SweetDreams Sleep Study for Children With Autism

Overseen By Bennett Leventhal, MD

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: University of California, San Francisco

Must not be taking: Sleep medications

Disqualifiers: Sleep apnea, severe asthma, diabetes

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

Duration: 4 weeks

20 Participants Needed

Trial Summary

What is the purpose of this trial?

This trial will test an online program called SweetDreams that helps preschool-aged children with Autism Spectrum Disorder (ASD) sleep better. The program provides videos and educational materials for parents, making it easier for them to get help. It can be accessed on computers, tablets, and phones.

Will I have to stop taking my current medications?

If you are regularly using sleep medications, you will need to stop, as the trial excludes participants who use them regularly. Occasional use of over-the-counter sleep aids is allowed.

What safety data exists for the treatment known as SweetDreams?

The research articles reviewed do not provide specific safety data for the treatment known as SweetDreams or its other names. ^{1 2 3 4 5}

Research Team

Lauren Asarnow, PhD

Principal Investigator

University of California, San Francisco

Eligibility Criteria

Inclusion Criteria

have poor sleep health

have a diagnosis of ASD

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants receive access to the SweetDreams intervention, an online delivery of educational materials and treatment strategies for sleep improvement

4 weeks

Online access available continuously

Follow-up

Participants are monitored for changes in sleep habits and quality after the intervention

4 weeks

Treatment Details

Interventions

• SweetDreams

Participant Groups

Group I: SweetDreams Experimental Treatment 1 Intervention

Access to a mobile and computer accessible adaptation of existing evidence based sleep interventions.

Group II: Waitlist Control Active Control 1 Intervention

Wait list condition with future access to a mobile and computer accessible adaptation of existing evidence based sleep interventions.

Who Is Running the Clinical Trial?

University of California, San Francisco

Lead Sponsor

Trials

2,636

Recruited

19,080,000+

Findings from Research

The study developed a method to extract comprehensive drug safety information from adverse event narratives using natural language processing (NLP), analyzing 3723 narratives from the Korea Adverse Event Reporting System (KAERS) between 2015 and 2019.

The KAERS-BERT model achieved high performance in extracting relevant data, improving data completeness by an average of 3.24% in structured fields, indicating that enhanced NLP techniques can significantly improve the quality of drug safety information in spontaneous reporting systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques. Kim, S., Kang, T., Chung, TK., et al.[2023]

In a study of 48,118 participants from four clinical trials on acute coronary syndromes, 50% reported adverse events within a year, with 14.4% classified as serious adverse events (SAEs) and 85.6% as nonserious adverse events (AEs).

The reporting of adverse events peaked shortly after hospital discharge and was influenced by factors such as chronic obstructive pulmonary disease and heart failure, while participants from Eastern Europe and Asia reported fewer SAEs, highlighting the need for improved adverse event collection methods in clinical trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Pooled analysis of adverse event collection from 4 acute coronary syndrome trials. Zimerman, A., Lopes, RD., Stebbins, AL., et al.[2016]

A new standardized strategy for reporting adverse events (AEs) and serious adverse events (SAEs) in substance use disorder (SUD) clinical trials was developed, which aims to reduce the reporting burden while maintaining safety monitoring.

In a review of 17 SUD trials involving 6737 participants, the new strategy showed a significant reduction in irrelevant safety event reporting, leading to a more consistent safety assessment system tailored to the risks associated with specific trial interventions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Strategies for safety reporting in substance abuse trials. Lindblad, R., Campanella, M., Styers, D., et al.[2013]

Among 156 clinical trials for new drugs, safety results were reported in ClinicalTrials.gov for 58.3% of trials and in peer-reviewed publications for 67.9%, indicating similar availability of safety data across both sources.

ClinicalTrials.gov provided more complete reporting for serious adverse events and adverse events compared to publications, suggesting it could be a valuable resource for monitoring drug safety post-approval.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Reporting of clinical trial safety results in ClinicalTrials.gov for FDA-approved drugs: A cross-sectional analysis. Chen, KY., Borglund, EM., Postema, EC., et al.[2022]

References

1. New Zealand pubmed.ncbi.nlm.nih.gov

Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Adverse event reporting in publications compared with sponsor database for cancer clinical trials. [2007]

3. United States pubmed.ncbi.nlm.nih.gov

Pooled analysis of adverse event collection from 4 acute coronary syndrome trials. [2016]

4. England pubmed.ncbi.nlm.nih.gov

Strategies for safety reporting in substance abuse trials. [2013]

5. England pubmed.ncbi.nlm.nih.gov

Reporting of clinical trial safety results in ClinicalTrials.gov for FDA-approved drugs: A cross-sectional analysis. [2022]