Math is not for girls: The stereotype begins early!

By Nestor Lopez-Duran PhD

Imagine yourself an elementary school teacher. One of your female students fails to complete an arithmetic assignment and offers an excuse that ‘‘Girls don’t do math.’’ What might be a pretext for avoiding homework could also be the outcome of social-cognitive development. Combining cultural stereotypes (‘‘Math is for boys’’) with the knowledge about one’s own gender identity (‘‘I am a girl’’) to influence one’s self-concept (‘‘Math is not for me’’) reflects the tendency to achieve what social psychologists (Heider, 1946) call cognitive balance.

What you just read is how Dario Cvencek and his colleagues at the University of Washington introduced a provocative study recently published in the prestigious journal Child Development. In the study, the authors wanted to know whether elementary school children, some as young as first graders, already hold on to the stereotype that girls dont do math. Research has shown that in the US both adults and older children believe that math is a male activity and this stereotype is believed to be responsible for the gender gap that exists in math achievement (that males tend to outperform females in math).

The researchers wanted to go beyond simply asking kids whether they thought math was for boys, which is called explicit stereotypes. Instead, the study examined whether kids held implicit stereotypes, or ideas about gender and math that are held without conscious awareness. To test this idea, the researchers conducted an experiment called the Implicit Association Test (IAT), which measures how much we associate concepts without knowing, or without being explicitly aware that we do, such as associating math with boys.

The experiment worked this way: Using a computer screen, a child is presented with words in the middle of the screen and he/she is asked to sort into a left or right pile by clicking a left or right arrow. First the child is asked to sort names based on whether they are boy (left) or girl (right) names. Then the child is asked to sort words based on whether the words are math words (addition, number, math), or reading words (read, book, letters). Then the task becomes more difficult because the child is presented with both names and math/reading words and is asked to sort all words. In one condition the child is asked to sort all boy names AND math words to the left, and all girl names AND reading words to the right (picture A in the figure below). In another condition the child is asked the boy names and the reading words to the left, and the girl names and math words to the right (picture B below).

The idea behind this experiment is that if you have an implicit stereotype that associates math with boys then you would be much faster when sorting words in condition A than when sorting words in condition B. That is, if you think that boys go with math then it is confusing to sort math words to the side that says girls because that is inconsistent with your stereotype. In that case you would be slower than when sorting math words to the side that says boys.

The results:

The important finding in the graph above is the middle section. Boys were faster when sorting math words and boy names to the same pile than when sorting math words and girl names into the same pile. Likewise, girls were faster when sorting math words and boy names into the same pile than when sorting math words and girl names into the same pile. Both boys and girls showed a clear gender stereotype in that it is easier for them to link the concepts of boys and math than it is to link the concepts girls and math.


One final result is worth noting. The researchers found that this effect was noticeable since 1st and 2nd grade! This is important because gender differences in math are not seen that early (girls perform just as well as boy at those grades). This suggests that the stereotype is not simply a reflection of actual performance but comes from socialization process that starts very early. For example, studies have shown that mothers tend to underestimate the math abilities of their elementary school daughters and overestimate the abilities of their sons, and this has a major impact on their kids perception of how good they are at math.

The reference:
Dario Cvencek, Andrew N. Meltzoff, & Anthony G. Greenwald (2011). Math–Gender Stereotypes in Elementary School Children Child Development

Pot use and early psychosis: Does smoking marijuana increase the risk for schizophrenia?

By Nestor Lopez-Duran PhD

Just wanted to share some quick thoughts on a large meta-analysis that was recently published in the prestigious Archives of General Psychiatry. In this publication, the authors reviewed all previous studies that have examined the association between substance use (Cannabis, Alcohol, and other substances) and the onset of psychotic disorders (such as schizophrenia). Although the research on the potential harmful effects of pot use is quite controversial in many issues (e.g., whether pot is a gateway drug or not*), one finding that appears to be more consistent is that the use of cannabis is related to an earlier onset of psychotic disorders.  This meta-analysis was intended to examine the results of all previous studies using statistical techniques that allow the researchers to reach some conclusions about what previous studies, as a whole, truly say about this topic.

The meta-analysis included 83 different studies with a total of 8,167 substance-using patients and 14,352 non-substance using patients. Note that both samples (substance and non-substance using) were patients with a confirmed diagnosis of a psychotic disorder. The study then compared the age of onset of the condition between the substance vs. non-substance using groups.

