AutismNCT05874466
SenseToKnow Autism Screening Device Validation Study
An observational study of Autism and Autism Spectrum Disorder, run by Duke University at 1 site in United States.
Trial summary
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the detection of autism spectrum disorder in children 16-36 months of age. Read more about this study
- Who is it for?
- Ages 16 to 36 monthsHealthy volunteers can take part
- Conditions the study is for
- Autism, Autism Spectrum Disorder
- How many people
- 350 participants
- Where
- 1 siteUnited States
- Study dates
- 7 Jul 2023to Dec 2027
- Phase
- Observational
- 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 16 to 36 months
- All sexes
- Healthy volunteers accepted
- Child
Study population: Participants will be patients 16-36 months of age recruited from > 6 sites comprised of pediatric medical clinics that are part of the broader Duke University Health System in North Carolina.
Who may be able to join
- Duke Health pediatric patient at enrollment
- 16-<37 months of age at enrollment
- Parent/legal guardian speaks English or Spanish
- Parent/legal guardian understands and voluntarily provides informed consent
Who cannot join
- Severe motor impairment that precludes study measure completion
- Known genetic disorders
- Severe hearing or visual impairment as determined on physical examination according to parent report
- Acute illnesses likely to prevent successful or valid data collection
- Uncontrolled epilepsy or seizure disorder
- History or presence of a clinically significant medical disease, or a mental state that could confound the study or be detrimental to the subject as determined by the investigator
Show all 12Show fewer
- Acute exacerbations of chronic illnesses likely to prevent successful or valid data collection
- Receiving therapies that affect vision
- Parent/legal guardian and/or investigator believes that the child will be unable/unwilling to sit in the parent's lap to watch the app videos
- Parent/legal guardian indicates that they or their child is unwilling or unable to complete the app administration, surveys, or diagnostic assessment
- Participants who are otherwise judged as unable to comply with the protocol by the investigator
- Any other factor that the investigator feels would make the study measures invalid
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Duke University
Durham, North Carolina, 27705
Geraldine Dawson, PhD, 919-668-0070
About this study
Official title: SenseToKnow STAR Study: A Study of Technologies for Assessing Children's Development
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the detection of autism spectrum disorder in children 16-36 months of age.
Read the detailed description
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the classification of autism spectrum disorder when administered by parents in a sample of patients 16-36 months of age. The trial design is a non-interventional cross-sectional study comparing the SenseToKnow device classification of autism spectrum disorder ("autism") versus non-autism with the patient's diagnostic status based on expert clinical diagnosis in a population of pediatric patients.
- Conditions
- Autism, Autism Spectrum Disorder
- Keywords
- Diagnosis, Digital device
- Medical subject headings
- Autistic Disorder, Autism Spectrum Disorder, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Pediatric patients, 16-36 months of age, recruited through pediatric medical clinics | Consecutive pediatric participants will be recruited and enrolled via >= 6 participating sites comprised of pediatric medical clinics (e.g., primary care and family medicine clinics) that are part of the broader Duke University Health System (DUHS) located in North Carolina. Enrollment will proceed until the targets of N = 150 participants diagnosed with autism spectrum disorder and N = 200 without autism are reached. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection
Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data and (2) expert clinical diagnosis / #participants positive for autism on both SenseToKnow and expert clinical diagnosis
Measured: Will be calculated based on data from Baseline/Timepoint 1
Specificity of the SenseToKnow screening device based on machine earning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection
Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data, and (2) expert clinical diagnosis / #participants negative for autism on autism by expert clinical diagnosis
Measured: Will be calculated based on data from Baseline/Timepoint 1
Secondary outcomes (8)
Positive Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis
The likelihood that a participant with a positive test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Negative Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis
The likelihood that a participant with a negative test result (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow screening device (using the SenseToKnow digital data and SenseToKnow Caregiver survey data) for autism versus non-autism classification
Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data, in comparison to expert clinical diagnosis.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Sensitivity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection
Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / # participants positive for autism on expert clinical diagnosis
Measured: Will be calculated based on data from Baseline/Timepoint 1
Specificity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection
Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data and (2) expert clinical diagnosis / #participants negative for autism on expert clinical diagnosis.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Positive Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis
The likelihood that a participant with a positive test result has a diagnosis of autism (based on expert clinical diagnosis). Positive Predictive Value will be calculated with and without adjustment for population prevalence.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Negative Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis
The likelihood that a participant with a negative test result does not have a diagnosis of autism (based on expert clinical diagnosis). Negative Predictive Value will be calculated with and without adjustment for population prevalence.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow device using only the SenseToKnow digital data for autism versus non-autism classification
Receiver Operating Characteristic Curve (ROC) is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Area Under the Curve (AUC) measures the area underneath the entire ROC curve. Accuracy of test is based on a machine learning algorithm using only the SenseToKnow digital data, in comparison to expert clinical diagnosis.
