Our study showed that the incentive is not what motivated people, at least in this design.

Mitesh Patel

Categorized in : Health News

Future studies should test ways to combine these technologies with effective engagement strategies to change patient behavior to treat or reduce their risk from these conditions, as more sensors of higher accuracy get integrated into smartwatches and smartphones... the prediction accuracy for various health conditions using machine learning techniques will continue to improve.

The study detected a low rate of atrial fibrillation in the overall population(.052 %) but the rates were higher in older patients with 1.3 % in ages 55 to 64 and 3.14 % in age 65 or older, so future work should consider which populations of patients are most appropriate for this method of screening.

The main takeaway is that the design of the incentive is critical to its success, our study can help them [wellness programs] to design these incentives in a way that can be more effective and engage employees that have more to benefit, especially those that are obese, and to take into account that simple changes in the way we frame incentives can have a dramatic outcome in how we influence adults to change their behavior.

People are more motivated by losses than gains, and they like immediate gratification.

In ours, we were pleasantly surprised that 96% stayed.

There is a large body of evidence in behavioral economics that has looked at ways of framing, it's the way our brains are wired that we tend to avoid wanting to lose things more than the benefit we get from gaining them. It makes people think like the money is theirs to lose from day one. By having skin in the game, it makes people more motivated, and we think we can leverage that in these types of programs.

Someone should be encouraged along the way, we've found from studies that if you want to motivate people they need regular feedback.