A REDCap-based model for online interventional research: Parent sleep education in autism

Sleep disturbance and other co-occurring conditions in autistic children: A network approach to understanding their inter-relationships

Autistic children frequently have one or more co-occurring psychological, behavioral, or medical conditions. We examined relationships between child behaviors, sleep, adaptive behavior, autistic traits, mental health conditions, and health in autistic children using network analysis. Network analysis is hypothesis generating and can inform our understanding of relationships between multiple conditions and behaviors, directing the development of transdiagnostic treatments for co-occurring conditions. Participants were two child cohorts from the Autism Treatment Network registry: ages 2-5 years (n = 2372) and 6-17 years (n = 1553). Least absolute-shrinkage and selection operator (LASSO) regularized partial correlation network analysis was performed in the 2-5 years cohort (35 items) and the 6-17 years cohort (36 items). The Spinglass algorithm determined communities within each network. Two-step expected influence (EI2) determined the importance of network variables. The most influential network items were sleep difficulties (2 items) and aggressive behaviors for young children and aggressive behaviors, social problems, and anxious/depressed behavior for older children. Five communities were found for younger children and seven for older children. Of the top three most important bridge variables, night-waking/parasomnias and anxious/depressed behavior were in both age-groups, and somatic complaints and sleep initiation/duration were in younger and older cohorts respectively. Despite cohort differences, sleep disturbances were prominent in all networks, indicating they are a transdiagnostic feature across many clinical conditions, and thus a target for intervention and monitoring. Aggressive behavior was influential in the partial correlation networks, indicating a potential red flag for clinical monitoring. Other items of strong network importance may also be intervention targets or screening flags.

E-Health Family Interventions for Parents of Children With Autism Aged 0-6 Years: A Scoping Review

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with onset in infancy. Early intervention is critical to improve the prognosis for these children. E-health interventions have tremendous potential. This review aimed to determine the status and effectiveness of family interventions for parents of children aged 0-6 years with ASD in the context of e-health.

METHODS

The review methodology was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. PubMed, Web of Science, and China National Knowledge Infrastructure were searched from inception to June 2022. The searches were limited to children with ASD of the age range between 0 and 6 years. We collated the available information and used descriptive statistics to analyze the synthesized data.

RESULTS

Our initial search identified 3,672 articles, of which 30 studies met the inclusion criteria. The 30 articles selected were released between 2012 and 2022. All articles are in English. Most articles reviewed were from high-income countries (27/30, 90.0%), especially from the United States (16/30, 53.3%). Four major themes emerged from the 30 studies that matched the inclusion criteria, as follows: 1) type of e-health interventions, 2) duration of interventions, 3) clinical aspects of e-health interventions, and 4) evidence for intervention effectiveness, looking into the positive, negative, and mixed findings of previous studies.

CONCLUSION

These findings suggest that a wide variety of e-health interventions may actually help support both children with ASD aged 0-6 years and their parents.

Sleep in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

SLEEP IN AUTISM SPECTRUM DISORDER AND ATTENTION DEFICIT HYPERACTIVITY DISORDER: Kanwaljit Singh, Andrew W. Zimmerman Seminars in Pediatric Neurology Volume 22, Issue 2, June 2015, Pages 113-125 Sleep problems are common in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Sleep problems in these disorders may not only worsen daytime behaviors and core symptoms of ASD and ADHD but also contribute to parental stress levels. Therefore, the presence of sleep problems in ASD and ADHD requires prompt attention and management. This article is presented in 2 sections, one each for ASD and ADHD. First, a detailed literature review about the burden and prevalence of different types of sleep disorders is presented, followed by the pathophysiology and etiology of the sleep problems and evaluation and management of sleep disorders in ASD and ADHD.

Decentralized clinical trials in the trial innovation network: Value, strategies, and lessons learned

New technologies and disruptions related to Coronavirus disease-2019 have led to expansion of decentralized approaches to clinical trials. Remote tools and methods hold promise for increasing trial efficiency and reducing burdens and barriers by facilitating participation outside of traditional clinical settings and taking studies directly to participants. The Trial Innovation Network, established in 2016 by the National Center for Advancing Clinical and Translational Science to address critical roadblocks in clinical research and accelerate the translational research process, has consulted on over 400 research study proposals to date. Its recommendations for decentralized approaches have included eConsent, participant-informed study design, remote intervention, study task reminders, social media recruitment, and return of results for participants. Some clinical trial elements have worked well when decentralized, while others, including remote recruitment and patient monitoring, need further refinement and assessment to determine their value. Partially decentralized, or "hybrid" trials, offer a first step to optimizing remote methods. Decentralized processes demonstrate potential to improve urban-rural diversity, but their impact on inclusion of racially and ethnically marginalized populations requires further study. To optimize inclusive participation in decentralized clinical trials, efforts must be made to build trust among marginalized communities, and to ensure access to remote technology.

The need for data management standards in public health nursing: A narrative review and case study

BACKGROUND

Data management is the key to the success of all projects and research. The ability to safely store, manipulate, and decipher data in real time is invaluable. Currently data management standards in public health are non-existent. Since the invention of computers real-time data retrieval and analysis has been possible but underutilized by researchers in the field. Historically, most small research studies and field-based projects have utilized spreadsheets for data management, which often proves problematic as the project grows. However, a viable and superior alternative exists in relational databases, such as REDCap. Relational databases allow for easier concatenation of multiple legacy datasets, facilitate data entry with surveys that incorporate branching logic, and allow for real time data entry in the field without the need for WIFI.

METHODS

One example of a public health project being transitioned from spreadsheet data management to a relational database is the Farmworker Family Health Program based out of the Lillian Carter Center for Global Health & Social Responsibility at Emory University's Nell Hodgson Woodruff School of Nursing. The data management transition from spreadsheets to REDCap has provided the team with unique insight into the data that has been collected in the 30 years the program has been running.

CONCLUSION

Through this case study, we identify the need for and recommend that those in public health nursing utilize relational databases when collecting data during research studies or as electronic medical records for field clinics.

Evaluating sleep quality using the CSHQ-Autism

BACKGROUND

Sleep problems are common in autistic children and adversely impact daytime functioning. The Children's Sleep Habits Questionnaire (CSHQ) [39] was developed from a community-based sample of children and has validated a cut-off score of 41. Katz et al. [50] developed an abbreviated 23-item four-factor version of the CSHQ, which may be useful when assessing sleep in autistic children. However, a cut-off value has not yet been developed.

OBJECTIVE

Our objective was to develop and validate a cut-off for the CSHQ-autism total score in order to identify sleep problems among autistic children. We hypothesized that the derived cut-off value for the CSHQ-autism would perform better than the original CSHQ cut at 41 on validation in a sample of autistic children.

METHODS

Age-specific cut-off values were developed and validated using receiver operating characteristic analysis.

RESULTS

The derived cut-off values for the CSHQ-autism total score were 34, 35, 33, and 35 for the 2-3, 4-10, 11-17, and 2-17 years age groups, respectively. On validation, all cut-off values performed with moderate to high sensitivity (76.6-82.4%) and moderate specificity (69.1-75.5%), while the original CSHQ cut at 41 had high sensitivity (89.9-93.0%) but low specificity (42.6-57.7%). Using McNemar's tests, the CSHQ-autism had significantly higher specificity but lower sensitivity than the original CSHQ cut at 41 in all age groups.

CONCLUSIONS

The CSHQ-autism cut-off values performed better overall than the original CSHQ cut at 41 in a sample of autistic children. The CSHQ-autism cut-off can help identify sleep problems among autistic children.