  • The age of onset of the psychosis for cannabis users was 2.70 years younger than non-substance users.
  • The age of onset of the psychosis of other (non-pot and non-alcohol) users was also 2 years younger than non-substance users.
  • The age of onset of alcohol users was not different than the age of onset of non-users.

However, these results do not address a basic question regarding the association of pot use and psychosis: Does pot use CAUSE an early onset of psychosis OR are those with early onset more likely to have used pot for other reasons (e.g., self-medication). The authors dispute this idea because they did not find alcohol to be related to earlier onset. Im not sure that argument is convincing because it is still possible that youth in early stages of psychosis find cannabis use to provide a function (self medication) that alcohol does not. It is also possible that cannabis and other drug users simply raise alarms with family and friends earlier leading to faster identification and diagnosis (e.g., reflected in earlier onset) than non-substance users. In addition, we do not know whether those who had early onset and were pot users would have developed psychosis a bit later had they not been using pot. So these results are intriguing but the nature of the association between cannabis use and psychosis is still a bit unclear.

* Studies consistently show that most people who use hard drugs had used cannabis before starting hard drugs BUT most cannabis users never go on to use hard drugs.

If you feel your drug or alcohol use has gotten out of control consider a non 12 step rehab program.

The reference: Large, M., Sharma, S., Compton, M., Slade, T., & Nielssen, O. (2011). Cannabis Use and Earlier Onset of Psychosis: A Systematic Meta-analysis Archives of General Psychiatry DOI: 10.1001/archgenpsychiatry.2011.5

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Video game addiction in teens: A problem or a myth?

By Nestor Lopez-Duran PhD

During the last decade there has been much debate about the dangers of excessive video game playing. Some have argued that excessive video game playing can become an addiction and can lead to many negative consequences. Yet, much of the research linking video game playing to negative factors had methodological limitations that prevented the researchers from determining what causes what. For example, it is difficult to determine whether video game leads to negative outcomes (e.g., excessive video game playing leads to depression) or instead whether negative factors lead to more video game playing (depression leads to more video game playing).

The study reported in Pediatrics provides data from over 3,000 children and adolescents attending 3rd, 7th, and 8th grade in Singapore and who were surveyed annually between 2007 and 2009.

The authors were interested in answering a number of questions, including:

1. What are the rates of pathological gaming? (see below for a definition)

2. What factors predict becoming a pathological gamer?

3. What are the negative consequences of pathological gaming?

To answer these questions the authors asked the participants to complete a series of surveys that measured the kids social skills, impulsivity, social phobia, depression, anxiety, parent-child relationship quality, and school performance. The surveys also included an assessment of gaming patterns and experiences in order to determine whether the child met the criteria for pathological gaming

What is pathological gaming?

The authors based their definition of pathological gaming on the criteria used by psychiatrists to diagnose pathological gambling.  Specifically, the authors classified a child as engaging in pathological gaming if the child endorsed 5 out of 10 specific symptoms. Although the authors did not explicatively define these symptoms in the article, here are the 10 symptoms of pathological gambling as adapted for pathological gaming:

Persistent or recurrent maladaptive gaming behavior as indicated by 5 or more of the following:

  1. is preoccupied with gaming
  2. needs to play more and more in order to achieve the desired excitement
  3. has repeated unsuccessful efforts to control, cut back, or stop gaming
  4. is restless or irritable when attempting to cut down or stop gaming
  5. plays games as a way to avoid problems or relieving sad mood
  6. after losing at gaming, often returns to get even
  7. lies to family or others to conceal the extend of involvement in gaming
  8. has committed illegal acts to pay for gaming
  9. has jeopardized or lost a significant relationship, job, or education/career opportunity because of gaming
  10. relies on others to provide money because he/she spends all her money on gaming.

So what did they find?

As you can see below, most of the kids (90%) did not engage in pathological gaming during the 3-year study. About 1% had high levels of pathological gaming symptoms during year 1 but then stopped completely by year 3. About 6% were chronic pathological gamers. And about another 1% were not pathological gamers during year one but became pathological gamers by year 3.

Some additional results:

Impulsivity, poor emotion regulation skills, and poor social competence predicted increases in pathological gaming symptoms over time.

Not surprisingly, more gaming at time 1 predicted becoming a pathological gamer at time 3. For example, those who became pathological gamers at time 3 played an average of 31 hours per week (almost a full time job!) at time 1. In contrast, those who did not become pathological gamers played an average of 19 hours per week at time 1 (still surprisingly high).


