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.

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.

Antidepressants during pregnancy and risk of persistent pulmonary hypertension of the newborn

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 2nd July 2015.

Persistent pulmonary hypertension of the newborn (PPHN) is associated with increased morbidity and mortality of infants and occurs in 10-20 per 10,000 births.

Those who survive face chronic lung disease, seizures, and neurodevelopmental problems as a result of hypoxemia and aggressive treatment (Walsh-Sukys et al 2000; Farrow et al 2005; Clark et al 2003; Glass et al 1995).

Based on a single study in 2006, the FDA issued a public health advisory that late pregnancy exposure to SSRIs may be associated with an increased risk of PPHN (FDA 2015; Chambers 2006). However, a review yielding conflicting findings led the FDA to conclude that they were premature in their conclusion.

This is the background to a new study by Huybrechts et al (2015), which sets out to investigate SRRI and non-SSRI antidepressants and the associated risk of PPHN in late stage pregnancy.

PPHN is a potentially fatal condition affecting mainly full-term babies, in which the blood flow to the lungs shuts down because the main arteries to the lungs constrict.

Methods

Cohort and data

Participants were drawn from the Medicaid Analytic eXtract (MAX) cohort, which holds the health records of medicate beneficiaries in the United States.

Antidepressants

If women filled 1 antidepressant prescription 90 days before delivery, they were considered ‘exposed.’ Antidepressant medications were classified as either SSRIs (Selective Serotonin Re-uptake Inhibitors) or non-SSRIs. Women exposed to both types of antidepressant were excluded from the analysis. A reference group of women was created, whom had not been exposed to either SSRI or non-SSRIs at any time during pregnancy.

Persistent Pulmonary Hypertension of the Newborn (PPHN)

PPHN was defined by the ICD-9 diagnostic criteria for persistent foetal circulation or primary pulmonary hypertension in the first 30 days following delivery.

Analysis

A sensitivity analysis was conducted to control for possible misclassification, with exposure status defined as filling 2 prescriptions during 90 days before delivery, and outcome redefined as only severe cases of PPHN (respiratory assistance, extracorporeal membrane oxygenation, or inhaled nitric oxide therapy).

This very large (3.8 million pregnant women) population-based study included mothers in the US on low income and with limited resources.

Results

Within 3,789,330 pregnancies, 3.4% of women used antidepressants in the 90 days before delivery, of which 2.7% were SSRIs and 0.7% were non-SSRI antidepressants.

Antidepressant versus non-use

  • 31.0 (95% CI, 28.1 to 34.2) per 10,000 infants exposed to antidepressant use had PPHN
  • 20.8 (95% CI, 20.4 to 21.3) per 10,000 infants not exposed to antidepressant use had PPHN

SSRI versus non-SSRI antidepressant use

  • 31.5 (95% CI 28.3 to 35.2) per 10,000 infants exposed to SSRIs had PPHN
  • 29.1 (95% CI 23.3 to 36.4) per 10,000 infants exposed to non-SSRIs had PPHN

Depression diagnosis

After restricting to a diagnosis of depression:

  • 33.8 (95% CI, 29.7 to 38.6) per 10,000 infants exposed to SSRIs had PPHN
  • 34.4 (95% CI, 26.5 to 44.7) per 10,000 infants exposed to non-SSRIs had PPHN
  • 14.9 (95% CI 23.7 to 26.1) per 10,000 infants not exposed to antidepressant use had PPHN

Sensitivity analysis

  • Women who filled 2 prescriptions in the 90 days before delivery did not have stronger associations
  • Changing the definition for PPHN did not alter associations in either SSRIs or non-SSRIs

The chances of a baby getting PPHN when its mother was not taking an SSRI are around 2 in 1,000, compared to around 3 in 1,000 when the mother had taken an SSRI in the last 90 days of pregnancy.

Discussion

Overall, the authors found evidence that SSRI exposure in the last 90 days of pregnancy may be associated with an increased risk of PPHN. However, the magnitude of risk observed is less than has previously been reported. Furthermore, sensitivity analyses did not amplify these risks.

The authors conclude by suggesting clinicians should take the increase of risk of PPHN into consideration when prescribing these drugs during pregnancy.

Limitations

There are a few limitations in this study to be noted:

  • Possible misclassification of the exposure or outcome, (e.g. filling a prescription does not guarantee it was taken as prescribed) which may bias the results. However, the authors did conduct a sensitivity analysis in order to control for this.
  • The baseline characteristics varied between women taking antidepressants and those who did not, with women prescribed antidepressants more likely to be older, white, taking other psychotropic medicines, be chronically ill, be obese, smoke, and have health care issues. While the SSRI and non-SSRI groups were more comparable, non-SSRI women had higher overall illness, more comorbidities, and co-medication use. Additionally, the participant population was drawn from a relatively low-income group, in which comorbid illness is likely to be higher than general populations, which may account for the difference in risk of previous studies.

This evidence would suggest that the benefits of antidepressants taken during pregnancy outweigh the risks of rare events such as PPHN.

Professor Andrew Whitelaw, Professor of Neonatal Medicine at the University of Bristol, said of the study:

Taking this study with the previous evidence, I conclude that there is a slightly increased risk of PPHN if a pregnant woman takes an SSRI but this only brings the risk up to 3 per 1000 births. I do not suggest that seriously depressed pregnant women should be denied SSRI treatment, but it would be wise for them to deliver in a hospital with a neonatal intensive care unit in case PPHN does occur.

