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Why I took part in the “Preregistration Challenge”

By Sarah Peters

The preregistration of study protocols has a long history in clinical trials, but is a more recent innovation in many other areas. The hope is that it will help counter the “reproducibility crisis” in psychological science – the failure of many published findings to replicate reliably. Here I discuss my experience with the Open Science Framework “Preregistration challenge”, and argue for more widespread adoption of preregistering reports.

There is an ongoing methodological crisis in psychological science – the reproducibility crisis refers to the failure of many scientific findings to be replicated. The Reproducibility Project, a recent initiative led by Professor Brian Nosek at the University of Virginia, aimed to identify the scale of this crisis. A large collaboration between 270 project members reran 100 published psychological experiments, and found that just 36% of the initial findings were replicated. Similarly, some classic textbook experiments have proven difficult to replicate, and publication bias – whereby positive findings are more likely to be published and negative findings to be dismissed – plagues the field.

Given this, scientists are exploring how to improve the way we conduct research and thereby improve the quality of what we produce. One suggestion is to preregister our research question, methods and analysis plan in advance of data collection. It is hoped that public preregistration will limit analytical flexibility and post hoc hypothesising, thereby improving the transparency and robustness of research findings.

Curious about the benefits of preregistration, and to see how it differed from the way I’d previously conducted my research, my colleagues and I published a preregistration for a recent study on Open Science Framework (OSF). We were interested in whether Cognitive Bias Modification, a psychological intervention designed to shift the emotional interpretation of faces, would impact clinically-relevant outcomes. We also entered the study into OSF’s (ongoing!) Preregistration Challenge, which offers the chance to win a $1,000 prize to 1,000 researchers who go from preregistration to publication.

Preregistering our study did require a greater time commitment prior to running it, but thinking about our predictions, design, and analyses meant that we could spot any potential issues and improve our experimental design before we collected data (i.e., before it was too late!). As a preregistration is public and cannot be changed after it’s published, it forced us to think more carefully about our decisions. For example, thinking more carefully about whether our data would truly answer our question made us wonder whether the emotional biases we wanted to study might be more prominent when an individual is under stress, so we decided to include another task to measure this. Also, by knowing which statistical analyses we would conduct before recruiting participants we could ensure that our study was adequately powered and would meet the assumptions of the planned analyses.

Initially I was concerned that this approach could be limiting. What if we found something interesting that we hadn’t expected and wanted to run additional analyses to probe it? But a preregistered report doesn’t prevent that – it simply means that you would (honestly and transparently!) report those analyses as exploratory. This protection against HARKing (hypothesising after the results are known) is important; separating analyses as planned versus exploratory can prevent overconfidence in weaker findings and the publication of attractive, but uncertain, positive findings.

Following data collection, we went back to our preregistration. It was here that our earlier time investment paid off; once our data were cleaned we could immediately run our planned analyses, and much of the manuscript writing (introduction and methods) was already done. We also ran a number of exploratory analyses, such as whether our results were moderated by participants’ anxiety scores. We subsequently published our findings in the academic journal Royal Society Open Science, and were thrilled to receive one of the latest $1,000 Preregistration Challenge prizes for bringing our study from preregistration to publication!

While interpreting findings and making discoveries is an important aim of scientific research, it is just as important to continuously scrutinise the scientific method. As a scientist, there is no question that seeing data can influence my decisions and interpretations. However, the adoption of preregistration can eliminate this, make the process easier in the long term, and improve research quality overall.

Professor Nosek and other members of the Reproducibility Project argue that, “Progress in science is marked by reducing uncertainty about nature”. But, if scientific findings have not or cannot be replicated, we can’t be certain that they exist. Preregistration is a simple change to the way we do research that can help to halt the reproducibility crisis and produce effective and credible science.

Read more about how to take part in the Preregistration Challenge here.

See Peters et al.’s preregistration here, and the published study here.

Sarah Peters can be contacted via email at: s.peters@bristol.ac.uk.

Can cognitive interventions change our perception from negative to positive, and might that be useful in treating depression?

By Sarah Peters

Have you ever walked away from a social interaction feeling uncomfortable or anxious? Maybe you felt the person you were talking to disliked you, or perhaps they said something negative and it was all you could remember about the interaction. We all occasionally focus on the negative rather than the positive, and sometimes ruminate over a negative event, but a consistent tendency to perceive even ambiguous or neutral words, faces, and interactions as negative (a negative bias), may play a causal role in the onset and rate of relapse in depression.

A growing field of psychological interventions known as cognitive bias modification (CBM) propose that by modifying these negative biases it may be possible to intervene prior to the onset of depression, or prevent the risk of subsequent depressive episodes for individuals in remission. Given that worldwide access to proven psychological and pharmacological treatments for mood disorders is limited, and that in countries like the UK public treatment for depression is plagued by long waiting lists, high costs, side effects, and low overall response rates, there is a need for effective treatments which are inexpensive, and both quick and easy to deliver. We thought that CBM might hold promise here, so we ran a proof of principle trial for a newly developed CBM intervention that shifts the interpretation of faces from negative to positive (a demonstration version of the training procedure can be seen here). Proof of principle trials test an intervention in a non-patient sample, which is important to help us understand a technique’s potential prior to testing it in a clinical population – we need to have a good idea that an intervention is going to work before we give it to people seeking treatment!

In this study, we had two specific aims. Firstly, we aimed to replicate previous findings to confirm that this task could indeed shift the emotional interpretation of faces. Secondly, we were interested in whether this shift in interpretation would impact on clinically-relevant outcomes: a) self-reported mood symptoms, and b) a battery of mood-relevant cognitive tasks. Among these were self-report questionnaires of depressive and anxious symptoms, the interpretation of ambiguous scenarios, and an inventory of daily stressful events (e.g., did you “wait too long in a queue,” and “how much stress did this cause you on a scale of 0 to 7”). The cognitive tasks included a dot probe task to measure selective attention towards negative (versus neutral) emotional words, a motivation for rewards task which has been shown to measure anhedonia (the loss of pleasure in previously enjoyed activities), and a measure of stress-reactivity (whereby individuals complete a simple task under two conditions: safe and under stress). This final task was included because it is thought that the negative biases we were interested in modifying are more pronounced when an individual is under stress.

We collected all of our self-report and cognitive measures at baseline (prior to CBM), after which participants underwent eight sessions (in one week) of either CBM or a control version of the task (which does not shift emotional interpretation). We then collected all of our measures again (after CBM). In order to be as sure of our results as possible, there were a number of critical study design features we used. Our design, hypotheses, and statistical analyses were pre-registered online prior to collecting data (this meant that we couldn’t fish around in our data until we found something promising, then re-write our hypotheses to make that result seem stronger). We also powered our study to be able to detect an effect of our CBM procedure. This meant running a statistical calculation to ensure we had enough participants to be convinced by any significant findings, and their potential to be clinically useful. This told us we needed 104 individuals split evenly between groups. Finally, our study was randomised (participants were randomly allocated to the intervention group or the control group), controlled (one group underwent an identical “placebo” procedure), and double-blind (only an individual who played no role in recruitment or participant contact knew which group any one participant was in).

