The New York Times recently posted an article about the return on investment around public health interventions and how American life expectancy at birth increased by 30 years in the time from 1900 to 1999. The article went on to stress that these additional years of life can largely be attributed to advances in public health. Interventions like vaccines, seat-belts, fluoridation of water, and family planning have all changed our behaviors and helped us live longer, happier, and healthier lives. However, not all types of interventions work well for everyone. A lot of people struggle to make long-lasting health behavior changes.
Medical Care recently published an article exploring the differences between responders and non-responders for a chronic disease-focused community health intervention. The intervention, Individualized Management for Patient-Centered Targets (IMPaCT), had been tested in two randomized controlled trials and was shown to improve control of obesity, smoking, mental health, diabetes, and quality of primary care. IMPaCT involved community health workers providing tailored support to help participants meet their chronic disease management goals. In the recent paper, the authors defined non-responders “as those individuals who had worsening of their chronic disease (as measured by an increase in blood sugar (HbA1c), body-mass index, systolic blood pressure, or cigarettes per day between enrollment and 6-month follow-up)” and responders as “those who improved, or at least maintained control of, their chronic disease.”
Upon analysis, the authors identified several similarities and differences across the responders and non-responders. The majority of participants described feeling motivated at the start of study enrollment, expressed a positive opinion of their community health worker, and classified chronic disease management as a high priority. Common barriers across both responders and non-responders included limited access to healthy food, trauma, stress, disability, and insurance issues. Differences included responders moving away from negatively influencing social norms (like eating unhealthy food or smoking). If family or friends were encouraging bad behavior, responders would create new social norms seeking assistance from their community health worker or support group. Responders also identified more concrete barriers to behavior change (such as bad weather or pain), whereas non-responders identified vague barriers (such as “it was situations beyond my control”) that were a harder to address. Finally, and what the authors found most surprising, was how the two groups reacted to failure. Responders could bounce back from failure easier. Non-responders would typically start off optimistic but then become discouraged and disengage when setbacks occurred.
These findings raise an important question with potentially broad implications: why are two similar groups of people having such different responses to failure? The authors posit that when failure is attributed to controllable causes, this can enhance motivation and improve behavior. On the other hand, when failure is attributed to an uncontrollable cause, this can trigger a sense of hopelessness and avoidance.
A related article, titled “Why is changing health-related behavior so difficult?” makes a different argument for why we might be failing at behavior change: policy-making around health behavior change is setting us up for failure. The authors provide six common errors, which we have summarized below:
Source: Created by the authors for this post
To wrap up the post, we wanted to share a creative intervention we recently learned about, described in The New York Times and originally published in the New England Journal of Medicine. The articles describe how barbershops were used to host pharmacist-led interventions to lower blood pressure for African American men. The study enrolled 319 participants, who were patrons of 52 barbershops. Barbers at the intervention shops encouraged participants to follow-up with on-site pharmacists to have their blood pressure measured. Pharmacists had established a collaborative practice agreement and were then able to prescribe anti-hypertensive medications. Participants at control shops received encouragement for lifestyle changes and doctor’s appointments from their barbers. At the end of the six-month intervention, the mean systolic blood pressure of the intervention group fell 27.0 mmHg and 9.3 mmHg in the control group. The mean diastolic blood pressure of the intervention group fell 17.5 mmHg and 4.3 mmHg in the control group.
This intervention found a creative way to circumvent one of the errors described above, that knowledge and information alone drive behavior change. Participants in the intervention group received an extra nudge to obtain treatment for their hypertension, since medication could be prescribed immediately after being screened. Ultimately, the intervention group had better outcomes with their blood pressure compared to the control group. The intervention removed barriers by coming directly to community locations (barbershops), and arranged for information to be delivered from a trusted source (the participant’s barber). It was a testament that we are complex creatures, and there’s a lot more that influences our behavior than just having the right information.
Alexa Ortiz
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Catherine Gupta