Links

Primary paper

Huybrechts K, Bateman B, Palmsten K, Desai R, Patorno E, Gopalakrishnan C, Levin R, Mogun H, Hernandez-Diaz S. (2015) Antidepressant Use Late in Pregnancy and Risk of Persistent Pulmonary Hypertension of the Newborn. 2015: 313(21). [Abstract]

Other references

Walsh-Sukys MC, Tyson JE, Wright LL et al. (2000) Persistent pulmonary hypertension of the newborn in the era before nitric oxide: practice variation and outcomes. Pediatrics. 2000;105(1 pt 1):14-20. [PubMed abstract]

Farrow KN, Fliman P, Steinhorn RH. (2005) The diseases treated with ECMO: focus on PPHN. Semin Perinatol. 2005;29(1):8-14. [PubMed abstract]

Clark RH, Huckaby JL, Kueser TJ et al. (2003) Clinical Inhaled Nitric Oxide Research Group.  Low-dose nitric oxide therapy for persistent pulmonary hypertension: 1-year follow-up. J Perinatol. 2003;23(4):300-303. [PubMed abstract]

Glass P, Wagner AE, Papero PH et al. (1995) Neurodevelopmental status at age five years of neonates treated with extracorporeal membrane oxygenation. J Pediatr. 1995;127(3):447-457. [PubMed abstract]

US Food and Drug Administration. (2006) Public health advisory: treatment challenges of depression in pregnancy and the possibility of persistent pulmonary hypertension in newborns.

Chambers  CD, Hernández-Diaz  S, Van Marter  LJ,  et al.  Selective serotonin-reuptake inhibitors and risk of persistent pulmonary hypertension of the newborn. N Engl J Med. 2006;354(6):579-587. [PubMed abstract]

– See more at: http://www.nationalelfservice.net/treatment/antidepressants/antidepressants-during-pregnancy-and-risk-of-persistent-pulmonary-hypertension-of-the-newborn/#sthash.kEFM7Ik8.dpuf

Smoking and risk of schizophrenia: new study finds a dose-response relationship

by Marcus Munafo @MarcusMunafo

This blog originally appeared on the Mental Elf site on 1st July 2015.

Almost exactly a year ago, a landmark study identified 108 genetic loci associated with schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). In a Mental Elf post on that study I wrote: “Genetic studies also don’t rule out an important role for the environment – [genome-wide association studies] might even help identify other causes of disease, by identifying loci associated with, for example, tobacco use.”

I mentioned this because one of the loci identified is strongly associated with heaviness of smoking. There are two possible explanations for this: either this locus influences both smoking and schizophrenia, or smoking causes schizophrenia.

Smoking and schizophrenia are highly co-morbid; the prevalence of smoking among people with a diagnosis of schizophrenia is much higher than in the general population. It is widely believed that this is because smoking helps to alleviate some of the symptoms of schizophrenia, or the side-effects of antipsychotic medication.

The possibility that smoking itself may be a risk factor for schizophrenia has generally not been widely considered. Now, however, intriguing evidence has emerged that it may be, from a large study of data from Swedish birth and conscript registries (Kendler et al, 2015).

The leading causes of premature mortality in people with schizophrenia are ischaemic heart disease and cancer, both heavily related to smoking.

Methods

The authors linked nationwide Swedish registers via the unique 10-digit identification number assigned at birth or immigration to all Swedish residents. Data on smoking habits were collected from the Swedish Birth Register (for women) and the Military Conscription Register (for men). The date of onset of illness was defined as the first hospital discharge diagnosis for schizophrenia or non-affective psychosis.

Cox proportional hazard regressions were used to investigate the associations between smoking and time to schizophrenia diagnosis. To evaluate the possibility that smoking began during a prodromal period (where symptoms of schizophrenia may emerge prior to a full diagnosis), buffer periods of 1, 3 and 5 years were included in the models. In the female sample, data from relatives (siblings and cousins) were also used to control for familial confounding (genetic and environmental).

Results

Smoking status information was available for 1,413,849 women, and 233,879 men.

There was an association between smoking at baseline and a subsequent diagnosis of schizophrenia for:

  • Women
    • Light smoking: hazard ratio 2.21, (95% CI 1.90 to 2.56)
    • Heavy smoking: hazard ratio 3.45 (95% CI 2.95 to 4.03)
  • Men
    • Light smoking: hazard ratio 2.15 (95% CI 1.25 to 3.44)
    • Heavy smoking: hazard ratio 3.80 (95% CI 1.19 to 6.60)

Adjustment for socioeconomic status and prior drug abuse (i.e., confounding) weakened these associations slightly.

Taking into account the possibility of smoking onset during a prodromal period also did not weaken these associations substantially, irrespective of whether the buffer period (from smoking assessment to the date at which a first schizophrenia diagnosis would be counted) was 1-, 3- or 5-years. Theoretically, if prodromal symptoms of schizophrenia lead to smoking onset (i.e., reverse causality) the smoking-schizophrenia association should weaken with longer buffer periods.

Finally, the co-relative analyses compared the association between smoking and schizophrenia in the female sample, within pairs of relatives of increasing genetic relatedness who had been selected on the basis of discordance for smoking (i.e., one smoked and one did not). If the smoking-schizophrenia association arises from shared familiar risk factors (genetic or environmental) the association should weaken with increasing familial relatedness. Instead, only modest decreases were observed.

As a validation check on the accuracy of their measure of smoking behaviour, the authors confirmed that heavy smoking was more strongly associated with both lung cancer and chronic obstructive pulmonary disease, two diseases known to be caused by smoking.

These results show a dose-response relationship between smoking and risk of schizophrenia, i.e. the more you smoke, the stronger the association. 