Measured: Will be calculated based on data from Baseline/Timepoint 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 May 2023
- Study start7 Jul 2023
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AutismStudies of the same condition, open and under way.
- More trials run by Duke UniversityThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 350 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Duke University
- Sponsor type
- University, hospital or other
- Collaborators
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- Responsible party
- Sponsor
- Investigators
- Geraldine Dawson, PhD, Principal investigator, Duke University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05874466
- Sponsor's protocol ID
- Pro00111371
- Other IDs
- 2P50HD093074
- Status last verified
- Feb 2026
- First submitted
- 12 May 2023
- Last updated
- 6 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All individual-level data that meets PHI and IRB confidentiality requirements will be submitted to the NIH/NIMH Data Repository by the end of the grant period.
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- SenseToKnow STAR Study: A Study of Technologies for Assessing Children's Development
- ClinicalTrials.gov ID
- NCT05874466
- Acronym / sponsor ID
- Pro00111371
- Conditions
- Autism, Autism Spectrum Disorder
- Keywords
- Diagnosis, Digital device
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 350 participants (estimated)
- Lead sponsor
- Duke UniversityUniversity, hospital or other
- Collaborators
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
- Study start
- 7 Jul 2023 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 25 May 2023 (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.
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the detection of autism spectrum disorder in children 16-36 months of age.
Detailed description
This is a pivotal, prospective, double-blind, study to evaluate the sensitivity and specificity of the SenseToKnow device for the classification of autism spectrum disorder when administered by parents in a sample of patients 16-36 months of age. The trial design is a non-interventional cross-sectional study comparing the SenseToKnow device classification of autism spectrum disorder ("autism") versus non-autism with the patient's diagnostic status based on expert clinical diagnosis in a population of pediatric patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric patients, 16-36 months of age, recruited through pediatric medical clinics | – | –Consecutive pediatric participants will be recruited and enrolled via >= 6 participating sites comprised of pediatric medical clinics (e.g., primary care and family medicine clinics) that are part of the broader Duke University Health System (DUHS) located in North Carolina. Enrollment will proceed until the targets of N = 150 participants diagnosed with autism spectrum disorder and N = 200 without autism are reached. |
Who may qualify
- Ages
- 16 to 36 months
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- Participants will be patients 16-36 months of age recruited from > 6 sites comprised of pediatric medical clinics that are part of the broader Duke University Health System in North Carolina.
Key inclusion criteria (4 listed)
- Duke Health pediatric patient at enrollment
- 16-<37 months of age at enrollment
- Parent/legal guardian speaks English or Spanish
- Parent/legal guardian understands and voluntarily provides informed consent
Key exclusion criteria (12 listed)
- Severe motor impairment that precludes study measure completion
- Known genetic disorders
- Severe hearing or visual impairment as determined on physical examination according to parent report
- Acute illnesses likely to prevent successful or valid data collection
- Uncontrolled epilepsy or seizure disorder
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Sensitivity of the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Sensitivity = #participants positive for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data and (2) expert clinical diagnosis / #participants positive for autism on both SenseToKnow and expert clinical diagnosis
Specificity of the SenseToKnow screening device based on machine earning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Specificity = #participants negative for autism on both (1) the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with the SenseToKnow Caregiver Survey data, and (2) expert clinical diagnosis / #participants negative for autism on autism by expert clinical diagnosis
8 secondary outcomes
Positive Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Negative Predictive Value of SenseToKnow screening device (based on a machine learning algorithm using the SenseToKnow digital data, combined with the SenseToKnow Caregiver Survey data) for autism detection in comparison to expert clinical diagnosis
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow screening device (using the SenseToKnow digital data and SenseToKnow Caregiver survey data) for autism versus non-autism classification
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Sensitivity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Specificity of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Positive Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Negative Predictive Value of SenseToKnow screening device based on a machine learning algorithm using only the SenseToKnow digital data for autism detection in comparison to expert clinical diagnosis
Time frame: Will be calculated based on data from Baseline/Timepoint 1
Receiver Operating Characteristic Curve and Area Under the Curve with respect to the accuracy of the SenseToKnow device using only the SenseToKnow digital data for autism versus non-autism classification
Time frame: Will be calculated based on data from Baseline/Timepoint 1
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 6 Mar 2026.
- Individual participant dataSharing planned: Yes. All individual-level data that meets PHI and IRB confidentiality requirements will be submitted to the NIH/NIMH Data Repository by the end of the grant period.
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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.
Geraldine Dawson, PhD principal investigator
Duke University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Autism and Autism Spectrum Disorder, run by Duke University at 1 site in United States.
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 4 inclusion and 12 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: looking for participants now, as of the sponsor's last update on 6 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
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.
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 Geraldine Dawson, PhD (9196680070, geraldine.dawson@duke.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the SenseToKnow screening device based on a machine learning algorithm that combines SenseToKnow digital data with data from the SenseToKnow Caregiver survey for autism detection (time frame: Will be calculated based on data from Baseline/Timepoint 1). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 6 Mar 2026.