So, what did we actually find? While the intervention successfully shifted the interpretation of facial expressions (from negative to positive), there was only inconclusive evidence of improved mood and the CBM procedure failed to impact most measures. There was some evidence in our predicted direction that daily stressful events were perceived as less stressful by those in the intervention group post-CBM, and weaker evidence for decreased anhedonia in the intervention group. In an exploratory analysis, we also found some evidence that results in the stress-reactivity task were moderated by baseline anxiety scores – for this task, the effects of CBM were only seen in individuals who had higher baseline anxiety scores. However, exploratory findings like this need to be treated with caution.

Therefore, as is often the case in scientific research, our results were not entirely clear. However, there are a few limitations and directions for future research that might explain and help us to interpret our findings. Our proof of principle study only considered effects in healthy individuals. Although these individuals were clearly amenable to training, and may indeed have symptoms of depression or anxiety without a clinical diagnosis, our observation that more anxious individuals appeared to be more affected by the intervention warrants research in clinical populations. In fact, a reasonable parallel to the effects observed in this study may be working memory training, which does not transfer well to other cognitive operations in healthy samples, but shows promise as a tool for general cognitive improvement in impaired populations.

Future research is also needed to disambiguate the tentative self-report stress and cognitive anhedonia effects observed here. One possibility, for example, is that the 104 participants we recruited were not enough to detect an effect of transference from CBM training to other measures (the size of which is unknown). Given the complexity of any mechanism through which a computerised task could shift the perception of faces and then influence behaviour, it is likely that a larger sample is necessary. While it could be argued that if such a large group of individuals is warranted to detect an effect, that effect is likely too small to be clinically useful, we would argue that even tiny effects can indeed be meaningful (e.g., cancer intervention studies often identify very small effects which can have a meaningful impact at a population level).

Another explanation for our small effects is that while one week was long enough to induce a change in bias, it may not have been long enough to observe corresponding changes in mood. For instance, positive interpretation alone may not be enough – it may be that individuals need to go out into the world and use this new framework to have personal, positive experiences that gradually improve mood, and this process may take longer than one week.

Overall, this CBM procedure may have limited impact on clinically-relevant symptoms. However, the small effects observed still warrant future study in larger and clinical samples. Given the large impact and cost of mood disorders on the one hand, and the relatively low cost of providing CBM training on the other, clarifying whether even small effects exist is likely worthwhile. Even if this procedure fails to result in clinical improvement, documenting and understanding the different steps in going from basic scientific experimentation to intervening in clinical samples is crucial for both the scientific field and the general public to know. The current study is part of a body of research which should encourage all individuals who are directly or indirectly impacted by depression or other mood disorders. Novel approaches towards understanding, preventing, and treating these disorders are constantly being investigated, meaning that we can be hopeful for a reduction in the devastating impact they currently have in the not so distant future.

Read the published study here

Sarah Peters can be contacted via email at: s.peters@bristol.ac.uk 

Does calorie and unit information influence our drinking behaviour?

By Olivia Maynard

Over the past two years we’ve invited hundreds of people into the lab to drink beer. Unfortunately, we weren’t there to socialise; this was in the name of science. We wanted to know whether giving people information about the number of units and or calories in their beer influenced how much they drank and their perceptions of drinking.

There are strong arguments for including this information: providing unit information may increase knowledge about alcohol consumption and calorie information may help drinkers choose lower calorie (and as a result lower unit) beverages. However, we also wondered whether there might be some unintended consequences of providing this information, particularly for those who are highly motivated to drink. What if unit information simply allows these drinkers to choose higher strength drinks and calorie information only discourages them from eating more, not drinking less? What if discussion around mandatory unit and calorie labelling is distracting us from the bigger issues: health warnings, minimum unit pricing, improving treatment for alcohol dependence and stopping alcohol advertising to young people, to name a few?

So, with this healthy level of scepticism, we set about inviting 264 regular alcohol consumers (mostly undergraduate students) to attend a lab session where they were given some beer and completed some taste ratings. What participants didn’t know was that they had been randomly assigned to one of four conditions. One group had information about the calorie and unit content of the beers, one group just got calorie information, another had just unit information, and the final group got no information at all. As well as measuring how much beer they drank, we also asked participants to reflect on the likely impact of unit and calorie information on their drinking behaviour.

You can read all the results in our (open access) paper that was published this week in the journal Alcohol and Alcoholism. If you want the concise version: we found no evidence that either unit or calorie information influenced how much beer people consumed and we found a lot of variation in the amount people drank.

However, it was our analysis of participants’ thoughts on unit and calorie information that proved vital to understanding what was going on here. Our participants told us that their main motivation for drinking alcohol was usually to get drunk; where unit information was perceived as being helpful, this was to help them choose the highest strength drink. Unit and calorie information was seen as distracting from the social aspect of drinking, and although some participants felt that calorie information might reduce consumption, most thought it would affect others, not themselves. Some people thought that calorie information could be misused by encouraging people to eat less (to compensate), rather than drink less.

It’s interesting that even though the unit and calorie information was very visible in our study (on a piece of paper, presented for 10 minutes), those who had received this information were still very inaccurate when it came to reporting how many units and calories were in their drinks. They basically didn’t seem to have read or engaged with it. If they’re not reading it in this context, is it likely that drinkers will read this information when it’s printed in tiny font on the back of the bottle?

So, what does this all mean for any plans to introduce unit and calorie information? Our study only really tells us about the potential impact of unit and calorie information among young adults (many of whom were students) who tend to drink to get drunk. However, our findings do call into question whether mandatory unit and calorie labelling on its own would reduce how much people drink, and also highlights potential negative unintended consequences of providing this information.

Despite some of these potential unintended consequences, there are still reasons to include unit and calorie information, if only because it’s a consumer right (you know how many calories are in just about everything else you consume). However, perhaps more effort needs to be placed on making this information more engaging and embedding it into public understanding of recommended drinking levels. Coincidentally, an analysis of the public’s awareness of new national alcohol guidelines was also published yesterday. This report argues that although the public have a relatively high awareness of what the guidelines are, they should be put into context by increasing the public’s awareness of the links between alcohol and cancer. Perhaps using health messages such as ‘Drinking alcohol regularly is linked to long-term risks such as cancer’, alongside unit and calorie information, might result in more meaningful changes in attitudes and behaviours around drinking. I feel another study coming on….

Olivia Maynard can be found on Twitter at @OliviaMaynard17

Action for Brain Injury Week

By Eleanor Kennedy

It’s Action for Brain Injury Week this week (8th – 14th May), a campaign run by the non-profit brain injury association Headway.  This year the campaign is all about “A New Me”, giving a platform to survivors and their families to discuss how life-changing a brain injury can be. In honour of the campaign, I’m writing a summary about my PhD research on mild traumatic brain injury.

Traumatic brain injury (TBI) is an injury to the head that results in an alteration in consciousness. My work focuses on mild TBI, which injury involves symptoms such as confusion/disorientation, loss of consciousness of less than 30 minutes and/or memory loss around the event that led to the injury.