Conclusion

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia. However, observational associations are notoriously problematic, because these associations may arise because of confounding (measured and unmeasured), or reverse causality. Since these analyses were conducted on observational data, these limitations should be borne in mind and we cannot say with certainty that smoking is a causal risk factor for schizophrenia.

Nevertheless, the authors conducted a number of analyses to attempt to rule out different possibilities. First, the associations were weakened only slightly when adjusted for socioeconomic status and prior drug abuse, so the impact of measured confounders appears to be modest (although other confounding could still be occurring). Second, the inclusion of a buffer period to account for smoking onset during a prodromal period also weakened the associations only slightly, which is not consistent with a reverse causality interpretation. Finally, the co-relative analysis did not indicate that the association differed strongly across levels of familial relatedness, suggesting that the impact of unmeasured familial confounders (both genetic and environmental) is relatively modest.

This study provides clear evidence of a prospective association between cigarette smoking and a subsequent diagnosis of schizophrenia.

Limitations

There are some limitations to the study that are worth bearing in mind:

  1. First, there were no data on lifetime smoking, although the authors validated their measure of smoking against outcomes known to be caused by smoking.
  2. Second, the authors used clinical diagnoses, and included both schizophrenia and non-affective psychosis, so the specificity of the findings to these outcomes is uncertain.
  3. Third, because of the small number of schizophrenia diagnoses the co-relative analyses used non-affective psychosis only.

This study is not enough to say with certainty that smoking is a causal risk factor for schizophrenia.

Summary

There are three main ways in which the association between smoking and schizophrenia might arise:

  1. Schizophrenia causes smoking,
  2. Smoking causes schizophrenia, and
  3. The association arises from risk factors common to both.

This study suggests that the first mechanism cannot fully account for the association; if anything there was more support for the third mechanism, including stronger evidence for a role for familial factors than for socioeconomic status and drug abuse. However, critically, this study also finds support for the second mechanism, including a dose-response relationship between smoking and risk of schizophrenia.

Despite this study’s strengths, and the care taken by the authors to explore the three possible mechanisms that could account for the association between smoking and schizophrenia, no single study is definitive. However, evidence is emerging from other studies that support the possibility that smoking may be a causal risk factor for schizophrenia.

Recently, McGrath and colleagues have reported that earlier age of onset of smoking is prospectively associated with increased risk of non-affective psychosis (McGrath et al, 2015).

In addition, Wium-Andersen and colleagues report that tobacco smoking is causally associated with antipsychotic medication use (but not antidepressant use), in a Mendelian randomisation analysis that uses genetic variants as unconfounded proxies for heaviness of smoking (Wium-Andersen et al, 2015).

Identifying potentially modifiable causes of diseases such as schizophrenia is a crucial part of public health efforts. There is also often reluctance among health care professionals to encourage patients with mental health problems (including schizophrenia) to attempt to stop smoking. If smoking is shown to play a causal role in the development of schizophrenia, there may be more willingness to encourage cessation. Since the majority of the mortality associated with schizophrenia is due to tobacco use (Brown et al, 2000), helping people with schizophrenia to stop is vital to their long-term health.

There is now mounting evidence that supports the possibility that smoking may be a causal risk factor for schizophrenia.

Links

Primary paper

Kendler, K.S., Lonn, S.L., Sundquist, J & Sundquist, K. (2015). Smoking and schizophrenia in population cohorts of Swedish women and men: a prospective co-relative control study. American Journal of Psychiatry. doi: 10.1176/appi.ajp.2015.15010126 [Abstract]

Other references

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421-427. doi: 10.1038/nature13595

McGrath, J.J., Alati, R., Clavarino, A., Williams, G.M., Bor, W., Najman, J.M., Connell, M. & Scott, J.G. (2015). Age at first tobacco use and risk of subsequent psychosis-related outcomes: a birth cohort study. Australian and New Zealand Journal of Psychiatry. [PubMed abstract]

Wium-Andersen, M.K., Orsted, D.D. & Nordestgaard, B.G. (2015). Tobacco smoking is causally associated with antipsychotic medication use and schizophrenia, but not with antidepressant medication use or depression. International Journal of Epidemiology, 44, 566-577. [Abstract]

Brown S, Inskip H, Barraclough B. (2000) Causes of the excess mortality of schizophrenia. Br J Psychiatry. 2000 Sep;177:212-7.

– See more at: http://www.nationalelfservice.net/mental-health/schizophrenia/smoking-and-risk-of-schizophrenia-new-study-finds-a-dose-response-relationship/#sthash.u3UiDOlG.dpuf

CBT for substance misuse in young people

by Eleanor Kennedy @Nelllor_

This blog originally appeared on the Mental Elf site on 26th May 2015.

According to 2011 figures for the UK, over 11% of people seeking treatment for substance use were aged between 15-19 years old (Emcdda.europa.eu, 2015).

Cognitive-Behavioural Therapy (CBT) is a treatment that uses cognitive and behavioural techniques to target drug-related beliefs and to alter how these beliefs impact on actions. The individualised nature of CBT may especially be beneficial for young people whose needs differ from those of an adult due to the developmental stage of adolescence.

The factors that moderate the success of CBT treatment among young people are not well-defined. The authors of the current review aimed “to assess the effectiveness of CBT for young people in outpatient non-opioid drug use and to explore any factors that may moderate outcomes” (Filges et al 2015). Non-opioid drugs refers to cannabis, cocaine, ecstasy and amphetamines.

The non-opioid drugs covered by this review were cannabis, cocaine, ecstasy and amphetamines.

