Understanding addiction relapse: Let’s start with the individual

*Disclaimer: Please note that parts of this blog have been automatically translated.*

For someone suffering from substance use addiction, it is an immense challenge to quit using. Receiving specialized treatment is often helpful. But what may even be harder than quitting is to then never start again. A relapse is when someone returns to previous levels of substance use dependence.

Studying risk factors for relapse is evidently relevant. By comparing those who relapse and those who do not, relapse has been associated with biological factors (e.g., genetic¹ or neurological structures²), psychological factors (e.g., coping skills³), or social factors (e.g., marital status⁴). These are just some studied risk factors – the range is enormous. What they essentially convey is, based on chance, who may be at risk of relapse. Of course, this information is extremely informative – it enables evidence-based decisions about the broad contours of addiction after-care. However, translating findings from groups of participants back to the individual patient is far from straightforward. If it were, then at discharge from the clinic, clinicians would go through their checklist: inspect the genes, scan the brain, administer surveys, check marital status, etcetera. Then, depending on how many ‘risk factor boxes’ are ticked, they could tell Jack that he will relapse and Mary that she will not. Unfortunately, it’s not that simple. Like Burrhus Skinner said: “the most difficult subject ever submitted to scientific analysis is human behavior”. This certainly applies precision prediction of relapse as well. Therefore, in addition to asking who is at-risk, would it not be crucial to better understand when there is risk for relapse? I’ll use the movie Flight to illustrate. Denzel Washington plays a pilot with a history of alcohol dependence who has managed to stay sober for the past months. One night in a hotel, he discovers a full minibar (see picture). It is not the first time he has been around alcohol after his sobriety, but for some reason, this is the moment he drinks again. From here on out, he rapidly spirals back into his former excessive drinking patterns. Why that moment? Was it pure chance, or did something set the stage for this old pattern to (re-)emerge then? Had he stumbled upon that minibar the day before, or a month ago, would this then too have meant relapse? Asking when someone may be extra susceptible to relapse is the million-dollar question, but how should addiction science get close to an answer? In my opinion, we ought to look in ways counterintuitive to most scientists. We have been drilled to find the one explanation that fits all through big data that reflects many people. From group-level findings, we produce knowledge aimed at helping the individual. But there’s another way. The individual (as opposed to the group) can also be the starting point to produce this knowledge⁵. But first, the uniqueness of each individual has to be appreciated by 1) really listening to the person’s experiences and 2) really looking at what happens with that individual over time. After we’ve drawn conclusions for one person, we move on to the next, to eventually look for between-person (dis)similarities. I believe this will bring us closer to eventually recognizing relapse risk ‘live’. That would enable preventive efforts tailored and timed to the individual. Although we’re not (nearly) there yet, I think we may be further than most of us realize. We have the methods to track individuals intensively over time⁶. We have relapse models that integrate the who and when, with promising results⁷. We have a theory that informs us about personalized signals preceding immediate behavioral change⁸. All it may now take is to take the individual as a starting point. Therapists already do this, but science is (for good reasons) about the large numbers. However, truly understanding when there is a relapse risk first requires rigorous person-specific research efforts.   This blog was written by Daan Hulsmans, Radboud University, for RAD-blog, the blog about smoking, alcohol, drugs and diet.   Relapse scene from Flight:

https://www.youtube.com/watch?v=R4lSEO26Qqo

  References

1. Wojnar et al. (2009). https://doi.org/10.1111/j.1530-0277.2008.00886.x
2. Bach et al. (2019). https://doi.org/10.1111/adb.12571
3. Moore et al. (2014). https://doi.org/10.1037/a0034127
4. Walitzer & Dearing (2006). https://doi.org/10.1016/j.cpr.2005.11.003
5. Hekler et al. (2018). https://doi.org/10.1186/s12916-019-1366-x
6. Shiffman (2009). https://doi.org/10.1037/a0017074
7. Witkiewitz & Marlatt (2007). http://dx.doi.org/10.1016/j.cpr.2007.01.002
8. Olthof et al. (2023). https://doi.org/10.31234/osf.io/f68ej