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Posts Tagged ‘research’

Quitting smoking without help is hard: Effects of motivation and other personality factors

October 14th, 2012

Quitting smoking is hard, but that suggestion probably isn’t terribly exciting all on its own since most of our readers probably knew it already. Still, while we’ve talked about quitting smoking using nicotine replacement and medication, we haven’t really touched the subject of all those people out there who just decide to give quitting smoking a try one day without those patches, gums, or pills.

Since something like 95% of those who try their hand at quitting smoking relapse within one year, and most of these people try to quit unaided, I think this is an important topic to touch on. Fortunately, recent research conducted in the U.K. tried to assess the personality and cognitive aspects that end up predicting who will succeed, or fail, in their quit attempt.

The effects of expectation, motivation, and impulsivity when quitting smoking

Quite a bit of research has already shown that when smokers are trying to quit (so we’re talking early on during abstinence), their brains react differently to stimuli in the environment depending on the relationship between those stimuli and nicotine. Stimuli that aren’t associated with smoking (or some other form of nicotine intake) get less attention and show overall less activation of important brain circuits while nicotine associated cues light up the brain just as if nicotine was on board (even though participants were drug free at the time). Essentially, if a stimulus predicts getting a hit, the brain gets smokers to pay attention to it so that they can do whatever is necessary and get a little drug in. Throw in some of that reduced ability to control behavior that we talk about so much (like impulsivity), and which is common not only in smokers but in users of almost every other drug (heroin might be the exception) and you have a recipe for disaster, or at least for a good bit of smoking relapse. And yet if we want to fight the horrible health consequences of cigarettes, then quitting smoking has to be made easier, which nicotine replacement and medications like bupropion have done to some extent.

As part of this equation, knowing the specific predictors of early relapse in people who are quitting smoking may be useful so that professionals planning smoking interventions can do a better job of targeting the most important factors. The study recently published the journal Psychopharmacology tried to assess the relationship between the severity of smoking, the above-mentioned personality factors, and the success of the quitting attempt.

The cool thing about this study is that the 141 people who participated were assessed on a whole set of these cognitive tests twice – once after a smoking free night and a nicotine lozenge and another time after a smoking free night followed by a nicotine-free lozenge. While they couldn’t tell which was which, the procedure gave the researchers an assessment off how different participants’ reactions were with or without nicotine on board. Following the assessments participants were directed to begin their attempt at quitting smoking. While they were asked not to use nicotine replacement options or other medications, they were allowed to use any other resource available and were given a set of information pamphlets that explained expected side effects and likely difficulties during the quit attempt. They were then followed up after 1 week, 1 month, and 3 months. Quitting was identified as minimal smoking (less than 2 cigarettes per week) and was verified both by self report and cotinine testing. There was a small financial incentive to quitting, with people who relapsed after a week getting only £40 (about $60) and those who made it through month 3 getting £150 (about $250), though I’m pretty sure that if $200 was enough to make people quit we’d have just paid up already…

The first thing to note in the results was that 24% of the participants were still not smoking at the 33 month followup. This seems to be about on par with the usually low success rates at 1 year though I’m sure this research group will try to continue following these participants at least up to the 1 year mark and hopefully produce another paper.

The overall most reliable predictor of who quit and who relapsed ended up being the level of nicotine dependence as measured by the participants’ pre-quit attempt cotinine levels and the number of cigarettes they smoked every day. Since cotinine assessments are less biased, it was the most predictive of all throughout the experiment (# of daily cigarettes was no longer predictive at 3 months). Interestingly, self reported impulsivity and smokers’ initial ratings of cravings for cigarettes didn’t end up predicting relapse at all, but those cognitive tests assessing the quitters’ reactions to nicotine associated cues told a pretty interesting story: It seems that early on during their quitting attempt smokers who had more general interference with their cognitive function relapsed sooner. These cognitive problems can be thought of as interfering with normal thinking by nicotine-related cues and maybe even more general interference with brain function. After the 1-week follow-up, at the 1 and 3 month assessment, the odds of quitting had more to do with baseline assessments of motor impulsivity as well as those initial cotinine levels assessing the degree of nicotine dependence.

