<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03261076</url>
  </required_header>
  <id_info>
    <org_study_id>H-40232</org_study_id>
    <nct_id>NCT03261076</nct_id>
  </id_info>
  <brief_title>Reading Remediation and Outcomes in Detention</brief_title>
  <acronym>SERIOUS</acronym>
  <official_title>Severe LD in Juvenile Delinquents: Presentation, Course, and Remediation</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Baylor College of Medicine</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Houston</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Harris County Juvenile Probation Department</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Connecticut Court Support Services Division</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>MindTrust Labs</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Baylor College of Medicine</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The current literature on academic skill difficulties, whether considered as part of the&#xD;
      continuum of ability or as a specific learning disability (LD), indicates that these problems&#xD;
      often coexist with conduct problems and juvenile delinquency, and are risk factors for&#xD;
      initial law-breaking behavior and for its persistence. However, less is understood about how&#xD;
      this relationship develops. It is these broad questions that this project seeks to address.&#xD;
      First, what is the causal pathway? Does LD cause delinquency, delinquency cause LD, or are&#xD;
      both caused by something else? And can big data analytics applied to statewide datasets of&#xD;
      information about juvenile justice (JJ) involvement help to answer this question? Second, as&#xD;
      it is known that learning to read and do math (and thus becoming more employable) increases&#xD;
      the likelihood of desistance (i.e., not committing any more illegal acts), what are the&#xD;
      necessary parts of an intervention designed to teach these skills? And what role might&#xD;
      technology play in such an intervention? To answer these questions, we will implement a study&#xD;
      that includes two components, (a) a big data component and (b) an intervention component. For&#xD;
      (a), we will work with a large historical dataset from the Harris County Juvenile Probation&#xD;
      Department. For (b), we will work, in total, with 192 (48 per year) delinquent youth with&#xD;
      severe LD in residential placement. These individuals, in a nonconcurrent multiple baseline&#xD;
      design, will be offered an educational therapy designed to address severe reading problems in&#xD;
      juvenile detainees using a novel mixed media intervention in which the person-to-person&#xD;
      intensive 1:1 component is completed while youth are in residential settings (24 sessions,&#xD;
      delivered in 90 minute settings 3 times a week) and a &quot;gamified&quot; educational smartphone&#xD;
      learning tool follow-up completed upon release (with appropriate network fidelity monitoring&#xD;
      and participant reinforcement). The person-to-person component is developed specifically for&#xD;
      juvenile offenders with severe LD, combining two well-established and highly-regarded&#xD;
      intervention programs designed to systematically build students' repertoire of&#xD;
      grapheme-phoneme correspondence rules as well as develop comprehensive reading skills, from&#xD;
      beginning reading to proficiency.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Learning disabilities (LD) are among the most common types of disabilities in juvenile&#xD;
      offenders that have been linked to delinquency. Nationwide, children and youth with special&#xD;
      education needs are overrepresented in the US justice systems. Reports estimate delinquent&#xD;
      juveniles with a disability to comprise about 30% to 60% of the entire delinquent population.&#xD;
      A national survey in the US states an average prevalence rate of 33.4% of incarcerated&#xD;
      juveniles with disabilities in correctional facilities. Moreover, concerns have long been&#xD;
      raised on the recidivism rates of youth with disabilities and special education backgrounds.&#xD;
      In general, regarding educational performance, academic deficits such as a lack of basic&#xD;
      skills in reading, writing, and mathematics have been associated with recidivism. However,&#xD;
      these studies largely neglect the dynamic nature of delinquent and criminal behavior that has&#xD;
      been documented within the field of developmental criminology with a focus on the onset,&#xD;
      continuity, and extinction of deviant behavior. In light of this research, the identification&#xD;
      and remediation of LD as a risk factor for repeat offending has been a persistent challenge&#xD;
      given the accumulation of and overlap with other risk factors such as poverty, familial&#xD;
      patterns of criminality, influence of delinquent peers, and the differential impact of risk&#xD;
      factors across an individual's developmental trajectory. Altogether, there is a challenge&#xD;
      around the implementation of sophisticated methodology to model the complex longitudinal and&#xD;
      reciprocal links between juvenile delinquency and educational problems such as LD, and how&#xD;
      they relate to other risk factors over time. This challenge is intensified by the required&#xD;