Children with high levels of pathological gaming at time 1 had higher symptoms of depression, anxiety, social phobia, and academic difficulties at time 3. However, I could not determine from the article if the authors controlled for the levels of depression, anxiety, etc. at time 1.

In sum, about 7% of the kids in this study had symptoms related to their video game playing habits that are similar to those observed in adults with pathological gambling problems. It seems that impulsivity and poor social skills are one of the best predictors of developing these problems. These kids also had significantly more mental health symptoms and problems in the schools.

The Reference: Gentile, D., Choo, H., Liau, A., Sim, T., Li, D., Fung, D., & Khoo, A. (2011). Pathological Video Game Use Among Youths: A Two-Year Longitudinal Study PEDIATRICS, 127 (2) DOI: 10.1542/peds.2010-1353

While I was out: Autism, Anxiety Treatments, and how depression affects teen romance.

By Nestor Lopez-Duran PhD

Hello everyone, I decided to briefly mention three new studies published while I was out and let you suggest which study I should discuss in more detail next week. Make sure you express your preference in the comments section.

While I was out:

1. Closely spaced pregnancies increase risk of autism.

A study of over 600,000 siblings conducted by a team at Columbia University suggests that second-born children are at a higher risk for autism the closer in age they are to their older siblings. For example, siblings born within 12 months after their older siblings are over 240% more likely to develop autism than siblings born 3 years after their older siblings.
The reference: Cheslack-Postava, K., Liu, K., & Bearman, P. (2011). Closely Spaced Pregnancies Are Associated With Increased Odds of Autism in California Sibling Births PEDIATRICS, 127 (2), 246-253 DOI: 10.1542/peds.2010-2371

2. Early evidence for the efficacy of a computerized attention retraining program for childhood anxiety.

An article published in the Journal of Child Psychology and Psychiatry reports the findings of a randomized control trial of a computer game-like procedure that may reduce the symptoms of anxiety in affected children by modifying their tendency to attend to threatening information (e.g., scary faces, etc.).
The reference: Bar-Haim, Y., Morag, I., & Glickman, S. (2011). Training anxious children to disengage attention from threat: a randomized controlled trial Journal of Child Psychology and Psychiatry DOI: 10.1111/j.1469-7610.2011.02368.x

3. Teen depression may impact their romantic relationships into adulthood.

A 5-year longitudinal study of teens showed that depression during middle adolescence (10th grade) has a significant negative impact in romantic relations for years. For example, teens who had elevated symptoms of depression in 10th grade experienced more increases in relationship conflicts and less increases in positive problem solving during the next 5 years as compared to their non-depressed peers.
The reference:Hana M. Vujeva, & Wyndol Furman (2010). Depressive Symptoms and Romantic Relationship Qualities from Adolescence Through Emerging Adulthood: A Longitudinal Examination of Influence.  Journal of Clinical Child & Adolescent Psychology

Let me know which study I should discuss next week!

Child-Psych will be back tomorrow.

By Nestor Lopez-Duran PhD

Hi all, I was unable to update child-psych in January due to some work-related issues, but Im finally back and I will start posting again this week. Im also in discussions with a number of colleagues who will write some guest posts during the upcoming months. Thank you to all the readers who sent emails during the absence and appreciate your support and your patience. See you all tomorrow! Nestor.

Jaundice and autism: It depends on when youre born.

By Nestor Lopez-Duran PhD

Many parents of children with autism may remember answering a long list of questions regarding their childs early development during their autism evaluations: Was he/she premature? Were there any complications? Jaundice?  Most often these questions are asked to gather information about other possible developmental factors that may explain the symptoms. Thus, this neonatal information is usually not directly relevant to the diagnostic criteria of autism, but it is used to rule out other disorders (such as a specific genetic syndrome). So I was intrigued when I read an article published this week in the journal Pediatrics that examined the association between jaundice and a number of developmental disorders, including autism.

The authors examined practically all children born in Denmark between 1994 and 2004 using the countrys Medical Birth Register. They examined several neonatal factors, including birth weight, parental age, gestational age, parental smoking, congenital malformations, and jaundice exposure. Developmental disorder diagnoses were obtained from the country health registers and included speech and language disorders, learning disabilities, mental retardation, and autism spectrum disorders.