I’m interested in how mild TBI in youth may be associated with later behaviour. Initially I conducted a systematic review of the literature and found that there was evidence for an association between childhood mild TBI and behaviours such as substance use, committing crimes and behavioural issues. However, this was based on a small number of studies and there were some limitations to be addressed.

A key issue was the use of appropriate control participants. In this kind of research, the behaviour of participants with mild TBI has been compared to that of participants with no injuries. These control participants are usually similar to the mild TBI group in terms of demographic factors such as age, gender and socioeconomic background. However, these similarities do not consider injury factors that could also have an impact on behaviour, for example pain, absence from school, and the trauma of having an injury. A second control group that includes participants with a non-head-related injury addresses this issue.

In my own research, I use data from the Avon Longitudinal Study of Children and Adolescents (ALSPAC). This is a birth cohort that began in the early nineties when over 14, 000 pregnant women were recruited; biological, genetic, environmental and psychological information has been gathered on participating families ever since. Participants and their parents have answered questions relating to head injury and fractures at many time points across the children’s life time. It is possible to have a group with mild TBI, a group with broken bone history and a group with neither injury.

So far, we have explored the association between mild TBI from birth to age 16 years and risk behaviour at age 17 years. We found that participants with a mild TBI were more likely to use alcohol to a hazardous level than participants with a broken bone and participants with no injury. This is in line with previous research, and has important implications for recurrent TBI and recovery from TBI. Another finding was that participants with either a mild TBI or a broken bone were more likely to commit offences – suggesting that there may be common risk factors for acquiring an injury and criminal behaviour. For example, an individual who has the personality trait of sensation seeking could potentially be more likely to get into risky situations leading to injuries and to commit offences.

I recently presented these findings at the International Brain Injury Association’s 12th World Congress in New Orleans. At the conference, there was an exhibition of masks created as part of a project called ‘Unmasking Brain Injury’. Each mask was designed and decorated by a survivor of brain injury to share their experience; each mask was as unique as the individuals’ story. Projects that give a voice to people living with a brain injury, such as ‘A New Me’ campaign, are a reminder of the challenges that are faced when dealing with a brain injury. It’s a privilege to contribute research to this field and to listen to the voices of those experiencing it to promote awareness and compassion.

Smoking and chronic mental illness: what’s the best way to quit or cut down?

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 11th December 2015.

Smoking rates in the US and UK are 2-4 times higher in people with mental illnesses compared to those without (Lasser at al., 2000; Lawerence et al., 2009).

What’s more, smokers suffering from mental illness have higher nicotine dependence and lower quit rates (Smith et al.,2014; Weinberger et al., 2012; Cook et al 2014).

About half of deaths in people with chronic mental illness are due to tobacco related conditions (Callaghan et al., 2014; Kelly et al 2011).

A new ‘state of the art’ review in the BMJ by Tidey and Miller (2015) is therefore much needed, focusing as it does on the treatments currently available for smoking and chronic mental illness, such as schizophrenia, unipolar depression, bipolar depression, anxiety disorders and post-traumatic stress disorder (PTSD).

42% of all cigarettes smoked in England are consumed by people with mental health problems.

42% of all cigarettes smoked in England are consumed by people with mental health problems.

Methods

Tidey and Miller (2015) identified studies by searching keywords in PubMed and Science Direct, using relevant guidelines, reviews and meta-analyses, and data from the authors’ own files. Two authors reviewed the references and relevant studies were chosen and summarised. Only peer-reviewed articles published in English were reviewed.

It’s important to stress that this was not a systematic review, so the included studies were not graded, but simply summarised with a particular focus on outcomes.

BMJ State of the Art reviews are not systematic reviews, so are susceptible to the same biases as other literature reviews or expert opinion pieces.  

BMJ State of the Art reviews are not systematic reviews, so are susceptible to the same biases as other literature reviews or expert opinion pieces.

Results

Schizophrenia

Nicotine replacement therapy (NRT) plus psychosocial

Overall, in studies of NRT with psychosocial treatment (such as CBT) 13% of smokers with schizophrenia averaged 6 to 12 month quit rates. Additionally, those continuing to receive NRT had reduced relapse rates.

Bupropion

Studies investigating bupropion in smokers with schizophrenia found initial abstinence, but were followed by high relapse rates with treatment discontinuation, suggesting the need for longer treatment duration. One study found bupropion coupled with NRT and CBT reduced relapse rates. 

Varenicline

Studies investigating varenicline in smokers with schizophrenia achieved abstinence at the end of the trial (compared to placebo), but not at 12-month follow up. One study found varenicline and CBT had higher abstinence rates at 52 weeks (compared to controls). Psychiatric side effects reported did not differ between groups, suggesting varenicline is well tolerated in schizophrenia.

Psychosocial

Studies investigating psychosocial treatments in smokers with schizophrenia were varied. Studies implementing CBT displayed high continuous abstinence, and those receiving motivational interviewing were more likely to seek treatment. However, in contingency management trials (receiving monetary reward for abstinence) it appeared individuals might only be staying abstinent long enough for their reward, therefore longer trials are needed.

E-cigarettes

One (uncontrolled) study provided e-cigarettes for 52 weeks to smokers with schizophrenia, finding half reduced their smoking by 50% and 14% quit. None of the participants were seeking treatment for cessation at the start of the trial, suggesting a need for further RCTs of e-cigarettes in smokers with schizophrenia.

The Mental Elf looks forward to reporting on RCTs of e-cigarettes in smokers with schizophrenia.

The Mental Elf looks forward to reporting on RCTs of e-cigarettes in smokers with schizophrenia.

Unipolar depression

A review of the cessation treatments available to smokers with unipolar depression found little differences in outcomes between individuals with and without depression. However, women with depression were associated with poorer outcomes. Previous studies indicate bupropion, nortriptyline, and NTR with mood management all effective in smokers with depression. Additionally, a long-term study of varenicline displayed continuous abstinence up to 52 weeks without any additional psychiatric side effects.

Bipolar depression

Few studies investigated cessation treatments in smokers with bipolar depression; two small-scale studies of bupropion and varenicline indicated positive results. However a long-term varenicline study found increased abstinence rates at the end of the trial, but not at 6 month follow-up. Some individuals taking varenicline reported suicidal ideation, but this did not differ from the control group.

Anxiety disorders

An analysis investigating both monotherapy and combination psychotherapies found anxiety disorders to predict poor outcomes at follow-up. Despite combination psychotherapy doubling the likelihood of abstinence in non-anxious smokers, neither monotherapy or combination therapy were more effective than placebo in smokers with a lifetime anxiety disorder. However, unipolar and bipolar only touched on pharmaceutical treatments.

PTSD (Post Traumatic Stress Disorder)

Studies investigating cessation in PTSD sufferers found higher abstinence rates in integrative care treatment, in which cessation treatment is integrated into pre-existing mental healthcare where therapeutic relationships and a set schedule already exist. A pilot study investigating integrative care with bupropion found increased abstinence at 6 months. However, a contingency management trial found no differences between controls, although it’s possible this was due to small numbers.

Standard treatments to help people quit smoking are safe and effective for those of us with mental illness.

Standard treatments to help people quit smoking are safe and effective for those of us with mental illness.

