Once the preserve of Hollywood movies, the age of AI is now upon us. AI has a wide range of uses within society, and the list is always growing. AI is now being used in security and surveillance, social media, driverless planes and cars, and more.
Researchers have also recently found that AI can even help predict when someone might be about to experience a relapse from alcohol. This means that someone with an alcohol problem may be able to attend a recovery meeting or even go to rehab for alcohol addiction before they relapse.
A study recently published in the journal Alcoholism: Clinical & Experimental Research found that AI could be used to predict indicators of an impending relapse. The research found that the most important information that could help predict relapse included factors like liver enzyme levels, the age that alcohol dependence began, and patient reports on drinking behaviors and psychological symptoms they were experiencing. The algorithm is then able to accurately predict the likelihood that someone will relapse.
While this is still in a very early stage of research, it is hoped that this kind of AI-based approach could eventually be used to help reduce the rates of relapse among people with alcoholism. It is thought that it could also be applied to other substance abuse disorders, such as addiction to opioids.
At present, there is no sure-fire way to prevent relapse among people with alcoholism. However, this research provides a glimpse of how AI might one day be used to help people stay on the road to recovery.
It is important to note that this kind of AI-based approach is still in its infancy, and it will be some time before it is ready for widespread use. However, the potential for this kind of technology to help reduce rates of relapse is very exciting, and it will be interesting to see how this research develops in the future.
What Can Cause Alcohol Relapse?
There are a number of signs that suggest someone might be at risk of alcohol relapse. These include exposure to triggers, stress, interpersonal problems, positive moods, pain due to injuries or medical issues, lack of social support, peer pressure, and being hungry or tired.
Each of these causes will be discussed in more detail below, along with how AI might reduce the chance of relapse.
Exposure to Triggers
One of the main signs that someone might be at risk of alcohol relapse is exposure to triggers. These triggers can be anything that reminds the person of drinking or makes them want to drink.
Some common triggers include being in places where alcohol is served, such as bars or clubs, or being around people who are drinking. Other triggers can be more personal, such as certain emotions or situations that make the person feel like they need a drink.
AI can help to identify these triggers and warn the person when they are at risk of exposure. For example, if a person is using a social media app, AI can analyze their posts and comments to look for triggers. If the person is using a GPS system, AI can track their location and warn them if they are getting close to a trigger.
Stress
Another sign that someone might be at risk of alcohol relapse is stress. Stress can come from many different sources, such as work, family, or personal problems.
AI can help to identify when someone is feeling stressed and can provide them with support or resources to help deal with the stress. For example, if the person is using a chatbot, AI can analyze their conversation to look for signs of stress. If the person is using a fitness tracker, AI can track their heart rate and warn them if it starts to increase.
Interpersonal Problems
Interpersonal problems can be another sign that someone might be at risk of alcohol relapse. These problems can include arguments with family or friends, relationship problems, or financial difficulties.
AI can assist in identifying when someone is having interpersonal problems and can provide them with support or resources to help resolve the issue. If the person is using a financial management app, AI can track their spending and warn them if they are at risk of financial difficulties. This may reduce issues with loved ones regarding money.
Positive Moods
Positive moods can also be a sign that someone might be at risk of alcohol relapse. This is because people often drink to celebrate or relax, and positive moods can trigger these desires. Again, chatbots may help. In the future, AI may also be able to analyze brain waves to tell when someone is in a heightened mood which may predispose them to relapse.
Pain Due to Injuries or Medical Issues
Pain due to injuries or medical issues can also be a sign that someone might be at risk of alcohol relapse. This is because people often drink to self-medicate, and pain can trigger this desire.
AI may be able to recognize when a user is in pain that is serious enough to cause a relapse and identify and suggest strategies that will help the user to decrease their pain and therefore the chance of relapsing.
Lack of Social Support
Another sign that someone might be at risk of alcohol relapse is a lack of social support. This can include feeling isolated from family and friends, or feeling like there is no one to turn to for help.
AI might not only be able to help figure out if someone is at risk of alcohol relapse due to a lack of social support, but also give them social support to help prevent the relapse.
Being Hungry, Angry, Lonely, or Tired (HALT)
Finally, being hungry, angry, lonely, or tired can also be a sign that someone might be at risk of alcohol relapse. This is because these emotions cause someone discomfort that they may wish to extinguish with alcohol.
Wearables are currently being developed that may be able to analyze when wearers are in any of these states, warn them that their risk of relapse has increased, and suggest steps that they can take to reduce their relapse risk.
Conclusion
In conclusion, AI shows promise in helping to reduce the rates of relapse among people with alcoholism. This is still a very early stage of research, but the potential for this kind of technology to help people stay on the road to recovery is very exciting. It will be interesting to see how this research develops in the future.
