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1.4 percent: Behind the numbers of ADHD prevalence

A question closely tied to the issue of prescribing ADHD medication is the actual growth of the disorder. Media reports have cited substantial increases in Ritalin use in the UK, and from looking at prescribing trends in the US one possibility is that the diagnosis of ADHD increases in parallel with growing Ritalin prescriptions. It is particularly important to understand how the increased prescribing of ADHD medication in the UK relates to the actual prevalence of the disorder. However, a key issue in the ADHD debate is whether the diagnosis represents a core developmental disorder. The growth in Ritalin prescriptions and the disproportionately high rates of ADHD in the US in particular have often been used to suggest that ADHD diagnosis is socially motivated.

When we look more closely at the numbers around ADHD, however, three important observations emerge.  First, we can see that although there has been a consistent growth in ADHD medication prescriptions in the UK, there is no evidence speaking to whether this also corresponds to an increase in ADHD diagnoses. Second, the common perception of the US being disproportionately affected by ADHD is surprisingly unsupported. Rather, we can see that different methods and approaches to ADHD diagnosis are what best explain its relative prevalence.  Third, thinking about ADHD as a label for a highly heterogeneous group can perhaps more readily account for the differential rates of ADHD based on the diagnostic criteria used.

Has the prevalence of ADHD in the UK increased in line with Ritalin prescriptions?

As we saw in our previous blog post, the number of Ritalin prescriptions dispensed in England increased by 156% from 2004 to 2014. However, this does not mean that the prevalence of ADHD has increased by a similar proportion. Why? Factors that can influence the number of prescriptions for ADHD medication written include GPs being more likely to prescribe such medication due to more positive peer-reviewed articles about its effects, changes in prescription protocols (such as more frequent repeat prescriptions being provided), and more awareness amongst parents and the public about the possible benefit of ADHD medication meaning a higher proportion of people ask for medication1. Further, although NICE guidelines published in 2013 recommended treatment for ADHD with medication as part of a comprehensive treatment programme (including psychological, behavioural and educational support), reductions in funding available for such support may contribute to a higher prescription of medication by GPs as an alternative. All of these factors can lead to a higher number of prescriptions being written, and this means the number of prescriptions does not necessarily align with the number of individuals diagnosed with ADHD.

So we need to take a different approach to estimating the prevalence of ADHD diagnoses, by looking at the proportion of actual ADHD diagnoses in the population. However, there are currently no available public health records about ADHD prevalence in the UK. Instead, one useful source of data come from population-based studies, which take a large sample of children (often around 8000-14,000) and estimate the prevalence of ADHD through the percentage of children with parent reports of symptoms and/or ADHD diagnoses. These studies have the benefit of estimating the prevalence of ADHD through a non-referred sample, avoiding the biases arising from estimates reached by only counting the number of children brought to medical attention.

One such study conducted by Green and colleagues in 20042 estimated the UK prevalence of ADHD in 5-15 year-olds at approximately 1.5%. (A similar report from 2003 estimated the prevalence in this age group at 2.2%3). A more recent report by Russell et al. (2014)4 based on the Millenium Cohort Study estimated ADHD prevalence in the UK at 1.4%. This estimate came from data collected in 2008-2009 when children were 6-8 years old, and parents reported whether ADHD had been identified by a doctor or other health professional.

At face value, these figures are a striking contrast to the prescription data: the parent-report data suggest no increase in ADHD diagnoses (from 2004-2009, at least), in contrast to the yearly increase in Ritalin prescriptions over the same time period. However, it is important to consider that the more recent Millennium Cohort Study figures may slightly underestimate the prevalence of children affected by ADHD: this is because the Millennium Cohort sample children were younger, and more children are likely to be diagnosed when they are older.

So whilst it is possible that ADHD diagnoses in the UK have increased, we do not have the data to accurately evaluate this change. As we saw in our previous blog post, data from the US would suggest that growing use of ADHD medication does align with increasing diagnoses, but it is difficult to evaluate this relationship for the UK because we do not have statistics from comparable samples of children at different time points. It is particularly important to verify if the actual number of individuals diagnosed with ADHD has increased to understand whether such a growth in prescriptions stems from more diagnoses, a growing trend to provide medication in lieu of other treatment options, or a combination of the two.

But the current wave of Millennium Cohort Study data were recently collected when children were 11 years old, and using these data researchers can assess if more children in the sample have now been diagnosed with ADHD as they have gotten older – so we might have an answer soon.

Is the United States disproportionately affected by cases of ADHD, and does this tell us anything about causality?

The growth of ADHD medication use and the concurrent prevalence of the disorder is closely related to the debate around whether it reflects an increase in a genuine developmental disorder, or whether such growth is part of a cultural trend towards diagnosis. The geographical vagaries of ADHD in the US particularly have often been invoked as an argument against its legitimacy as a disorder, with some suggestion that the US peak in ADHD rates indicates its diagnosis can be culturally motivated. Sir Ken Robinson, an author, speaker and education adviser, gave a popular TED talk in 2010 in which he showed a map of ADHD prescription rates across the USA, indicating that they increased as one moved east across the country – or as put by Sir Ken, “people start losing interest in Oklahoma, they can hardly think straight in Arkansas, and by the time they get to Washington they’ve lost it completely.”

This brings us to our next question: is it really the case that ADHD is over diagnosed in the US relative to the rest of the world, as media discussion often suggests? The geographical distribution of ADHD is important for two reasons. First, consistent differences in the geographical spread of ADHD may offer clues as to its aetiology: possible environmental causation in areas of high rates, and preventative factors in areas of low rates. Second, the worldwide distribution of ADHD informs the debate on whether it stems from cultural factors which make it an ‘American’ disorder – whatever this may mean – in which case we would expect it to be more prevalent in the US than anywhere else.

When we look at just the US and UK, it appears that US does have higher diagnosis rate of 6.3% (as reported by Russell et al., 2014). This is the closest estimate using the same sampling methods as the UK figure of 1.4% from the Millennium Cohort Study. However, as pointed out in the Millennium Cohort report by Russell et al., there is an explanation for this: a difference in the measurement of ADHD between the US and UK. When the data were collected the DSM-IV was used in the US, but researchers in the UK from whom the data were collected mainly used the ICD-10. The ICD-10 is more stringent, in which a child must show symptoms in all three dimensions of inattention, hyperactivity, and impulsivity to be diagnosed with ADHD, with no comorbid disorders. The DSM-IV is less stringent in the classification of ADHD, whereby children can be diagnosed with symptoms in one dimension only (such as inattention) alongside comorbid disorders. This difference in relative preference of diagnostic criteria can at least partly explain why the DSM-IV-using US has an epidemic compared to the relative calm of the ICD-10-preferring UK5.

When we look at the geographical spread of ADHD across the rest of the world, the figures are particularly striking. A meta-analysis by Polanczyk et al. in 20076 combined 102 studies, comprising data from 171,756 children, to estimate the average prevalence rates reported from samples worldwide in the previous 25 years. This figure was 5.3%. Second, they evaluated whether the US prevalence rates really did exceed everywhere else in the world, relative to Africa, Asia, Europe (excluding the UK), the Middle East, Oceania, and South America. The US rate of 6.2% did not significantly differ from the European rate (4.6%), or the highest rate in South America (11.8%). And importantly, there was no overall association between geographic location and ADHD prevalence rates when the methodology of the studies was controlled for. The figure below shows the prevalence estimates for each region (the bars represent the variability in this estimate).Polanczyk et al. (2007) Worldwide Figure

Finally, Polanczyk and colleagues verified that the different ADHD prevalence estimates between the US and Europe in particular were indeed explained by the different diagnostic criteria of the DSM and ICD. They did this by coding for all the methodological features in each study, and found that US diagnoses were more frequently made on DSM criteria, and European diagnoses more so on ICD-10 criteria. Another meta-analysis by Willcutt (2012)7 also found that differences in ADHD prevalence across countries were not significant after diagnostic approaches were controlled for. So these data suggest that ADHD is not disproportionately prevalent in the US; rather, its relative prevalence seems to depend more on the diagnostic tool used.

But on an individual level, any parent of a child with ADHD, or any individual with ADHD, is unlikely to attest that their difficulties with learning, concentration and communication are because they are living in Bolivia rather than Finland. The critical ongoing issue is therefore what constitutes our diagnosis of ADHD. In practice, a child is likely to be identified with ADHD because of how their various cognitive difficulties are expressed, and how this expression is interpreted by clinicians. Part of this interpretation may be due to less quantifiable environmental, social and cultural factors: in particular, Conrad and Bergey (2014)8 recognise that the historical approach to ADHD and medication use in a country can greatly influence whether clinicians classify children’s difficulties under the ADHD diagnostic label. But the different diagnostic criteria of the DSM and ICD do play a role. As noted by Russell et al. (2014), children with hyperactive behaviour and social difficulties may be more likely to be diagnosed with autism spectrum disorder in the UK (due to ICD-10 criteria ruling out a combined diagnosis), and ADHD in the US (due to the DSM-IV allowing ADHD to be diagnosed alongside other developmental disorders). It therefore seems that ADHD is not disproportionately prevalent in the US when we take diagnosis methods into account, but that fact that different tools can yield different prevalence rates makes it important to verify what the ADHD diagnostic label is mapping onto.

A dimensional approach to ADHD

At first glance, quantifying the prevalence of ADHD would suggest counting all children and adults struggling with attention, hyperactivity, and impulsivity and suggesting those individuals reflect the relative rate of ADHD in a particular population. In practise, however, there is no natural threshold between affected and unaffected individuals with ADHD. ADHD can frequently co-occur with other developmental disorders, such as autism spectrum disorder (ASD); in the Millennium Cohort dataset, 24% of the children with ADHD were also diagnosed with ASD. This individual variation supports a dimensional approach to classifying developmental disorders, which considers the extent to which various cognitive difficulties are present rather than a clear-cut diagnostic label.