Discussion

Clinical practice should prioritise cessation treatments for individuals suffering mental illnesses, in order to protect against the high rates of tobacco related death and disease in this population.

This review shows that smokers with mental illness are able to make successful quit attempts using standard cessation approaches, with little adverse effects.

Several studies suggested bupropion and varenicline effective in schizophrenia, and varenicline in unipolar and bipolar depression. However, it should be noted, these studies only investigated long-term depression, not situational depression.

Furthermore, all the participants in the studies reviewed were in stable condition, therefore it’s possible outcomes may be different when patients are not as stable. Individuals whom are not stable will have additional psychiatric challenges, may less likely to stick with their treatment regime, and may be more sensitive to relapse.

It should be noted that this was a ‘state of the art’ review, rather than a systematic review or meta-analysis. Therefore- as all literary reviews-it’s subject to bias and limitations, with possible exclusion of evidence, inclusion of unreliable evidence, or not being as comprehensive as if this were a meta analysed. For example, some of the author’s own files are used along side the literary search, but (presumably unpublished) data from other researchers are not sought out or included. Many of the studies included differed in design (some placebo controlled, some compared against a different active treatment ect.) therefore caution should be taken when drawing comparisons across studies.

Additionally, some sections appeared to be much more thorough than others. For example, schizophrenia is covered extensively, including NTR, psychosocial, and pharmaceutical approaches. While all anxiety disorders appeared to be gaped together as one (as opposed to looking at social anxiety, GAD, or panic disorder) and were not explored in detail, drawing little possible treatment conclusions. Finally, this was great literary review, which provided much information, but at times it did feel a bit overwhelming to read and difficult to identify the key information from each sections.

Service users who smoke are being increasingly marginalised, so practical evidence-based information to support quit attempts at the right time is urgently needed.

Service users who smoke are being increasingly marginalised, so practical evidence-based information to support quit attempts at the right time is urgently needed.

Links

Primary paper

Tidey JW and Miller ME. Smoking cessation and reduction in people with chronic mental illness. BMJ 2015;351:h4065

Other references

Lasser K, Boyd JW, Woolhandler S, et al. Smoking and mental illness: a population-based prevalence study.JAMA 2000;284:2606-10 [PubMed abstract]

Lawrence D, Mitrou F, Zubrick SR. Smoking and mental illness: results from population surveys in Australia and the United States. BMC Public Health 2009;9:285

Smith PH, Mazure CM, McKee SA. Smoking and mental illness in the US population. Tob Control 2014;23:e147-53.[Abstract]

Weinberger AH, Pilver CE, Desai RA, et al. The relationship of major depressive disorder and gender to changes in smoking for current and former smokers: longitudinal evaluation in the US population. Addiction 2012;107:1847-56. [PubMed abstract]

Cook BL, Wayne GF, Kafali EN, et al. Trends in smoking among adults with mental illness and association between mental health treatment and smoking cessation. JAMA 2014;311:172-82 [PubMed abstract]

Callaghan RC, Veldhuizen S, Jeysingh T, et al. Patterns of tobacco-related mortality among individuals diagnosed with schizophrenia, bipolar disorder, or depression. J Psychiatr Res 2014;48:102-10 [PubMed abstract]

Kelly DL, McMahon RP, Wehring HJ, et al. Cigarette smoking and mortality risk in people with schizophrenia. Schizophr Bull 2011;37:832-8 [Abstract]

Photo credits

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/smoking-and-chronic-mental-illness-whats-the-best-way-to-quit-or-cut-down/#sthash.NvTaK7E6.dpuf

A behavioural insights bar: How wine glass size may influence wine consumption

by Olivia Maynard @OliviaMaynard17

Now that the festive season is almost upon us, I’ve been wading through the list of jobs I’ve been putting off for longer than I can remember, with the hope of starting afresh in 2016.

One of these jobs is wrapping up some of the studies I’ve been running this year, tidying up the data files and deciding what to do with the results. Obviously it’s best practice to write up all studies for publication in peer-reviewed journals, but sometimes this isn’t possible straight away (for example, when we’ve collected pilot data which will inform larger studies or research grants), although journals specifically for pilot and feasibility work do exist. However, it’s still important to share the findings, at the very least to prevent other research groups from running exactly the same pilot study (avoiding the file drawer effect).

The pilot study I’m trying to wrap up was conducted in September this year and is worth reporting, not only because the research is interesting, but also because the method of data collection was novel.

In December 2014 we were approached by the Behavioural Insights Team (BIT), who asked whether we’d be interested in running an experiment at their annual conference. Alongside a star-studded list of speakers, the BIT had planned to demonstrate to conference delegates the power of behavioural insights, by running a series of mini-experiments throughout the conference. We were asked to contribute, not only because I had previously worked in the BIT as part of a placement during my PhD, but also because of TARG’s track record in running behavioural experiments to influence alcohol consumption, both in the lab and in the ‘real-world’.

glassThe team asked us to run an experiment in the Skylon bar in the Royal Festival Hall – the venue of the conference drinks reception. After an initial assessment of the bar (yes, this is a tough job!) and discussing various possible experiments we could conduct, we finally decided to examine the impact of glass size on alcohol consumption. While considerable previous research has shown that plate size is an important driver in food consumption, and we have shown that glass shape (i.e., curved versus straight) influences alcohol consumption, there is very little research on the impact of glass size on alcohol consumption. Larger wine glasses are increasingly common and these may increase wine consumption and drinking speed by suggesting larger consumption norms to consumers, or by tricking consumers into thinking there’s a smaller amount in the glass than in a smaller glass which is equally full.

The primary aim of this pilot study was to determine the feasibility of implementing a glass size intervention study in a real-world drinking environment in order to inform future studies in this area.

Method

Prior to starting the study, as with every TARG study, we published the protocol online on the Open Science Framework. Depending on the side of the bar they were stood in, delegates attending the drinks reception were provided with either a small or a large wine glass, each of which was filled to the same volume. Every 15 minutes we counted the number of delegates on the two sides of the bar and every hour (for three hours) we counted the number of empty wine bottles on each side of the bar. We calculated the average volume of wine consumed per delegate each hour and then compared these between the two groups.

Results

From a feasibility point of view, the study worked as well as expected. Follow-up interviews with the manager of the bar indicated that bar staff enjoyed the process of participating in a study and were happy to participate again in future studies.

However, because we were conducting this in the real-world, rather than in our carefully controlled laboratory environment, we encountered a few logistical challenges. Here are the key points we learned from running this pilot study:

  1. In the real-world, there’s a necessary trade-off between collecting the data and not disrupting normal behaviour

bottles

Ideally we would have counted the number of empty bottles more frequently than every hour in order to get a more accurate measure of how much was consumed by the delegates. However prior to the start of the study, the bar manager suggested that this would interfere with their service and the bar staff reiterated this after the study had finished. As the bar staff were vital to the success of this pilot study, we didn’t think it was appropriate to push for more data collection than they felt comfortable with.

