Autism Spectrum DisorderPhase 1/2TREAAT2+NCT06204536
Treatment Planning for ABA Employing Auxiliary Tools V2+ (TREAAT2+)
A Phase 1/2 study testing Tech-enabled ABA treatment planning in people with Autism Spectrum Disorder, run by Montera Health Texas LLC.
Trial summary
Technology enhancement in Applied Behavior Analysis (ABA) treatment planning may increase confidence, efficiency, consistency, and satisfaction for Board Certified Behavior Analysts (BCBAs) which, in turn, can provide for better clinical outcomes for patients on the autism spectrum. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Autism Spectrum Disorder
- What is being tested?
- Tech-enabled ABA treatment planning
- Type of treatment
- Pill or oral medicine
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 20 participants
- Where
- No sites listed yet
- Study dates
- 1 Jul 2025to 30 Jun 2027
- Phase
- Phase 1/2
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- BCBAs will be eligible for enrollment if they are actively employed by Montera and are actively providing ABA treatment to Montera patients.
Who cannot join
- BCBAs will be excluded from the study for one or more of the following reasons:
- BCBA requests that their data is not used in the study;
- BCBA does not complete the required assessments;
- BCBA does not have the aforementioned data required for inclusion.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: The Efficacy of Technology-Based Tools in Applied Behavior Analysis Treatment Planning for Autism Spectrum Disorder
Technology enhancement in Applied Behavior Analysis (ABA) treatment planning may increase confidence, efficiency, consistency, and satisfaction for Board Certified Behavior Analysts (BCBAs) which, in turn, can provide for better clinical outcomes for patients on the autism spectrum. To this end, the investigators will examine the use of >3 technology-based tools that will be implemented in the BCBAs' clinical workflow to aid with treatment planning. The study will initially involve two aims that are non-interventional (these processes will occur in the background and will have no impact on any cohorts), followed by an interventional aim that includes two arms (i.e., two BCBA cohorts). BCBAs within both arms will observe and practice the standard of care for ABA, and thus patient care will not be impacted. The outcome measures are primarily focused on the BCBAs as follows: Arm 1: An experimental group (BCBA Tech cohort) will receive the full tech package (TREAAT2+) from the start. Arm 2: The control group (BCBA non-Tech cohort) will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package.
Read the detailed description
Autism spectrum disorder (ASD) is a complex, heterogeneous neurodevelopmental disorder that accounts for >$250 billion in direct and indirect annual expenditures and is estimated to occur in 1 out of 36 children <8 years of age in the US. Validated ASD treatments, such as Applied Behavior Analysis (ABA), rely upon Board Certified Behavior Analysts (BCBAs) to develop highly individualized treatment plans via a complex and labor-intensive process. Integrating technology-based approaches for treatment planning (largely unexplored in the context of ABA), such as data-driven clinical decision support (CDS) systems and assistive technology, can modernize and optimize the BCBA workflow, which, in turn, can enhance patient care. There has been a high demand for BCBAs in recent years, which has led to shortages straining care providers, with about 72% of BCBAs experiencing significant burnout, increasing BCBA turnover. One contributing factor to the BCBA burnout rate is the workload, which can fuel exhaustion and disengagement.The BCBA workflow includes significant time spent on developing effective individualized treatment plans via a tedious, non-automated, and heterogeneous process lacking standardized tools. Thus, there is a need to automate existing workflows to increase BCBA efficiency, confidence, consistency, and satisfaction, in order to mitigate burnout and consequently improve the patient management process, and by extension patient clinical outcomes.
