The psychology behind New Year’s resolutions that last

Don Stenhoff and Adam Hahs of the MSABA Program

Arizona State University’s Adam Hahs (right) and Don Stenhoff are clinical assistant professors of psychology in the Master's in Applied Behavior Analysis Program. Photo by Robert Ewing/ASU


Each year, millions of Americans commit to changing something in the New Year, like making better financial decisions, improving their fitness or simply enjoying life more. But, approximately 80 percent of resolutions made in January fail by February.

How can people beat that resolution failure rate? Arizona State University’s Adam Hahs and Don Stenhoff know how promote behavior change that will last long past next February. Hahs is a clinical assistant professor in the Department of Psychology and directs the Master’s in Applied Behavior Analysis (MS ABA) program. Stenhoff is a clinical assistant professor of psychology.

“Applied behavior analysis can help discover why people do things, how they respond to reinforcement and their environment, but we take it in a socially significant direction. We improve lives, and improve relationships with other people,” Stenhoff said.

Applied behavior analysis (ABA) changes or modifies behavior by assessing how a targeted behavior is related to the environment. While ABA is often associated with interventions for autism spectrum disorder, it is actually capable of assessing and developing support plans for all sorts of behaviors like parenting challenges, education and strategies to overcome or deal with substance abuse or eating disorders.

ASU’s MS ABA program began three years ago, and the innovative curriculum of the program changes the way behavior analysts are thinking about socially sigificant behavior.


Question: How can someone begin to make a long-term behavioral change?

Hahs: It’s a tricky dance when we consider long-term goals. A lot of the work that behavior analysts do pertaining to goals is to set smaller short-term goals that, when accomplished, provide reinforcement of approximations of more robust repertoires of behavior. In the science of human behavior, we use data that help to support the decisions that we make. When you break down behaviors and related reinforcers, you can better understand the ways in which we can occasion longer-lasting behavior.

Stenhoff: When we discover that we have something we want to change, such as weight loss, improving our relationships or changing a behavior, one of the things you need to look at is whether the behavior has actual value for you? Other people might want that change, but it might not match the values you have for your life.

Q: What are some barriers to behavior change?

Stenhoff: Beyond the personal, internal values of behavior, the other thing that we look at is whether the environment will actually support the new behavior.

If I want to start a weight-loss program in January but I don’t have the time to actually do it successfully, that can be an obstacle to changing my behavior. Or, if I want to make changes with how I interact with other people, do I actually have the opportunity to do so?

One of the things that causes the failure of resolutions is when we set large goals, but they are too large, we are unable to see the intermediate steps that provide the necessary reinforcement to make it to the finish line.

If I set the goal of losing 50 pounds, that could be too large in one chunk. If instead, I set up a goal of losing 6 pounds per month, that intermediate step could help me get to my end goal without being discouraged.

Hahs: One of the biggest barriers to sustained behavior change is not seeing results. In the example of fitness, you might not see an immediate payoff from the effort you are putting in, and the behavior related to trying to do that tapers off.

By setting smaller incremental goals, such as “this week I will run a mile three times,” you are able to track those health behaviors much better.

Q: How important is motivation?

Hahs: Motivation is key. We talk about it a little different in ABA by using the terms "reinforcement" or "values." By values we mean verbally constructed consequences of behavioral variability that establish reinforcers for engagement in that behavior, and engagement in the behavior becomes intrinsically reinforcing in and of itself. We use goal setting such that when we reach those goals we are engaging in behaviors that are in line with our values.

Stenhoff: Reinforcement matters a great deal. It can come from internal sources or from the environment, such as someone to whom we are accountable. Another external motivator is tracking data on graphs or through apps on smart devices.

Without data, we would have no idea if we were making any progress because progress is often not discernable on a daily basis. We really need data to help achieve behavior change.

Q: Does social support matter?

Hahs: The more social support you have for a goal, the better! It is always easier to accomplish behavior change with consistency and with reinforcing feedback. Social support is essential to both.

Stenhoff: We can look at social support in a few ways. Often, we look at it from an external perspective. Maybe we have a “cheater” day or maybe we are getting feedback from the people with whom we interact. Setting up that time to have increased accountability will improve the chance of accomplishing your goals.

More Science and technology


Galaxy PJ0116-24, known as an Einstein ring

Telescopes in Atacama Desert capture extreme starburst galaxy warped into fiery ring

Ten billion years in the past, a rare population of extreme galaxies formed stars at rates more than 1,000 times faster than our…

Graphic illustration of daphnia, a form of zooplankton.

Study challenges traditional views of evolution

In new research, Arizona State University scientists and their colleagues investigated genetic changes occurring in a naturally…

A studio portrait of Kyle Jensen, wearing a white shirt on a dark background lit with orange lighting

Understanding how our perception of AI affects its use

Editor's note: This expert Q&A is part of our “AI is everywhere ... now what?” special project exploring the potential (and…