This is critical in deciding how we give children help in the classroom. For example, we have seen previously that children with poor working memory and children with ADHD have highly similar working memory and executive function deficits, indicating that these shared difficulties are not limited to children with ADHD. Further, although an estimated 25-40% of children with ADHD also have dyslexia9, the treatment approaches for ADHD do not usually address reading difficulties, even though these can be a main constraint on a child’s progress at school. The diagnostic label of ADHD thus represents a highly heterogeneous group of children, and what best helps one child with ADHD may not be what best helps another.

But one might argue that neuroimaging data suggesting reduced prefrontal cortex activity in ADHD is indicative of a clear group of children who suffer from a specific neurobiological disorder. However, even such neurobiological ‘markers’ of ADHD are present to a varying degree in individuals without ADHD. Shaw et al. (2007)10 reported that the age of reaching peak cortical thickness – a measure of brain maturation, before cortical thinning proceeds throughout late childhood and adolescence – was delayed in children with ADHD relative to typically developing children. This finding suggested that ADHD may reflect a delay in typical brain development, rather than a qualitative difference between children with and without an ADHD diagnosis. And Shaw et al. (2011)11 found that this rate of cortical thinning was related to the degree of hyperactive and impulsive symptoms in typically developing children without an ADHD diagnosis. Children with higher ratings of hyperactive/impulsive symptoms had a slower rate of cortical thinning, similar to what was observed in children with ADHD. These data align with the idea that ADHD may be a disorder that is better understood dimensionally, with those who are affected being at the end of a continuum of inattention and/or hyperactivity symptoms, rather than there being a distinct cut-off between ADHD and ‘typical’ development.

How does this inform the question on whether ADHD exists as a developmental disorder? From the evidence above it seems that the label of ADHD does not identify a separate category of children who are inattentive and hyperactive. Rather, what we think of as ADHD is a complex cluster of cognitive and behavioural difficulties which fall on a scale for all children, from unaffected to severe. It is how these difficulties are expressed by children and interpreted by different diagnostic approaches, in different cultural contexts, which determine the cut-off at which a child is diagnosed. Whilst difficult to unpick, these factors may best capture how ADHD is identified and why prescriptions have steadily grown; however, we critically need further data to accurately evaluate ADHD growth in the UK. And on an individual level, the symptoms of ADHD have a substantial impact on the day-to-day lives of those who are affected. It is important we ensure that diagnosis appropriately reflects individual differences between children, and think about how to best to commission education and health services to help affected children and adults.

References

  1. Prescribing in primary care: Understanding what shapes GPs’ prescribing choices and how might these be changed, RAND Technical Report. Available at http://www.nao.org.uk/wp-content/uploads/2007/05/TR443_3C.pdf
  2. Green, H., McGinnity, A., Meltzer, H., & Ford, T. G., R. (2004). Mental health of children and young people in Great Britain, 2004. Report commissioned by the Office for National Statistics.
  3. Reported in the NICE Guidelines pp. 26, available here: https://www.nice.org.uk/guidance/cg72/resources/cg72-attention-deficit-hyperactivity-disorder-adhd-full-guideline-2
  4. Russell, G., Rodgers, L. R., Ukoumunne, O. C., & Ford, T. (2014). Prevalence of parent-reported ASD and ADHD in the UK: findings from the Millennium Cohort Study. J Autism Dev Disord, 44(1), 31-40. doi: 10.1007/s10803-013-1849-0
  5. For interested readers, there is also an excellent timeline here depicting ADHD rates in the US as diagnostic tools have changed from 1970 to the present day.
  6. Polanczyk, G., de Lima, M. S., Horta, B. L., & Biederman. J. (2007). The Worldwide Prevalance of ADHD: A Systematic Review and Metaregression Analysis. American Journal of Psychiatry, 164(6). 942-948.
  7. Willcutt, E. G. (2012). The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics, 9(3), 490-499. doi: 10.1007/s13311-012-0135-8
  8. Conrad, P., & Bergey, M. R. (2014). The impending globalization of ADHD: notes on the expansion and growth of a medicalized disorder. Soc Sci Med, 122, 31-43. doi: 10.1016/j.socscimed.2014.10.019
  9. Reported in Eden, G. F., & Vaidya, C. J. (2008). ADHD and developmental dyslexia: two pathways leading to impaired learning. Ann N Y Acad Sci, 1145, 316-327. doi: 10.1196/annals.1416.022
  10. Shaw, P., Eckstrand, K., Sharp, W., Blumenthal, J., Lerch, J. P., Greenstein, D., . . . Rapoport, J. L. (2007). Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A, 104(49), 19649-19654. doi: 10.1073/pnas.0707741104
  11. Shaw, P., Gilliam, M., Liverpool, M., Weddle, C., Malek, M., Sharp, W., . . . Giedd, J. (2011). Cortical development in typically developing children with symptoms of hyperactivity and impulsivity: support for a dimensional view of attention deficit hyperactivity disorder. Am J Psychiatry, 168(2), 143-151. doi: 10.1176/appi.ajp.2010.10030385

Ritalin and ADHD: What is the story so far?

The use of Ritalin to treat ADHD has become a highly contentious issue over the past decade. Part of this contention stems from a marked growth in use, with a 156% increase in the number of Ritalin prescriptions dispensed in England over the last ten years. Another source of contention comes from fragmented information about what the precise effects of Ritalin are (and ADHD medications in general) and what governs whether it is prescribed. We can unpick at least some of this debate by asking two questions: what is the evidence for what Ritalin does, and the extent to which it can help children with ADHD? And why could the number of Ritalin prescriptions have steadily increased over the past decade?

Methylphenidate medication in ADHD: What does Ritalin do, and can it help children with ADHD?

ADHD is a developmental disorder which, despite having a single diagnostic label, presents substantial individual differences between children who are affected. ADHD is characterised by deficits of inattention, impulsiveness, and hyperactive behaviour. These difficulties are often understood to be tied to reduced functionality of the prefrontal cortex (PFC) in individuals with ADHD. The PFC is important for attentional control and executive functions – a broad term for cognitive functions governing organised and controlled behaviour – and this reduced PFC functionality is suggested to result in the core ADHD symptoms of attentional and behaviour control deficits. However, ADHD also frequently co-occurs with other developmental disorders, such as dyslexia and autism. This means that children who are diagnosed with ADHD will range from those severely affected by difficulties in concentrating and sitting still, to those who struggle to pay attention and have comorbid difficulties in areas such as reading and communication. From the outset it is therefore important to recognise that there is huge individual variation in the children (and adults) whom fall under the ADHD diagnostic umbrella.

Ritalin, also known as methylphenidate, is a stimulant medicine which is commonly used to treat ADHD symptoms of inattention and hyperactivity. To understand the effects of Ritalin on cognition, research studies compare performance on tasks when participants have taken Ritalin to when they have taken a placebo. A meta-analysis of these studies by Coghill et al. (2014)1 combined papers investigating the effects of Ritalin in children and adolescents with ADHD. They found that the medication improved short term memory accuracy, working memory capacity, response speeds, and response inhibition relative to a placebo. Because the meta-analysis combined results across at least ten published studies for each aspect of cognition, these results are more representative than findings from a single study. A similar review by Epstein et al. (2014)2 assessed the effects of Ritalin medication in adults with ADHD, and observed that it improved inattentiveness, hyperactivity and impulsive behaviour. These findings overall suggest that Ritalin has positive effects on cognition and controlled behaviours compared to a placebo, in both children and adults with ADHD.

A suggested neurophysiological mechanism for Ritalin’s effect on cognition and hyperactivity is via increased functionality of the prefrontal cortex (Spencer et al., 2015)3. Ritalin prevents the reuptake of the neurotransmitters dopamine and norepinephrine in the PFC. Because these neurotransmitters allow nerve cells to communicate, their increased levels in the PFC strengthen its signalling and therefore improve its functionality. This increased activity of the PFC is measured in studies using fMRI, and is suggested to be tied to Ritalin’s effects of improving PFC-dependent cognitive skills such as attentional control, working memory, impulse control, and organised behaviour (Berridge & Devilbiss, 2011)4.

Importantly, however, these effects do not mean that the Ritalin treats the causes of ADHD. A key piece of evidence comes from the finding that Ritalin has the same ‘cognition-enhancing’ effects on individuals without ADHD. A review by Linssen et al. (2014)5 found that when adults without ADHD were given Ritalin their performance improved on tests of working memory and processing speed, and to a lesser extent attention and vigilance. Whilst these data do not allow us to assess if the magnitude of these methylphenidate effects were statistically identical to those in individuals with ADHD, they are an important indicator that Ritalin can improve these cognitive markers irrespective of an ADHD diagnosis (as current American college trends may also attest). These findings suggest that whilst Ritalin can be beneficial, it does not operate on the root aetiology of ADHD.

But interestingly, despite the cognitive benefits measured in short-term studies, Ritalin does not seem to improve children’s academic achievement in the long term. A large-scale longitudinal study reported by Molina et al. (2009)6 assessed the long-term outcomes of 579 children with ADHD who were randomly allocated to one of four treatment groups: methylphenidate medication, behaviour therapy, combined medication and behaviour therapy, or community care. At the end of the fourteen-month treatment period, all groups had improved relative to their pre-treatment baseline on a range of outcomes including parent and teacher-rated ADHD symptoms, teacher-rated social skills, and reading and maths achievement. Interestingly, the greatest improvements were for the medication group and combined medication plus behaviour therapy group. At the first follow-up ten months after treatment, the initial advantage of the medication and combined groups was still present (although the magnitude of this effect was about half what it had been immediately after treatment).

However, when the children were followed up several years later, this added benefit of medication had levelled out. At the three-year follow-up the four treatment groups remained above their pre-treatment baseline, but there was no difference between the treatment options; that is, the relative advantage of the medication groups was no longer present. After eight years the groups continued to perform above a pre-treatment baseline, but there remained no differential treatment benefit of medication. These findings suggest that medication can have a benefit during a relatively short-term treatment period, but beyond this initial treatment there is no evidence for a lasting medication advantage for the majority of children.