In this SBIR project, the investigators propose to integrate a data-driven technological package (TREAAT2+) into the BCBA's workflow to assist with streamlined and consistent ABA treatment planning. TREAAT2+ consists of (1) a machine learning algorithm (MLA)-based CDS tool that analyzes data from electronic health records (EHRs) and recommends treatment dosage in terms of hours, where the MLA is integrated into a proprietary application ("app"); (2) a treatment planning software tool integrated with the proprietary app to facilitate highly accessible treatment oversight; and (3) individual patient progress reports pushed onto the proprietary app from Autism Analytica (AA). The predecessor of the app-integrated MLA, TREAAT, was validated and achieved excellent performance (AUROC of 0.895) for a binary treatment dosage recommendation (<20 or >20 hours/week). The investigators will enhance the capacity of the MLA for more granular treatment dosage recommendations, deploy a treatment planning software tool in the app for BCBA use, and provide pushed AA patient data assessments in the app. This will improve BCBA efficiency and confidence within their workflow, and thereby significantly reduce the burden related to the manual and subjective nature of the treatment planning process. TREAAT was validated with proprietary data from patients of Montera Health TX LLC ("Montera"), and the investigators will use a larger number of patients to fine-tune and validate the app-integrated MLA to improve generalizability. The investigators expect that the MLA will perform as well as or better than the original TREAAT in this expanded patient population and that the MLA, in conjunction with the treatment planning software tool and the pushed AA data, will significantly improve the BCBA workflow. The lack of current workflow automation coexists with significant BCBA burnout rates, and TREAAT2+ provides the solution of modernizing time-consuming tasks within treatment planning. By bridging the technological gap in the BCBA workflow, TREAAT2+ will mitigate BCBA burnout, and by extension improve patient care.
Study Aim 1: Retraining and upgrading the MLA of TREAAT2+. The investigators will use a larger set of retrospective and prospective Montera patient data than was employed for the original TREAAT training, and will design the MLA output as increments of treatment dosage recommendation (in 10 hr increments). The investigators will additionally integrate the MLA into our proprietary app. The investigators expect MLA performance metrics to be comparable to or better than retrospective benchmarks from the pilot study (AUROC: 0.895; 95% CI: 0.811 - 0.962).
Study Aim 2: Test the robustness of the MLA of TREAAT2+ in treatment plan development. The investigators will conduct a non-interventional prospective evaluation of the app-integrated MLA. The agreement between the incremental treatment dosage suggested by the MLA and the dosage prescribed by the BCBA will be assessed. MLA performance will be evaluated on demographic subpopulations to ensure bias minimization. The investigators expect that there will be substantial agreement between the treatment dosage prescribed by the BCBA and the dosage suggested by MLA, as measured by minimum inter-rater reliability (e.g., Cohen's Kappa) of greater than 0.6, which indicates substantial agreement according to the Landis and Koch's classification system.
Study Aim 3: Evaluate the impact of deploying TREAAT2+ within the BCBA workflow. The investigators will utilize prospective data from two BCBA cohorts. An experimental group (BCBA Tech cohort) will receive the full tech package (TREAAT2+) from the start. The control group (BCBA non-Tech cohort) will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package. The investigators expect to demonstrate the efficacy of TREAAT2+ with statistically significant improvement (p < 0.05) over baseline in qualitative endpoints (efficiency, confidence, consistency, and satisfaction; as measured by BCBA self-reported Likert questionnaire scales) and quantitative endpoints (time allocated to treatment plan development).
- Conditions
- Autism Spectrum Disorder
- Being tested
- Tech-enabled ABA treatment planning
- Keywords
- Autism Spectrum Disorder, Autistic Disorder, Applied Behavior Analysis, Decision Support Systems, Clinical
- Medical subject headings
- Autism Spectrum Disorder, Autistic Disorder
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Tech-enabled ABA treatment planningOther
The tech-enabled ABA treatment planning will involve the use of a proprietary MLA integrated within a proprietary software application for use on a tablet, phone, or other smart device by BCBAs (one proprietary software application, one non-proprietary software application). The proprietary application provides functionality for treatment plan development, including templates and centralized resource availability. This tech will be used as adjunct or auxiliary tools to develop and manage ABA treatment plans. The proposed period of time for this intervention (the latter portion of the study subsequent to the non-interventional phases) will be 24 months.