Methods

Numerous online databases were searched and studies were included if:

  • The study design was either a randomised, quasi-randomised or non-randomised controlled trial (RCT, QRCT or NRCT)
  • Participants were aged 13-20 years old
  • Participants were enrolled in outpatient treatment for non-opioid drug treatment
  • CBT was the primary intervention, although CBT interventions with an add-on component, such as motivational interviewing, were included

The primary outcome measure was abstinence or reduction of drug use as measured by biochemical test, self-report estimates or psychometric scales. Secondary outcomes of interest were social and family functioning; education or vocational involvement; retention; risk behaviour such as crime rates.

Two separate meta-analyses were conducted.

Seven

Results

Study characteristics

Seven studies, reported in seventeen papers, were included in the review. All seven studies were RCTs; six were conducted in the US and one was carried out in The Netherlands. The seven studies were quite different; sample sizes ranged from 43 to 320 participants and the gender of participants enrolled ranged from 54% to 81% male.

CBT was compared to a range of interventions, namely adolescent community reinforcement approach; multidimensional family therapy; chestnut’s Bloomington outpatient program; interactional treatment; psychoeducational substance abuse treatment and functional family therapy. Three evaluated CBT only, while four studies looked at CBT with an add-on component including Assertive Continuing Care, Motivational Enhancement Intervention or Integrated Family therapy.

The studies also differed in terms of CBT delivery; one study provided individual CBT, two had group CBT session, one study included family sessions alongside peer-group therapy, another study had family sessions at the beginning and end of the treatment period, while another study provided a home-based continuing care approach.

Main findings

Separate meta-analyses were conducted on the four studies that looked at CBT with an add-on component and on the three studies that evaluated CBT without an add-on component. Analyses had differing numbers of included studies depending on the variable in question.

Outcome measures were evaluated in three different intervals: short term (beginning of treatment to < 6 months later); medium term (6 months to < 12 months after beginning treatment) and long term (12 months + after the beginning of treatment).

Drug use

  • Overall, studies that reported on the effects of CBT with an add-on component did not show a reduction of drug use relative to the comparison treatment in the:
    • Short term (SMD 0.14 95% CI -0.64 to 0.36);
    • Medium term (SMD -0.06 95% CI -0.44 to 0.32) or
    • Long term (SMD -0.15 95% CI -0.36 to 0.06)
  • The studies that evaluated CBT without an add-on component were not found to be significantly more effective than their respective comparison treatment in the
    • Short term (SMD -0.13 95% CI -0.68 to 0.42);
    • Medium term (SMD 0.08 95% CI -0.48 to 0.31) or
    • Long term (SMD 0.02 95% CI -0.48 to 0.52)

Recovery

  • Studies that reported on CBT with an add-on component showed a statistically significant relative effect on recovery status in the long term (OR = 0.63 (95% CI 0.39 to 1.00)
  • Only one study with CBT without an add-on component reported recovery status, this was not statistically significant (OR = 2.89 (95% CI 0.72 to 11.56)

Secondary outcomes

  • CBT with an add-on component was not found to have a significant relative effect on retention or risk behaviour
  • CBT without an add-on component also did not have a significant relative effect on psychological problems, family problems, school problems, retention or risk behaviour

Unfortunately, this review does not tell us whether CBT is more or less effective than other treatments for substance misuse in young people.

Strengths and limitations

The review had some strengths. A large number of databases were searched and there were no language restrictions on the literature included. Additionally, all included studies were RCTs with none of the studies classified as having a very high risk of bias.

The small number of studies included in this review is not problematic by itself, however, the choice to carry out separate meta-analyses based on the inclusion of an add-on component to the CBT, reduced the power of the analyses even further.

Additionally, caution must be taken when interpreting the findings of the meta-analyses as the studies were all very different. There was significant heterogeneity between the studies in all but one analysis and also many of the analyses were conducted on only two studies.

The qualitative review of the paper was weak, it was merely a description of the included studies without an evaluation of the differences between them.

Conclusions

The review is inconclusive in terms of CBT being more or less effective than other therapies, as the authors themselves note. No qualitative comparisons were drawn between the studies, this may have been more beneficial given the array of differences between all seven studies.

The review did not consider any factors that may moderate the efficacy of CBT as a treatment for non-opioid drug use and the authors suggest that future studies should include more information about the heterogeneity of treatment effects so that this can be explored.

Given the differences between the included studies, a meta-analysis was probably not appropriate and a good quality systematic review may have been more useful.

More qualitative analysis of the included studies may have shed more light on this discussion.

Links

Primary paper

Filges T, Knudsen ASD, Svendsen MM, Kowalski K, Benjaminsen L, Jørgensen AMK. Cognitive-Behavioural Therapies for Young People in Outpatient Treatment for Non-Opioid Drug Use: A Systematic Review. Campbell Systematic Reviews 2015:3 10.4073/csr.2015.3

Other references

Emcdda.europa.eu, (2015). EMCDDA | European Monitoring Centre for Drugs and Drug Addiction — information on drugs and drug addiction in Europe. [online] Available at: http://www.emcdda.europa.eu/ [Accessed 15 May 2015].

– See more at: http://www.nationalelfservice.net/mental-health/substance-misuse/cbt-for-substance-misuse-in-young-people/#sthash.xWsGpoWk.dpuf

The effect of smoking-free psychiatric hospitals on smoking behaviour: more evidence needed

By Olivia Maynard @OliviaMaynard17 

This blog originally appeared on the Mental Elf site on 18th May 2015.

One in three people with mental health illnesses in the UK smoke, as compared with one in five of the general population. In addition, smokers with mental illnesses smoke more heavily, are more dependent on nicotine and are less likely to be given help to quit smoking. As a result, they are more likely to suffer from smoking-related diseases, and on average die 12-15 years earlier than the general population.

