Behavioral Theory: The Missing Ingredient for Digital Health Tools to Change Behavior and Increase Adherence

Abstract

Behavioral theory is an important factor for designing digital health tools for diabetes to increase adherence to treatment. Many digital health products have not incorporated this method for achieving behavior change. This oversight might explain the disappointing outcomes of many products in this class. Four theories reported to be capable of enhancing the performance of digital health tools for diabetes include (1) Integrate, Design, Assess, and Share (IDEAS); (2) the Behaviour Change Wheel; (3) the Information-Motivation-Behavioral skills (IMB) model; and (4) gamification. Well-designed digital health tools are most likely to be effective if they are deployed in a patient-centered care setting established upon principles of sound behavioral theory. Behavioral theory can increase the effectiveness of digital tools and promote a receptive environment for their use.

Introduction

Achieving adherence to a therapeutic regimen in chronic diseases is difficult to achieve, which often prevents the full benefits of treatments from being realized.¹ Behavioral theory is a key factor in human motivation and adherence. The concept of behavioral theory generally means that it is important to take into account the patients’ psychology in care. Behavioral theories describe the various attempts made by psychologists to propose models of human behavior. An important goal of digital health is to change behavior and increase adherence to recommended treatments. Insufficient emphasis on behavioral theory may be a significant reason why the impact of digital health on health behavior change and related outcomes has been limited.²

Digital health has been the “next big thing” in diabetes for several years.³ Many reasons have been proposed. The most widely expressed reason has been a dearth of high quality statistically significant outcomes data to support use of digital health tools in diabetes from large long-duration randomized controlled trials.^4,5

If inadequate supporting data was merely due to poorly designed trials of existing mobile apps, then the problem could be mostly overcome simply by spending more money to improve the structure of many digital health trials. One solution could involve conducting clinical trials on larger sized randomized controlled study populations to increase the power of the outcome and decrease the p value.⁶ A second solution could be to compare outcomes in very large nonrandomized populations between users and nonusers of the digital health intervention by way of real world data collections. A third solution could be to study the digital intervention for longer periods of time to satisfy digital health skeptics who are concerned about recidivism from healthy behavior inspired by a digital health intervention back to unhealthy behavior over as little as 6-12 months.⁷ While these three approaches might lead to more robust outcomes for some digital interventions, the problem with many digital health tools appears to be more fundamental. A personalized (or one size does not fit all) diabetes mobile app grounded in behavior theory, which accounts for individual differences in psychological traits, may be associated with improved adherence and outcomes.⁸ Many apps are not well designed, and because of this flaw, they do not adequately alter behavior sufficiently to justify their development costs, marketing costs, and time investments by patients and health care professionals. In this article, a solution is proposed to the problem of poorly designed digital health tools.

Unintentional and Intentional Nonadherence

Unintentional and intentional nonadherence should be distinguished.⁹ Although this distinction is not usually taken into account by behavioral theories, it might be useful to predict the chances of success of digital health tools in improving patient adherence to long-term medications. Unintentional nonadherence does not depend on the choice of the patient but on other potentially modifiable factors, such as poor understanding of the treatment,¹⁰ forgetfulness,¹¹ low health literacy/numeracy,¹² or simply irrationality, such as when a patient knows the importance of a treatment but simply does not adhere.¹³ Here digital health tools might be useful to nudge a patient into adherence.¹⁴ In intentional nonadherence, however, patients weigh the pros and cons of following a treatment and rationally decide to not adhere.¹⁵ Here e-tools might not be useful to increase adherence, whereas a good physician-patient relationship can be useful.¹⁶ Therefore, the use of behavioral theory for digital health tools to change behavior and increase adherence by way of mobile applications pertains primarily to unintentional nonadherence.

Behavioral Theory

Mobile health behavior intervention development could benefit from greater application of health behavior theories.¹⁷ At an international workshop on how to create, evaluate, and implement effective digital interventions in 2015, the consensus was that data about behavior change interventions should be collected to test theories of behavior change. The purpose is to account for how outcomes vary across individuals, contexts, and over time so that the results can refine models and allow for individualization of this software.¹⁸

Four purposes of digital health are to increase knowledge, promote healthy behavior, increase adherence to prescribed treatment, and control an effector. While digital health apps that present data have been shown to increase knowledge and apps to control effectors have been demonstrated by engineering labs to control devices, sustained success in promoting healthy behavior and adhering to prescribed treatment has been difficult to demonstrate. Modifying health behaviors is difficult and digital health has not provided a consistent solution to date. A major reason for disappointing results of mobile apps for improving behavior to date is a failure to methodically incorporate behavioral theory into the design of these apps.

