A protocol for applying health equity-informed implementation science models and frameworks to adapt a sleep intervention for adolescents at risk for suicidal thoughts and behaviors

Abstract

BackgroundEffective and equitable strategies to prevent youth suicidal thoughts and behaviors (STB) are an urgent public health priority. Adolescent sleep disturbances are robustly linked to STB but are rarely addressed in preventive interventions or among Black and/or Hispanic/Latinx youth for whom STB risk is increasing disproportionately. This paper describes an application of health equity-informed implementation science models and frameworks to adapt and evaluate the evidence-based Transdiagnostic Sleep and Circadian (TSC) intervention for primary care implementation with adolescents of minoritized backgrounds with depression and STB risk.MethodsThis multiphase study protocol uses the Assessment, Decision, Adaptation, Production, Topical Experts-Integration, Training, Testing (ADAPT-ITT) model to adapt and evaluate TSC for primary care implementation with adolescents who are depressed, at risk for STB, and of primarily Black and/or Hispanic/Latinx backgrounds. We integrate the Consolidated Framework for Implementation Research (CFIR) in an initial qualitative inquiry of adolescent, caregiver, and clinician perceptions of TSC. Subsequent ADAPT-ITT phases include systematically and iteratively testing adaptations based on the qualitative inquiry, with ongoing key informant input, and then evaluating the adapted TSC for feasibility, acceptability, and efficacy in a pilot randomized trial.Anticipated resultsBased on youth depression and sleep health disparities research, we expect that TSC adaptations will be needed to enhance intervention content for adolescents with depression, STB risk, and primarily Black and/or Hispanic/Latinx backgrounds. We also anticipate adaptations will be needed to align TSC delivery methods with primary care implementation.ConclusionsAdapting evidence-based interventions with end-users and contexts in mind can help ensure that intervention strategies and delivery methods are acceptable to, and feasible with, health disparate populations. Although TSC has shown effectiveness for adolescents with sleep disturbances, we expect that additional multiphase research is necessary to optimize TSC for primary care delivery with Black and/or Hispanic/Latinx adolescents with depression and STB risk.

Machine learning data sources in pediatric sleep research: assessing racial/ethnic differences in electronic health record–based clinical notes prior to model training

“…However, these disparities are often preventable and linked to social determinants of health at the individual, family, healthcare, and broader community/societal levels ( Billings et al, 2021 ; Fanta et al, 2021 ; Yip et al, 2022 ; Clarkson-Townsend et al, 2023 ; Gueye-Ndiaye et al, 2023 ). Pediatric primary care is ideal for preventing pediatric sleep disparities at the population level, yet providers in this setting typically lack the time and resources necessary to identify sleep problems ( Honaker and Saunders, 2018 ; Mosher and Piccinini-Vallis, 2022 ; Williamson et al, 2022 ; Golden et al, 2023 ). Efficient machine learning and clinical decision support tools embedded in the pediatric primary care electronic health record (EHR) are needed to support universal screening of pediatric sleep problems at the population level ( Anan et al, 2023 ).…”