It is particularly interesting that there was no evidence for medication specifically improving long-term academic attainment in light of its short-term improvement of attention, short-term memory, and working memory. Because we know these cognitive skills are significant predictors of educational outcomes, we might have expected their improvement with Ritalin to also boost educational outcomes. Of course, the reason for no longer-term effects of medication may simply be that it can improve cognitive control during treatment, but these effects of the drug do not continue to have additive benefits after treatment is discontinued. There is also relatively little existing research on the effects of taking Ritalin for a longer time period, which limits our understanding of its effects from longer-term use.

However, another interesting explanation for the lack of such an effect may come from the heterogeneity of children diagnosed with ADHD. In their report Molina et al. (2009) identified three subgroups of children in the three-year follow-up, each with different ADHD trajectories as a result of treatment. One group showed an increasing steady benefit of medication use over three years; a second group showed a very large initial benefit for medication use which then plateaued, and was maintained three years after treatment; and a final group returned to pre-treatment levels with no ongoing benefit of treatment. These different profiles of children continued to be associated with their performance on the outcome measures of cognitive, social, and academic attainment at the eight-year follow-up. Importantly, these findings suggest that children’s longer-term outcomes may be determined more by their individual profile of symptoms than the specific type of treatment they receive.

So overall, what do these data us about the decision to use Ritalin for individuals with ADHD? Whilst research studies conducted over the short-term indicate that Ritalin can improve aspects of controlled cognition such as working memory and attention relative to a placebo, there is no evidence that medication continues to have an added benefit to non-medication treatments (such as behavioural therapy) after about two years following the start of treatment. The varying effects of medication may come from the heterogeneity of children diagnosed with ADHD. For many children, comorbid difficulties with reading or communication, for example, could be a key limiter on their performance at school, which Ritalin may do little to redress. Although Ritalin can undoubtedly be beneficial for children who substantially suffer with attention or behavioural control difficulties, the fact it is not universally helpful is an indicator of how much children diagnosed with ADHD can differ in their profile of strengths and weaknesses. Importantly, the effects of medication may therefore depend more on the symptoms of an individual child than the presence of an ADHD diagnosis.

156 percent: What are the reasons behind growing Ritalin use?

A herein lies the controversy: despite the above findings, and an anecdotal lack of consensus about its effects, the number of Ritalin prescriptions dispensed in England increased by 156% between 2004 and 2014. These data, published by the Health and Social Care Information Centre in 2015 and shown on the figure below, show Ritalin prescriptions at 359,068 in 2004 and 922,206 in 20147. (Interestingly, whilst the HSCIC data show that prescriptions for Ritalin are of a larger quantity than the other ADHD medicines on the market, the average use of other ADHD medicines also increased by a modest 136%).

PrescriptionsRitalinUK_FigureBut could it just be the case that all prescriptions have increased this much in general over the last decade? No; the average number of prescriptions dispensed across England rose by 55.2% over the same ten-year period. This discrepancy prompted the National Institute for Health and Care Excellence (NICE) to release guidelines in 2013 that initial treatment of moderate ADHD should not include methylphenidate medication, prompting substantial debate as to what constituted ‘moderate’. So whilst it is agreed that the prescription of ADHD medication has grown substantially, there is no consensus on what is driving this change.

What reasons could there be behind a growing number of Ritalin prescriptions? One obvious reason is an increase in the number of children (and adults) diagnosed with ADHD. In the United States at least, this argument for a relationship between prescription rates and ADHD diagnoses seems to hold. The Centre for Disease Control and Prevention collected comprehensive data on the percent of 4-17 year-olds with parent-reported ADHD diagnoses in each state, and the percent of children taking ADHD medication in each state, in 2007 and 20118. These data mean we can look at whether there is a relationship between each state’s change in ADHD diagnoses in this period, and their simultaneous change in medication use. The figure below shows the relationship between the change in the percentage of children with ADHD diagnoses and percent of children taking ADHD stimulant medication in each state, and there is indeed a significant positive correlation between the two. This positive correlation means that states with a larger increase in ADHD diagnoses also had a larger increase in the number of children prescribed stimulant medication. Whilst we cannot infer causality from these data (that is, we cannot assume that more ADHD diagnoses cause more medication prescriptions) they do suggest that the growth in diagnoses and prescriptions are related.ADHD medication and diagnosis change US states, 2007-2011

The main question these data raise – apart from what is going on in Texas – is why ADHD diagnoses have increased. In a recent paper investigating reasons behind the global expansion of ADHD diagnoses and medication use, Conrad and Bergey (2014)9 suggested that increased promotion of ADHD drugs by the pharmaceutical industry, the increased adoption of DSM (the Diagnostic and Statistical Manual of Mental Disorders, an American publication) diagnostic criteria, and more accessible information about ADHD online could all contribute to its steady growth. ADHD information being more visible and accessible online can prompt more individuals to seek and thus obtain a diagnosis, and promotions by pharmaceutical companies can result in medication treatment being more frequently offered to individuals diagnosed with ADHD. Further, the diagnostic criteria used are particularly important: Conrad and Bergey suggested that a shift towards the DSM criteria in the 1990s contributed to increasing rates of ADHD, as the DSM uses a lower threshold for diagnosis and allows the co-occurrence of ADHD with other developmental disorders. Whilst these are broad brush strokes for reasons for which Ritalin use has grown, they nonetheless suggest possible mechanisms for the increase in prescriptions.

However, it is important for us to recognise that measuring the number of prescriptions dispensed may not be the best indicator of how many individuals are actually taking the medication. A good example of this comes from looking at the headline finding from a 2008 study which reported huge variation in the number of Ritalin prescriptions dispensed across England, with as much as a ­23-fold difference between the lowest and highest-prescribing areas of the UK (Stoke-on-Trent and Wirral, respectively). However, the prescription rate for each region was measured by the number of prescriptions dispensed as a proportion of the population in that region. These figures meant that differences in prescribing practises across regional primary care trusts, such as the standard frequency of repeat prescriptions, could contribute to a lot of this variation. The overall population of a region also influenced its ‘rate’ of prescribing; this might explain why the relatively small Isle of Wight had the second-highest rate at 107 prescriptions per 1000 children. Although just one example, this points to the need to look at Ritalin use in terms of the number of individuals prescribed Ritalin and ask how this aligns with the number of individuals actually diagnosed with ADHD.

So the story about Ritalin seems to have two parts: first, it can have beneficial effects on cognition, but these benefits may critically depend on children’s individual profile of symptoms. Second, it is possible that Ritalin use has increased in line with growing ADHD diagnoses, but we need to carefully evaluate the evidence we have to assess this. Possible vehicles for growth include more individuals seeking a diagnosis due to the information now available about ADHD, promotion by pharmaceutical companies for the use of medication, and a shift in diagnostic criteria meaning a higher proportion of people are identified as having ADHD. In the UK, however, we know relatively little about how increasing Ritalin use relates to the number of individuals with ADHD taking medication, and the wider growth of ADHD as a disorder. This is the question we will turn to in our next post.

References

  1. Coghill, D. R., Seth, S., Pedroso, S., Usala, T., Currie, J., & Gagliano, A. (2014). Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry, 76(8), 603-615. doi: 10.1016/j.biopsych.2013.10.005
  2. Epstein, T., Patsopoulos, N. A., & Weiser, M. (2014). Immediate-release methylphenidate for attention deficit hyperactivity disorder (ADHD) in adults. The Cochrane Review, (9). Retrieved from http://www.cochrane.org/CD005041/BEHAV_ritalin-for-adult-attention-deficit-hyperactivity-disorder-adhd
  3. Spencer, R. C., Devilbiss, D. M., & Berridge, C. W. (2015). The cognition-enhancing effects of psychostimulants involve direct action in the prefrontal cortex. Biol Psychiatry, 77(11), 940-950. doi: 10.1016/j.biopsych.2014.09.013
  4. Berridge, C. W., & Devilbiss, D. M. (2011). Psychostimulants as cognitive enhancers: the prefrontal cortex, catecholamines, and attention-deficit/hyperactivity disorder. Biol Psychiatry, 69(12), e101-111. doi: 10.1016/j.biopsych.2010.06.023
  5. Linssen, A. M., Sambeth, A., Vuurman, E. F., & Riedel, W. J. (2014). Cognitive effects of methylphenidate in healthy volunteers: a review of single dose studies. International Journal of Neuropsychopharmacology, 17(6), 961-977. doi: 10.1017/S1461145713001594
  6. Molina, B. S., Hinshaw, S. P., Swanson, J. M., Arnold, L. E., Vitiello, B., Jensen, P. S., . . . Group, M. T. A. C. (2009). The MTA at 8 years: prospective follow-up of children treated for combined-type ADHD in a multisite study. J Am Acad Child Adolesc Psychiatry, 48(5), 484-500. doi: 10.1097/CHI.0b013e31819c23d0
  7. http://www.hscic.gov.uk/catalogue/PUB17644/pres-disp-com-eng-2004-14-rep.pdf, pp. 139-140. Data available in ‘Appendix 2 Tables’ Excel Spreadsheet here: http://www.hscic.gov.uk/catalogue/PUB17644
  8. The researchers measured medication prevalence by the number of individual children who were prescribed ADHD medication, rather than the number of prescriptions being written.
  9. Conrad, P., & Bergey, M. R. (2014). The impending globalization of ADHD: notes on the expansion and growth of a medicalized disorder. Soc Sci Med, 122, 31-43. doi: 10.1016/j.socscimed.2014.10.019

The developmental cognitive neuroscience of the Great British Bake-off

The Great British Bake-off has quickly become a fixture in the cultural life of the UK. For a few weeks the country is obsessed with the fluffiness of the ultimate Victoria sponge, the moistness of the ideal carrot cake, and the layering of the perfect Black Forest Gateau. For the poor souls who are not familiar with the format, a selection of amateur bakers is charged with making a certain type of baked good. Usually the contestants have some artistic licence to add interesting flavour combinations to the standard recipe. The creations are then judged on taste, presentation, texture and other aspects.