The take-home: Quitting smoking is hard for different reasons in the first week and later on

If you’ve ever tried to quit you’ve been told you that the first week is the hardest and that once you make it through that the rest is a piece of cake. While this research doesn’t necessarily support that notion, since about 25% of the sample relapsed between each of the followups, it does seem to indicate that the reasons for relapse change after that first week.

It seems that the first week may be difficult because of general cognitive interference by stimuli and cues that are nicotine associated. Those cues make it hard to pay attention to much else and they interfere with normal thinking and attention process, making sticking to the quit attempt difficult. After that point, successfully quitting smoking seems to be associated more with the level of initial smoking and that damn motor impulsivity test. The finding that heavier smokers have a harder time quitting isn’t new and isn’t surprising, but the fact that cognitive effects and predictors of relapse change does suggest that the interventions likely to help smokers quit may need to be different during week 1 and afterward.

Overall, these findings suggest that the cognitive function problems associated with quitting smoking (or smoking in general) may recover faster than do some of the other physiological factors associated with quitting since the initial levels of smoking continued to be highly predictive throughout the 3 month period of followup. Another explanation could be that initial smoking levels affected brain function in ways not assessed by these researchers.

Since so many smokers relapse within the first week (more than 50%), it seems to me that interventions that really focus on the cognitive interference and the extreme attention towards nicotine associated cues and stimuli would be helpful for those quitting smoking. Maybe if we can reduce relapse numbers at 1 week we can have a more gradual fall-off for the following month resulting in significantly higher quit rates.

Interestingly, NIDA and other research organizations are getting really interested in the use of technologies like virtual reality for help in addiction training. It seems that in this context, these sorts of treatments might be useful in helping early quitters train to avoid that cognitive interference. Additionally, medications like modafinil, and maybe even other ADHD medication could be used very early on for those quitting smoking to help recover some of their ability to control their attention thereby reducing the power nicotine associated stimuli have over them. I guess we’ll have to wait and see as those who develop interventions start integrating this research. In the meantime, I’d love to hear from readers who have quit or tried to quit: Does this research seem to support your own experiences?

Citation:

Jane Powell, Lynne Dawkins, Robert West, John Powell and Alan Pickering (2010). Relapse to smoking during unaided cessation: clinical, cognitive and motivational predictors, Psychopharmacology.

 

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The first thing to  note in the results was the 24% of the participants were still not smoking at the 33 month followup. This seems to be about on track for the normally low success rates at 1 year though I’m sure this group will try to follow these individuals up at that point and hopefully produce another paper. The overall most reliable predictor of who quit and who relapsed ended up being the level of nicotine dependence as measured by the participants’ pre-quit attempt cotinine levels and the number of cigarettes they smoked every day. Since cotinine assessments are less biased, it was the most predictive of all throughout the experiment (# of daily cigarettes was no longer predictive at 3 months). Interestingly, self reported impulsivity and smokers’ initial ratings of cravings for cigarettes didn’t end up predicting relapse at all, but those cognitive tests assessing the quitters’ reactions to nicotine associated cues told a pretty interesting story: It seems that early on during their quitting attempt smokers who had more general interference with their cognitive function relapsed sooner. These cognitive problems can be thought of as interruption with normal thinking by nicotine-related cues and maybe even more general interference with brain function. After that point, at the 1 and 3 month follow-ups, had more to do with baseline assessments of motor impulsivity as well as those initial cotinine levels assessing the degree of nicotine dependence.