| Study group | Role | What they receive |
|---|---|---|
| technology integrated care planning cohort | Experimental | This cohort will consist of 15 BCBAs that will receive a minimum of 3 technology-based tools (2 proprietary tools and at least 1 non-proprietary tool) for use in conjunction with the standard of care to develop and manage ABA treatment plans for active patients. Receives: Other: Tech-enabled ABA treatment planning |
| non-technology integrated care cohort | No intervention | This cohort will consist of 5 BCBAs that will follow the standard of care that does not involve the use of the tech-based tools to develop and manage ABA treatment plans for active patients receiving ABA. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Retraining and upgrading the MLA (non-interventional)
Performance of MLA as measured by AUROC, sensitivity, and specificity
Measured: 6 months
Prospective evaluation of the app-integrated MLA (non-interventional)
Attain substantial agreement between the treatment dosage prescribed by the BCBA and the dosage suggested by MLA, as measured by minimum inter-rater reliability (e.g., Cohen's Kappa) of greater than 0.6, which indicates substantial agreement according to the Landis and Koch's classification system.
Measured: 6 months
Evaluate the impact of deploying the tech package within the BCBA workflow
Endpoint: Efficiency (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in efficiency at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Measured: 24 months
Evaluate the impact of deploying the tech package within the BCBA workflow
Endpoint: Efficiency (Quantitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in efficiency at 6 month intervals.The time allocated to treatment plan development by individual BCBAs for individual patients will be measured. The minimum and maximum values are represented by number of hours, with fewer hours (minimum value: 4 hours) associated with better outcomes (i.e., increased efficiency) and a higher number of hours (maximum value: 40 hours) associated with worse outcomes (i.e., no change or decreased efficiency).
Measured: 24 months
Evaluate the impact of deploying the tech package within the BCBA workflow
Endpoint: Confidence (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in confidence at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Measured: 24 months
Evaluate the impact of deploying the tech package within the BCBA workflow
Endpoint: Consistency (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in consistency at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Measured: 24 months
Evaluate the impact of deploying the tech package within the BCBA workflow
Endpoint: Satisfaction. Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in satisfaction at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jan 2024
- Study start1 Jul 2025 (estimated)
- Primary completion30 Jun 2027 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Autism Spectrum DisorderStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase 1/2
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Subsequent to conducting 2 non-interventional aims, the investigators will employ technology-based tools interventionally for the BCBAs for developing and managing an ABA treatment plan. The investigators divide the BCBAs into two interventional cohorts: one BCBA Tech cohort and one BCBA non-Tech cohort. The BCBA Tech cohort will have access to the full tech package from the start of this interventional aim; the BCBA non-Tech cohort will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package.
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 20 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Montera Health Texas LLC
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06204536
- Sponsor's protocol ID
- Mont_2023
- Acronym
- TREAAT2+
- Status last verified
- Nov 2024
- First submitted
- 12 Dec 2023
- Last updated
- 22 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Maharjan J, Garikipati A, Dinenno FA, Ciobanu M, Barnes G, Browning E, DeCurzio J, Mao Q, Das R. Machine learning determination of applied behavioral analysis treatment plan type. Brain Inform. 2023 Mar 2;10(1):7. doi: 10.1186/s40708-023-00186-8.
PubMed 36862316Garikipati A, Ciobanu M, Singh NP, Barnes G, Decurzio J, Mao Q, Das R. Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment. Cureus. 2023 Mar 27;15(3):e36727. doi: 10.7759/cureus.36727. eCollection 2023 Mar.