      large samples to detect robust and interpretable patterns and predictive relationships for&#xD;
      groups of youth with severe LDs that, by definition, are small in size and censored with&#xD;
      regard to various educational outcomes (e.g., academic performance). This research is&#xD;
      conceived to contribute to the field's understanding of the connection between LD and&#xD;
      delinquent behavior. We hope to generate unique findings capitalizing on the availability of&#xD;
      the relevant big data, the clinical strengths of its members and their capacity to develop&#xD;
      and administer educational therapy to juvenile offenders, and its embedment within&#xD;
      communities empowering the creation and processing of multi-level longitudinal datasets,&#xD;
      merging sociological (i.e., criminological), behavioral, neurophysiological, and&#xD;
      genetic/genomic data.&#xD;
&#xD;
      Big Data:&#xD;
&#xD;
      The Big Data component seeks to address three objectives: (1) to conduct big data analytics&#xD;
      and data mining to quantify/qualify the complexities of severe LD among juvenile justice&#xD;
      (JJ)-involved youth using statistical methods applied to big datasets that capture multiple&#xD;
      indicators (i.e., offending and educational outcomes) cross-sectionally (for one-time&#xD;
      offenders) and longitudinally (for repeat offenders); (2) to utilize big data analytics to&#xD;
      identify a group of youth with severe LD who will serve as a comparison group for individuals&#xD;
      participating in a reading intervention; and (3) to evaluate the findings from the first two&#xD;
      objectives based on the statistical assumptions underlying the models applicable to the big&#xD;
      data at hand.&#xD;
&#xD;
      Hypotheses. Extending previous research, Big Data seeks to identify factors that relate LD to&#xD;
      recidivism, frequency of re-offending and re-arrest, and the time to post-involvement&#xD;
      recidivism. We hypothesize that (a) overall recidivism rates (i.e., regardless of the&#xD;
      specific type of offense) will be higher among JJ-involved youth with severe LD&#xD;
      (operationalization based on standardized achievement tests and special education status)&#xD;
      than in youth without LD; (b) youth with severe LD will show more incidents of school-related&#xD;
      problems after their first involvement (e.g., attendance problems, disciplinary incidents&#xD;
      such as in-school and out-of-school suspensions and expulsions) than youth without LD; (c)&#xD;
      youth with severe LD who had school-related problems prior to their first JJ involvement will&#xD;
      be at higher risk of recidivism than youth without LD; (d) lower levels of reading&#xD;
      achievement will be associated with shorter periods of time to repeated offending; and (e)&#xD;
      participation in the intervention will significantly decrease recidivism risk compared to&#xD;
      other youth with severe LD who did not participate in the intervention.&#xD;
&#xD;
      Intervention:&#xD;
&#xD;
      The intervention component seeks to address three objectives: (1) to improve the reading&#xD;
      skills of an understudied, high-risk population (JJ-involved youth) using an educational&#xD;
      therapy that has been specifically designed for JJ-involved youth, provided in a 1:1 setting,&#xD;
      and expanded with technology, including material relevant to daily living skills and&#xD;
      appropriate reinforcement that will extend the program into the post-release period; (2) to&#xD;
      reinforce skills and maintain reading gains made during the educational therapy, and increase&#xD;
      the automaticity of skill use by using a high-interest &quot;gamified&quot; learning tool that youth&#xD;
      will play on a SmartPhone given to them when they leave detention; and (3) to investigate the&#xD;
      individual response to therapy with respect to other variables (e.g., attention, executive&#xD;
      functioning, impulse control, learning ability, other academic achievement) collected at&#xD;
      baseline.&#xD;
&#xD;
      Hypotheses. The key hypotheses of the intervention component seek to identify the factors&#xD;
      associated with response to therapy for severe LD relative to pre-therapy functioning. We&#xD;
      hypothesize that (a) overall improvement in reading will be related to pre-therapy academic&#xD;
      skills, attention, and executive functioning; (b) inclusion of a metacognitive component in&#xD;
      the therapy will provide carry-over effects, improving other academic skills; and (c) the&#xD;
      gamification of the learning tool will result in robust technology usage, with a&#xD;
      dose-response relationship between time spent on task and improvement in skills trained (and&#xD;
      measured) via game play.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">May 14, 2018</start_date>
  <completion_date type="Anticipated">June 30, 2022</completion_date>