But how do we find the perfect cake and tell it apart from a mediocre one? The first line of judgment is pretty obvious. Cakes that are misshapen, under-baked, or horrible in taste are pretty easy to spot. However, the difference between good bakes is more subtle. Furthermore, there is a great variety of cakes and full marks in one category do not guarantee full marks in another. For instance, a sponge cake is supposed to be very light and fluffy, not too moist and with little taste on its own. In contrast, the carrot cake is much denser, contains more moisture, and is richly flavoured with spices. These categories are almost exclusive – one would not want a dense sponge cake or a flavourless carrot cake. It seems that bakers and cake connoisseurs posses a representation of cake categories that they can use to judge creations against something like the Platonic ideal of a certain cake type.

The formation of (cake) categories

Surely, even Mary Berry wasn’t born with a representation of the most angelic Angel cake and must have learned these categories along the way. Probably, the representation differentiated from simple categories like “sweet stuff” to a more refined representations liked “baked sweet stuff with a light texture and golden brown colour”. An important aspect of the formation of these representations is exposure to many different examples that enable the extraction of communalities that allow for category formation. This process is known as statistical learning. A classic example of statistical learning comes from studies about language acquisition in infants by Jenny Saffran and colleagues. One of the first steps in language learning for infants is to segment the continuous stream of speech into chunks and then to identify words from these chunks. For example, the infant would have to segment the speech stream ‘prettybaby’ into the constituent parts ‘pret-ty-ba-by’ and then identify their association with words, i.e. ‘pret-ty ba-by’ rather than ‘pre ty-ba by’. A possible way for the infant to achieve this feat is to learn which syllables frequently go together. In order to investigate this possibility, Saffran and colleagues exposed infants to different combinations of three syllables to create pseudowords that do not actually exist in the infants’ native language. After a short exposure, infants reacted differently to re-ordered combinations of the pseudowords compared to entirely new syllable combinations indicating that the infants had learned the statistical association between the syllables. Similarly, by exposure to many different examples of Sponge cake, we learn that they occupy a particular corner in the cake representational space in which features like golden colour, light texture, and buttery taste co-occur.

Neural correlates of category formation

Now, what are the underlying mechanisms in the brain that help us to achieve such a remarkable ability to extract communalities and form categories from the vast complexity of objects that we encounter? Unsurprisingly multiple brain areas need to interact to accomplish this task. On one hand, there are primary sensory regions that extract the lower level features of the stimulus in each modality, e.g. the basic shape and colour of the cake, the lemony smell etc. Many of these features are shared between different types of cake and are not specific to a cake category. However, these features can be connected to previous encounters in memory. This is mediated by a medial temporal system, particularly circuits of the hippocampus. Particular combinations of features may evoke a memory that links to a particular type of cake, e.g. the memory of the marvellous madeleines that Grandma Proust used to make.

However, category learning extends beyond retrieval of explicit memories. Freedman and colleagues identified a neural correlate of this categorisation in neurons in the prefrontal cortex. They trained monkeys to categories pictures of cats and dogs that were morphed to lie along a continuum. Neurons in the prefrontal cortex corresponded to their categorisation, e.g. “on” for picture categorised as a cat and “off” for a picture categorised as a dog, even though the stimuli themselves were continuous. These sharp categorisations may arise through feedback loops between the prefrontal cortex and the mesolimbic system involved in reward and reward prediction. Through the integration of these systems, learning about the association between the a particular stimulus and a reward is generalised to other similar stimuli.

As these examples show, the art of baking links to the deepest mysteries of how mind and brain make sense of a complex world based on limited experience and from a very early age. So, enjoy your next bake – it might expand the palette of your categorical representation!

References

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical Learning by 8-Month-Old Infants. Science (New York, N.Y.), 274(5294), 1926–1928. http://doi.org/10.1126/science.274.5294.1926

Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33(1), 203–219. http://doi.org/10.1146/annurev.neuro.051508.135546

Do children with stronger functional connectivity at rest have better working memory capacity?

You are driving to the house of a friend, and you are getting directions from a GPS. “Drive to the end of the road, turn right, drive straight ahead and turn down the third road on your left, and the house is at the end of the road.”  As you are driving you need to remember each instruction, and hold that information in mind whilst completing each step. This is an example of a task that requires working memory. Working memory is the ability to hold relevant information in mind for a short period of time, whilst processing or using other material to complete a task. Working memory is critically important for learning at school, and failures of working memory can be a key predictor of children showing poor academic performance.

One way in which we can start to think about why some children seem to do far better on working memory tasks than others is by exploring the underlying neurophysiological processes that support working memory. With a technique called magnetoencephalography (MEG) we are able to look at how and when different brain areas are communicating, by seeing whether activity between different brain regions co-occurs in time. Importantly, it is possible to observe the strength of communication – functional connectivity – between different brain areas at rest.

In a new paper, we found that a child’s spatial working memory capacity can be predicted on the basis of particular patterns of brain activity at rest. We measured the resting-state functional connectivity of 8 to 11-year-olds, and assessed whether the strength of functional connectivity could be predicted by the child’s capacity. To measure working memory, we used well-validated behavioural measures of spatial and verbal working memory, taken outside the MEG scanner. These measures are routinely used in educational settings and are closely related to children’s literacy and numeracy levels. To examine functional connectivity, we measured the extent to which the activity of different brain regions was coordinated whilst the children were at rest inside the scanner. We then selected specific networks of coordinated regions that we considered ought to relate to working memory, based on adult networks responsible for cognitive control, because this is thought to be a key component of working memory. We reasoned that better these control areas can become coordinated with other brain systems, the better the child ought to do at control-demanding working memory exercises.

And this was what we found. We identified that the strength of connectivity between areas of the frontal and parietal lobe, and a portion of the temporal lobe,  was related to children’s spatial working memory capacity. The stronger the coordination between these areas, the higher the child’s capacity. Importantly, this relationship could not be explained simply by differences in motivation or strategy during the spatial working memory tasks, as children were at rest when the MEG was measured. Interestingly, we did not find a relationship between verbal working memory scores and any functional networks at rest – this may have been because the brain networks involved in verbal working memory tasks are less discrete or easily detectible with MEG.

Why is this important? Working memory is a key predictor of educational outcomes, and being able to relate individual differences in working memory to the functional coordination of different brain regions helps us understand the mechanisms supporting successful (and less successful) performance. The specific network related to children’s spatial working memory was the bilateral superior parietal cortex and middle frontal gyri, areas often associated with spatial attentional control, and lower-level processing areas. This suggests that children with better spatial working memory may be those with stronger connections between these frontal control regions and lower-level sensory areas. We can use this knowledge to begin to ask questions such as whether training can strengthen this connectivity (yes, as we have seen in a previous blog post), and to identify and redress the ways in which it can impact on learning.

References

Barnes, J. K., Woolrich, M. W., Baker, K., Colclough, G. L., & Astle, D. E. (2015). Electrophysiological measures of resting state functional connectivity and their relationship with working memory capacity in childhood. Developmental Science, doi: 10.1111/desc.12297.

Why should we take a dimensional approach to studying developmental disorders?

Developmental disorders like attention deficit disorder, attention deficit hyperactivity disorder (ADD/ADHD), autism spectrum disorder (ASD), language, learning and movement disorders are relatively common, more common then we might think. Furthermore, these disorders have a considerable impact upon the daily lives of those who struggle with them. Because some of these disorders are more apparent in some contexts than others, and their severity is highly variable, they may go unrecognised for some time. Indeed, in many cases these disorders are only formally recognised when a child has already progressed through years of formal schooling. This means that they may already have had a largely negative experience of learning, may have lost motivation, and may already have fallen far behind their peers.

When trying to study these disorders, researchers normally use a case-control approach. It’s an observational study in which two groups differing in outcome are identified and compared on the basis of some presumed causal attribute. Researchers use this method to identify factors that may contribute to a medical condition with the help of comparing subjects who have that condition (“cases”) with patients who do not have the condition but are otherwise similar (“controls”).

Case-control studies are relatively cheap and they are a frequently used type of epidemiological study that can be carried out by small teams or individual researchers in single facilities in a way that more complex experimental studies often cannot be. This design is often used in the study of rare diseases or as an exploratory study where little is known about the connection between the risk factor and the disease. In several cases they have bigger statistical power than cohort studies. This approach has largely been translated from the clinical sphere to study developmental disorders.

Well-designed observational studies, like case-control designs, can provide valuable evidence. It is however worth noting that they are quasi-experimental in nature and thus do not bring the same level of evidence as randomized controlled experiments. That said, case-control designs can sit well alongside complementary randomized controlled experiments. There are however other problematic features of case-control designs, which are particularly highlighted when studying developmental disorders. Selecting an outcome of choice, indeed the basis for choosing one particular group, may produce unintended biases that can have a strong effect in overall findings.

One such example is the exclusion of children with any comorbid symptoms – this is routine practice when using a case-control design to study developmental disorders. The most important disadvantage in case-control studies relates to the problem of acquiring reliable information about an individual’s status over time, and then using this as a basis for choosing some children whilst excluding others. The children actually included may be atypical of those with a particular disorder. This exemplifies why this design does not always translate well into the study of developmental disorders; in developmental disorder comorbidity is more the rule than the exception. This approach can also give a false impression of the nature of these disorders, which can be graded rather than discrete.

However, it also is possible to use a dimensional approach instead of the case-control approach. A dimensional approach puts focus on the kind of problem a person is experiencing and on the extent to which that aspect of cognition is impaired. It doesn’t place people into diagnostic categories but along dimensions. Diagnosis then becomes not a process of deciding the presence or absence of a symptom or disorder, but the degree to which particular characteristic is present. This is entirely the approach taken by the Centre for Attention Learning & Memory (CALM; http://calm.mrc-cbu.cam.ac.uk/). Children are referred not on the basis of any discrete disorder or diagnosis, but because they are experiencing problems in the areas of attention, learning and memory. The researchers are taking a dimensional approach to exploring the nature of these impairments.