Since July 2008, mental health facilities in England have had indoor smoking bans. However, NICE guidelines recommend that all NHS sites, including psychiatric hospitals become completely smoke-free, a recommendation previously examined by the Mental Elf.

This NICE recommendation has been criticised by those who argue that:

  1. Tobacco provides necessary self-medication for the mentally ill;
  2. Smoking cessation interferes with recovery from mental illness;
  3. Smoking cessation is the lowest priority for those with mental illnesses;
  4. People with mental illnesses are not interested in quitting;
  5. People with mental illness cannot quit smoking.

Many people argue that forcing people to quit smoking when they are having an acute mental health episode is tantamount to abuse.

Judith Prochaska, a researcher at Stanford University, has previously addressed each of these arguments (she calls them ‘myths’) (Prochaska, 2011). The abridged summary of the evidence surrounding myths 1, 2 and 3 is that:

  1. Smoking actually worsens mental health outcomes; in fact, the argument that nicotine provides self-medication is one which has been promoted by the tobacco industry itself;
  2. Smoking cessation does not exacerbate mental health outcomes;
  3. Smoking cessation should be a high priority, given that mental health patients are much more likely to die from tobacco-related disease than mental illness.

These are interesting and important arguments and more evidence surrounding them is also available here (Prochaska, 2010).

However, in this blog post I focus on ‘myths’ 4 and 5, drawing on a recent systematic review investigating the impact of a smoke-free psychiatric hospitalisation on patients’ motivations to quit (myth 4) and smoking behavior (myth 5) (Stockings et al., 2014).

This systematic review brings together mostly cross-sectional studies that look at the impact that smoke-free hospitals have on psychiatric inpatients who smoke.

Methods and results

Stockings and colleagues searched for studies examining changes in patients’ smoking-related behaviours, motivation and beliefs either during or following an admission to an adult inpatient psychiatric facility.

Study characteristics

Fourteen studies matched these inclusion criteria, two of which were conducted in the UK. The majority of the studies used a cross-sectional design and none were randomised controlled trials. The studies were all quite different, with the number of participants ranging from 15-467 and the length of admission ranging from 1-990 days. Crucially, the type of smoking ban varied considerably between the studies, so I’ll consider these separately.

Facilities with complete smoking bans

Six studies were conducted in facilities with complete bans. All of these offered nicotine dependence treatment, including nicotine replacement therapy (NRT) or brief advice.

  • Only one of these statistically assessed smoking behaviour, finding that cigarette consumption was lower during admission compared with prior to admission.
  • Three studies assessed smoking behaviour after discharge, finding that the majority of patients resumed smoking within five days. However, there was some evidence from the two larger studies that smoking prevalence was still lower at two weeks and three months post-discharge compared with prior to admission.
  • The one study to statistically assess smoking-related beliefs and motivations found that patients expected to be more successful at quitting following discharge compared with at admission. Higher doses of NRT were related to higher expectations of success.

Facilities with incomplete bans

Eight studies were conducted in facilities with incomplete bans. 

  • Four banned smoking indoors and all of these offered nicotine dependence treatment:
    • Only one of these statistically assessed smoking behaviour, finding that quit attempts increased from 2.2% when smoking was permitted in specific rooms, to 18.4% after the ban.
    • One study that assessed smoking prevalence post-discharge found that all participants (n = 15) resumed smoking.
    • One study found that participants expected to be more successful in smoking cessation post-discharge as compared with at admission.
  • Three allowed smoking in designated rooms, with no nicotine dependence treatment:
    • There were mixed results among the two studies which assessed smoking prevalence during admission.
    • Compared with at admission, there was some evidence of increased motivation to quit smoking.
  • One restricted smoking to five pre-determined intervals per day, with no nicotine dependence treatment:
    • Motivation to quit was lower at discharge compared with at admission.

This review suggests that complete bans are the most effective at encouraging smoking cessation and that NRT or brief advice are crucial.

Conclusions

The authors concluded that:

Smoke-free psychiatric hospitalisation may have the potential to impact positively on patients’ smoking behaviours and on smoking-related motivation and beliefs.

Strengths and limitations

The fourteen studies included in this review were all quite different from each other and had a number of limitations including:

  • Small sample sizes;
  • Incomplete reporting of key outcomes;
  • Failure to use controlled, experimental research designs;
  • Differences in the types of smoking bans in place;
  • Inconsistent provision of nicotine dependence treatment.

These key differences and limitations prevented statistical examination of the results as a whole. This means that making firm conclusions is difficult. There is clearly a need for more research in this area.

This area of research is far from complete, so we cannot make any firm conclusions about smoke-free psychiatric hospitals at this stage.

Summary

There is evidence that people with mental illnesses are interested in quitting smoking (myth 4) and that they are able to (myth 5). However, we still need more studies to examine these questions with well-powered (i.e. large sample sizes), high-quality (i.e., experimental) research designs.

The evidence presented in this systematic review suggests that complete bans are the most effective at encouraging smoking cessation and that the provision of nicotine dependence treatment, such as NRT or brief advice, is also crucial.

Although a handful of the studies assessed smoking behaviour after discharge, none of the facilities viewed this as an important outcome. Given the high level of smoking-related disease among those with mental health illnesses, ensuring that individuals remain abstinent from smoking after discharge is important for the continuing good health of these individuals.

Importantly, none of the studies in this review explored the impact of smoke-free legislation on mental health outcomes. Although the evidence suggests that smoking cessation actually improves mental health outcomes, future research should continue to examine this relationship.

Over to you

Do you have a mental health illness yourself, or support someone who does? Do you work with people with mental health illnesses? Should psychiatric hospitals become smoke-free?

