[This is a preview of some of the exciting mHealth research being presented at the upcoming Medicine 2.0 Congress in September. This abstract and others are candidates for the iMedicalApps-Medicine 2.0 mHealth Research Award]
Liana Lianov MD, MPH, FACPM – Medicine 2.0/iMedicalApps Award Finalist
Changing health behaviors to scientifically recommended actions has the potential to prevent 80% of all chronic illnesses. Not only are healthy behaviors the key to preventing chronic disease, but they are also the mainstay for treating chronic illness.
Yet, one of the greatest challenges clinicians face is successfully facilitating health behavior change among their patients. Experts in behavior modification and lifestyle medicine are calling for innovative and tailored approaches to assist clinicians.
A new psychological framework
One new tailoring approach focuses on patients’ individual personalities. Health behavior change interventions can be aligned with personality-based individual learning and decision making styles. Leveraging these individual traits can lead to successful user engagement and sustainable health outcomes.
Studies have shown that personality types as defined by the Myers-Briggs Type Indicator (MBTI) -the most widely used personality theory across the globe—can determine learning and decision making styles. The theory is based on the work of renowned psychologist Carl Jung who posited that eight cognitive processes influence how individuals perceive information and make judgments and hence can affect motivation and decision-making regarding health habits.
We developed a new psychological framework that merges the Jung’s personality theory with another model (stages-of-change/trans-theoretical). Interventions at each stage-of-change are customized for each personality type focusing on the dominant and auxiliary (most preferred) cognitive processes of that type. We refer to these processes as the individual’s personality strengths. Note: “We” reference undefined.
The framework also incorporates the individual’s preference for judging or perceiving. Those who prefer judging are attracted to a planned approach and those who prefer perceiving are attracted to an open-ended approach to interacting with the world.
Application of personality theory to mobile technologies
Persuasive mobile technologies for health behavior change can be designed to incorporate this framework and leverage individual cognitive strengths. Motivational, action and follow-up prompts are aligned with users’ personality preferences. Users are given the choice of entering their personality type as identified by the MBTI or taking an abbreviated questionnaire to identify their preferred cognitive processes.
The program integrates an algorithm for each personality type in a virtual coach format of feedback and advice. The user enters a desired health action plan and receives guidance customized to his personality at key points during the process of building a new habit, such as when monitoring his progress, looking for ways to stay on track, or dealing with a set back.
Users can experiment by choosing a variety of personality types and receive different coaching algorithms to identify the most comfortable approach. The objective for offering these options is to engage users in the tools and help them quickly move forward with health behavior change plans rather than distracting users with identifying their best-fit personality type.
Prompting users to experiment with cognitive-behavioral tools will likely lead to greater success with engagement and sustainability. Enhanced versions of the program will allow the user to interface with the program according to the user’s preferred learning style. For example, design and user interface might emphasize step-by-step versus open-ended instructions.
In addition to these personality-based tailored approaches for promoting engagement, the program incorporates evidence-based behavior change principles. Examples include prompting users to take small action steps and checking confidence levels to build self-efficacy, providing ongoing feedback, rewarding short and long term goals, and assuring support from social networks. The framework can be incorporated into a variety of mobile technologies to assist clinicians and their patients. The clinician can prescribe a health behavior treatment plan and enter the plan into the program that can support the patient between clinical encounters.
Enhancing clinical practice and examples
Several standard health behavior change approaches may benefit from this new approach. Health coaches are generally encouraged to develop a specific action plan for each patient who is ready to start a health action plan. Ideally, the plan includes the specific activity, frequency, schedule, and social supports to assure adherence.
Digital coaching programs often apply this model as well. Such an approach works best for individuals who have a judging preference. In other words, they prefer to plan in advance and are motivated by seeking a sense of closure when completing a pre-planned task.
This approach, however, would not be as helpful to an individual who has a perceiving preference and needs flexibility and spontaneity to stay engaged. A mobile program with reminders to complete a task could easily be resisted or ignored by someone who prefers this open-ended way of accommodating tasks.
Mobile programs might be more successful for such individuals by allowing flexibility and spontaneity to achieve broad scientifically recommended goals.
A great example applies to physical activity. Instead of guiding the individual to schedule a 50 minute walk three times a week on a set schedule, the program could guide the individual to commit to walking three times a week without a schedule. The individual could even simply commit to being active throughout the week for a certain number of minutes. He checks-in or uses sensors to track his activity. The mobile program sends reminders about the remaining number of walks or minutes of activity in the plan as the week progresses. Rewards are built in as long as the goal is met by the end of the week.
This personality-based model was tested for feasibility with eleven individuals participating in a patient support group. Qualitative analysis of individual participant interviews suggests that users find it engaging and one consistent pattern emerged. Interviewees reported that an action plan emphasizing their preference for engaging with the external or internal world was most useful. Next steps to study the model will include quantitative analysis of process and outcomes measures of mobile application users.
Mobile technologies offer an opportunity to tailor health behavior change interventions based on individual cognitive process preferences in order to achieve and sustain healthy habits. Busy clinicians can refer patients to use such tools between clinical encounters. Applications that can be customized to specific medical conditions, as well as different personalities, can be prescribed by busy clinicians to enhance patient self-management.
Author: Liana Lianov MD, MPH, FACPM
Dr. Lianov is a physician entrepreneur, board certified in internal medicine and preventive medicine, focusing on lifestyle medicine. She serves as president of the American College of Lifestyle Medicine, assistant professor at the University of California Davis, and coordinator for the health and wellness interest group of the Association of Psychological Type International. She founded HealthType™ as a result of her passion to develop and study the impact of scalable personalized interventions to support healthy behaviors. She previously directed the Healthy Lifestyles Division of the American Medical Association.