Instead of making judgements of “present or not?” the dimensional approach asks the question “how much?”. It ranks disorder on a continuum based upon multiple domains of cognition, assessed using standardised materials. A dimension is viewed as a cluster of related psychological/behavioural characteristics that occur together. This approach generates profiles, rather than discrete diagnostic categories. Of course, one could argue that this approach is far ‘messier’ than a simple case-control approach. However, one might also argue that this unique profiling is far more informative about the nature and extent of the impairments themselves, and provides a far clearer picture about the pattern of deficits actually present in a population of children with problems of learning.

Whilst working at CALM I have been trying to understand how children attend, listen and remember and how these skills impact on learning. These include difficulties in language, literacy and maths. By improving our understanding of the cognitive and brain processes involved in learning, we hope to develop ways of identifying and overcoming problems that might appear during childhood. We also hope to provide an information hub for researchers and professionals in children’s services, and to run regular workshops.

A child visiting the CALM clinic is profiled by grading the severity of symptoms from a number of dimensions using standardised tests. For example these dimensions include working memory, attentional control, short-term memory, phonological skills, the ability to inhibit and control responses, to initiate, plan, organise and set goals, inattention, hyperactivity, aggression, conduct problems, emotional symptoms, peer relations, prosocial behaviour, as well as aspects of communication (speech, syntax, semantics, coherence, initiation, use of context and non-verbal communication). This information can be fed back to the referrer and can then be used to help guide the support that the child receives. In parallel to this we are building a large and rich dataset. A dimensional approach is better able to capture the complexities that a categorical approach may miss. Of course, this approach is not without its challenges also. How does one define a dimension? What statistical approaches ought we to use? And what kind of scores would warrant some form of intervention?

Despite these challenges, we think that the dimensional approach will provide a way of capturing the rich complexities of these data. Whilst other disciplines may strongly favour an approach with rigid categorical boundaries, this approach is not always appropriate for studying developmental disorders. Whilst strict case-control studies can be valuable, reliance on these designs alone can provide a biased and unrealistic view of the children with problems of learning.

References

Gelder M, Harrison P, Cowen P. Classification and diagnosis. In: Shorter oxford textbook of psychiatry. 5 th ed. Oxford: Oxford University Press; 2006. p. 21-34.

Helzer, J. E., Kraemer, H. C., & Krueger, R. F. (2006). The feasibility and need for dimensional psychiatric diagnoses. Psychological Medicine(36), 1671–1680. http://psych.colorado.edu/~willcutt/pdfs/Helzer_2006.pdf

Kendell RE. Five criteria for an improved taxonomy of mental. In: Helzer JE, Hudziak J, editors. Defining psychopathology in the 21 st century: DSM-V and beyond. Washington DC: American Psychiatric Publishing; 2002. p. 3-18.

Lewallen, S., & Courtright, P. (1998). Epidemiology in Practice: Case-Control Studies. Community Eye Health(11(28)), 57–58. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/

http://www.healthknowledge.org.uk/elearning/epidemiology/practitioners/introduction-study-design-ccs

Does working memory training change neurophysiology in childhood?

The short answer to that question is ‘yes’.

We have known for some time that training particular cognitive skills, like working memory, can produce improvements in cognition. These improvements transfer to other untrained tasks, provided that they are similarly structured. However, we know very little about how these kinds of intensive cognitive training programmes change children’s performance.

The study has been under embargo whilst it awaits publication in the Journal of Neuroscience, but the embargo has just been lifted, and we can now tell you all about it (the paper itself should be published very soon – open access, naturally). We used magnetoencephalography – a technique for measuring electrical brain activity – to explore patterns of brain activity as children rested in the scanner with their eyes closed. We repeated this procedure before and after the children underwent working memory training. Importantly, only half of the children underwent an intensive version of the training, with the other half doing a low intensity version. This latter group acted as a control, and the children were randomly allocated to the high or low intensity conditions.

After the training, children’s working memory performance improved. We used standardised assessments of working memory to show this. Importantly, these improvements were specific to the group of children who had undertaken the intensive training programme, with the control group showing little or no improvement. This pattern could not have resulted from us expecting that the children in the intensive group ought to show bigger improvements than the controls, because the researcher doing the assessments did not know which group the children were assigned to.

The magnetoencephalography data allowed us to explore whether there were any significant changes in children’s brains, and whether these changes mirrored the improvements in performance that we observed. We used the spontaneous electrical activity in the children’s brains to explore network connectivity – that is, how different brain areas are coordinated. After the training, connectivity within networks involved in attentional control were significantly enhanced. Furthermore, the bigger the change in connectivity, the bigger the improvement in the child’s working memory.

We have a lot more data on these children, which we are slowly crunching our way through. So there will be more to come!

A Summary: Working Memory Differences between Children Living in Rural and Urban Poverty – Michele Tine

Working memory (WM) is the process by which we transiently hold and process information required for obtaining a short-term goal. It can be broadly considered to have two main components, verbal WM and visuospatial WM. WM is essential to everyday life, for example, it is used when remembering your friends’ order in a coffee shop or adding up the correct change to pay the man at the checkout counter. WM is essential to higher-processing and cognition including decision-making, learning, complex problem-solving, strategic thinking and sustained attention. In recent years, research has indicated that children from a low socioeconomic background display deficits in WM capacity when compared with children from a high socioeconomic background. (For further reading on this see: Fernald, Weber, Galasso and Ratisfandrihamanana, 2011; Noble, Norman and Farah, 2005.) With the knowledge that a lower WM capacity impacts on attention and learning this finding is of particular significance when considering children’s education. It has been shown that children with a low WM capacity can exhibit poor attention in class, poorer academic performance, learning difficulties and more difficult behaviours. (See our previous post for more insight into the relationship between WM and ADHD https://forgingconnectionsblog.wordpress.com/2015/02/05/adhd-and-low-working-memory-a-comparison/ ).

Given that a low socioeconomic background can impact on WM in children, it is important to explore why this occurs and which socioeconomic factors are the largest contributors. The following paper, by Michele Tine, (http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule) considers how coming from a low-income background can impact on working memory (WM) in children whilst distinguishing between participants based on their ‘developmental context’ (settlement area), either rural or urban. Whilst it has been established that low-income children have deficits in verbal and visuospatial WM when compared with their high-income counterparts, this paper is the first, to my knowledge, that considers whether poverty is associated with a greater deficit in verbal or visuospatial WM and how settlement area influences this relationship.

Considering settlement area is a good way of determining if environmental factors mediate the relationship between low-income and decreased WM capacity. Whilst each settlement area will be unique in it is own way we can assume that some features are more prominent in one environment than another. For example, stress is thought to be a highly important factor when considering the impacts of socioeconomic background on an individual. Grouping via settlement area, takes into account exposure to different stressors in different environments.

Some of the differing stressors identified in the paper for those from a low-socioeconomic  background  include:

For an URBAN environment For a RURAL environment
Substandard and crowded housing Isolation from people
Excessive noise Isolation due to reduced access to technologies the internet and reduced computer ownership
Inadequate health care, education and inadequate health care Isolation from institutions and services
Increased crime rate Decreased social support*
Increased physiological disorders Less everyday visual stimulation
Increased divorce rates
Exposure to chronic aircraft noise

* Social support is a known buffer for stressors

Prior research has determined that the relationship between socioeconomic status and visuospatial WM is ‘fully mediated by allostatic load’, where allostatic load is the strain on the individual that accumulates over time with chronic stress. Thus, we would expect that the higher the amount of stress experienced, the greater the reduction in visuospatial WM capacity. On this basis we would perhaps expect there to be a difference in WM deficits once settlement area is considered as a result of exposure to differing stressors. Whilst the relationship between stress, socioeconomic status and visuospatial WM has been investigated, there has been little definitive research into how this compares to the relationship between stress, socioeconomic status and verbal WM.

 The individuals that participated in this study were recruited via their schools and their income measures (high or low) were determined by the following thresholds:

Low-income:

The individual attends a school that ‘serves a community with a median family income below the national median family income’. The school must have >75% of students qualified for free or reduced school meals and the individual themselves must qualify for free school meals.

High-income:

The individual attends a school that ‘serves a community with a median family income above the national median family income’. The school must have <25% of students qualified for free or reduced school meals and the individual must not qualify for reduced or free school meals.

To determine if an area was rural or urban The US Census Bureau (2010) and McCracken and Barcina’s (1991) work was utilised along with population per county and the average number of students per class in the county.

With these thresholds, four groups of individuals could be defined:

1) Urban and high-income

2) Rural and high-income

3) Urban and low-income

4) Rural and low-income

There were no significant differences in gender between the four groups but there was, however, a significant difference in ethnicity distribution across the groups.

Each individual completed four WM tasks from the Automated Working Memory Assessment battery. Two of the subtests measured verbal WM and two measured visuospatial WM.  As expected, the low-income children were found to have significantly lower composite WM percentile scores than the high-income students and there were no differences between the urban high-income and the rural high-income groups. The urban low-income group had significantly lower WM scores than both the high-income groups and the rural low-income group showed the same trend, having significantly lower WM scores than both of the high-income groups.

However, a comparison of the two low-income groups showed that they had differing WM profiles. The rural low-income group had a significantly higher verbal WM profile than the urban low-income group but also a significantly lower visuospatial WM profile than the urban low-income group. The urban low-income group showed no significant differences across the verbal and visuospatial WM scores, whilst the rural low-income group had significantly different verbal and visuospatial WM scores.

Based on past literature, Tine speculates that the differences in verbal WM between the two low-income groups may be accounted for by chronic noise pollution amongst the low-income urban population, for example through living close to airports and train tracks. However, the reason for this difference and for the decreased visuospatial WM capacity in the rural low-income group needs further investigation before any conclusion can be drawn.