  <primary_completion_date type="Anticipated">June 30, 2022</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <intervention_model_description>This study uses a multiple baseline design, in which participants enter into the single-arm intervention at different times, yet have data collected at all time points. Approximately one third of participants will have their entry into the intervention shifted later by one week, and approximately another third will have their entry shifted by two weeks. This will happen on a rotating basis. All individuals will receive progress monitoring during the weeks that the other individuals are receiving intervention and/or assessments. The multiple baseline design allows for all individuals to receive the study intervention, and still provides a mechanism for monitoring change (or lack thereof) during non-intervention periods before and after intervention.</intervention_model_description>
    <primary_purpose>Treatment</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Change in Reading Skills from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>Reading skills will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):&#xD;
Letter &amp; Word Recognition&#xD;
Reading Comprehension&#xD;
This standard score will be obtained based on the sum of subtest standard scores according to published norms.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in Decoding Skills from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>Decoding skills will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):&#xD;
Letter &amp; Word Recognition&#xD;
Nonsense Word Decoding&#xD;
This standard score will be obtained based on the sum of subtest standard scores according to published norms.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in Reading Understanding from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>Reading understanding will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):&#xD;
Reading Comprehension&#xD;
Reading Vocabulary&#xD;
This standard score will be obtained based on the sum of subtest standard scores according to published norms.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in Comprehension from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>Comprehension will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):&#xD;
Reading Comprehension&#xD;
Listening Comprehension&#xD;
This standard score will be obtained based on the sum of subtest standard scores according to published norms.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in Reading Fluency from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>The Test of Reading Efficiency, Second Edition (TOWRE-2) will be used to measure an individual's ability to pronounce printed words and phonemically regular nonwords accurately and fluently. Standard scores will be obtained according to published norms.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Change in Other Academic Skills from pre-intervention (baseline) to the week after intervention</measure>
    <time_frame>Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.</time_frame>
    <description>Other academic skills will be measured as a composite of remaining subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3) and Woodcock-Johnson-IV Tests of Academic Achievement (WJ-IV):&#xD;
KTEA-3 Math Concepts &amp; Application&#xD;
KTEA-3 Math Fluency&#xD;
KTEA-3 Rapid Automatized Naming Letters&#xD;
KTEA-3 Spelling&#xD;
WJ-IV Science&#xD;
WJ-IV Social Studies&#xD;
WJ-IV Humanities&#xD;
This standard score will be obtained based on the z-score of the sum of subtest standard scores at pre-intervention.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Recidivism within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>Recidivism will be computed as a dichotomous variable (yes/no) indicating whether an individual committed an offense within 1 year after participating in the intervention.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of days to recidivism within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>For individuals who recidivated within 1 year after participating in the intervention, the number of days until the first offense post-intervention will be computed.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Detention within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>This secondary outcome will be computed as a dichotomous variable (yes/no) indicating whether an individual has been detained within 1 year after participating in the intervention.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of days to detention within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>For individuals who were detained within 1 year after participating in the intervention, the number of days until the first detention post-intervention will be computed</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Adjudication within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>This secondary outcome will be computed as a dichotomous variable (yes/no) indicating whether an individual has been adjudicated within 1 year after participating in the intervention.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of days to adjudication within 1 year post-intervention</measure>
    <time_frame>Within 1 year post-intervention</time_frame>
    <description>For individuals who were adjudicated within 1 year after participating in the intervention, the number of days until the first adjudication post-intervention will be computed.</description>
  </secondary_outcome>
  <other_outcome>
    <measure>Changes in decoding skills as assessed by a SmartPhone App</measure>
    <time_frame>Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.</time_frame>