  1. Complete control of the environment isn’t possible in the real-world

controlkey

To prevent delegates from moving between the two sides of the bar we placed physical barriers between them, such as sofas, plants and lamps. However, inevitably, some delegates who wanted to ‘work the room’ at what was essentially a networking event did make their way past the barriers we set up. Other than instructing the waiters to replace the glass of those who had moved sides with the glass size appropriate for the side of the bar they were now in, there was very little we could do about this, short of frog-marching delegates back to their original side (which we thought wouldn’t go down very well on this occasion!)

  1. Accurate enforcement of study conditions is more difficult in the real-world

pouring

If we had conducted this study in the laboratory, we would have randomised participants to receive one of two glass sizes and carefully poured the exact volume of wine into their glass. In this real-world study, however, we had to rely on the waiters to accurately pour the wine into the glasses. Although highly trained, the waiters may also have fallen foul of the visual illusion the different glasses present (an effect which has been shown in previous real-world experiments). Future studies could monitor waiter pouring behaviour before and during the study.

  1. Studies in real bars have some other unexpected challenges…

full glassess

The BIT had asked that we present the results at 9am the following morning, allowing a nine hour turnaround from the end of the study to data presentation. This time pressure was not helped by the large quantities of complimentary champagne being served at the event, which considerably slowed down data entry and analysis at midnight!

Despite this substantial challenge, the results of the study were presented the following morning.

These data suggested that there was no difference in volume of wine consumed between the groups drinking from larger glasses and those drinking from tablesmaller glasses. As this study wasn’t powered to detect a meaningful difference between the two groups, we weren’t really surprised by this finding. However, these pilot data, along with the lessons learned from conducting the study will be used to inform our future research studies and grant applications.

And there we have it – another pilot study out of the file drawer and another item crossed off my ‘to-do’ list.

I’d like to thank the entire Behavioural Insights Team, in particular Ariella Kristal and Gabrielle Stubbs, for making this study happen, Carlotta Albanese from the Skylon bar and David Troy and Jim Lumsden from TARG for helping with all the data collection (and data entry at midnight).

Drug-using offenders with co-occuring mental illness

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 15th October 2015.

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Many individuals in the criminal justice system have both mental health and substance use problems. There is little evidence targeting the treatment programmes for offenders, alongside the additional challenges faced by those with co-occurring mental illnesses.

The Cochrane Drugs and Alcohol Group have published a set of four reviews centred on interventions for drug-using offenders. This is an updated review, targeting offenders with co-occurring mental illnesses, which was originally published in 2006. We blogged about the review when it was last updated in March 2014, but this new version has more evidence (3 new RCTs) included.

About 30% of acquisitive crime (burglaries, theft and robberies) are committed by individuals supporting drug use.

Methods

The review authors searched the usual comprehensive list of databases to identify randomised controlled trials (RCTs) to identify whether treatments for drug using offenders with co-occurring mental illnesses:

  • Reduced drug use
  • Reduced criminal activity
  • Whether the treatment setting affected the intervention
  • Whether the type of treatment affected the outcome

All participants, regardless of gender, age or ethnicity, were included in this analysis.

The updated search (from March 2013 – April 2014) added 3 new trials to the review, totalling 14 publications representing 8 trials published between 1999 and 2014.

Study characteristics

  • 6 studies were conducted in secure settings and 2 studies were conducted in a court setting
  • No studies assessed pharmacological treatments or were conducted in the community
  • All studies were conducted in the United States
  • Study duration varied from 3 months to 5 year follow-up
  • 7 studies investigated adult offenders, while one study investigated adolescent offenders (aged 14 -19)
  • 3 studies included female offenders, while adult male offenders filled the majority of the population in the remaining studies.

Results

Therapeutic community and aftercare versus treatment as usual

Impact on drug use (self-report)

  • Two studies reported a reduction in drug use:
    • (Sacks, 2004) (RR 0.58 95% CI 0.36 to 0.93, 139 participants)
    • (Sacks, 2008) (RR 0.73, 95% CI 0.53 to 1.01, 370 participants)
  • One study reported no reduction:
    • (Wexler, 1999) (RR 1.11 95% CI 0.82 to 1.49, 576 participants)

Impact on criminal activity

  • Two studies reported no reduction in re-arrests following treatment:
    • (Sacks, 2008) (RR 1.65, 95% CI 0.83 to 3.28, 370 participants)
    • (Wexler, 1999) (RR 0.96, 95% CI 0.82 to 1.13, 428 participants)
  • Three studies evaluated the impact of therapeutic community treatment using re-incarceration measures
    • Two studies reported reductions:
      • (Sacks, 2004) (RR 0.28, 95% CI 0.13 to 0.63, 193 participants)
      • (Sacks 2011) (RR 0.49, 95% CI 0.27 to 0.89, 127 participants)
    • One study found no effects:
      • (Sacks, 2008) (RR 0.73, 95% CI 0.45 to 1.19, 370 participants)

Mental health court and case management versus treatment as usual (standard court proceedings)

Impact on drug use (self-report)

  • No data available

Impact on criminal activity

  • One study reported no reduction in criminal activity:
    • (Cosden, 2003) (RR 1.05, 95% CI 0.90 to 1.22, 235 participants)

Motivational interviewing and cognitive skills versus relaxation therapy

Impact on drug use (self-report)

  • Two studies reported no reduction in drug use:
    • (Stein 2011) (MD -7.42, 95% CI -20.12 to 5.28, 162 participants)
    • (Lanza 2013) (RR 0.92, 95% CI 0.36 to 2.33, 41 participants)

Impact on criminal activity

  • No data available

Interpersonal psychotherapy versus a psychotherapy versus a psycho-educational intervention

Impact on drug use (self-report)

  • One study reported no reduction in drug use:
    • (Johnson 2012) (RR 0.67, 95% CI 0.30 to 1.50, 38 participants)

Impact on criminal activity

  • No data available

This review suggests that mental health programmes and drug interventions can help reduce criminal activity and re-incarceration rates, but are less effective at reducing drug use.

Discussion

This updated review included eight studies conducted within secure settings and in the judicial system. There were no studies for drug abusing offenders with mental illnesses under parole identified for inclusion within this review. Therefore, it’s difficult to compare if interventions are more beneficial within the community or under probation services.

Additionally, as all studies were conducted in the United States, it’s possible the treatments may not be generalisable outside the American judicial system, and as drug-use was self-report rather than biological measures, some caution needs to be taken when interpreting the results.

Generally, there was large variation across the studies, making comparisons difficult. However, two of the five trials displayed some evidence for therapeutic aftercare in relation to reducing subsequent re-incarceration.

All of the studies in this review were conducted in the US, so there may be issues of generalisability to other countries and judicial/health systems.

Links

Primary paper

Perry AE, Neilson M, Martyn-St James M, Glanville JM, Woodhouse R, Godfrey C, Hewitt C. Interventions for drug-using offenders with co-occurring mental illness. Cochrane Database of Systematic Reviews 2015, Issue 6. Art. No.: CD010901. DOI: 10.1002/14651858.CD010901.pub2.