The take-home: Quitting smoking is hard for different reasons in the first week and later on

If you’ve ever tried to quit you’ve heard someone telling you that the first week is the hardest and once you make it through that the rest is a piece of cake. Well, this research doesn’t really support that notion since about 25% of the sample relapsed between each of the followups, but it does seem to indicate that the reasons for relapse change after that first week. It seems that the first week may be difficult because of general cognitive interference by stimuli and cues that are nicotine associated. Those cues make it hard to pay attention to much else and they interfere with normal thinking and attention process, making sticking to the quit attempt difficult. After that point, successfully quitting smoking was associated more with the level of initial smoking and that damn motor impulsivity test. The finding that heavier smokers have a harder time quitting isn’t new and isn’t surprising, but the fact that cognitive effects and predictors of relapse change does suggest that the interventions likely to help smokers quit may need to be different during week 1 and afterward. Overall, these findings suggest that the brain function problems associated with quitting smoking (or smoking in general) may recover faster than do some of the other physiological factors associated with quitting since the initial levels of smoking continued to be highly predictive throughout the 3 month period of followup. Another explanation could be that initial smoking levels affected brain function in ways not assessed by these researchers.

Since so many smokers relapse within the first week (more than 50%), it seems to me that interventions that really focus on the cognitive interference and the extreme attention towards nicotine associated cues and stimuli would be helpful for those quitting smoking. Maybe if we can bring the relapse numbers down at 1 week we can have a more gradual fall-out for the following month resulting in significantly higher quit rates. Interestingly, NIDA and other research organizations are getting really interested in the use of technologies like virtual reality for help in addiction training. It seems that in this context, these sorts of treatments might be useful in helping early quitters train to avoid that cognitive interference. Additionally, medication like modafinil, and maybe even other ADHD medication could be used very early on for those quitting smoking to help recover some of their ability to control their attention thereby reducing the power that nicotine associated stimuli have over them. I guess we’ll have to wait and see as those who develop interventions start integrating this research. In the meantime, I’d love to hear from readers who have quit or tried to quit: Does this research seem to support your own experiences?

Citation:

Jane Powell, Lynne Dawkins, Robert West, John Powell and Alan Pickering (2010). Relapse to smoking during unaided cessation: clinical, cognitive and motivational predictors, Psychopharmacology.


Posted in:  Drugs, Education, Tobacco
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Decision-making and alcoholism: what’s the risk?

March 18th, 2012

By James R. Ashenhurst

Every day we are faced with decisions to make, both simple and complex: should I buy the bargain brand cereal or do I really want to pay more for those Cheerios? Sometimes, we’re faced with decisions that carry a bit more risk to our health and safety: should I jump out of this plane and skydive like I planned to, or is the risk that something might go wrong too high? In the addiction world, decisions must be made about the risks of buying and using drugs and alcohol: Should I really be driving home from the bar now, risking a DUI? What if the police catch me buying crystal meth?

People naturally vary in the amount of risk they are generally willing to take. Especially when potential rewards are great, some people will take rather extreme risks, while others are more hesitant. Clearly, the world needs risk-takers to brave the waters with new business ideas, or to risk rejection to gain romance. Risk-taking is by no means a uniformly bad trait. But, when it comes to drug use, how might having a risk-taking personality affect how people choose to use? Understanding how risk-taking relates to drug and alcohol dependence (alcoholism) might help clinicians and addiction treatment centers be more effective by making patients aware of how their own risk-propensity influences their disease.

The difficult part of answering these questions is deciding how you’re going to figure out exactly how risk-taking a person is. In the past, many researchers used simple self-report questionnaires that boil down to essentially asking participants how risk-taking they think that they are. However, there is a good deal of self-report bias when using these questionnaires; in other words, the accuracy of a person’s answer depends on how self-aware they are and how well they evaluate themselves compared to others (which also requires them to evaluate others objectively). To deal with this problem, Carl Lejuez developed an elegantly simple experimental task that avoids self-report bias: the Balloon Analogue Risk Task [1] (named the BART in honor of The Simpsons, they also made a task called the MRBURNS).

Balloon analog risk-taking taskIt works like this: you see a balloon on a computer screen and you can press a button to inflate it by a small amount. Every time you inflate it, you get a small amount of money. But, there is always a chance that when you inflate it, the balloon will pop and you’ll loose all the money you’ve accumulated for that balloon. You can also decide to “cash out” at any point and add the money you’ve earned to a guaranteed bank and move on to the next balloon to pump. Participants actually receive the money they’ve banked in the task. So, how far would you go?