    <description>A SmartPhone App will be used to assess changes in decoding skills over a 14-week game-play period. Specifically, changes in decoding skills will be measured by tracking the level participants reach in the SmartPhone game. Level is determined by word frequency (a proxy for difficulty).</description>
  </other_outcome>
  <other_outcome>
    <measure>Changes in word reading automaticity as assessed by a SmartPhone App</measure>
    <time_frame>Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.</time_frame>
    <description>A SmartPhone App will be used to assess changes in word reading automaticity over a 14-week game-play period. Specifically, word reading automaticity will be measured by tracking the speed at which participants are able to make decisions regarding spellings and sounds of words presented during the SmartPhone game</description>
  </other_outcome>
  <other_outcome>
    <measure>Changes in breadth of vocabulary as assessed by a SmartPhone App</measure>
    <time_frame>Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.</time_frame>
    <description>A SmartPhone App will be used to assess changes in breadth of vocabulary over a 14-week game-play period. Specifically, vocabulary will be measured by tracking the percent correct participants reach on vocabulary tasks in the SmartPhone game.</description>
  </other_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Anticipated">192</enrollment>
  <condition>Specific Learning Disorder (MeSH Unique ID: D000067559)</condition>
  <condition>Dyslexia (MeSH Unique ID: D004410)</condition>
  <condition>Conduct Disorder (MeSH Unique ID: D019955)</condition>
  <arm_group>
    <arm_group_label>SERIOUS Intervention</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>All youth will be juveniles in a residential facility for post-adjudicated youth. They will be recruited into the study as they are being placed in the facility post-adjudication, to ensure that they meet criteria and will be in the facility long enough to complete the program. All qualified youth will be invited to participate in the SERIOUS intervention; once a group of youth are recruited to complete the multiple baseline design and interventionists are free (from running interventions with other participants) to work with the youth, an intervention cycle will begin.</description>
  </arm_group>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Strategies Enhancing Reading in Older Underperf. Students</intervention_name>
    <description>Strategies for Enhancing Reading in Older Underperf. Students (SERIOUS) combines two scientific, research based intervention programs, each meeting specific need of youth in detention: (1) Stevenson Reading Program, manualized and uniquely suited to students with phonological awareness, attention, and memory difficulties that may impact reading; and (2) Dr. Sharon Vaughn's well-documented, validated program, explicitly designed for older, severely reading-impaired students. SERIOUS also includes: (3) metacognitive strategies for self-regulated learning, and (4) A gamified extension of the intervention on a SmartPhone, designed to reinforce word-level skills (e.g., decoding, automaticity, and vocabulary), and including features efficacious for learning.</description>
    <arm_group_label>SERIOUS Intervention</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Between ages 14 and 16&#xD;
&#xD;
          -  Conversational English ability (sufficient to benefit from the intervention program)&#xD;
             based on the CASL-2 core score&#xD;
&#xD;
          -  Reading below the 3rd grade level on the KTEA-3 pseudoword decoding subtest and the&#xD;
             TOWRE-2 phonemic decoding efficiency subtest&#xD;
&#xD;
          -  IQ at or above 70 on the KABC-II&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Hearing or vision problem that would preclude the use of visual and auditory materials&#xD;
&#xD;
          -  Developmental delay, autism spectrum disorder, or intellectual disability&#xD;
&#xD;
          -  Known genetic syndrome&#xD;
&#xD;
          -  Neurological disorder (e.g., epilepsy)&#xD;
&#xD;
          -  Current psychosis or suicidal ideation&#xD;
&#xD;
          -  Recent (within 6 months) traumatic brain injury&#xD;
      </textblock>
    </criteria>
    <gender>Male</gender>
    <minimum_age>14 Years</minimum_age>
    <maximum_age>16 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Elena L Grigorenko, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>BCM</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Lesley A Hart, PhD</last_name>
    <phone>713-743-8600</phone>
    <email>lahart@central.uh.edu</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Sergey Kornilov, PhD</last_name>
    <email>sergey.kornilov@bcm.edu</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Burnett-Bayland Rehabilitation Center (BBRC)</name>
      <address>
        <city>Houston</city>
        <state>Texas</state>
        <zip>77081</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Jennifer Hunley</last_name>
      <phone>713-222-4421</phone>
      <email>jennifer.hunley@hcjpd.hctx.net</email>
    </contact>
    <contact_backup>
      <last_name>Raymond Livingston</last_name>
      <email>raymond.livingston@hcjpd.hctx.net</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <link>
    <url>http://www.stevensonlearning.com/</url>
    <description>Stevenson Learning Skills</description>