Among full term babies, jaundice was associated with:

– A 56% increase in the risk for speech and language disorders

– A 56% increase in the risk for autism.

Among premature babies, jaundice was not associated with a higher risk for any disorder.

A few more interesting findings:

Jaundice was associated with a risk for autism only among kids born from October to March. For these kids, jaundice increased the risk for autism by 97%. In contrast, jaundice did not increase the risk for autism among kids born from April to September. Why? The authors suggested two possibilities. One, exposure to daylight is a standard treatment for jaundice. It is possible that kids born from October to March had significantly less light exposure (remember these kids are in Denmark, which has limited daylight during winter months). It is also possible that kids born during the winter are exposed to more viruses, infections and other conditions that may be responsible for the increased risk for autism.


Jaundice increased the risk for autism only among babies born to woman having their second+ child. For these kids, jaundice increased the risk for autism by 129%. In contrast, jaundice did not increase the risk for autism in kids who were the mothers first child. Why? The authors also offered two possible explanations. First, it is possible that mothers having their second + child have accumulated more antibodies during pregnancy that could affect development. However, it is also possible that this is a byproduct of the Danish health system. The authors explained that mothers who had a successful first pregnancy are discharged from the hospital faster during their second + pregnancies (usually the same day of delivery). This limits access to care for second-born kids with jaundice during the first days of life. In contrast, babies who are the first-born receive more extensive care and are not discharged until the 3rd or 4th day after delivery. This could result in better management of jaundice and a reduction of the risk for autism.

But the question still remains, what is the mechanism by which jaundice increases the risk for autism among these full term babies?

And finally, just to put these results into context. Jaundice affects 80% of preterm babies and 60% of full term babies. This means that the vast majority of babies who are exposed to jaundice will not develop autism.

The reference: Maimburg, R., Bech, B., Vaeth, M., Moller-Madsen, B., & Olsen, J. (2010). Neonatal Jaundice, Autism, and Other Disorders of Psychological Development PEDIATRICS, 126 (5), 872-878 DOI: 10.1542/peds.2010-0052

Social skills training for children with autism: not all group therapies are the same

By Nestor Lopez-Duran PhD

Last week, while discussing a study that compared medication and psychotherapy for the treatment of teen depression, I mentioned how the current research suggests that the efficacy of Cognitive Behavioral Therapy (CBT) as treatment for depression in adolescents may vary significantly as a function of small variations between the different versions of CBT used by clinicians. That is, not all CBTs are the same and some types appear to be more effective than others. Are other interventions also sensitive to subtle variations?

Last night I read a recent article published in the Journal of Autism and Developmental Disorders that compared two similar social skills intervention groups for children with Autism. Social Skills Group Intervention, or SS-GRIN, is one of the most effective social skills training programs for typically developing children and teens with social difficulties. This intervention uses CBT and social learning techniques to teach social skills and peer relations to at-risk kids, such as those experiencing severe peer rejection.

Social skills therapy generally has been found to be of mixed effectiveness when used with children and teens with high functioning autism (HFA). Some studies have suggested that it works while others suggest that it doesnt. However, the type of social skills programs used in previous studies has varied significantly and some studies included implementation of programs designed to teach social skills to typically developing kids, such as SS-GRIN.

In an effort to design a more appropriate, and hopefully more effective, intervention for kids with HFA, a group of clinicians and researchers developed a version of SS-GRIN especially modified for HFA.  They called it the SS-GRIN-HFA.

The intervention consists of 15 sessions:

They then tested the intervention in 55 children age 8 to 12 with a diagnosis of HFA, Aspergers disorder, or PDD NOS. The kids were randomly assigned to either SS-GRIN-HFA or the traditional SS-GRIN. Before and after the intervention the parents completed the following measures:

  • Social Responsiveness Scale (SRS), which assesses 5 domains including social awareness, social cognition, social communication, social motivation, and autistic mannerisms.
  • The Achieved Learning Questionnaire (ALQ), which measures social skills learned during the intervention.
  • Social Self-efficacy Scale (SSS), which measures the childs perceived competence in social skills.

In addition, the child also completed the SSS and the Social Dissatisfaction Questionnaire, which measures the childs perceived social isolation.

So which therapy was more effective?