As it turns out, how people behave in this task relates pretty well to how they behave in the world (this is known as external validity); a person who inflates a lot (and probably pops more than a few balloons) is more likely to not wear a seatbelt, practice unsafe sex and, yes, experiment with drugs and drink problematically [1] [2, 3]. Also, a twin study has shown that risk-taking in the BART is heritable in males [4] and I have demonstrated that behavior is heritable in a rat version of the task [5], suggesting that at least some of it is due to nature and some due to nurture. This is good news for medical research, because it means that there is some discoverable biological pathway that determines, in part, how people behave in the BART.

Still, this preliminary research about the BART and alcohol was gathered from young undergraduates who do not have long histories of alcoholism or drug dependence. Thus, for my research, I wanted to know how older folks who are diagnosably alcoholic might behave in the BART [6]. We invited 158 gracious volunteers from the Los Angeles community (who identified themselves as having problems with alcohol) to the lab and evaluated their dependency severity under the same guidelines used by psychiatrists in the DSM-IV. We also had them play the BART. My prediction was that participants with more severe alcoholism would also tend to be bigger risk-takers.

To my surprise, everything flipped around. People who were more risk-taking (inflated the balloons bigger) actually had fewer alcoholism symptoms. In other words, the more severe the case of alcoholism, the less risks they would take in the BART. How could this be, and what does this tell us about the role of risk-taking in alcoholism?

There are several possibilities. For one, it could be the case that while young risk-takers tend to drink problematically, as alcoholism develops, it is actually the problem-drinkers who are more risk-averse who tend to go on into more severe cases of alcoholism. This theory relies on the idea that risk-taking personality is fixed and doesn’t change much in adulthood; it might be a stable trait that influences the developmental course of alcoholism.

It could be, however, that the trait is not always stable across a lifetime, and experience with alcohol changes one’s risk-taking personality. If we assume instability, it could be either social and/or biological factors that cause the change. Maybe people with more severe alcoholism face more problems in their personal life, and this changes their temperament to be more risk averse. Or, it could be that the continued exposure to a lot of alcohol changes the parts of the brain that evaluate risks and underlie the decision-making process. It is well-known that chronic exposure to alcohol at high levels for long periods of time changes the quantity and subtypes of neurotransmitter receptors in the brain as part of an adaptive process; the brain adjusts itself to tolerate the constant signals it’s getting from alcohol. Thus, it is a reasonable idea that decision-making parts of the brain could change too.

Lastly, it could also just be an observation that is specific to this task in this population. While the task has been shown to be externally valid in the college-aged sample, we didn’t reassess that here for older alcoholics. We’re talking about people taking small risks to earn relatively small amounts of money by the end of the task. Usually, participants are rewarded with somewhere between $5 to $20, depending on the study.

What if larger sums of money were at play? Or access to alcohol was at risk? Once a person is an active alcoholic, what feels risky and what’s not might change too. Acknowledging that you have a problem and starting to try to cut down or abstain might feel more risky than continuing as normal. Nevertheless, even if this flip is specific to behavior in a laboratory task, it means that the relationship between risk-taking and alcoholism is not as straightforward as we might expect.

So, what do you think? In your experience, are the more severely alcoholic people you’ve known not big risk-takers? If you’re an alcoholic in recovery, does it seem like your risk-taking personality changed over time? Hopefully, we’ll get more clues down the line and we’ll be better positioned to say which theory is correct, and this can then help alcoholics in their own pathway to addiction recovery.

 

1. Lejuez, C.W., et al., Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 2002. 8(2): p. 75-84.

2. Fernie, G., et al., Risk-taking but not response inhibition or delay discounting predict alcohol consumption in social drinkers. Drug and Alcohol Dependence, 2010. 112(1-2): p. 54-61.