Multiple behavioral theories have been used to develop tools to increase adherence in chronic diseases.¹⁹ With respect to creating mobile apps to increase adherence to diabetes treatment regimens, four iterations of behavioral theory are particularly promising for this purpose. These include the Integrate, Design, Assess, and Share (IDEAS) method, the behavioral wheel method, the information, behavioral, and Information-Motivation-Behavioral skills (IMB) method, and gamification. Each offers various levels of documented efficacy worthy of consideration to support future mobile diabetes apps for achieving behavior change and increasing adherence to treatment.

Integrate, Design, Assess, and Share (IDEAS)

IDEAS is a framework and toolkit of strategies for the development of more effective digital interventions to change health behavior.²⁰ This set of processes, which was first published in 2016, is derived from five overarching concepts: (1) behavioral theory, (2) design thinking, (3) user-centered design, (4) rigorous evaluation, and (5) dissemination. IDEAS principles are classified into four categories (Integrate, Design, Assess, and Share). The categories are manifested in 10 phases as listed in Table 1.

Table 1.

List of 10 Phases of the IDEAS Framework.

1. Empathize with target users

2. Specify target behavior

3. Ground in behavioral theory

4. Ideate implementation strategies

5. Prototype potential products

6. Gather user feedback

7. Build a minimum viable product

8. Pilot test to assess potential efficacy and usability

9. Evaluate efficacy in a randomized controlled trial

10. Share intervention and findings

Developing a mobile app using the IDEAS framework includes the following activities for each phase of the framework: Phase 1: Empathize with target users by gathering qualitative insights from users from in-depth interviews and focus groups and understand the needs and motivations of the target group. Phase 2: Specify target behavior by translating broad behavioral goals into highly specific target behavior that is aligned with research findings. Phase 3: Ground the intervention in behavioral theory by identifying behavioral strategies best suited to the target users. Phase 4: Ideate implementation strategies by brainstorming creative strategies for translating theory and insights into app features and after generating many ideas, quickly winnow them down. Phase 5: Quickly develop prototypes and winnow down features to the most promising ones. Phase 6: Gather user feedback through interviews and questionnaires. Phase 7: Build a minimum viable product with only the most essential features and incorporate analytics to collect data on app usage patterns. Phase 8: Conduct a pilot test of the intervention for efficacy and usability using a small-scale evaluation, analysis of usage behavior, interviews, and questionnaires. Phase 9: Evaluate the tool’s efficacy in a randomized controlled trial, while studying the app’s analytics and assessing its broader effects on behaviors and risk factors. Phase 10: Share the results by publishing the results and working with industry partners to also disseminate the results and meanwhile continue to refine the product for potency and usability.

The third phase, which uses behavior theory, can apply a variety of process motivators, which are strategies to initiate and sustain behavior change. Fifteen behavioral strategies that can promote adherence within the context of using the IDEAS framework in a digital health tool are presented in Table 2. Unfortunately, this third phase might not be considered by mobile app developers, if they are unaware of the importance of grounding a mobile app in behavioral theory.

Table 2.

Fifteen Process Motivators That Can Lead to Increased Adherence With a Digital Health Tool.

Challenge

Choice/control

Community

Competence

Competition

Context

Curiosity

Growth mind-set

Identity

Personalization

Piggybacking

Pride

Reframing

Taste

Teamwork

By integrating designing, development, and evaluation of mobile apps for interventions to change health behavior IDEAS combines behavior theory with design thinking by way of a stepwise process. An important feature of this process is that it aligns intervention goals with user goals to avoid mismatches that typically result in poor adherence with the behavior being promoted. IDEAS has been used to successfully develop a mobile app to increase vegetable consumption by overweight adults.²¹

Other multicomponent frameworks have also been proposed for mobile app development.²² None are as comprehensive in covering all the ten phases of IDEAS, and none provides as much granular detail for developing and integrating all the phases for product development as does IDEAS.

Behaviour Change Wheel

The Behaviour Change Wheel (BCW) was first reported in 2011 as a method for characterizing and linking behavior frameworks and behavioral change interventions.²³ This method is based on a wheel-shaped figure with three layers: sources of behavior, intervention functions, and policy categories (Figure 1). The inner behavior system layer involves three essential conditions: capability, opportunity, and motivation. The middle layer contains nine intervention functions, including education, persuasion, incentivization, coercion, training, enablement, modeling, environmental restructuring, and restrictions. The outer ring contains seven categories of policy that could enable those interventions to occur, including communication/marketing, guidelines, fiscal, regulation, legislation, environmental/social planning, and service provision. These rings can be used to characterize behavioral interventions and policies to change behavior. This behavioral theory claims that a successful intervention can be deconstructed into the features of the wheel’s three layers and a new intervention can then be developed using successful combinations of attributes. Based on a planned intervention’s combination of three traits, the likelihood of success can be predicted.