Other references

Sacks S, Sacks JY, McKendrick K, Banks S, Stommel J. Modified TC for MICA inmates in correctional settings: crime outcomes. Behavioural Sciences and the Law 2004;22(4):477-501. [PubMed abstract]

Sullivan CJ, McKendrick K, Sacks S, Banks S. Modified therapeutic community treatment for offenders with MICA disorders: substance use outcomes. American Journal of Drug and Alcohol Abuse 2007; Vol. 33, issue 6:823-32. [0095-2990: (Print)] [PubMed abstract]

Sacks JY, McKendrick K, & Hamilton ZK. A randomized clinical trial of a therapeutic community treatment for female inmates: outcomes at 6 and 12 months after prison release. Journal of Addictive Diseases 2012;31(3):258-69. [PubMed abstract]

Sacks JY, Sacks S, McKendrick K, Banks S, Schoeneberger M, Hamilton Z, et al. Prison therapeutic community treatment for female offenders: Profiles and preliminary findings for mental health and other variables (crime, substance use and HIV risk). Journal of Offender Rehabilitation 2008;46(3-4):233-61. [: 1050-9674] [Abstract]

Prendergast ML, Hall EA, Wexler HK. Multiple measures of outcome in assessing a prison-based drug treatment program. Journal of Offender Rehabilitation 2003;37:65-94. [Abstract]

Prendergast ML, Hall EA, Wexler HK, Melnick G, Cao Y. Amity prison-based therapeutic community: 5-year outcomes. Prison Journal 2004;84(1):36-50. [Abstract]

Wexler HK, DeLeon G, Thomas G, Kressel D, Peters J. The Amity prison TC evaluation – re incarceration outcomes. Criminal Justice and Behavior 1999a;26(2):147-67. [Abstract]

Wexler HK, Melnick G, Lowe L, Peters J. Three-year re incarceration outcomes for Amity in-prison therapeutic community and aftercare in California. The Prison Journal1999b;79(3):321-36. [Abstract]

Cosden M, Ellens JK, Schnell JL, Yamini-Diouf Y, Wolfe MM. Evaluation of a mental health treatment court with assertive community treatment. Behavioral Sciences and the Law2003;21(4):415-27. [Abstract]

Stein LA, Lebeau R, Colby SM, Barnett NP, Golembeske C, Monti PM. Motivational interviewing for incarcerated adolescents: effects of depressive symptoms on reducing alcohol and marijuana use after release. Journal of Studies on Alcohol and Drugs2011;72(3):497-506. [PubMed abstract]

Lanza PV, Garcia PF, Lamelas FR, Gonzalez-Menendez A. Acceptance and commitment therapy versus cognitive behavioral therapy in the treatment of substance use disorder with incarcerated women. Journal of Clinical Psychology 2014;70(7):644-57. [DOI:10.1002/jcip.22060]

Johnson JE, Zlotnick C. Pilot study of treatment for major depression among women prisoners with substance use disorder. Journal of Psychiatric Research 2012;46(9):1174-83. [DOI: 10.1016/j.jpsychires.2012.05.007]

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/drug-using-offenders-with-co-occurring-mental-illness/#sthash.CnpCuCWr.dpuf

SMS text messaging interventions for healthy behaviour change

by Olivia Maynard @OliviaMaynard17

This blog originally appeared on the Mental Elf site on 28th September 2015.

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There’s a lot to like about text messaging (Short Message Service: SMS) interventions for behaviour change: they can deliver cost-effective, brief, real-time and tailored messages at moments when individuals need them most. They reduce time demands on both the individual and health care practitioners, maintain the privacy of the individual and what’s more, given that the majority of the world’s population own a mobile phone, text messaging interventions can be delivered at a global scale.

Given these advantages, there’s been a great deal of research on the effectiveness of SMS interventions for health behaviours, finding mixed results. Last month, I blogged about a meta-analysis which found weak evidence for the efficacy of SMS interventions for smoking cessation. However, smoking cessation is a complex health behaviour and recent reviews have found that SMS interventions are more effective for more simple health behaviours such as medicine adherence and attending medical appointments.

Another recent meta-analysis by Orr and King (2015) published in Health Psychology Review was the first to examine the overall effectiveness of SMS interventions to enhance healthy behaviour, rather than focus on any one health behaviour (i.e. smoking cessation). Similar to the meta-analysis I blogged about last month, the authors also aimed to identify which SMS features have the biggest impact on intervention effectiveness.

Mobile phones are now so ubiquitous that they are a great tool to deliver interventions globally.

Methods

The authors searched for randomised controlled trials (RCTs) which compared SMS interventions targeting health behaviour change to a non-SMS control, which did not attempt to change behaviour (i.e. RCTs which compared two active treatments were not included).

The authors examined the influence of six moderators on the effectiveness of SMS interventions:

  1. SMS dose (i.e. the frequency of the messages: multiple per day, weekly, once only etc);
  2. SMS message tailoring (i.e. standardised, tailored, personalised);
  3. SMS directionality between researcher and participant (i.e. one-way, two-way);
  4. Category of health behaviour targeted (i.e. unhealthy behaviour modification, chronic disease management, medication adherence, appointment attendance, disease or pregnancy preventive behaviours);
  5. Complexity of these behaviours (i.e. complex: chronic disease management, disease-related medication adherence, unhealthy behaviour modification; simple: appointment attendance, non-disease-related medication adherence, preventive behaviour);
  6. Participants’ mean age.

Results

Thirty eight studies met the criteria for inclusion in this meta-analysis.

The meta-analysis found that there was an overall (pooled) positive effect of SMS interventions on healthy behaviour (g = 0.291, 95% CI = 0.219 to 0.363, p < 0.001). The heterogeneity between studies was low (I2 = 38.619, p = 0.009­).

Planned sub-group analyses explored the impact of the six moderators on health behaviour change. There was little evidence that any of the six moderators impacted SMS intervention efficacy. However, when the authors regrouped the studies (i.e. unplanned analyses) for the tailoring, dose and complexity moderators, only SMS dose was found to impact SMS efficacy, with studies using multiple messages per day being more effective than those with reduced frequency.

The effect size for SMS interventions was small, but given that this is a cheap and simple intervention to deliver, it may be worthwhile.

Strengths

The quality of the evidence in the 38 studies was judged to be relatively high, with all but three of the studies assessed as having high to moderate methodological quality. However, the authors judged that the risk of incomplete data was high in 40% of studies, despite efforts to contact the authors of the original studies for further information.

Further analyses of the data found that publication bias was not a threat to the validity of the estimated effect of SMS interventions for healthy behaviour.

Weaknesses

The majority of included studies relied on self-report outcome measures, rather than actual behaviour, which is likely to have increased the observed effects of the studies. Future studies should use objective outcome measures.

The observation that more frequent text messages are more effective than less frequent messages was only found after regrouping the studies and running multiple unplanned comparisons between groups. This finding should therefore be treated with caution.

The authors only included studies which compared SMS intervention to no intervention at all. These findings therefore tell us nothing about how SMS interventions compare to other established interventions, such as verbal or other written reminders and messages.

Very few of the studies included in this meta-analysis were grounded in any health behaviour theory. The authors suggest that future research should examine the impact of established theoretical components on health behaviour change outcomes.

This study does not provide reliable evidence that more frequent text messages are more effective than less frequent messages.