In the above table the treatment group is the SS-GRIN-HFA and the control group is the traditional SS-GRIN. The scores below the columns M reflect the mean change from before the intervention to after the intervention. In the case of the SRS, negative scores mean improvement after the intervention. For example, when looking at the SRS social awareness difficulties, the SS-GRIN-HFA had a score change of -.33 while the control group had a score change of +.38. That is, those in the SS-GRIN-HFA had a reduction in social awareness difficulties (as reported by parents) while those in the traditional group actually got worse! For all other scores (ALQ, Self-Efficacy, and Social Dissatisfaction) positive numbers mean improvement after the intervention. The last column tells us about the strength of the difference between the SS-GRIN-HFA and the traditional group. Values higher than .50 or lower than -.50 suggest a sizable difference between the groups.

So in the table above  we can see that when compared to the traditional  SS-GRIN intervention, the modified SS-GRIN-HFA resulted in significant improvements in parental reports of:

  • Social Awareness
  • Social Communication
  • Social Motivation
  • Autism Mannerisms
  • Perceived social competence

In contrast, there was no difference between the two interventions in the levels of the childs report of social competence or perceived social isolation.


The authors concluded:

Overall, the current study provides evidence for the efficacy of the S.S.GRIN-HFA group intervention for enhancing the social skills of children with high functioning ASD. As increasing numbers of children are diagnosed with high functioning ASD, there is an urgent need and demand for efficacious treatment protocols for use with this population (IACC 2009). The S.S.GRIN-HFA treatment program pro- vides a manualized research-based option to aid mental health professionals in their work with children with high functioning ASD. In particular, the S.S.GRIN-HFA intervention offers professionals several advantages compared to more generic SST, including its active engagement of parents, use of community exercises to promote generalization, and focused social skills training. In particular, results of the current study provide clear evidence that when children with high functioning ASD are in a group with more similar peers (i.e., diagnostic, social and developmental profiles), outcomes are enhanced by following a protocol that is designed specifically to meet their social skill needs.

The reference: DeRosier, M., Swick, D., Davis, N., McMillen, J., & Matthews, R. (2010). The Efficacy of a Social Skills Group Intervention for Improving Social Behaviors in Children with High Functioning Autism Spectrum Disorders Journal of Autism and Developmental Disorders DOI: 10.1007/s10803-010-1128-2

Teenage depression treatment: Medication, therapy or both? It may depend on marital conflict.

By Nestor Lopez-Duran PhD

Recently I discussed a study that examined the rates of psychiatric conditions in children and adolescents. I mentioned how by age 18, about 16% of girls and 8% of boys will experience a depressive disorder (major depressive disorder or dysthymia). Depression is one of the most prevalent psychiatric conditions in adolescents and unfortunately it is not just a temporary phase. Research suggests that depression that starts in childhood or adolescence is actually more chronic and more impairing than adult onset depression.

Although we have some effective interventions for depression, including medications, not all teens respond to these interventions. Because of this finding, researchers have attempted to identify factors that may make a child more or less likely to respond to the intervention. This month the Journal of Abnormal Child Psychology published a large study examining whether parental conflict negatively impacted the effectiveness of interventions for depressed adolescents. Given that parental conflict is one factor associated with depression in teens, it was sensible to think that depressed kids living in families with high parental conflict would be less likely to respond to interventions than depressed kids living in low conflict families.

Is this the case?

This study included 260 adolescents with a depressive disorder living in two parent households (122 girls and 138 boys). These teens were randomly assigned to one of four treatment groups:

1. Fluoxetine (Prozac)

2. Cognitive Behavior Therapy (CBT)

 

 

3. Fluoxetine + CBT

4. A placebo (fake pill)

Below you can see the basic results across all groups.

As you can see, the combination of CBT and Fluoxetine was just as effective as Fluoxetine alone, and both of these treatments were more effective than CBT alone and the placebo. Sadly, CBT alone was not any better than the fake pill. Thus across all participants, only Fluoxetine made a significant difference. Parental marital discord did not change these results.

However, a slightly different pattern emerged when looking at the effect of parental marital discord separately for boys and girls.

The next graph shows the effect of the interventions for girl with high levels of parental marital discord.

The results for this group were identical to those for the entire sample. The combination of CBT and Fluoxetine or Fluoxetine alone was very effective. CBT alone was not significantly different from the placebo.

Now here we can see the effect of the therapy for girls living in families with low parental marital discord.

As you can see, the results are a bit striking. Only 20% of those receiving CBT alone actually improved. That compares to 36% of those receiving a fake pill. The fluoxetine alone and the combination of CBT/Fluoxetine were significantly better than CBT alone.