PubMed 36998917
Study at a glance
From the registry record, last updated by the sponsor on 22 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Efficacy of Technology-Based Tools in Applied Behavior Analysis Treatment Planning for Autism Spectrum Disorder
- ClinicalTrials.gov ID
- NCT06204536
- Acronym / sponsor ID
- TREAAT2+ / Mont_2023
- Conditions
- Autism Spectrum Disorder
- Keywords
- Autism Spectrum Disorder, Autistic Disorder, Applied Behavior Analysis, Decision Support Systems, Clinical
- Phase
- Phase 1/2
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 20 participants (estimated)
- Lead sponsor
- Montera Health Texas LLCIndustry (pharma, biotech, devices)
- Study start
- 1 Jul 2025 (estimated)
- Primary completion
- 30 Jun 2027 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 12 Jan 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Technology enhancement in Applied Behavior Analysis (ABA) treatment planning may increase confidence, efficiency, consistency, and satisfaction for Board Certified Behavior Analysts (BCBAs) which, in turn, can provide for better clinical outcomes for patients on the autism spectrum. To this end, the investigators will examine the use of >3 technology-based tools that will be implemented in the BCBAs' clinical workflow to aid with treatment planning. The study will initially involve two aims that are non-interventional (these processes will occur in the background and will have no impact on any cohorts), followed by an interventional aim that includes two arms (i.e., two BCBA cohorts). BCBAs within both arms will observe and practice the standard of care for ABA, and thus patient care will not be impacted. The outcome measures are primarily focused on the BCBAs as follows: Arm 1: An experimental group (BCBA Tech cohort) will receive the full tech package (TREAAT2+) from the start. Arm 2: The control group (BCBA non-Tech cohort) will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package.
Detailed description
Autism spectrum disorder (ASD) is a complex, heterogeneous neurodevelopmental disorder that accounts for >$250 billion in direct and indirect annual expenditures and is estimated to occur in 1 out of 36 children <8 years of age in the US. Validated ASD treatments, such as Applied Behavior Analysis (ABA), rely upon Board Certified Behavior Analysts (BCBAs) to develop highly individualized treatment plans via a complex and labor-intensive process. Integrating technology-based approaches for treatment planning (largely unexplored in the context of ABA), such as data-driven clinical decision support (CDS) systems and assistive technology, can modernize and optimize the BCBA workflow, which, in turn, can enhance patient care. There has been a high demand for BCBAs in recent years, which has led to shortages straining care providers, with about 72% of BCBAs experiencing significant burnout, increasing BCBA turnover. One contributing factor to the BCBA burnout rate is the workload, which can fuel exhaustion and disengagement.The BCBA workflow includes significant time spent on developing effective individualized treatment plans via a tedious, non-automated, and heterogeneous process lacking standardized tools. Thus, there is a need to automate existing workflows to increase BCBA efficiency, confidence, consistency, and satisfaction, in order to mitigate burnout and consequently improve the patient management process, and by extension patient clinical outcomes.
In this SBIR project, the investigators propose to integrate a data-driven technological package (TREAAT2+) into the BCBA's workflow to assist with streamlined and consistent ABA treatment planning. TREAAT2+ consists of (1) a machine learning algorithm (MLA)-based CDS tool that analyzes data from electronic health records (EHRs) and recommends treatment dosage in terms of hours, where the MLA is integrated into a proprietary application ("app"); (2) a treatment planning software tool integrated with the proprietary app to facilitate highly accessible treatment oversight; and (3) individual patient progress reports pushed onto the proprietary app from Autism Analytica (AA). The predecessor of the app-integrated MLA, TREAAT, was validated and achieved excellent performance (AUROC of 0.895) for a binary treatment dosage recommendation (<20 or >20 hours/week). The investigators will enhance the capacity of the MLA for more granular treatment dosage recommendations, deploy a treatment planning software tool in the app for BCBA use, and provide pushed AA patient data assessments in the app. This will improve BCBA efficiency and confidence within their workflow, and thereby significantly reduce the burden related to the manual and subjective nature of the treatment planning process. TREAAT was validated with proprietary data from patients of Montera Health TX LLC ("Montera"), and the investigators will use a larger number of patients to fine-tune and validate the app-integrated MLA to improve generalizability. The investigators expect that the MLA will perform as well as or better than the original TREAAT in this expanded patient population and that the MLA, in conjunction with the treatment planning software tool and the pushed AA data, will significantly improve the BCBA workflow. The lack of current workflow automation coexists with significant BCBA burnout rates, and TREAAT2+ provides the solution of modernizing time-consuming tasks within treatment planning. By bridging the technological gap in the BCBA workflow, TREAAT2+ will mitigate BCBA burnout, and by extension improve patient care.