Whilst this paper presents solid and much needed research into how income and settlement area/developmental context impacts on WM capacity in children, the author states that caution must be taken in inferring any causal relationships from studies such as this one. Due to a lack of information on the participants’ parents, although unlikely, it is possible that ‘low-income parents (and in turn, children) with particularly weak verbal WM abilities tend to seek out urban low-income areas, or on the contrary, low-income parents (and children) with relatively weak visuospatial WM capacity self-select into rural low-income areas.

Additionally, as previously mentioned, there was a significant difference in racial distribution across the groups and thus it cannot be ruled out that some of the differences seen in the WM profiles are a result of cultural differences and the ‘experience of race-based stereotype threat’ which has been shown to reduce WM capacity (Schmader and Johns, 2003). The study also does not consider language ability amongst the populations or the proportion of non-native English speakers, which is also known to be related to WM ability. Whilst the insights this paper provides are unique, the research could be furthered still by taking into account factors other than income and developmental context, for example, maternal education, parental employment or family structure, other well recognised and substantial components of what it means to be from a low-socioeconomic background.

So, in conclusion, whist we cannot assume causality from the findings, this paper presents, in my opinion, a very good first step into developing a much deeper understanding of the impact of SES on cognitive ability in children.  The hope is that this kind of research will eventually allow us to develop a successful intervention to aid those children from disadvantaged backgrounds so that they do not experience the cognitive deficits and disadvantages in education that their high-socioeconomic peers evade.

The full paper can be accessed here: http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule)

References

Noble, K.G. Norman, M.F. and Farah, M.J. (2005) Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8, 74-87.

Fernald, L.C.H. Weber, A. Galasso, E. and Ratisfandrihamanana, L. (2011) Socioeconomic gradients and child development in a very low income population: Evidence from Madagascar. Developmental Science, 14, 832-847.

McCracken, J.D. and Barcinas, J.D. (1991) Differences between rural and urban schools, student characteristics, and student aspirations in Ohio. Journal or Research in Rural Education, 7, 29-40.

Schmader, T. and Johns, M. (2003) Converging evidence that stereotype threat reduces working memory capacity. Journal of Personality and Social Psychology, 85, 440-452.

ADHD and sugar: new avenues for an old question?

There is something about sugar consumption and hyperactivity that seems very intuitive. Perhaps, because everyone has experienced kids going crazy at a birthday party after having consumed industrial amounts of sugar through cakes, sweets, and lemonade. According to questionnaire studies, this opinion is also shared by parents of children with attention deficit hyperactivity disorder (ADHD). For some parents, dietry restrictions that cut-out all refined carbohydrates are preferred over gold standard stimulant treatment (Sciutto, 2015). However, the evidence that sugar has any effect on children’s behaviour and cognitive performance is not well supported. A number of studies were conducted in the 1980s and 1990s that investigated the link between sugar consumption and behaviour in children. The general design of these studies involved having children consume food items that contained either sugar or an artificial sweetener (aspartame or saccharine) without the child or the experimenter being aware which group the individual child was assigned to (double blind experiment). Next, the outcome of the food intake was measured as performance on a particular cognitive task (mostly standard assessments of general cognitive ability). A meta-analysis of these studies found no effect on test performance or observed behaviour (Wolraich, Wilson, & White, 1995). These results suggest that there is no effect of sugar consumption on general measures of behaviour and performance in typical children. But what about children with an ADHD diagnosis?

Some studies that looked at associations between environmental influences and ADHD in a large number of people found links between a diet high in carbohydrates and saturated fats and ADHD scores in independent samples in the UK, Western Australia, and Korea (Howard et al., 2011; Woo et al., 2014). However, it is difficult to draw firm conclusions from these population-based studies as both dietary patterns and ADHD behaviours may be influenced by a common underlying factor, e.g. differences in family lifestyle or socio-economic status. Conversely, some studies investigated the effect of specific diets as a treatment for ADHD. A recent review reported no effects of sugar vs apartame/saccharine on ADHD symptoms in 3 out of 4 studies (Heilskov Rytter et al., 2015). Interestingly, the one study that found differences reported differences on a measure of attention, while a behavioural assessment of aggressive behaviour was not influenced by sugar ingestion (Wender & Solanto, 1991). The other studies were based on playroom observations or assessments of general learning and memory (Gross, 1984; Milich & Pelham, 1986; Wolraich, Milich, Stumbo, & Schultz, 1985). The possibility that sugar consumption only affects performance on certain cognitive tasks but not general behaviour remains to be further investigated. In addition, research in the last decades has established that children diagnosed with ADHD present a very heterogeneous sample. This lead to the inclusion of subtypes in the last revision Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). It is possible that different subtypes of the disorder are differently affected by sugar consumption and that different studies find different results due to differences in the inclusion of subtypes within the study sample. The scientific interest in links between sugar and ADHD has ebbed away in the new millennium, while parents and educators continue to belief in a link. It seems that future work should address the questions that have not been satisfyingly answered in the previous work.

Some support for an association between ADHD and sugar comes from studies on the physiological level. Children are typically less able to regulate blood sugar levels compared to adults and this seems to be especially the case for children with ADHD (Lindblad, Eickhoff, Forslund, Isaksson, & Gustafsson, 2015). The brain is the most energy-hungry organ consuming 25% of the energy while only accounting for 2% of the body’s mass. Differences in energy metabolism are also known to influence behaviour, e.g. in the case of hypoglycemia (Millichap & Yee, 2012). Sugar intake was also found to increased EEG beta activity over frontal regions in children with “food-induced” hyperactivity (Uhlig, Merkenschlager, Brandmaier, & Egger, 1997), which related to measures of problematic behaviour. Based on these findings and others, a recent extensive review suggested that differences in energy metabolism are at the heart of ADHD symptomatology (Killeen, Russell, & Sergeant, 2013). According to the neuroenergetics theory, ADHD is characterised by a less efficient regulation of energy supply to neurons, which results in a reduction of around 15% in energy capacity. Further, based on this model inconsistencies between studies are also expected when the demands of the task vary. However, it is currently not clear from the model how differences in neuronal energy metabolism relate to dietary patterns. It is possible that attraction to high energy foods is an attempt of the system to counter-balance inefficient energy supply, but further empirical and theoretical work will be needed to fully understand the link between dietary preferences and brain metabolism.

In summary, the discussed studies do not support that there is a strong link between sugar consumption and ADHD. However, evidence from populations studies and physiological investigations indicate that energy regulation is affected in children with ADHD. These studies highlight that ADHD needs to be understood as a complex systemic disorder that affects many different levels of observation from cell biology to behaviour, which will hopefully be addressed in future research.

This line of investigation also poses questions that are extremely important for parents and educators, but it also raises concerns about current research practices. If food intake has an immediate effect on behavioural performance that differs systematically between ADHD and comparison groups, any study that aims to investigate neuro-cognitive differences should be controlling this factor.

Photo credit: Moyan Brenn (https://www.flickr.com/photos/aigle_dore/)

References:

Gross, M. D. (1984). Effect of sucrose on hyperkinetic children. Pediatrics, 74(5), 876–878.

Heilskov Rytter, M. J., Andersen, L. B. B., Houmann, T., Bilenberg, N., Hvolby, A., Mølgaard, C., et al. (2015). Diet in the treatment of ADHD in children – a systematic review of the literature. Nordic Journal of Psychiatry, 69(1), 1–18. doi:10.3109/08039488.2014.921933

Howard, A. L., Robinson, M., Smith, G. J., Ambrosini, G. L., Piek, J. P., & Oddy, W. H. (2011). ADHD is associated with a “Western” dietary pattern in adolescents. Journal of Attention Disorders, 15(5), 403–411. doi:10.1177/1087054710365990

Killeen, P. R., Russell, V. A., & Sergeant, J. A. (2013). A behavioral neuroenergetics theory of ADHD. Neuroscience & Biobehavioral Reviews, 37(4), 625–657. doi:10.1016/j.neubiorev.2013.02.011

Lindblad, F., Eickhoff, M., Forslund, A. H., Isaksson, J., & Gustafsson, J. (2015). Fasting blood glucose and HbA1c in children with ADHD. Psychiatry Research. doi:10.1016/j.psychres.2015.01.028

Milich, R., & Pelham, W. E. (1986). Effects of sugar ingestion on the classroom and playground behavior of attention deficit disordered boys. Journal of Consulting and Clinical Psychology, 54(5), 714–718.

Millichap, J. G., & Yee, M. M. (2012). The diet factor in attention-deficit/hyperactivity disorder. Pediatrics, 129(2), 330–337. doi:10.1542/peds.2011-2199

Sciutto, M. J. (2015). ADHD knowledge, misconceptions, and treatment acceptability. Journal of Attention Disorders, 19(2), 91–98. doi:10.1177/1087054713493316

Uhlig, T., Merkenschlager, A., Brandmaier, R., & Egger, J. (1997). Topographic mapping of brain electrical activity in children with food-induced attention deficit hyperkinetic disorder. European Journal of Pediatrics, 156(7), 557–561. doi:10.1007/s004310050662

Wender, E. H., & Solanto, M. V. (1991). Effects of sugar on aggressive and inattentive behavior in children with attention deficit disorder with hyperactivity and normal children. Pediatrics, 88(5), 960–966.

Wolraich, M. L., Wilson, D. B., & White, J. W. (1995). The Effect of Sugar on Behavior or Cognition in Children: A Meta-analysis, 274(20), 1617–1621. doi:10.1001/jama.1995.03530200053037

Wolraich, M., Milich, R., Stumbo, P., & Schultz, F. (1985). Effects of sucrose ingestion on the behavior of hyperactive boys. The Journal of Pediatrics, 106(4), 675–682.