What does this mean? Before we try to interpret these findings lets talk about what is truly happening in the placebo (fake pill) condition. Most people interpret the response to the fake pill as the placebo effect. That is, we get better because we think or hope that the intervention (fake pill) is supposed to help even though there is no real mechanism by which the intervention (fake pill) could actually help. We always include a placebo condition when examining the efficacy of an intervention because we want to know if the intervention is actually working BEYOND what is expected from the patients high hopes and beliefs.

But in the case of depression the response to the fake pill is not only due to the placebo. It is actually due to the placebo effect PLUS the effect of time. Depression episodes come in cycles and although adolescent depression is chronic (many depressed adolescents will continue to experience depressive episodes throughout their lives), many people will eventually come out of an acute episode given enough time.  Thus, when we see that 35% of depressed kids get significantly better after 12 weeks of taking the fake pill we have to remember than some of these kids would have improved significantly even if they had just continued with their normal life without taking the fake pill.

So given these thoughts, in this study the story about CBT as treatment of adolescent depression is actually quite sad. Not only was CBT not effective in the treatment of depressed teens, but actually it was a very poor placebo among girls in low conflict households. Thus, depressed teens, and especially girls in low parental conflict households, seem to respond to Prozac but not to CBT.

Now, the discussion among clinical researchers on this issue is that this finding is not really representative of CBT because in this study the clinicians used a specific version of CBT called TADS CBT. Some argue that other CBT versions have actually been found to be effective in treating depressed adolescents, and by effective I mean better than a placebo (but not necessarily better than fluoxetine). This is true. Some studies have shown that some versions of CBT are actually very effective in treating depressed teens. However, in practice this is bad news because it means that details matter. It means that not ANY version of CBT would work and the minor nuances between one manual and another (one version of CBT and another) actually make the difference between being effective or being potentially worse than a sugar pill!

Why bad news?

Because even among clinicians who say they use CBT, the use of a manualized approach is rare at best (I say most likely non-existent). Instead, most CBT clinicians use their own flexible version of CBT that borrows from many different CBT versions. The problem with this method is that parents have no way of knowing if the hybrid version of CBT that their kids therapist is using is actually the one that works. Clinicians have argued that details do not matter and that as long as they use principles of CBT the work they do is effective. But the research clearly suggests that this is not true.

There is one last caveat though. This study examined the effect of the interventions at 12 weeks after starting treatment. Thus, these results dont tell us much about whether these interventions prevented relapse or at least shortened the time to the next depressive episode. Thats a discussion for another day, but sadly the data on this last issue are not encouraging at all.

Disclaimer: I do not prescribe medications in my clinical work (Im a clinical researcher) and do not receive any royalties or payments from any pharmaceutical company.

The reference: Amaya, M., Reinecke, M., Silva, S., & March, J. (2010). Parental Marital Discord and Treatment Response in Depressed Adolescents Journal of Abnormal Child Psychology DOI: 10.1007/s10802-010-9466-2

Fast ForWord for reading disabilities and language delays: Does it work?

By Nestor Lopez-Duran PhD

Monday BRIEFS: Quick musings in child related research.

Fast ForWord is a series of computer programs designed to improve language and reading skills in 4-14 year-old kids with language difficulties. The system is sold and marketed by the Scientific Learning Corporation (www.scilearnglobal.com/the-fast-forword-program/).

The system has been adopted extensively by schools across the USA, Canada, and Australia. Yet, despite such popularity, there is significant controversy regarding the effectiveness of this intervention as the evidence for its efficacy is mixed at best.

In the last issue of the Journal of Child Psychology and Psychiatry, Dr. Gemma Strong and her colleagues at the University of York and the University of Birmingham in the UK published the largest comprehensive meta-analysis of the past studies that have tested whether Fast ForWord is effective. A meta-analysis is a statistical process that groups the results from all previous relevant studies to provide an overall conclusion regarding the outcome of interest. In this case, the authors were interested in four specific outcomes; namely, whether Fast ForWord was effective in improving:

  1. Standardized measures of single word reading
  2. Passage reading comprehension
  3. Receptive Vocabulary
  4. Expressive Vocabulary

The meta-analysis was conducted with 6 studies that met a strict inclusion criteria. The studies had to include a comparison group (whether active or non-active treatment) and had to include a baseline measure that allows for the examination of change from before to after the intervention. Out of 79 potential studies, only 6 met the inclusion criteria. The rest were eliminated because of lack of control groups, lack of baseline measures, poor randomization, and inadequate sample size.