We'd love to hear your views about this systematic review and more generally on this often emotive topic. Please use the comment box below to share your knowledge and experience.

Links

Primary paper

Stockings EA. et al (2014) The impact of a smoke-free psychiatric hospitalization on patient smoking outcomes: a systematic review. Aust NZ J Psychiatry 2014 May 12;48(7):617-633. [PubMed abstract]

Other references

Prochaska, J. J. (2010). Failure to treat tobacco use in mental health and addiction treatment settings: A form of harm reduction? Drug and Alcohol Dependence, 110(3), 177-182. doi: http://dx.doi.org/10.1016/j.drugalcdep.2010.03.002

Prochaska, J. J. (2011). Smoking and Mental Illness — Breaking the Link. New England Journal of Medicine, 365(3), 196-198. doi: doi:10.1056/NEJMp1105248

 

Promoting smoking cessation in people with schizophrenia

by Meg Fluharty @MegEliz_

This blog originally appeared on the Mental Elf site on 14th May 2015.

shutterstock_276469196People with schizophrenia have a considerable reduction in life expectancy compared to the general population (Osborn et al 2007; Lawrence et al 2013). A number of factors lead to cardiovascular disease (Osborn et al 2007; Lawrence et al 2013; Nielsen et al, 2010) one of which is smoking.People with schizophrenia smoke at much higher rates and more heavily than the general population (Ruther et al 2014, Hartz et al 2014).Stubbs et al (2015) carried out a review to assess the current cessation interventions available for individuals with serious mental illnesses and establish if any disparities currently lie in the delivery of these interventions.60% of premature deaths in people with schizophrenia are due to medical conditions including heart and lung disease and infectious illness caused by modifiable risk factors such as smoking, alcohol consumption and intravenous drug use.

Methods

The authors searched several electronic databases (Embase, PubMed, and CINAHL) using the following keywords: “smoking cessation”, “smoking”, “mental illness”, “serious mental illness” and “schizophrenia.”

Studies were eligible if they included individuals with a DSM or ICD-10 diagnosis of schizophrenia and reported a cessation intervention.

The authors included both observational and intervention studies as well as systematic-reviews and meta-analyses.

This paper is a clinical overview (not a systematic review) of a wide range of different studies relevant to smoking cessation in schizophrenia and other severe mental illnesses.

Results

Pharmacological interventions

 Non-pharmacological interventions

  • The evidence for E-cigarettes was inconsistent, with the authors concluding more evidence was needed before clinicians consider e-cigarettes within mental health settings. Additionally, e-cigarette use in people with schizophrenia should have side effects monitored closely.
  • There was little research on exercise in schizophrenia, but one study found a reduction in tobacco consumption.

Behavioural approaches

  • Behavioural approaches such as offering smoking cessation advice alongside pharmacotherapy have been found successful with no harmful side effects.

Disparities in smoking cessation interventions

  • An investigation of GP practices found individuals with schizophrenia did not receive smoking cessation interventions proportional to their needs.

Support while quitting

  • People with serious mental illnesses experience more severe withdrawal symptoms compared to the general population, and therefore should be given extra support during cessation attempts (Ruther et al 2014).
  • Psychiatrists should re-evaluate choice and the dose of antipsychotic medicine being given after abstinence from smoking is achieved. This is because of nicotine’s metabolic influence on antipsychotic medicine.
  • Alongside smoking cessation, exercise should be promoted among people with schizophrenia to combat weight gain and the increased metabolic risk.

People with serious mental illness are likely to need more support when quitting smoking, because they generally suffer more severe withdrawal symptoms.

Discussion

In light of the findings, the authors suggest several steps for clinicians to help people with schizophrenia quit smoking:

  • Patients’ current smoking status, nicotine dependency, and previous quit attempts should be assessed. Assessing nicotine dependency will help predict the level of withdrawal symptoms the patient is likely to experience upon quitting.
  • Cessation attempts are best timed when the patient is stable. Patients should be thoroughly advised on the process needed to give them the best chance of quitting smoking, Thus, allowing the patient to formulate their quit plan and take ownership of their own quit attempt.
  • Cessation counselling should be provided, particularly what to expect with withdrawal symptoms (e.g. depression and restlessness) and how to cope.
  • Pharmacological support should be provided (Bupropion recommended) when there is even mild tobacco dependence.
  • Clinicians should carefully monitor patients’ medication and fluxions in weight for a minimum of 6 months after quitting smoking, and when needed recommended exercise to combat weight gain.

The authors provide a well laid out summary of their findings, alongside some excellent suggestions for clinicians to consider on how to best promote cessation in practice.

However, it should be stressed that Stubbs et al (2015) only searched for high qualities studies and provided an overview of them –  this is not a systematic review or meta-analysis. They included several types of studies, set little inclusion criteria and listed no exclusion criteria. This is quite different from a systematic review with a meta-analysis, which would set stricter predefined search and eligibility criteria, which identify a set of studies all tackling the same question, thus allowing for the statistical pooling and comparison of these studies.

This is not a systematic review, but it does offer some very useful practical advice for clinicians who are trying to promote smoking cessation.

Links

Primary paper

Stubbs B, Vancampfort D, Bobes J, De Hert M, Mitchell AJ. How can we promote smoking cessation in people with schizophrenia in practice? A clinical overview. Acta Psychiatrica Scandinavica. 2015: 1-9. 
[PubMed abstract]

Other references

Osborn DPJ, Levy G, Nazareth I, Petersen I, Islam A, King MB. Relative risk of cardiovascular and cancer mortality in people with severe mental illness from the United Kingdom’s General Practice Research Database. Arch Gen Psychiatry 2007;64:242–249.

