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Archive for December, 2015

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.

shutterstock_314454056

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