3. Lejuez, C.W., et al., Differences in risk-taking propensity across inner-city adolescent ever- and never-smokers. Nicotine Tob Res, 2005. 7(1): p. 71-9.

4. Anokhin, A.P., et al., Heritability of risk-taking in adolescence: a longitudinal twin study. Twin Research and Human Genetics, 2009. 12(4): p. 366-71.

5. Ashenhurst, J.R., M. Seaman, and J. David Jentsch, Responding in a Test of Decision-Making Under Risk is Under Moderate Genetic Control in the Rat. Alcoholism: Clinical and Experimental Research, in press.

6. Ashenhurst, J.R., J.D. Jentsch, and L.A. Ray, Risk-Taking and Alcohol Use Disorders Symptomatology in a Sample of Problem Drinkers. Experimental and Clinical Psychopharmacology, 2011. 19(5): p. 361-70.

 

 


Posted in:  Education
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Addiction research – Who are we studying?

March 4th, 2012

I teach a class on the psychology of addiction (Psych 477 at California State University in Long Beach) and as I have been preparing the lectures something has become very clear to me – textbooks patently gloss over important details about the addiction research they cite. One of the most obvious gaps I’ve noticed this semester concerns the population of research subjects most addiction research is conducted on. An example will clarify:

A student group in my class had to read a study assessing the residual effects of methamphetamine on mood and sleep. They were amazed that no changes in mood were observed and that participants slept a full 6-8 hours the night after being administered meth! Would you have been surprised with these results given that we all have been told that crystal meth improves mood and causes insomnia?

Would it matter at all if I told you that the participants in the study were current meth abusers who use an average of 4 times every week?

For anyone not aware of the tainted history of health research in the U.S. (I’m including psychological research in this group), go ahead and read about the Tuskegee Syphilis Experiment and Stanford Prison Experiment (video here). There are other examples including Stanley Milgram‘s obedience studies, and more but as exciting as the discussion of these studies is, it’s time to get back to my main point.

It is mostly due to the ethically-questionable, psychologically damaging, research above that research institutions are now required to vet proposed research studies using Institutional Review Boards (IRBs) to assure that human participants in studies are consenting to participate of their own free will, are not coerced, and are not suffering undue damage. This is also true of addiction research. Rarely does the public consider this fact however when they are being reported on research relevant to addiction. I know this because the kids in my class never gave it a second thought.

When reading about addiction research, think about the subjects participating in itNearly all addiction research, especially studies utilizing “hard” drugs like cocaine, meth, opiates, etc., are required to make use of a very limited part of society – drug using individuals with a history of use of the specific drug of interest who are specifically not interested in treatment. Individuals who have never tried the drug or who want to be treated for drug abuse or dependence (addiction) are excluded due to ethical concerns. In most studies, participants can not qualify if they are addicted to drugs other than those being studies (except smoking, for which exceptions are usually made since we’d be able left with no participants otherwise) or have any associated mental health disorders, which are very common among addicted individuals. I would further assert that for at least a substantial portion of these research participants, the term “addicts” may not be appropriate since many addicts would not willingly give up using their favorite substance for a week or two to be replaces with a hospital bed and an experimenter controlled dose of drug or placebo. Taken together, our research subjects are pretty obviously not representative of all drug users, or all addicts, or all anything else. They make up a very specific group – less than perfect, but what we have to work with.

In some studies that attempt to make a direct comparison between controls (or drug naive participants) and drug users, this is likely less of an issue. This can happen when researchers try to examine brain structure differences, or performance on a specific psychological or physical test. In such cases researchers can at least statistically identify contributions of length of use, method of use, and other relevant data on differences between people who use and those that don’t. There are probably still some serious differences between “true” addicts, recreational users, and semi-chronic users that would be important to understand here, but we can’t so we don’t. But when it comes to assessing mood effects, or indeed any of a number of subjective effects of drugs, drug cravings, and withdrawal, this limitation in the population to be studied is something that often needs to be made explicitly clear to most public consumers of research. Since we can’t assess changes in mood, absorption rate, anxiety, or any other such measure (some exceptions for very low doses in very specific circumstances) among people who are new to the drug, we end up assessing them among people with a lot of experience, but not enough of a problem to want addiction treatment. Again, this should be considered a pretty specific type of drug user in my opinion.