Lawrence D, Hancock KJ, Kisely S. The gap in life expectancy from preventable physical illness in psychi- atric patients in Western Australia: retrospective analysis of population based registers. BMJ 2013;346: f2539-f.

Nielsen RE, Uggerby AS, Jensen SOW, McGrath JJ. Increasing mortality gap for patients diagnosed with schizophrenia over the last three decades – a Danish nationwide study from 1980 to 2010. Schizophr Res 2013;146:22–27.  
[PubMed abstract]

Ruther T, Bobes J, de Hert M et al. EPA guidance on tobacco dependence and strategies for smoking cessation in people with mental illness. Eur Psychiatry 2014;29:65– 82. 
[PubMed abstract]

Hartz SM, Pato CN, Medeiros H et al. Comorbidity of severe psychotic disorders with measures of substance use. JAMA Psychiatry 2014;71:248–254.

 

Motivational interviewing may help people quit smoking, but more research is needed

by Olivia Maynard @OliviaMaynard17

This blog originally appeared on the Mental Elf site on 30th April 2015.

Both pharmacological (i.e. bupropion and varenicline) and non-pharmacological (i.e. brief advice from physicians) interventions have been shown to be effective in assisting people to stop smoking. Evidence also suggests that combining both these types of interventions can help people to stop smoking and both are considered equally important in quitting success.

Motivational interviewing (MI) is a counselling-based intervention which focusses on encouraging behaviour change by helping people to explore and resolve their uncertainties about changing their behaviour. MI avoids an aggressive or confrontational approach and aims to increase the self-belief of the individual. MI was initially developed to treat alcohol abuse, but may be helpful in encouraging smoking cessation.

In a recent Cochrane systematic review, Lindson-Hawley and colleagues from the Cochrane Tobacco Addiction Group aimed to determine whether or not MI is an effective method of smoking cessation (Lindson-Hawley et al, 2015).

Motivational interviewing focusses on encouraging behaviour change by helping people to explore and resolve their uncertainties about changing their behaviour.

Methods

The authors searched online databases and studies were included if:

  • Participants were tobacco users and were not pregnant or adolescents;
  • The intervention was based on MI techniques;
  • The control group received brief advice or usual care;
  • Some monitoring of the quality of the MI intervention was included;
  • Smoking abstinence was reported at least 6 months after the start of the programme.

The main outcome measure was smoking abstinence, using the most rigorous definition of abstinence for each study. Biochemically-validated measures of abstinence (i.e., carbon monoxide breath testing or saliva cotinine samples) were also used where available. Those participants lost to follow-up were considered to be continuing to smoke.

Results across studies were combined in a meta-analysis.

Results

Twenty eight studies published between 1997 and 2014 were found to match the strict inclusion criteria.

The total dataset included over 16,000 participants and studies varied in:

  • The length of the MI sessions (ranging from 10 to 60 minutes)
  • The number of sessions (one to six sessions)
  • Who the sessions were delivered by (primary care physicians, hospital clinicians, nurses or counsellors)

Some of the main findings included:

  • A modest (26%) increase in quitting among those receiving MI as compared with control (although the true value is likely to lie between 16-36%).
  • Sub-group analyses found that:
    • MI delivered by primary care physicians increased the likelihood of successful quitting by 349% (53-794%) as compared with control
    • When it was delivered by counsellors, quit rates increased by only 25% as compared with control
    • MI delivered by nurses was not found to be more effective than control
  • Shorter sessions (less than 20 minutes) increased the chances of quitting relative to control by 69%, as compared with longer sessions, which only increased the chances of quitting by 20%.
  • There was little difference in the likelihood of quitting between single MI sessions (26%) and multiple sessions (20%) as compared with control.
  • There was little difference between MI delivered face-to-face as compared with via the telephone only.
  • There was no evidence for a difference for MI delivered to smokers who were motivated to quit as compared with those with low levels of motivation.

Compared with brief advice or usual care, motivational interviewing yielded a significant increase in quitting. However, study quality means that these results should be interpreted with caution.

Strengths

This review adds 14 additional studies to a previous review conducted in 2010. The addition of these new studies altered the results of the original review very little, providing strong support for the validity of these findings.

Two previous systematic reviews have also examined the effectiveness of MI for smoking cessation, observing modest positive effects of MI (Heckman et al., 2010, Hettema and Hendricks, 2010), although these studies used a broader inclusion criteria than used here and therefore may have underestimated the effects of MI.

The majority of studies included in this review adequately reported their design and methods. Some studies did not report information about blinding of the outcome assessment or how participants were allocated to conditions. However, sensitivity analyses indicated that these factors did not influence the findings of the review.

Limitations

The authors report some evidence for publication bias, such that studies reporting a positive effect of MI were more likely to be published, potentially compromising the results of this systematic review.

Eight of the 24 studies did not use biochemically-validated measures of abstinence. When analyses excluded these studies, the size of the beneficial effect of MI increased. Future research should use the biochemically-validated abstinence measures so as to ensure that smoking cessation is reliably reported.

Conclusions

These results indicate that MI is more effective at promoting smoking cessation than usual care or brief advice, although the effect is modest.

Some components of MI counselling appear to increase the effectiveness of MI for smoking cessation, including delivery by a primary care physician. The reviewers suggest that physicians may be better placed to use the MI approach given their established rapport with the patient. However, this effect is based on only two studies and therefore the importance of physician delivery should not be overstated.

Shorter sessions and fewer follow-ups were also found to be more effective than longer sessions with more follow-up sessions. One explanation given by the authors is that a single session is enough to motivate someone to quit smoking. Prolonging the time before the quit date may mean participants lose focus on their goal to stop smoking.