  </link>
  <reference>
    <citation>Grigorenko EL. Learning disabilities in juvenile offenders. Child Adolesc Psychiatr Clin N Am. 2006 Apr;15(2):353-71, viii. doi: 10.1016/j.chc.2005.11.001.</citation>
    <PMID>16527660</PMID>
  </reference>
  <reference>
    <citation>Archwamety, T. &amp; Katsiyannis, A. Academic remediation, parole violations, and recidivism rates among delinquent youths. Remedial and Special Education 21, 161-170 (2000).</citation>
  </reference>
  <reference>
    <citation>Walker, H. M. &amp; Sprague, J. R. The path to school failure, delinquency, and violence: Causal factors and some potential solutions. Intervention in School and Clinic 35, 67-73 (1999).</citation>
  </reference>
  <reference>
    <citation>Edmonds MS, Vaughn S, Wexler J, Reutebuch C, Cable A, Tackett KK, Schnakenberg JW. A Synthesis of Reading Interventions and Effects on Reading Comprehension Outcomes for Older Struggling Readers. Rev Educ Res. 2009 Mar 1;79(1):262-300. doi: 10.3102/0034654308325998.</citation>
    <PMID>20072704</PMID>
  </reference>
  <reference>
    <citation>Wexler, J., Pyle, N., Flower, A., Williams, J. L. &amp; Cole, H. A synthesis of academic interventions for incarcerated adolescents. Review of Educational Research 84, 3-46 (2014).</citation>
  </reference>
  <reference>
    <citation>U.S. Department of Education, O. o. E. T. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. (U.S. Department of Education, Washington, DC, 2012)</citation>
  </reference>
  <reference>
    <citation>Baker, R. S. J. D. &amp; Yacef, K. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining 1, 3-16 (2009).</citation>
  </reference>
  <verification_date>February 2022</verification_date>
  <study_first_submitted>July 21, 2017</study_first_submitted>
  <study_first_submitted_qc>August 22, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 24, 2017</study_first_posted>
  <last_update_submitted>February 25, 2022</last_update_submitted>
  <last_update_submitted_qc>February 25, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">March 2, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Baylor College of Medicine</investigator_affiliation>
    <investigator_full_name>Elena</investigator_full_name>
    <investigator_title>Professor</investigator_title>
  </responsible_party>
  <keyword>Specific Learning Disorder</keyword>
  <keyword>Learning Disorders</keyword>
  <keyword>Neurodevelopmental Disorders</keyword>
  <keyword>Mental Disorders</keyword>
  <keyword>Developmental Reading Disability</keyword>
  <keyword>Reading Disorder</keyword>
  <keyword>Conduct Disorder</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Dyslexia</mesh_term>
    <mesh_term>Learning Disabilities</mesh_term>
    <mesh_term>Conduct Disorder</mesh_term>
    <mesh_term>Specific Learning Disorder</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03261076
org study id: H-40232
nct id: NCT03261076
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The current literature on academic skill difficulties, whether considered as part of the
      continuum of ability or as a specific learning disability (LD), indicates that these problems
      often coexist with conduct problems and juvenile delinquency, and are risk factors for
      initial law-breaking behavior and for its persistence. However, less is understood about how
      this relationship develops. It is these broad questions that this project seeks to address.
      First, what is the causal pathway? Does LD cause delinquency, delinquency cause LD, or are
      both caused by something else? And can big data analytics applied to statewide datasets of
      information about juvenile justice (JJ) involvement help to answer this question? Second, as
      it is known that learning to read and do math (and thus becoming more employable) increases
      the likelihood of desistance (i.e., not committing any more illegal acts), what are the
      necessary parts of an intervention designed to teach these skills? And what role might
      technology play in such an intervention? To answer these questions, we will implement a study
      that includes two components, (a) a big data component and (b) an intervention component. For
      (a), we will work with a large historical dataset from the Harris County Juvenile Probation
      Department. For (b), we will work, in total, with 192 (48 per year) delinquent youth with
      severe LD in residential placement. These individuals, in a nonconcurrent multiple baseline
      design, will be offered an educational therapy designed to address severe reading problems in
      juvenile detainees using a novel mixed media intervention in which the person-to-person
      intensive 1:1 component is completed while youth are in residential settings (24 sessions,
      delivered in 90 minute settings 3 times a week) and a "gamified" educational smartphone
      learning tool follow-up completed upon release (with appropriate network fidelity monitoring
      and participant reinforcement). The person-to-person component is developed specifically for
      juvenile offenders with severe LD, combining two well-established and highly-regarded
      intervention programs designed to systematically build students' repertoire of
      grapheme-phoneme correspondence rules as well as develop comprehensive reading skills, from
      beginning reading to proficiency.