Woo, H. D., Kim, D. W., Hong, Y.-S., Kim, Y.-M., Seo, J.-H., Choe, B. M., et al. (2014). Dietary patterns in children with attention deficit/hyperactivity disorder (ADHD). Nutrients, 6(4), 1539–1553. doi:10.3390/nu6041539

ADHD and Low Working Memory: A comparison

I was recently directed to a paper titled ‘Children with low working memory and children with ADHD: same or different?’ From the title alone this paper piqued my curiosity. As scientists we often focus on collecting large amounts of data, and in some cases forget the importance of forming new theories and ideas. For me, this paper casts the profiles of an ADHD diagnosis and a deficit in working memory, a common childhood issue, in a new light. What does it mean (if anything) to have a clinical diagnosis? And what is the best way of characterising children with problems of attention and memory?

Whilst I will summarise the main findings below, for those that want to read the paper for themselves it can be accessed here:

http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00976/full

This paper provides a novel comparison of cognitive skills, executive function, educational attainment and behaviour between those with a low WM and those with a diagnosis of ADHD. It utilised three groups of children (8-11yrs): one group identified by routine screening as having a low WM, one group with diagnoses of ADHD and one group of typically developing children.

Age was controlled for across the groups and for each group the following measures were taken: Working memory (Automated working memory assessment); Executive function (Delis-Kaplan Executive Function System, The K-test of the Continuous Performance Test and Walk/Don’t Walk from the Test of Everyday Attention for Children); Academic ability (Wechsler reading, spelling, reading comprehension, mathematical reasoning and number operations and the four WASI subtests); And classroom behaviour problems (Connors teacher rating and the Behaviour rating inventory of executive function). Those children taking medication for their ADHD stopped taking it at least 24hrs prior to the session to ensure the effects of the drugs were eliminated by the time of testing.

The theory that makes up the basis for the research, includes the fact that both ADHD and low WM in childhood are significantly associated with poor educational attainment. It has also been established in the literature that there are associations between poor WM and inattentive behaviour and poor WM and executive function deficits. These elements combined suggest a high degree of overlap between the profiles of those with a deficit in WM and those with an ADHD diagnosis. In ADHD, we see executive function problems (deficits in response inhibition, attentional switching, planning and sustained attention) and also excessively high levels of motor activity and impulsive behaviour.

Some have argued that the executive function deficits in ADHD are a result of an underlying low WM, whilst others have proposed that ADHD involves two distinct neurodevelopmental systems:

1) a ‘cool/cognitive system’ that affects executive functions and WM

and

2) a ‘hot/affective system’ that leads to ‘aversion to delay that manifests as impulsive behaviour’.

It is this impulsive behaviour that manifests as excessive motor activity and problems in impulse control that are core characteristics of ADHD and are not associated with low WM. Thus, this study hypothesised that the two groups share a common deficit in the ‘cool executive function system’ reflected in the fact that both profiles exhibit lack of attentional control, but only those with the ADHD diagnosis have impairments in the ‘hot executive function system’ that results in hyperactivity and impulsivity.

The ADHD and low WM group, as expected, performed more poorly than the typically developing children on the tests of working memory (necessarily so for the latter group, because they were selected on this basis). Performance was significantly decreased for the low WM group in the two WM subtests that they were initially screened on. This suggests a selection artefact as ‘the two groups did not differ on the verbal WM task that was not used at screening’. In the executive function switching task no significant difference was seen between the low WM and ADHD group when considering accuracy, however, the low WM group showed a significantly slower performance than the ADHD and typical group. The sustained attention executive function task showed that both the ADHD and low WM children were significantly less accurate than the typically developing group and that they both made a significantly greater number of omissions than the typically developing group. The ADHD group also further made a significantly greater number of commission errors than the other two groups. No other significant differences were seen between the low WM group and the ADHD group on any other test of executive function. It is also worth noting that although there are a number of different models proposed for the functional structure of WM, the findings of this paper remain consistent across models on the basis that the ‘storage-only capacities of short-term memory (STM) and the capacity-limited attentional control functions of WM can be distinguished’.

They concluded that the ADHD and low WM groups have very similar profiles of WM ability and executive function impairment. They did however find two important differences: the low WM group were ‘slower to respond on several tasks’ and the ADHD group were ‘more hyperactive and exhibited more difficulties in controlling impulsivity in sustained attention’.  The processing speed impairments in the low WM group were an unexpected finding. They state that ‘it does not appear to be a part of a broader problem in fluid intelligence, as controlling statistically for performance IQ had little impact’. They present the idea that the impairment in response time may be a result of sluggish cognitive tempo (SCT). Sluggish cognitive tempo is a ‘set of symptoms strongly associated with the predominantly inattentive form of ADHD that includes high levels of daydreaming, slow response times, poor mental alertness and hypoactivity.’ It could perhaps be the case that those with a low WM may correspond to those with SCT, a predominantly inattentive subtype of ADHD. At present SCT is not a commonly utilised diagnosis in the UK. The more hyperactive behaviour in the ADHD children however was expected. ‘They violated rules more frequently during a planning task, and made more commission errors on the Continuous Performance Test of sustained attention’. This elevated hyperactivity was also shown in the Connor’s teacher rating as increased impulsivity and oppositional behaviour.

Interestingly they found the two groups to have equivalently high levels of inattentive behaviour. Whilst it has been shown before that children with low WM often exhibit inattentiveness, this paper is the first to show that this inattentive behaviour is of a very similar degree to that of  children with ADHD. In terms of the deficits in working memory, both groups exhibited deficits in the same regions and of a similar magnitude. They had impairment in ‘visuospatial short term memory, verbal working memory and visuo-spatial working memory’; but ‘their verbal short term memory scores fell within the typical age range’. The novel finding here is that there is such a high degree of correspondence between the profile and severity of the WM impairments in the two groups.

Previous research has also suggested a close relationship between ‘controlled cognitive attention and working memory’. This paper takes this understanding one step further suggesting that it is now looking more likely that there is a ‘link between poor working memory and overt inattentive and distractible behaviours’ as seen through the inattentiveness in the behaviour of those children with a low WM. In addition to being comparable in inattentiveness and working memory impairment, the two groups both exhibited ‘high rates of problem behaviours across a wide range of executive function behaviours’. Previous studies have also indicated that problem behaviours associated with executive function are seen in children with a low WM. This paper supports and extends this idea by showing that both groups ‘performed poorly on direct measures of switching, inhibition, sorting, planning, sustained attention and response suppression’.

When considering again, the cool (cognitively based) and the hot (affective) model of executive function, the pattern found in the results from this study suggest that there is some shared general executive deficit between the ADHD and low WM groups. The cool deficits manifest as inattention and low WM in both groups whilst the hot deficits, only present in the ADHD group, manifests as the hyperactivity. The additional delay in response times in the low WM group is the only component that does not fit with this theory, but the authors have identified that it ‘may be symptomatic of a subgroup of children with the predominantly inattentive form of ADHD who are characterised by SCT’.

In conclusion, this paper shows some very interesting preliminary findings into two conditions that are generally considered distinct. Both groups showed equal levels of underachievement when IQ differences were taken into account (‘they are indistinguishable in terms of their poor learning progress in mathematics and reading’). This is of great significance as those with a diagnosis of ADHD will almost certainly have learning support provided to them whilst those with a low WM will not. The educational needs of the group of children with a low WM are being overlooked as they do not show the disruptive hyperactivity of ADHD, even though in many respects the groups are indistinguishable.

Holmes J, Hilton KA, Place M, Alloway TP, Elliott JG and Gathercole SE (2014) Children with low working memory and children with ADHD: same or different? Front. Hum. Neurosci. 8:976. doi: 10.3389/fnhum.2014.00976

Tools of the Mind: An Effective Intervention for High Poverty Schools?

Early childhood interventions are believed to be a key step towards ending the cycle of poverty. This belief is based upon the large evidence base that demonstrates that good childhood development (measured in various ways) is highly predictive of a large variety of positive outcomes later in life.  If we give children the emotional, social and cognitive support they need in their early years it is hoped that this will make a lasting improvement that persists for the rest of their lives and halt the transmission of negative social problems from one generation to the next.

However, high quality intervention studies are rare, making it difficult to know which type of approach will work (if any).  The lack of research is unsurprising due to the fact that this kind of study is very hard to do well. They usually require a huge investment in time, money and effort. Furthermore, they involve a number of complex factors that might affect both the validity and applicability of the results, such as individual teaching style and child demographics.

This is what makes the recent study into the effectiveness of Tools of the Mind [1], an early intervention programme, by Drs Blair and Raver from New York University [2] a particularly interesting research piece. They argue that children receiving the Tools of the Mind program showed a significant improvement in learning in comparison to children in typical kindergarten classrooms. Importantly, the authors claim that some of these benefits continue into the first grade, after the program had finished, and that many of these effects were stronger in high-poverty schools.

In this post, I will be putting Tools of the Mind to the test, loosely using Dorothy Bishop’s framework for identifying red flags in interventions (It’s a good read. For those interested see [3]). So, what is Tools of the Mind? Is it credible? How solid is the scientific evidence behind it? Does it really work and, if so, are the effects worth the effort and cost involved?

What is Tools of the Mind?

Tools of the Mind is an educational program, developed over 18 years and now used in prekindergartens and kindergartens across the USA and Canada. I will be focussing on the kindergarten program here (that’s ‘reception’ for the British). It is based on the Vygotskian approach: the idea that it is important to teach children to master ‘mental tools’ such as attention and emotion-regulation skills that promote intentional and self-regulated learning. This is expected to develop their executive functions, social and emotional competence at a greater rate. In practice, it involves 60 or more Vygotskian-based activities including activities that require children to create their own learning plans, reflect on their learning and work in pairs with a strong focus on intentional make-believe play tied to stories and literature. Tools of the Mind was named an exemplary education program by the International Bureau of Education at UNESCO in 2001. Maybe this sounds marvellous, but does it actually work?

Who is behind the program and what are their credentials?

The program has largely been developed by Drs Bodrova and Leong. Encouragingly, both appear to be experts in the field. Dr Elena Bodrova was, until recently, Principal Researcher at Mid-continent Research for Education and Learning, a non-profit, non-partisan education research and development corporation, and Dr Deborah Leong is the Professor Emerita of Psychology, Metropolitan State College of Denver. They’ve both written a number of papers, articles and books, and, to the best of my knowledge, have each authored 10 papers in peer review journals in topics related to the Tools of the Mind program. I can’t find any red flags here!