There are other types of studies – those conducted with abstinent ex-users or addiction treatment intervention studies utilizing addicts who want, or who reported to, treatment on their own or in response to advertisements. While these studies make use of populations that can be considered at least closer to the individuals they are specifically aimed at – assessing the return of  cognitive function after short or long term abstinence or testing a new intervention on those who want treatment – they still bring on limitations that need to be specifically considered.

An important point – most researchers recognize these issues and make them explicitly part of their research publications, in a specific section called “Limitations” but what seems troubling is that the public doesn’t have any awareness of these issues. So when someone tells you that “they just found out meth doesn’t actually make people lose sleep,” take a second to ask “for who?”


Posted in:  Education
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A3 Verified – Matrix Institute on Addictions

February 16th, 2012

A3 is doing its RehabFinder work this month and we have a brand new and exciting addition to our Verification roster – The West Los Angeles clinic of the Matrix Institute on Addictions (they can be reached at 310-935-1322). We’ve already featured one of the amazing founders of Matrix, Ms. Jeanne Obert, but during these past few weeks, we’ve gotten to have a more in depth look under the hood…

Matrix Institute on Addictions – Research based outpatient treatment

The Matrix Institute’s treatment protocol, manual, and method, were developed under a grant from the Substance Abuse and Mental Health Services Administration (SAMHSA), which we have mentioned many times in our writing on All About Addiction. Research  using the Matrix Institute manual has shown it to be successful enough that SAMHSA lists it on it National Registry of Evidence-based Programs and Practices (NREPP), a prestigious list of effective treatment approaches.

The Matrix Institute on AddictionsMatrix Institute is nationally and internationally recognized for its structured, outpatient treatments and research-supported elements and is accredited by the Commission on Accreditation of Rehabilitation Facilities (CARF). The Matrix Institute is also a proud member of the National Association of Addiction Treatment Providers (NAATP). One of the best things about the Matrix Institute addiction treatment program is that they accept almost all insurance carriers and have an amazingly affordable cost of treatment of only $1,900 per month! For a treatment program with such a track record, these are amazing statistics.

The Matrix Institute and ongoing training and research

Matrix Institute is absolutely one of the leaders in the field of addiction treatment when it comes to working with researchers to  find new, effective treatments for substance abuse.  Research at Matrix has helped in the development of new treatments that keep patients in treatment longer, and help them have greater success.  Some of the research we have participated in has resulted in new medications for alcohol dependence, (Campral and Revia) and opioid dependence (Suboxone or Buprenorphine).

Currently, Matrix is working with the National Institute on Drug Abuse (NIDA) to find a medication to help people with methamphetamine dependence.

The Matrix Model has been developed, refined and evaluated through research over the past 25 years. This is why All About Addiction (A3) is proud to stand together with The Matrix Institute on Addictions in improving the kind of addiction treatment available by making standardized, affordable treatment a reality.

(Disclosure – Dr. Jaffe is a group facilitator and educator at the Matrix Institute in West Los Angeles)


Posted in:  Education, Rehab-Finder
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Correlation, causation, and association – What does it all mean???

July 28th, 2011

A comment posted by a reader on a post reprimanded me for suggesting that marijuana caused relationships to go bad.

In this instance the reader was mistaken, as I had specifically used the word “associated”, but the comment made me think that maybe I should explain the differences between correlation, causation, and association. I’m a scientist studying addiction, and in the field, it’s very important to be clear about what each of the words you use means.

Being clear about inferences in research

Correlation – When researchers find a correlation, which can also be called an association, what they are saying is that they found a relationship between two, or more, variables. For instance, in the case of the marijuana post, the researchers found an association between using marijuana as a teen, and having more troublesome relationships in mid, to late, twenties.