Learning disabilities (LD) are among the most common types of disabilities in juvenile
      offenders that have been linked to delinquency. Nationwide, children and youth with special
      education needs are overrepresented in the US justice systems. Reports estimate delinquent
      juveniles with a disability to comprise about 30% to 60% of the entire delinquent population.
      A national survey in the US states an average prevalence rate of 33.4% of incarcerated
      juveniles with disabilities in correctional facilities. Moreover, concerns have long been
      raised on the recidivism rates of youth with disabilities and special education backgrounds.
      In general, regarding educational performance, academic deficits such as a lack of basic
      skills in reading, writing, and mathematics have been associated with recidivism. However,
      these studies largely neglect the dynamic nature of delinquent and criminal behavior that has
      been documented within the field of developmental criminology with a focus on the onset,
      continuity, and extinction of deviant behavior. In light of this research, the identification
      and remediation of LD as a risk factor for repeat offending has been a persistent challenge
      given the accumulation of and overlap with other risk factors such as poverty, familial
      patterns of criminality, influence of delinquent peers, and the differential impact of risk
      factors across an individual's developmental trajectory. Altogether, there is a challenge
      around the implementation of sophisticated methodology to model the complex longitudinal and
      reciprocal links between juvenile delinquency and educational problems such as LD, and how
      they relate to other risk factors over time. This challenge is intensified by the required
      large samples to detect robust and interpretable patterns and predictive relationships for
      groups of youth with severe LDs that, by definition, are small in size and censored with
      regard to various educational outcomes (e.g., academic performance). This research is
      conceived to contribute to the field's understanding of the connection between LD and
      delinquent behavior. We hope to generate unique findings capitalizing on the availability of
      the relevant big data, the clinical strengths of its members and their capacity to develop
      and administer educational therapy to juvenile offenders, and its embedment within
      communities empowering the creation and processing of multi-level longitudinal datasets,
      merging sociological (i.e., criminological), behavioral, neurophysiological, and
      genetic/genomic data.

      Big Data:

      The Big Data component seeks to address three objectives: (1) to conduct big data analytics
      and data mining to quantify/qualify the complexities of severe LD among juvenile justice
      (JJ)-involved youth using statistical methods applied to big datasets that capture multiple
      indicators (i.e., offending and educational outcomes) cross-sectionally (for one-time
      offenders) and longitudinally (for repeat offenders); (2) to utilize big data analytics to
      identify a group of youth with severe LD who will serve as a comparison group for individuals
      participating in a reading intervention; and (3) to evaluate the findings from the first two
      objectives based on the statistical assumptions underlying the models applicable to the big
      data at hand.

      Hypotheses. Extending previous research, Big Data seeks to identify factors that relate LD to
      recidivism, frequency of re-offending and re-arrest, and the time to post-involvement
      recidivism. We hypothesize that (a) overall recidivism rates (i.e., regardless of the
      specific type of offense) will be higher among JJ-involved youth with severe LD
      (operationalization based on standardized achievement tests and special education status)
      than in youth without LD; (b) youth with severe LD will show more incidents of school-related
      problems after their first involvement (e.g., attendance problems, disciplinary incidents
      such as in-school and out-of-school suspensions and expulsions) than youth without LD; (c)
      youth with severe LD who had school-related problems prior to their first JJ involvement will
      be at higher risk of recidivism than youth without LD; (d) lower levels of reading
      achievement will be associated with shorter periods of time to repeated offending; and (e)
      participation in the intervention will significantly decrease recidivism risk compared to
      other youth with severe LD who did not participate in the intervention.

      Intervention:

      The intervention component seeks to address three objectives: (1) to improve the reading
      skills of an understudied, high-risk population (JJ-involved youth) using an educational
      therapy that has been specifically designed for JJ-involved youth, provided in a 1:1 setting,
      and expanded with technology, including material relevant to daily living skills and
      appropriate reinforcement that will extend the program into the post-release period; (2) to
      reinforce skills and maintain reading gains made during the educational therapy, and increase
      the automaticity of skill use by using a high-interest "gamified" learning tool that youth
      will play on a SmartPhone given to them when they leave detention; and (3) to investigate the
      individual response to therapy with respect to other variables (e.g., attention, executive
      functioning, impulse control, learning ability, other academic achievement) collected at
      baseline.