The results:

After the intervention,

Kids using Fast ForWord did not differ from untreated controls participants in word reading (Effect Size = .07)

Kids using Fast ForWord did not differ from untreated controls participants in passage comprehension (Effect Size = .17)

Kids using Fast ForWord did not differ from untreated controls participants in receptive vocabulary (Effect Size = .01)

Kids using Fast ForWord did not differ from untreated controls participants in expressive vocabulary (Effect Size = -.04)

Likewise, when compared to treated controls, kids using Fast ForWord were not better at any of the four outcomes.

The authors concluded:

We believe that the pattern shown by our analyses isclear and consistent: whether comparing Fast For-Word with untreated or alternative treatment control groups, we found no sign of a reliable effect of treatment in any analysis. There is no evidence from this review that Fast ForWord is effective as a treatment for children’s reading or expressive or receptive vocabulary weaknesses. In contrast, evidence suggests that conventional forms of therapy can effect modest but reliable improvements in these skills.

The last sentence is key. The authors argued that there is more evidence for more conventional methods, such as phoneme awareness training and phonetically-based reading instructions, than for Fast ForWord, and thus parents and educators should utilize conventional methods when addressing reading and language difficulties.

The reference:
Strong, G., Torgerson, C., Torgerson, D., & Hulme, C. (2010). A systematic meta-analytic review of evidence for the effectiveness of the ‘Fast ForWord’ language intervention program Journal of Child Psychology and Psychiatry DOI: 10.1111/j.1469-7610.2010.02329.x

Storm and Stress? Many psychiatric disorders increase during adolescence.

By Nestor Lopez-Duran PhD

Psychiatric disorders in children and adolescents III: Increases in psychiatric disorders during adolescence.

Today is the third of a series of brief posts about the recent results of the latest National Comorbidity Survey (NCS).

The NCS is a large nationally representative study of over 10,000 adolescents aged 13 to 18. The study aims to examine the prevalence of psychiatric disorders in youth across the United States by conducting a comprehensive face-to-face structured diagnostic assessment of every participant. Such diagnostic interview is currently the gold standard in diagnostic assessment, which arguably provides us with the most accurate picture of the true prevalence of these disorders in the population. The study included 4,945 boys and 5,170 girls. The racial/ethnicity breakdown was 65% non-Hispanic whites, 15.1% non-Hispanic black, 14.4% Hispanic, and 5% other.

Monday I discussed the overall prevalence rates of the most common psychiatric disorders in children and adolescents. Yesterday I also discussed the overall proportion of affected kids that actually experience severe functional impairment. Today, I want to comment on the age differences in life time prevalence of psychiatric disorders in early and late adolescence.

Below you can see age differences in prevalence rates for mood and anxiety disorders among kids aged 13-14, 15-16, and 17-18. The Y (vertical) axis shows the life time prevalence at each age and the X (horizontal) axis shows the age groups.

As you can see above, there is an increase in life time prevalence in most mood and anxiety disorders. The greatest increase is in depression. At age 13-14, about 8% of kids have a life time history of depression. However, this doubles to over  15% by age 17-18, which suggests that 1/2 of all cases of depression by age 18 occur between the ages of 14 to 18.

Now, the graph below shows the same information for more behavioral disorders.

Although not surprising, the drastic increases in prevalence of alcohol and drug abuse/dependence during adolescence is remarkable. For example, the prevalence of drug abuse/dependence increases from 3% at age 13-14 to over 16% by age 17-18. That is an increase of over 400%. In contrast, eating disorders remain relatively stable. This is actually very surprising as I assumed that we would see an increase in eating disorder in high school. You will also note that the prevalence of ADHD remains virtually static. This is actually not surprising at all since the diagnosis of ADHD requires that the symptoms are present during early childhood. According to current diagnostic conventions, there is no such thing as adolescence-onset ADHD.

Also in this series:

  • How common are psychiatric disorders in children and adolescents?
  • How many kids with psychiatric disorders experience severe impairment?
  • SOON! A close look at race: Are there race biases in diagnostic practices?

When a loved one turns to self medication brought on by stress or depression, a drug rehabilitation program may be the best option. Dont wait to get help.

The reference: Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, & Swendsen J (2010). Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey ReplicationAdolescent Supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 49 (10), 980-9 PMID: 20855043