Correlations can be positive – so that as one variable (marijuana smoking) goes up, so does the other (relationship trouble); or they can be negative, which would mean that as one variable goes up (methamphetamine smoking) another goes down (grade point average).

The trouble is that, unless they are properly controlled for, there could be other variables affecting this relationship that the researchers don’t know about. For instance, education, gender, and mental health issues could be behind the marijuana-relationship association (these variables were all controlled for by the researchers in that study). Researchers have at their disposal a number of sophisticated statistical tools to control for these, ranging from the relatively simple (like multiple regression) to the highly complex and involved (multi-level modeling and structural equation modeling). These methods allow researchers to separate the effect of one variable from others, thereby leaving them more confident in making assertions about the true nature of the relationships they found. Still, even under the best analysis circumstances, correlation is not the same as causation.

Causation – When an article says that causation was found, this means that the researchers found that changes in one variable they measured directly caused changes in the other. An example would be research showing that jumping of a cliff directly causes great physical damage. In order to do this, researchers would need to assign people to jump off a cliff (versus lets say jumping off of a 12 inch ledge) and measure the amount of physical damage caused. When they find that jumping off the cliff causes more damage, they can assert causality. Good luck recruiting for that study!

Most of the research you read about indicates a correlation between variables, not causation. You can find the key words by carefully reading. If the article says something like “men were found to have,” or “women were more likely to,” they’re talking about associations, not causation.

Why the correlation-causation difference?

The reason is that in order to actually be able to claim causation, the researchers have to split the participants into different groups, and randomly assign some to the behavior or condition they want to study (like taking a new drug), while the rest receive something else. This is in fact what happens in clinical trials of medication because the FDA requires proof that the medication actually makes people better (more so than a placebo). It’s this random assignment to conditions (or randomization) that makes experiments suitable for the discovery of causality. Unlike in association studies, random assignment assures (if everything is designed correctly) that its the behavior being studied, and not some other random effect, that is causing the outcome.

Obviously, it is much more difficult to prove causation than it is to prove an association.

Should we just ignore associations?

No! Not at all!!! Not even close!!! Correlations are crucial for research and still need to be looked at and studied, especially in some areas of research like addiction.

The reason is simple – We can’t randomly give people drugs like methamphetamine as children and study their brain development to see how the stuff affects them, that would be unethical. So what we’re left with is a the study of what meth use (and use of other drugs) is associated with. It’s for this reason that researchers use special statistical methods to assess associations, making certain that they are also considering other things that may be interfering with their results.

In the case of the marijuana article, the researchers ruled out a number of other interfering variables known to affect relationships, like aggression, gender, education, closeness with other family members, etc. By doing so, they did their best to assure that the association found between marijuana and relationship status was real. Obviously other possibilities exist, but as more researchers assess this relationship in different ways, we’ll learn more about its true nature.

This is how research works.

It’s also how we found out that smoking causes cancer. Through endlessly repeated findings showing an association. That turned out pretty well, I think…


Posted in:  Education
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A word about animal research and animal rights

June 26th, 2011

Animal research is a controversial topic in some circles.

ucla-van-on-fireAs some of you may know already, a UCLA group has recently banded together to counter-protest the fear-mongering tactics used by animal rights activists. Before UCLA Pro-Test became a reality, researchers on campus would hide away when on campus demonstration came our way. No more.

Dr. David Jentsch, who was one of my UCLA advisors, had his car burned and his work, and life, threatened by one of the more extreme, terrorist, animal-rights groups. I’m all for debate, but blowing up cars makes you lose your place at the table as far as I’m concerned.

So what are the animal-rights arguments?

Animal rights groups claim that animal research is simply sadistic and that it does not benefit us at all.

The notion that animal researchers enjoy hurting animals is so wrong as to be insulting. I’ve conducted animal research myself and know dozens of others who have. Not one of us enjoys hurting animals and we do our best to conduct everything in ways that minimize any discomfort to the animals. Additionally, government regulations regarding animal welfare in research are very strict and highly regulated. Research involving animals is always done while considering its necessity and weighing alternative options (like using cells, tissue, computer models, etc.).