While MI seems to be effective in promoting smoking cessation, future research should continue to explore the components of MI which optimise the success of this intervention. The relationship between non-pharmacological interventions such as MI and pharmacological interventions should also be considered.

This review confirms that motivational interviewing for smoking cessation is supported by moderate level evidence.

Links

Primary paper

Lindson-Hawley N, Thompson TP, Begh R. Motivational interviewing for smoking cessation. Cochrane Database of Systematic Reviews 2015, Issue 3. Art. No.: CD006936. DOI: 10.1002/14651858.CD006936.pub3.

Other references

Heckman, C. J., Egleston, B. L. & Hofman, M. T. (2010). Efficacy of motivational interviewing for smoking cessation: a systematic review and meta-analysisTobacco Control, 19, 410-416.

Hettema, J. E. & Hendricks, P. S. (2010). Motivational interviewing for smoking cessation: A meta-analytic review. Journal of Consulting and Clinical Psychology, 78, 868-884. [DARE summary]

 

Researching abroad: Cannabis and decision making in the Big Apple

by Michelle Taylor @chelle_bluebird

Setting off for TARGs 2013 annual retreat to Cumberland Lodge in Great Windsor Park, I was looking forward to hearing a talk from an invited guest speaker. Gill had flown in from Columbia University to talk to the group about a recent drug administration study her lab group had completed. The research being conducted by their lab was very different to the epidemiological research that I am used to. Now don’t get me wrong, I thoroughly enjoy the research that I do, but these studies sounded new and exciting. After listening to the talk, the evening activities began with dinner and a quiz. Luckily, I ended up on the same quiz team as Gill, giving me the opportunity to ask more about her research. I decided to grab the bull by the horns and offer my help in one of her future studies, and so my trip to the Big Apple began…

central park 1Nine months later I was on my way to Heathrow for a two month stint collecting data on a cannabis administration study. I was both excited and apprehensive. I have never lived more than a 3 hour drive away from family, and have always been in a city where I have known people. I didn’t know whether I would get homesick, or whether I would make friends on my trip abroad. These feelings of apprehension soon disappeared in the first few hours of my first day at the New York Psychiatric Institute. Everyone I met was so friendly and welcoming, even the many morning commuters who stopped to help the lone Brit who was obviously puzzled by the subway map at 7.30am.

yankeesI was to spend the next six weeks collecting data for a study examining the neuro-behavioural mechanisms of decisions to smoke cannabis at the Substance Use Research Center in the New York Psychiatric Institute at Columbia University. This research centre is unique; it is one of the largest drug administration centre in the world and has licenses to administer a wide variety of drugs, including cannabis, cocaine and heroin. This means that much of the research conducted here is cutting edge. The aim of the study that I would be working on was to shed light on how and why drug abusers repeatedly make decisions to take drugs despite substantial negative consequences. The study used brain imaging (fMRI) to examine the neural and behavioural processes involved in decisions to self-administer cannabis, compared to decisions to eat food, in regular cannabis users. We also examined the influence of drug and food cues on the processes underlying these decisions. To do this, participants were recruited as inpatients and stayed with us in the lab for a week. Data collection for this study is still ongoing, but I will be sure to write another blog post with what we found when the results are available.

coney_2I found this research fascinating and it was a pleasure to be involved in the work carried out in this department. The experience was made even more enjoyable by the people I was working with. There were many office conversations about the British and American slang that was being used, many lunchtime trips to Chipotle (an American fast food restaurant that I am definitely missing since my return to the UK), and several Friday evening trips to the local Irish bar. One office memory that will always stick in my mind was meeting a very accomplished researcher in the field of my PhD, a researcher that was definitely someone I should be impressing. Upon entering this individuals office on an extreme
ly hot New York day, the fan was turned to the meeting area and the smell of cannabis filled the room as the flow of air reached me (I had been administered the drug to a participant earlier that afternoon). Probably not the best first impression I have ever made!

milkshakeI did, of course, take every opportunity to explore New York. I was lucky enough to get tickets to watch the New York Yankees beat the Boston Red Sox at the Yankee Stadium, which was also one of the last games played by baseball-legend Derek Jeter. I made several trips to the American Natural History Museum (my favourite type of museum, and this one cannot be done in a day), and while there saw a live spider show, a 3D film about Great White Sharks and a full T-Rex skeleton. The glorious weather allowed for several leisurely strolls around Central Park. And, of course, the American food definitely needs a mention. If anyone reading this takes a trip over the Atlantic, I would definitely recommend visiting Big Daddy’s Diner for what could be the best milkshake on the planet. And don’t be shy about trying a hotdog from one of the carts that can be found on nearly every street corner. The reason there are so many of them is that they’re delicious! I would also recommend a trip to the Russian Tea Rooms for caviar afternoon tea, an evening at the New York Metropolitan Opera (if that’s your cup of tea), and a trip to Coney Island.

t_rexAlthough it was daunting going abroad for that length of time to begin with, I don’t think I would be having those feelings again and I would definitely jump at any opportunity to work in a different environment in the future. I am very grateful that I am a PhD student in a large working group like TARG, as without this I probably would not have come across opportunities such as this one. This experience has taught me the importance of inter-disciplinary research, and the need for several fields contributing evidence to a much larger research question. Since this trip, I have been successful in a fellowship application allowing me 9 months in a different department at the University of Bristol, an application that I probably would not have made if it wasn’t for my experience at the Columbia University. I am an epidemiologist and do not have any plans to change that; however I do plan to conduct more interdisciplinary research in the future. I would like to that Gill (and everyone in her lab group) for welcoming me and making this trip possible. I look forward to hopefully working with you again in the future…