use is low and compromises long-term adherence. To engender long-term use of mHealth, it is important to address modifiable and nonmodifiable barriers. Although mHealth solutions cannot fully address the inhibitory effects of nonmodifiable barriers to adherence, they offer real and novel strategies to overcome and circumvent modifiable barriers. Some of these barriers are poor engagement, lack of motivation, limited support to achieve healthy behavior change across different contexts, inadequate health literacy, and limited access to evidence-based strategies to support healthy behavior change in real time.

One major patient-level modifiable barrier is lack of motivation, as patients may find it difficult to complete recommended tasks to achieve healthy behaviors and lifestyle. mHealth tools can modify motivation by providing timely motivators (eg, rewards, conditioned stimuli, and reinforcers) and information that may activate intrinsic beliefs and attitudes and thus induce positive behavior change. One system-level modifiable barrier is access to evidence-based strategies to support healthy behavior changes in real time. Unlike a health care provider, mHealth solutions can provide ubiquitous support (eg, out of office) to patients via mobile devices. Mobile solutions can be easily integrated into a patient’s daily routine to increase adherence to medical treatment and advice and primary prevention and management of chronic health conditions.

First, mHealth solutions can make adherence easier and more user-friendly for patients by reducing the cognitive demand and fatigue required for optimal adherence. Specifically, mHealth solutions can optimize prospective memory and information processing, improve health literacy, reduce recall bias on health behaviors and lifestyle practices, provide social support through networks of patients with similar conditions, and facilitate effective and timely patient-provider communications. Second, mHealth solutions can also address psychosocial and health system barriers to adherence by reducing the cost of health care and increasing access to specialized and expensive health care providers. Third, mHealth solutions also allow patients to monitor their health on their own time, and access and communicate with health care providers at convenient times.

Health care providers also benefit from the use of mHealth solutions. Since providers are heavily reliant on subjective and sometimes unreliable patient reporting, an mHealth solution would allow providers to continuously monitor patient performance and adherence, reducing the likelihood of recall bias, and increasing the possibility of more personalized and adaptive interventions. mHealth solutions allow providers to be more accessible to their patients with less effort to provide real-time support through several engineering technologies, such as push-notification reminders and alerts to comply with recommended treatments, automated bots with artificial intelligence knowledge banks that can provide dynamic information to patients based on their unique conditions, and effective strategies and tips to optimize medication and healthy behavior/lifestyle adherence through the internet of things⸺things/devices that independently or jointly with other things/devices collect, store, and process data via digital or sensor-based technology, for example, wearable sensors.

The development of such a health service delivery system requires novel engineering and a paradigmatic shift in practicing medicine from a warehouse, a one-size-fits-all approach to a personalized and optimized system. An mHealth solution rooted in a pantheoretical framework (Figure 1) can revolutionize the delivery of health services from a one-size-fits-all to a personalized and optimized approach. A personalized and optimized approach can optimize adherence to prevention strategies and management of chronic health conditions, which will go at the patient’s pace, thus optimizing the likelihood of adhering to recommended medical treatment and advice, making health behavior goals more achievable.

Despite the potential benefits and successes of a personalized and optimized mHealth solution for adherence, there are some people for whom this strategy may not work. Therefore, it is important to identify those patients who are ideal candidates. There are two strategies that can be employed to determine a patient’s candidacy. The first approach utilizes an a priori strategy to generate a behavioral profile that includes the patient’s likelihood of being engaged, ready to participate, and adhere to an mHealth solution. The second strategy utilizes a trial-and-error run-in phase. This strategy exposes patients to the mHealth solution and monitors their level of engagement, participation, and adherence, and if after a certain period, the patient is not responsive, then it is likely that they may not be a good candidate. However, if it is critical that the patient participates in this mHealth solution, it is highly recommended to enroll the patient in a pretreatment preparatory training, which will help them be more responsive to an mHealth solution. For example, individuals above 65 years (older adults), with a chronic health condition, low-income background, racial/ethnic minority background, or inadequate health literacy are traditionally not ideal candidates for an mHealth solution [85]. Unfortunately, these are the people who need a personalized and optimized mHealth solution most. Therefore, pretreatment preparatory training would entail identifying an individual’s unique barriers to engagement with mHealth solutions and creating a graduated and sequential curriculum that involves education, simulated, and real-world use of mHealth solutions.

Technical, psychosocial, and financial factors that affect the adoption and prolonged use of mHealth are often overlooked yet critical barriers. Users’ reluctance to embrace mHealth solutions may include fear of technology, inability to purchase, download, and navigate mHealth and digital apps and solutions, and overly stimulating and busy interfaces making following instructions and user experience and engagement more challenging. These barriers might explain why in general one-third of individuals stop using mobile and digital solutions within 6 months of download [86].

Cost, access, privacy concerns, user satisfaction, technology literacy, and proficiency are barriers to mHealth use. Despite the fact that 1 in 5 Americans use an mHealth solution, prolonged use depends on user satisfaction, learnability, efficiency, errors, and memorability [83,84]. Data privacy and security may be other areas of concern, particularly for shared personal health information [87]. Barriers to mHealth adoption may also be due to cost, given the moderate to high-income user base [84]. The cost of a mobile device and monthly telecommunication service charge (the traditional model in the United States) serve as two major barriers. Finally, many mHealth solutions require the purchase of expensive wearable or fitness tracking devices for real-time tracking and sophisticated data visualization of user performance. Individuals are often priced out of mHealth solutions, thus giving rise to health technology inequities, where only upper- and middle-class individuals will likely benefit. Despite these limitations, the proliferation of cheaper and more affordable mobile devices as well as their use in adjunctive services provided by health care facilities and payers in comprehensive prevention health programs serve as silver linings.