Discussion

Using strict inclusion criteria for studies, this meta-analysis found that SMS interventions have a positive, albeit small, effect on healthy behaviour change. There was little evidence that moderators such as tailoring, directionality, health behaviour category or complexity or participant age influence efficacy. There was some evidence that higher SMS dose might increase efficacy.

These findings echo those in a recent meta-analysis of studies exploring the effectiveness of SMS interventions for smoking cessation where no moderator was found to be more effective than any other at increasing quit success and only a small positive effect of SMS intervention was observed.

Although this and previous meta-analyses have found only modest benefits of SMS interventions over control, given the low cost of delivery of SMS interventions and the potential to target large numbers of individuals, the public health benefits are still considerable and future research should continue to examine the efficacy of these interventions.

Despite the small effect sizes, SMS text messages remain a potentially effective intervention that can work on a truly global scale.

Links

Primary paper

Orr JA, King RJ. (2015) Mobile phone SMS messages can enhance healthy behaviour: a meta-analysis of randomised controlled trialsHealth psychology review (just-accepted), 1-36.

Other references

Maynard O. (2015) SMS texting to quit smoking: a meta-analysis of text messaging interventions for smoking cessation. The Mental Elf, 26 Aug 2015.

A green man walked into a bar to the future…

By Jasmine Khouja (@Jasmine_Khouja), with contributions from Olivia Maynard, Suzi Gage, Gibran Hemani and David Troy

Alcohol and cigarettes may not seem out of place at a music festival; discussing the science behind alcohol, cigarettes and genetics may. This August, my colleagues and I from the MRC Integrative Epidemiology Unit (IEU) and Tobacco and Alcohol Research Group (TARG) at the University of Bristol took our research to the Green Man Festival 2015 (http://www.greenman.net/explore/areas/einsteins-garden/) which, though primarily a music festival, hosts the Einstein’s Garden where festival-goers can play, learn and converse about science. ‘Future’ was the theme so we took our ‘Bar to the Future’ to show where our research may lead.

Smokingcigexp

Since the smoking ban, bars have been smoke-free but the growing popularity of e-cigarettes has sparked debate about their use indoors. Using an attention-grabbing demonstration (pictured right), we asked the public’s opinions on smoking and vaping. Our demonstration, in which cigarettes and e-cigarettes were smoked/vaped into the two separate glass tubes by means of a battery powered bed pump, shows a brown residue (tar) in the conventional cigarette tube where the e-cigarette tube is clear. Tar is not present in e-cigarettes making them, in one way, less harmful than traditional cigarettes.

 

bartothefutureMembers of the public shared their mixed opinions with us (pictured right) ranging from extremely positive to extremely negative.  Many seemed concerned about the long term health effects of using e-cigarettes. Scientists are unsure of the long term effects of e-cigarette use because they have not existed long enough for these effects to be assessed and the rapidly changing devices make it hard for scientists to keep up. The wide spectrum of beliefs could reflect the inconsistency of information the public receive from the media; news sources reported Public Health England’s recent suggestion that e-cigarettes are 95% safer than conventional cigarettes and may eventually be prescribed on the NHS (http://www.bbc.co.uk/news/health-33978603) which received backlash in a Lancet Journal editorial which also received media coverage (http://www.theguardian.com/society/2015/aug/28/public-health-england-under-fire-for-saying-e-cigarettes-are-95-safer). Opinion on conventional cigarettes however, was less divided. The public are clearly aware of the dangers despite many choosing to continue smoking. Opinions on vaping indoors showed a similar pattern to the views on e-cigarettes harm; worries over normalising smoking again and second hand vape were among the concerns despite no evidence suggesting inhaling second hand vape is dangerous. The exhaled vapour usually contains traces of nicotine and flavouring as well as an FDA approved substance used in most fog machines (propylene glycol). Though positive about the use of e-cigarettes as a smoking cessation aid as they contain fewer and lower doses of toxicants, they do still contain toxicants so we would not describe them as safe for non-smokers but they are safer than conventional smoking.

art

The children of Green Man got involved by designing cigarette packaging warning labels. Here are a few of the designs placed on our giant cigarette packet which could show the future of cigarette health warnings.

Alcohol

Calorie information is now placed on food and soft drinks bought at supermarkets but rarely placed on the packaging of alcoholic drinks. Calls from public health officials and policy makers could see calorie content on labelling made mandatory. By asking the public to guess how many calories were in lager, whiskey, alcopops and wine it became apparent that the public have limited knowledge when it comes to the calorific content of alcohol. This is unsurprising as calorie content varies across brands, strength and size of alcoholic drink. Without the information being provided, it is difficult to know how many calories are in your drink (some information can be found on the drink aware website, https://www.drinkaware.co.uk/understand-your-drinking/unit-calculator).

Straight glasses may be used more in bars rather than curved glasses in the future. To demonstrate the effect of this we asked festival-goers to half fill a curved and a straight glass with water. The majority of festival-goers struggled to find the half-way point on the curved glass yet found it relatively easily on the straight glass. This finding has been shown in the lab too; people perceive the half-way point to be lower than it is on a curved glass and consume a half pint 4 minutes faster in a curved glass than from a straight glass and drink 1 minute slower from glasses marked with volume information displaying where the ½, ¼ and ¾ points are. By simply adding volume information to glasses and using straight instead of curved glasses the public may reduce their drinking speed meaning they drink less over the course of their drinking session.

 

snakesWe also shared some future research which involves the Rotating snakes illusion (pictured right). When viewing this illusion the motion perception areas in the brain are activated meaning the viewer perceives the snakes as rotating. We hypothesised that if festival-goers had consumed alcohol they would see less or no rotation due to their motion perception being impaired. Though we did not observe this, there was a lot of variation in quickly the public saw the snakes rotate even if they hadn’t consumed any alcohol. This information will be useful when designing our future lab studies so that each participant is tested with and without having consumed alcohol rather than comparing alcohol consumers to non-alcohol consumers.

Genetics

A popular part of our stall was the genetics of table football. This was no ordinary game of table football, each team was made up of a genetics and an environmental player to demonstrate how our genes and environment affect the person we are in the future. The genetics players rolled a dice to decide their genetic predispositions (e.g. to alcohol dependence) which represented the element of chance in who we are. This related to an advantage or disadvantage in the game (e.g. holding a cup while playing). The environment player then picked a card (representing the element of choice in our environment) giving the player an advantage or disadvantage. The players then battled it out. The team who scored the first goal got to pick a controlled environment card; they could either lose one of their disadvantages (e.g. put the cup down) or choose to disadvantage the other team before continuing play. The game sparked discussions on how neither your genes or environment solely determine who you are, it is a team effort.

Festival-goers also tried PTC (Phenylthiocarbamide), which has a rare property; 70% of people experience a bitter taste but 30% of people taste virtually nothing when they try this chemical due to their genetics. After asking festival-goers to lick a PTC strip they were asked if they liked four bitter foods. We expected to find that those who can taste PTC are more sensitive to bitter tastes and therefore like bitter tasting foods less than those who cannot taste PTC. Information like this could be used in the future to help people decide what alcoholic drinks may taste better to them. We found that PTC tasters were as likely to like bitter tasting foods as those who can’t taste PTC.

stallThe punch line

After three days of games and discussions with the public, our gazebo proved no match for the Welsh weather and was left broken beyond repair after heavy rainfall. We were forced to shut down the stall a day early but for those who missed out, we hope to see you next year!