Study Aim 1: Retraining and upgrading the MLA of TREAAT2+. The investigators will use a larger set of retrospective and prospective Montera patient data than was employed for the original TREAAT training, and will design the MLA output as increments of treatment dosage recommendation (in 10 hr increments). The investigators will additionally integrate the MLA into our proprietary app. The investigators expect MLA performance metrics to be comparable to or better than retrospective benchmarks from the pilot study (AUROC: 0.895; 95% CI: 0.811 - 0.962).
Study Aim 2: Test the robustness of the MLA of TREAAT2+ in treatment plan development. The investigators will conduct a non-interventional prospective evaluation of the app-integrated MLA. The agreement between the incremental treatment dosage suggested by the MLA and the dosage prescribed by the BCBA will be assessed. MLA performance will be evaluated on demographic subpopulations to ensure bias minimization. The investigators expect that there will be substantial agreement between the treatment dosage prescribed by the BCBA and the dosage suggested by MLA, as measured by minimum inter-rater reliability (e.g., Cohen's Kappa) of greater than 0.6, which indicates substantial agreement according to the Landis and Koch's classification system.
Study Aim 3: Evaluate the impact of deploying TREAAT2+ within the BCBA workflow. The investigators will utilize prospective data from two BCBA cohorts. An experimental group (BCBA Tech cohort) will receive the full tech package (TREAAT2+) from the start. The control group (BCBA non-Tech cohort) will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package. The investigators expect to demonstrate the efficacy of TREAAT2+ with statistically significant improvement (p < 0.05) over baseline in qualitative endpoints (efficiency, confidence, consistency, and satisfaction; as measured by BCBA self-reported Likert questionnaire scales) and quantitative endpoints (time allocated to treatment plan development).
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
- Model description
- Subsequent to conducting 2 non-interventional aims, the investigators will employ technology-based tools interventionally for the BCBAs for developing and managing an ABA treatment plan. The investigators divide the BCBAs into two interventional cohorts: one BCBA Tech cohort and one BCBA non-Tech cohort. The BCBA Tech cohort will have access to the full tech package from the start of this interventional aim; the BCBA non-Tech cohort will not have access to any tools from the tech package for the first 6 months. In the subsequent 18 months, they will receive one tool every 6 months until gaining access to the entire tech package.
| Arm | Type | Receives |
|---|---|---|
| technology integrated care planning cohort | Experimental | Other: Tech-enabled ABA treatment planningThis cohort will consist of 15 BCBAs that will receive a minimum of 3 technology-based tools (2 proprietary tools and at least 1 non-proprietary tool) for use in conjunction with the standard of care to develop and manage ABA treatment plans for active patients. |
| non-technology integrated care cohort | No intervention | –This cohort will consist of 5 BCBAs that will follow the standard of care that does not involve the use of the tech-based tools to develop and manage ABA treatment plans for active patients receiving ABA. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- BCBAs will be eligible for enrollment if they are actively employed by Montera and are actively providing ABA treatment to Montera patients.