The thought that animal research doesn’t benefit us is naive at best, but more likely purposefully misleading. Here’s a small, partial, list of advances that were made possible through animal research:

  1. Penicillin (mice)
  2. Insulin (dogs, mice, rabbits)
  3. Anesthetics (rats, rabbits, dogs)
  4. Polio Vaccine (mice, monkeys)
  5. Heart transplants (dogs)
  6. Meningitis Vaccine (mice)
  7. Cervical Cancer Vaccine (rabbits, cancer)
  8. Gene therapy for Muscular Dystrophy and Cystic Fibrosis (mice).
  9. Techniques such as bypass surgery, joint replacement, carcinogen screening & blood transfusions have all been developed & improved using animals

Now if anyone wants to claim that none of the above have significantly improved, or indeed saved, human lives, I’m ready for the debate.


Posted in:  Opinions
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Conversation with an addiction expert – Jeanne Obert

April 5th, 2011

Jeanne Obert of the Matrix InstituteFollowing up our successful and informative short interview with Chris Evans, we now turn our attention to Jeanne Obert, a founder and the Executive Director of the Matrix Institute. Matrix is an outpatient treatment center that is associated with the UCLA’s Integrated Substance Abuse Programs (ISAP).

Jeanne is a developer of the Matrix Model of Intensive Outpatient Treatment as well as the Matrix Model for Teens. She is a Licensed Marriage and Family Therapist and Supervisor.  Jeanne also has a master’s degree in business management (MSM) and works as a consultant for the National Institute on Drug Abuse and the Center for Substance Abuse Treatment.

Office conversations – 11 Questions for an addiction expert

1 ) How did you become interested/specialized in addiction research?

I was trained as a clinician and worked with a researcher who brought research into our clinics that we founded.

2 ) If you had to sum-up your “take” on substance use disorders (SUD’s) in a few sentences, what would those be?

SUD’s are a chronic relapsing condition from which recovery is entirely possible. Those people who are successful at recovery operate within the limits they recognize as necessary to sustain their sobriety. These people’s lives are quite often more meaningful and fulfilling than the lives of many people who never had to deal with SUDs.

3 ) What have been the most meaningful advances in the field in your view over the past decade?

The recognition and growing acceptance of #2. The emergence of brain imaging techniques and the degree to which those discoveries have advanced our understanding of these disorders.

4) What are the biggest barriers the field still needs to overcome?

There are still many people who believe people with addictive disorders “did it to themselves”. The continuing recognition of #2 is critical. There is also the distinct possibility that addiction disorders will become an underfunded and often ignored subset of mental health.

5) What is your current research focused on?

In our organization we do medication trials as well as behavioral research with many diverse foci.

6) What do you hope to see get more research attention in the near future?

Marijuana (THC) and it’s affect on users as well as the dissemination of evidence based practices.

7 ) How do you think the Health Care reform recently passed will affect SUD treatment?

Wide open question. The effects of the legislation will be totally determined by the political quagmire it needs to work its way through.

8 ) What is your view regarding the inclusion of behavior/process addictions in the field?

Just as important as any other aspect of the disease. We need to look at the disease of addiction from as many perspectives as possible.

9 ) The question of nature Vs. Nurture (or biology versus behavior) is an ever-present one. What is your view on the relative importance of each?

Neither can be ignored so we need to recognized the importance and contribution of each. Most people can understand they have to use behavioral change to overcome the biological hand they were dealt.

10 ) In your view, what are some of the biggest misconceptions that the public still holds about addiction?

See #4.

11 ) What is the most common question you get from others (public?) when it comes to addiction or when they find out you study addiction?

Right now many people ask, “Is pot addicting?” They also want to know how to tell whether a family member is “addicted”. The question of whether someone “can be cured” is also a frequent question.


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