 

Can we use the inhalation of 7.5% CO2 as a model to probe cognition and behaviour in anxiety?

by Alex Kwong @tskwong

A lot of the work conducted in the Tobacco and Alcohol research group (TARG) mainly focuses around tobacco and alcohol research (funny that…). However, when we’re not getting people intoxicated in the name of science (yes we do that), we’re also carrying research ranging from body perception, to emotion recognition and anxiety research. The latter is something that I’ve focused on, and to cut a long story short, we make people anxious by making them breathe in air enriched with carbon-dioxide (CO2), about 7.1% more than what you would normally breathe. Once people are anxious, we assess them on a number of outcomes, some clinically relevant, some more practical and applied.

Needless to say, breathing in about 7% more CO2 for a period of up to 20 minutes should make you anxious for a number of reasons (to be explained later on). But can breathing in a gas that is enriched with CO2 act as a viable model for anxiety, capable of assessing cognition and behaviours that are susceptible to anxiety? In this post I’ll explore some of the previous research utilising this model, and look at some of the future directions of the model and how it could be used as a training tool to help improve performance under anxiety. By then, hopefully you’ll agree with me that the model is good at experimentally inducing anxiety, and you’ll sign up for all our studies.

Possibly the most influential research on the inhalation of CO2 has been by Bailey et al. (2005) and work from David Nutt’s former lab in Bristol. They found that breathing in CO2 enriched gas for a period of 20 minutes decreased positive mood (feelings of happiness and relaxation) and increased negative mood (worry and fear). Since then, a plethora of research has supported this, and also found that the model induces symptoms such as sweating, increased heart rate and blood pressure and hypoxia, all common in generalised anxiety disorder (GAD). Interestingly, other research has found that we can actually reduce these responses to the CO2 model by giving people anxiolytic drugs. As such, the model of 7.5% CO2 has been considered a validated model of human anxiety induction that is generalisable to anxiety disorders such as GAD.

But why does breathing in a gas that is enriched with CO2 cause these sort of feelings? One explanation is that breathing in CO2 causes chemoreceptors to mislead the body into thinking that it is starved of oxygen. This leads to fear like responses, as well as increased breathing rates and higher blood pressure and heart rate. If you’ve ever had the pleasure of taking part in one of these CO2 experiments, you’ll likely agree that these things happen. I’ll just stress at this point that effects of the gas are transient and usually disappear quickly after the inhalation. Some people even enjoy the experience, so I hope I’m still selling this to you.

CO2 set-up

A typical experimental set up with the CO2.

So if it makes you feel like you’re experiencing physiological anxiety, then it’s obviously a model of human anxiety right? Well what about the psychological components? People with GAD often have a hypervigilance to threat, even when there is nothing threatening around. Additionally, their attention to negative stimuli is increased, even in the presence of other emotional content. Anxious sufferers also interpret ambiguous information as potentially dangerous or threatening. Can the CO2 model can tap into some of these psychological components that are common in GAD?

To address this, one study found that the inhalation of 7.5% CO2 caused quicker eye-movements to be made towards threatening stimuli. Another study found that CO2 caused attention to reflect a hyper vigilance to threatening information. Otherresearch in preparation has found that people were worse at correctly identify emotional faces during CO2. Lastly, Cooper et al. (2013) found that CO2 caused people to interpret ambiguous information in CCTV footage as threatening. These findings support the 7.5% CO2 model affecting psychological processes similar to those in GAD.

Great! So the model seems to be similar to the experience of GAD, what next? Well, what’s also quite fascinating is that if we have a model for anxiety, we could predict how people will behave in situations like sport, musical performances, decision-making, medical and security services etc – behaviours that can induce feelings of anxiety or be affected by anxiety, even in those without a disorder. Understanding how people will behave in stressful situations might help improve performances in the future.

The CO2 model has been used to investigate this. Attwood et al. (2013) found that 7.5% CO2 impaired the ability to match pairs of faces, a finding which has tremendous implications for military and forensic settings (e.g., border crossings and proof of sale purchases like alcohol and tobacco). More recently, we also found that the inhalation of 7.5% CO2 impairs the ability to remember faces that have previously been seen. Importantly, ‘witnesses’ did not report lower confidence of their choices despite this impaired ability, which has implications for the judicial system (e.g., courtrooms and line-ups).

Upcoming research has suggested that CO2 impairs decision-making on a gambling task, by making people choose more exploratory decisions which in turn causes less money earned. Other research has suggested that the CO2 causes excessive force production which could affect military, surgical and sporting behaviours. The same research also suggested that people speed up when asked to tap in time with a metronome, which could detriment musical performances and any task that requires accurate bodily timing. Together, this research shows that the inhalation of 7.5% CO2 may be a useful tool for examine how anxiety may affect behaviours.

Mask

The amount of Bane and Darth Vader impressions I got from participants was staggering – “It would be extremely anxious…, for you”

By now you should be getting the picture that a) the CO2 model is good for inducing anxiety and b) that I am incredibly biased in favouring this model. But I think there are good reasons to endorse this stance. Many previous studies that induce anxiety are time limited, meaning that ‘anxiety’ may only affect certain stages of the task. Other studies only produce one single ‘hit’ to cause anxiety (e.g., one phobic stimuli, one bodily stressor), which may not be characteristic of anxiety as a whole. However, one anxiety inducer that I think is quite neat is the threat of electric shock. Threatening people with electric shock is a great way to induce anxiety but in some experiments, the shock doesn’t actually come, so people quickly learn that there is no threat and thereby no longer remain anxious, which is a problematic for anxiety research.

The CO2 model is not without its flaws. Tasks can only be conducted within the 20 minute inhalation window. That said, there is no limit to how many times someone can be CO2’d. Practically, people may decide they no longer want to feel anxious during the inhalation and so drop out, but this is likely to be a problem in anxiety research generally. Perhaps most importantly, whilst we have conducted numerous CO2 experiments, we are still unsure exactly how the model works on all attentional and behavioural mechanisms. Future research is looking at how the CO2 model affects the brain, and our eye-movements. There is also research that has explored psychological interventions, such as mindfulness training, and whether this can reduce some of the symptoms brought on by the CO2 inhalation. It’ll also be really interesting to see whether the model can be utilised as a training tool for people who need to perform under anxious conditions. Research has shown that practising under conditions of anxiety can help improve performance at a later stage and so the next step would be to see if people can perform better in real life anxious situations, if they’ve practised on the CO2 model first.

In summary, the CO2 model seems to be a reliable way to induce anxiety that can impact on both cognition and behaviour. The model is validated by a wealth of research showing its similarity to GAD. Although the model is not perfect for inducing anxiety, it is one of the more promising tools we currently have, and subsequent research should continue to use the model as a viable probe for exploring cognition and behaviour under anxiety.