Key exclusion criteria (4 listed)
- BCBAs will be excluded from the study for one or more of the following reasons:
- BCBA requests that their data is not used in the study;
- BCBA does not complete the required assessments;
- BCBA does not have the aforementioned data required for inclusion.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Tech-enabled ABA treatment planningThe tech-enabled ABA treatment planning will involve the use of a proprietary MLA integrated within a proprietary software application for use on a tablet, phone, or other smart device by BCBAs (one proprietary software application, one non-proprietary software application). The proprietary application provides functionality for treatment plan development, including templates and centralized resource availability. This tech will be used as adjunct or auxiliary tools to develop and manage ABA treatment plans. The proposed period of time for this intervention (the latter portion of the study subsequent to the non-interventional phases) will be 24 months. | Other | technology integrated care planning cohort |
Primary outcome
Retraining and upgrading the MLA (non-interventional)
Time frame: 6 months
Performance of MLA as measured by AUROC, sensitivity, and specificity
Prospective evaluation of the app-integrated MLA (non-interventional)
Time frame: 6 months
Attain substantial agreement between the treatment dosage prescribed by the BCBA and the dosage suggested by MLA, as measured by minimum inter-rater reliability (e.g., Cohen's Kappa) of greater than 0.6, which indicates substantial agreement according to the Landis and Koch's classification system.
Evaluate the impact of deploying the tech package within the BCBA workflow
Time frame: 24 months
Endpoint: Efficiency (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in efficiency at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Evaluate the impact of deploying the tech package within the BCBA workflow
Time frame: 24 months
Endpoint: Efficiency (Quantitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in efficiency at 6 month intervals.The time allocated to treatment plan development by individual BCBAs for individual patients will be measured. The minimum and maximum values are represented by number of hours, with fewer hours (minimum value: 4 hours) associated with better outcomes (i.e., increased efficiency) and a higher number of hours (maximum value: 40 hours) associated with worse outcomes (i.e., no change or decreased efficiency).
Evaluate the impact of deploying the tech package within the BCBA workflow
Time frame: 24 months
Endpoint: Confidence (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in confidence at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Evaluate the impact of deploying the tech package within the BCBA workflow
Time frame: 24 months
Endpoint: Consistency (Qualitative). Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in consistency at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Evaluate the impact of deploying the tech package within the BCBA workflow
Time frame: 24 months
Endpoint: Satisfaction. Demonstrate the efficacy of the tech package with statistically significant improvement (p < 0.05) in satisfaction at 6 month intervals. Likert questionnaires will be used to analyze answers to questions individually and the sum of the entire assessment measure will be the endpoint. The Likert Scale will be applied as (1) Strongly Disagree, (2) Disagree, (3) Neither Agree Nor Disagree (4) Agree, and (5) Strongly Agree. The minimum value of 1 indicates worse outcomes and the maximum value of 5 indicates best outcomes.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Nov 2024.
- Background: PubMed 36862316Maharjan J, Garikipati A, Dinenno FA, Ciobanu M, Barnes G, Browning E, DeCurzio J, Mao Q, Das R. Machine learning determination of applied behavioral analysis treatment plan type. Brain Inform. 2023 Mar 2;10(1):7. doi: 10.1186/s40708-023-00186-8.
- Background: PubMed 36998917Garikipati A, Ciobanu M, Singh NP, Barnes G, Decurzio J, Mao Q, Das R. Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment. Cureus. 2023 Mar 27;15(3):e36727. doi: 10.7759/cureus.36727. eCollection 2023 Mar.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Questions about this trial
What is this clinical trial studying?
A Phase 1/2 study testing Tech-enabled ABA treatment planning in people with Autism Spectrum Disorder, run by Montera Health Texas LLC.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
What does this combined phase mean?
Phase 1 studies test a treatment in a small group of people for the first time or in a new way, mainly to check safety and find the right dose.
Phase 2 studies give the treatment to a larger group to see whether it has the intended effect and to keep checking safety.
Not every study follows the same path, and some studies (observational studies, device studies) have no phase.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Qingqing Mao, PhD (4158051725, qmao@fortahealth.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Retraining and upgrading the MLA (non-interventional) (time frame: 6 months). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06204536, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Nov 2024.