Is there credible science behind the program?

The program claims to improve academic achievement and socio-emotional skills through improving executive functions, and in particular, the ability to self- regulate. Executive functions encompass many different processes involved in the management of cognition and actions: the ability to pay attention to and remember relevant details, to plan, to solve problems strategically and to regulate emotions and behaviour successfully. If you think of your brain as an orchestra, executive functions would be the conductor,  organizing many different instruments to play together as a coherent whole, bringing some in and fading others outand changing the pace and intensity of the music. Indeed, they seem to be involved in pretty much every higher-order cognitive process, calling into question whether the idea of executive functions is too vague and general a concept to be of any use and they are sometimes seen as a controversial topic amongst cognitive scientists.

Putting this issue aside for now, performance on tasks purporting to tap executive functions are excellent predictors of educational progress. More and more research is pointing to the development of these as being one of the most important domains in early childhood for positive short and long-term outcomes. The argument goes something like this: in the same way that a bad conductor is likely to produce dreadful music, no matter how excellent the individual musicians are, poor development of executive function is a very strong predictor of poor social and academic outcomes even if the rest of the cognitive system is well developed. Great claims are made about the extent to which these skills can be boosted by intervention. When you combine this with the fact that growing up in a deprived environment is frequently found to have a profound negative effect on executive functions, it seems a credible and good target for a program like Tools of The Mind.

The approach used to develop executive functions is based on the relatively well known and researched Vygotskian Approach. Unfortunately, I don’t have time and space to review this here but the Tools of the Mind website provides a lot of information on the science behind this.

Is there evidence that the intervention is effective from controlled trials?

Blair and Raver provide the first cluster randomized controlled study into the effectiveness of the Tools of the Mind curriculum in comparison to current practices in kindergartens. They looked at its effects on a range of different cognitive and academic skills. During the two year study, an impressive 795 children took part from 79 different classrooms and 29 schools. The schools were randomly assigned to either the control group who simply continued business as usual or the treatment group who received training to implement the Tools for the Mind programme in their schools. Children were tested in the first term of kindergarten, with follow up tests at a mean of 5 months and 1 year later. It’s questionable as to whether this can be considered an active control group, given that there may have been a potential bias for teachers receiving the new and novel Tools training to expect better results in comparison to those that continued as before, but overall I think this is quite a good study design.

They found that children in the Tools of the Mind classrooms were significantly better at keeping information in their working memory (effect size, ES = 0.14), maintaining attention in the face of distractions (ES = 0.12) and processing information (ES = 0.08) at the first follow up. This was accompanied by a greater rate in academic improvements in mathematics (ES = 0.13), vocabulary (ES not given) and reading (ES = 0.07), in comparison to the control classrooms (although this was only conventionally significant for mathematics). The faster rate of improvement in reading (Figure 1, ES = 0.14) and vocabulary (ES = 0.1) extended into the first grade, becoming significant, after the program had finished, suggesting that it has long term effects.

Most of the effects seen were stronger in high-poverty schools where more than 75% of the pupils are eligible for free or reduced-price lunch. In particular, the effect of Tools of the Mind in comparison to the control group was significant in tests that measured the child’s ability to maintain attention despite emotionally arousing distractors (ES = 0.82), fluid IQ (the ability to solve problems in novel situations, ES = 0.46) and vocabulary (ES = 0.43) in high poverty schools.

Whilst this is the only study of the kindergarten program, it should be noted that both the National Institute for early education research (NIEER) and the Peabody research institute (PRI) have been investigating the pre-kindergarten program. Despite the fact that both studies have included only high-poverty classrooms (around 80%+ children receiving free or reduced price lunch) their results appear to have some substantial disagreements. In one study, NIEER compared 88 children in Tools classrooms with 122 children receiving the district’s balanced literacy curriculum. They found that by the end of the first year, children in the Tools classroom had significantly better classroom experiences, far fewer behavioural problems (an indicator of self-regulation) and some improvement in language performance, although not significant after correcting for multiple comparisons, in comparison to children in the control group. They found no improvement in reading or mathematics. In a subsequent study of the same program but with a slightly different sample (85 children in Tools, 62 in the control classrooms) they found that children in the Tools classrooms performed significantly better in tests of executive functions (stoop battery and flanker tasks) in comparison to children in the control classrooms and that this difference was greater, the more taxing the task.

In contrast, PRI have been conducting a much larger study comparing 498 pupils receiving the Tools curriculum with 379 pupils receiving a range of curricula that would normally be found in pre-school classrooms. They found that the Tools curriculum had no significant effect on direct assessments of achievement or executive function or any teacher ratings of language, executive function, or social behaviour by the end of pre-kindergarten in comparison to the control classrooms. Remarkably, when the children were assessed a year later at the end of kindergarten, they found that the comparison children that had received the normal curricula actually had significantly greater gains in achievement and executive function composite scores and on many of their subtests in comparison to those that received Tools of the Mind!

Are these effects worth the effort, time and cost involved?

Being subjective, this isn’t an easy question to answer. Note that the Tools curriculum can’t simply be taken off the shelf and implemented as other curricula might be: it requires about two years of teacher training including in-classroom coaching and a shift in the teacher’s role in the classroom. However, Blair and Raver appear to believe that: ‘teachers received typical levels of training and implemented the curriculum with materials that are well within the budget of the average kindergarten classroom. Information about the actual cost is difficult to find but in general sources place it at £5000 – £7000 per classroom, which seems reasonable [4, 5] and the materials required appear to be simple, inexpensive and readily available.

However, I think the key to answering this question is to consider the effect sizes produced by the program, the number of standard deviations between the mean score of the control and target groups. In general, the effect sizes found for the kindergarten version of Tools of the Mind in Blair and Raver’s study are relatively low: they all hover around the 0.1 mark, 5 months into the program. This is around half the average effect size for childhood interventions in 2006 found by Duncan et al. [6]. In addition, whilst the effects in high poverty schools look much more promising, it should be noted that the confidence intervals are also much larger for these effects.

To get a feel for what this means, we can get a rough estimate for how this effect size translates into months of development. Bloom et al. [7] found that children progress with an effect size of 1.52 in reading and 1.14 in maths over the first year of schooling. Averaging this and dividing by 12 gives as a rough expected progress estimate of 0.1 per month. This means that many of the effects of Tools of the Mind put children only one month ahead of children not receiving the program.  However, when considering vocabulary in high poverty schools, for example, with an effect size of 0.46, this offers nearly 5 months advantage. Of course, this analysis is something of an oversimplification, but it provides a little context as to whether these effect sizes are meaningful.

How do these effect sizes compare to differences between children coming from disadvantaged and advantaged children?  Whilst it is difficult to put numbers on these differences as many researchers use different measures of what counts  as ‘advantaged’ and ‘disadvantaged’, in order to get an idea of this, I’ll compare the ES to a few studies that found differences within the usual range. I’ll steer clear of the issues in measuring something as vague of ‘executive functions’ and concentrate on one aspect of them, working memory. Noble et al [8] found a difference of 0.31 SD between high and low SES children in the first year of school. Given that Tools of the Mind was found to improve working memory by 0.14, this would be the equivalent of closing half the income related gap. Vocabulary also shows promising results. A study comparing children receiving free school meals (FSM) and those that didn’t at the start of school found a gap of 0.62 SD between the groups [9]. The 0.46 ES for vocabulary in high poverty schools would close this gap by three quarters. Unfortunately, they don’t report what this figure was in high poverty schools one year later, which would be a particularly interesting result. Reading and maths are less promising however. The same study found gaps of 0.69 for reading and 0.68 for mathematics between children receiving FSM and those that didn’t. The ES of 0.13 in maths and 0.14 in reading won’t have much effect on closing this gap, which is a shame, because arguably these are the more valuable educational skills.

In conclusion, Tools of the Mind appears to be a relatively well-grounded intervention program. A random controlled study showed that Tools of the Mind improved children’s executive functions and academic skills in comparison to normal kindergarten classrooms. Despite not being one of the most effective interventions available, the costs, effort and time required seem reasonable and the results suggest that some of the benefits of the program are long term. It holds particular potential for high poverty schools, where the effects of the program appear to go some way to closing income related achievement gaps. However, it is questionable whether the control group used can be considered a full active control and it would perhaps be better to see these results replicated in a study that used a similarly new and novel curriculum or simply used just part of the Tools curriculum as the control group. In addition, results from studies of the pre-kindergarten program by two highly distinguished research bodies are inconclusive with a large study indicating that children receiving Tools of The Mind were at a disadvantage in comparison to normal curricula at the start of school. With this in mind, and the fact that we only have one study investigating the kindergarten program, I would suggest that more research needs to be done to establish the overall effectiveness of this curriculum (and ideally to identify which aspects of the Tools have the greatest effect) before we can advocate it as an effective intervention for kindergarten classes in high poverty schools.

[1] http://www.toolsofthemind.org/

[2] The full paper by Blair and Raver can be found here: http://dx.plos.org/10.1371/journal.pone.0112393

[3] http://deevybee.blogspot.co.uk/2012/02/neuroscientific-interventions-for.html

[4] http://www.washingtonpost.com/local/education/dc-school-reform-targets-early-lessons/2011/11/04/gIQAGZ2VCN_story.html

[5] http://economicdiscipleship.com/2010/12/23/profile-tools-of-the-mind/

[6] https://socialinnovation.usc.edu/files/2014/03/Duncan-Two-Policies-to-Boost-School-Readiness.pdf

[7] http://www.mdrc.org/sites/default/files/full_473.pdf

[8] http://onlinelibrary.wiley.com/doi/10.1111/j.1467-7687.2005.00394.x/pdf

[9] http://www.scotland.gov.uk/Publications/2005/02/20634/51605