      Hypotheses. The key hypotheses of the intervention component seek to identify the factors
      associated with response to therapy for severe LD relative to pre-therapy functioning. We
      hypothesize that (a) overall improvement in reading will be related to pre-therapy academic
      skills, attention, and executive functioning; (b) inclusion of a metacognitive component in
      the therapy will provide carry-over effects, improving other academic skills; and (c) the
      gamification of the learning tool will result in robust technology usage, with a
      dose-response relationship between time spent on task and improvement in skills trained (and
      measured) via game play.
allocation: N/A
intervention model: Single Group Assignment
intervention model description: This study uses a multiple baseline design, in which participants enter into the single-arm intervention at different times, yet have data collected at all time points. Approximately one third of participants will have their entry into the intervention shifted later by one week, and approximately another third will have their entry shifted by two weeks. This will happen on a rotating basis. All individuals will receive progress monitoring during the weeks that the other individuals are receiving intervention and/or assessments. The multiple baseline design allows for all individuals to receive the study intervention, and still provides a mechanism for monitoring change (or lack thereof) during non-intervention periods before and after intervention.
primary purpose: Treatment
masking: None (Open Label)
measure: Change in Reading Skills from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: Reading skills will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):
Letter & Word Recognition
Reading Comprehension
This standard score will be obtained based on the sum of subtest standard scores according to published norms.
measure: Change in Decoding Skills from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: Decoding skills will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):
Letter & Word Recognition
Nonsense Word Decoding
This standard score will be obtained based on the sum of subtest standard scores according to published norms.
measure: Change in Reading Understanding from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: Reading understanding will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):
Reading Comprehension
Reading Vocabulary
This standard score will be obtained based on the sum of subtest standard scores according to published norms.
measure: Change in Comprehension from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: Comprehension will be measured as a composite of two subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3):
Reading Comprehension
Listening Comprehension
This standard score will be obtained based on the sum of subtest standard scores according to published norms.
measure: Change in Reading Fluency from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: The Test of Reading Efficiency, Second Edition (TOWRE-2) will be used to measure an individual's ability to pronounce printed words and phonemically regular nonwords accurately and fluently. Standard scores will be obtained according to published norms.
measure: Change in Other Academic Skills from pre-intervention (baseline) to the week after intervention
time frame: Assessment of change will take place at pre-intervention (baseline), the week before intervention, after four weeks of intervention, and the week after intervention.
description: Other academic skills will be measured as a composite of remaining subtests from the Kaufman Test of Educational Achievement, Third Edition (KTEA-3) and Woodcock-Johnson-IV Tests of Academic Achievement (WJ-IV):
KTEA-3 Math Concepts & Application
KTEA-3 Math Fluency
KTEA-3 Rapid Automatized Naming Letters
KTEA-3 Spelling
WJ-IV Science
WJ-IV Social Studies
WJ-IV Humanities
This standard score will be obtained based on the z-score of the sum of subtest standard scores at pre-intervention.
measure: Recidivism within 1 year post-intervention
time frame: Within 1 year post-intervention
description: Recidivism will be computed as a dichotomous variable (yes/no) indicating whether an individual committed an offense within 1 year after participating in the intervention.
measure: Number of days to recidivism within 1 year post-intervention
time frame: Within 1 year post-intervention
description: For individuals who recidivated within 1 year after participating in the intervention, the number of days until the first offense post-intervention will be computed.
measure: Detention within 1 year post-intervention
time frame: Within 1 year post-intervention
description: This secondary outcome will be computed as a dichotomous variable (yes/no) indicating whether an individual has been detained within 1 year after participating in the intervention.
measure: Number of days to detention within 1 year post-intervention
time frame: Within 1 year post-intervention
description: For individuals who were detained within 1 year after participating in the intervention, the number of days until the first detention post-intervention will be computed
measure: Adjudication within 1 year post-intervention
time frame: Within 1 year post-intervention
description: This secondary outcome will be computed as a dichotomous variable (yes/no) indicating whether an individual has been adjudicated within 1 year after participating in the intervention.
measure: Number of days to adjudication within 1 year post-intervention
time frame: Within 1 year post-intervention
description: For individuals who were adjudicated within 1 year after participating in the intervention, the number of days until the first adjudication post-intervention will be computed.
measure: Changes in decoding skills as assessed by a SmartPhone App
time frame: Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.
description: A SmartPhone App will be used to assess changes in decoding skills over a 14-week game-play period. Specifically, changes in decoding skills will be measured by tracking the level participants reach in the SmartPhone game. Level is determined by word frequency (a proxy for difficulty).
