As the healthcare tech market matures, the focus of consumer oriented weight management products will shift from merely data gathering and generic weight loss plans to applied analytics for building successful personalized plans for change resistant users. To be most effective at addressing preventable health costs, efforts need to be focused on patients at the wrong end of disparities in health outcomes. The key will be identifying predictive frameworks through which data can be transformed into insights.
One of the frameworks that we employ within the algorithms behind Yabbit, our behavioral risk management platform, is the Health Belief Model. We talked earlier about the Health Belief Model and how it works. It states that an individual’s likelihood of engaging in a health related behavior is determined by his/her perception of the following six variables:
Perceived susceptibility (perceived risk for contracting the health condition of concern);
Perceived severity (perception of the consequence of contracting the health condition of concern);
Perceived benefit (perception of the good things that could happen from undertaking specific behaviors);
Perceived barrier (perception of the difficulties and cost of performing behaviors);
Cues to action (exposure to factors that prompt action);
Self-efficacy (confidence in one’s ability to perform the new health behavior).
These six health determinants identified by the Health Belief Model together provide a framework for designing both long and short-term health behavior interventions for conditions that extend further than tuberculosis screening programs: osteoporosis prevention, smoking cessation and healthy eating are all valid and potential uses of this model. The common denominator is that the Health Belief Model focuses on health determinants; therefore, it is most suitable for addressing problem behaviors that have health consequences.
So how predictive is the model in general?
The initial four HBM variables (susceptibility, severity, benefit, and barrier) have been shown to predict about 20% of variance in healthy behavior. In the pursuit of greater predictive power two additional variable, self-efficacy and cues to action were added by 1988. Domain-focused studies for areas such as HIV related mental health have also added domain specific variables to the HBM. For example, a 2003 HIV study extended the HBM to include HIV-related stigma as a variable. Such studies, when combined with the self-efficacy variable addition, have seen an average predicted variance around 40%. The HIV study saw predicted variance of 63%. The downside of the domain specific approach to extending the HBM is that the added variables are not broadly applicable to a wide range of health behaviors.
To further expand the broad predictive power of the HBM, we have also incorporated the variables used by Orji, Vassileva, and Mandryk of the University of Saskatechewan in their 2012 study:
Self-Identity, Perceived Importance, Consideration of Future Consequences, and Concern for Appearance.
Self identity refers to a patient’s self-perception in relation to a particular health behavior – ie “I am a health conscious person” or “I have an incurable sweet tooth.” Perceived importance refers to the value a person places on the outcomes of a particular behavior, both positive and negative. Consideration of future consequences refers to the extent to which a person’s decision-making is influenced by the potential outcomes of their current behavior. Concern for appearance refers to the importance a person places on behaviors that affect their physical appearance.
The HBM, when extended by the variables included by Orji et al, increases its predicted variance in healthy behavior to 71%. In conjunction with the biometric and GPS data we are also gathering with Yabbit, we provide a system with a high predictive threshold for patient behavior at a very granular level by various demographic characteristics. This presents the necessary insights for care providers and population health managers to work together with health plan administrators to build more cost effective models for supporting healthy behavior change.