Figure 1.

The Behaviour Change Wheel. This framework for characterizing and designing behavior change interventions consists of (1) a behavior system based on capability, opportunity, and motivation; (2) interventions aimed at changing behavior; and (3) policies supporting behavior change. Original source: Michie et al.²³

The BCW has been used to assess the benefits of mobile app interventions for improving medication adherence.²⁴ In one study, an app based on the BCW was used to encourage antiretroviral medication therapy. The BCW was able to (1) generate a behavioral diagnosis through mapping known antiretroviral therapy adherence barriers onto a Capability Opportunity Motivation-Behavior model of behavior; (2) specify the behavior change techniques that the app promotes; (3) link identified behavior change techniques to corresponding intervention functions of the BCW; and (4) connect these behavior change techniques and intervention functions to respective Capability Opportunity Motivation-Behavior influences on behavior to determine potential mechanisms of action. The authors concluded that their evaluation of this app with the BCW provided useful insights into how and why features of the app could enhance adherence. In another study, the app was used to promote medication adherence and health behavior in an outpatient hypertension intervention.²⁵ The BCW has been used to assess other digital behavioral interventions,^26,27 but it has not been reported to address adherence in any apps for diabetes patients to date.²⁵

The Information-Motivation-Behavioral Skills (IMB) Model

According to the IMB model, the performance of health-promotion behavior is supported by being (1) well informed about the behavior; (2) highly motivated to perform the behavior; and (3) sufficiently skillful to perform the behavior.²⁸ In patients with diabetes, having more information (more diabetes knowledge), personal motivation (less fatalistic attitudes), and social motivation (more social support) has been shown to be associated with healthy behavior, which in turn has been shown to be a useful predictor of glycemic control.²⁹ This approach can be used to build a mobile app for diabetes by using four stages: development—analysis, design, implementation, and evaluation. The development process can use goals extracted from clinical practice guidelines and can provide product features such as personalized education, blood glucose trends, encouragement to write a diabetes diary, and a social media platform to share data and experiences.³⁰ This approach can also be used to identify patient-specific barriers to diabetes medication adherence and suggest interventions targeting patient-specific barriers.³¹ There is little information in the literature about actual outcomes of mobile apps intended to promote improved self-care behavior that are based on this behavior theory.

Gamification

Gamification is a process that uses elements of game design, such as competition, rules, points, and rewards to achieve a goal. Serious games use elements of gamification to engage patients in order to achieve good health.³² New technologies, such as 3D video and virtual reality, are making it possible to build novel digital health tools containing serious games intended to improve adherence to treatment regimens.³³ However, there is little good evidence at this time that gamification is an effective approach for building digital health tools to increase adherence to diabetes regimens.³⁴ Although the rewards offered by gamification tools can lead to short-term improved behavior,³⁵ this method has not consistently been shown to lead to long-term increased motivation and improved outcomes.³⁶

A Framework for Deploying Digital Health Tools to Increase Adherence

Behavioral theory can contribute to optimized deployment of well-designed digital health tools for increased adherence by promoting patient-centered care. In this treatment paradigm, an individual’s health needs as well as their emotional, spiritual, and social needs are the primary basis for treatment decisions.³⁷ Behavioral change and increased adherence to treatment are most likely to occur in a patient-centered care setting.³⁸ Digital health tools for diabetes intended for long-term engagement are most likely to succeed in the context of an empathetic physician-patient relationship that is part of a patient-centered care paradigm.³⁹

A proposed alternative to this relationship is real time coaching via video or text by a remotely located professional, who represents the digital health product and may or may not be part of the traditional health care team,⁴⁰ or else a virtual coach programmed by artificial intelligence.³⁵ Without a human component of a health care professional relating to a patient, however, long-term success from electronic digital tools will be difficult to achieve.⁴¹

Conclusions

Behavioral changes are usually a necessary part of achieving adherence to diabetes therapy. Nevertheless, most digital health tools for diabetes do not apply principles of behavioral theory. In a recent review article about mobile apps to support diabetes self-management, only one of 11 apps studied applied behavioral theory.⁴² Perhaps not coincidentally, few digital tools have been shown to stimulate long-term success in improving outcomes in diabetes. Greater appreciation of the importance of behavior theory and individually designed tools by developers of diabetes digital health tools will likely achieve the behavioral changes and increased adherence to therapy that is definitely needed at this time. It would be worthwhile to formally compare outcomes of adherence to diabetes treatment regimens between digital health tools that do and those that do not apply principles of behavioral health.