measure: Changes in word reading automaticity as assessed by a SmartPhone App
time frame: Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.
description: A SmartPhone App will be used to assess changes in word reading automaticity over a 14-week game-play period. Specifically, word reading automaticity will be measured by tracking the speed at which participants are able to make decisions regarding spellings and sounds of words presented during the SmartPhone game
measure: Changes in breadth of vocabulary as assessed by a SmartPhone App
time frame: Participants will use the SmartPhone App over a 14-week game-play period. Data will be collected during game play.
description: A SmartPhone App will be used to assess changes in breadth of vocabulary over a 14-week game-play period. Specifically, vocabulary will be measured by tracking the percent correct participants reach on vocabulary tasks in the SmartPhone game.
arm group label: SERIOUS Intervention
arm group type: Experimental
description: All youth will be juveniles in a residential facility for post-adjudicated youth. They will be recruited into the study as they are being placed in the facility post-adjudication, to ensure that they meet criteria and will be in the facility long enough to complete the program. All qualified youth will be invited to participate in the SERIOUS intervention; once a group of youth are recruited to complete the multiple baseline design and interventionists are free (from running interventions with other participants) to work with the youth, an intervention cycle will begin.
intervention type: Behavioral
intervention name: Strategies Enhancing Reading in Older Underperf. Students
description: Strategies for Enhancing Reading in Older Underperf. Students (SERIOUS) combines two scientific, research based intervention programs, each meeting specific need of youth in detention: (1) Stevenson Reading Program, manualized and uniquely suited to students with phonological awareness, attention, and memory difficulties that may impact reading; and (2) Dr. Sharon Vaughn's well-documented, validated program, explicitly designed for older, severely reading-impaired students. SERIOUS also includes: (3) metacognitive strategies for self-regulated learning, and (4) A gamified extension of the intervention on a SmartPhone, designed to reinforce word-level skills (e.g., decoding, automaticity, and vocabulary), and including features efficacious for learning.
arm group label: SERIOUS Intervention
criteria: 
gender: Male
minimum age: 14 Years
maximum age: 16 Years
healthy volunteers: No
last name: Elena L Grigorenko, PhD
role: Principal Investigator
affiliation: BCM
last name: Lesley A Hart, PhD
phone: 713-743-8600
email: lahart@central.uh.edu
last name: Sergey Kornilov, PhD
email: sergey.kornilov@bcm.edu
facility: 
status: Recruiting
contact: 
contact backup: 
country: United States
url: http://www.stevensonlearning.com/
description: Stevenson Learning Skills
citation: Grigorenko EL. Learning disabilities in juvenile offenders. Child Adolesc Psychiatr Clin N Am. 2006 Apr;15(2):353-71, viii. doi: 10.1016/j.chc.2005.11.001.
PMID: 16527660
citation: Archwamety, T. & Katsiyannis, A. Academic remediation, parole violations, and recidivism rates among delinquent youths. Remedial and Special Education 21, 161-170 (2000).
citation: Walker, H. M. & Sprague, J. R. The path to school failure, delinquency, and violence: Causal factors and some potential solutions. Intervention in School and Clinic 35, 67-73 (1999).
citation: Edmonds MS, Vaughn S, Wexler J, Reutebuch C, Cable A, Tackett KK, Schnakenberg JW. A Synthesis of Reading Interventions and Effects on Reading Comprehension Outcomes for Older Struggling Readers. Rev Educ Res. 2009 Mar 1;79(1):262-300. doi: 10.3102/0034654308325998.
PMID: 20072704
citation: Wexler, J., Pyle, N., Flower, A., Williams, J. L. & Cole, H. A synthesis of academic interventions for incarcerated adolescents. Review of Educational Research 84, 3-46 (2014).
citation: U.S. Department of Education, O. o. E. T. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. (U.S. Department of Education, Washington, DC, 2012)
citation: Baker, R. S. J. D. & Yacef, K. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining 1, 3-16 (2009).
responsible party type: Principal Investigator
investigator affiliation: Baylor College of Medicine
investigator full name: Elena
investigator title: Professor
mesh term: Dyslexia
mesh term: Learning Disabilities
mesh term: Conduct Disorder
mesh term: Specific Learning Disorder
sharing ipd: No