Literature Collection

Introduction: Caregivers of children with chronic illness, such as hematology-oncology conditions, face numerous stressors, and a subset experience persistent distress and poor psychological outcomes. Many logistical and ethical barriers complicate the provision of mental health care to caregivers in children's hospital settings. Telemental health (TMH) is one method to increase access and reduce barriers. Methods: A partnership was established with an outside TMH agency to provide mental health care to caregivers of children with hematology-oncology conditions. Development and implementation strategies are described, and feasibility was measured on four dimensions. Results: One hundred twenty-seven (n = 127) caregivers were referred for TMH services in the first 28 months of program implementation. Of the total, 63/127 (49%) received TMH services for at least one session. Most caregivers had a child in active medical treatment (89%). A small portion (11%) of caregivers were bereaved or had a child in hospice care. Program feasibility was enhanced by hospital leadership support and availability of staffing, financial, and technology resources. Available resources also contributed to the practicality of program development and swift implementation and integration within the defined hospital system. Discussion: Partnership with an outside TMH agency increased access to care and reduced barriers to treating caregivers in a children's hospital setting. Offering mental health interventions to caregivers aligns with evidence-based standards of care. Future research will elucidate caregiver satisfaction with this modality of treatment and whether use of TMH reduces disparities in caregiver receipt of mental health care in children's hospital settings.

Introduction: Caregivers of children with chronic illness, such as hematology-oncology conditions, face numerous stressors, and a subset experience persistent distress and poor psychological outcomes. Many logistical and ethical barriers complicate the provision of mental health care to caregivers in children's hospital settings. Telemental health (TMH) is one method to increase access and reduce barriers. Methods: A partnership was established with an outside TMH agency to provide mental health care to caregivers of children with hematology-oncology conditions. Development and implementation strategies are described, and feasibility was measured on four dimensions. Results: One hundred twenty-seven (n = 127) caregivers were referred for TMH services in the first 28 months of program implementation. Of the total, 63/127 (49%) received TMH services for at least one session. Most caregivers had a child in active medical treatment (89%). A small portion (11%) of caregivers were bereaved or had a child in hospice care. Program feasibility was enhanced by hospital leadership support and availability of staffing, financial, and technology resources. Available resources also contributed to the practicality of program development and swift implementation and integration within the defined hospital system. Discussion: Partnership with an outside TMH agency increased access to care and reduced barriers to treating caregivers in a children's hospital setting. Offering mental health interventions to caregivers aligns with evidence-based standards of care. Future research will elucidate caregiver satisfaction with this modality of treatment and whether use of TMH reduces disparities in caregiver receipt of mental health care in children's hospital settings.

INTRODUCTION: Early detection and intervention are crucial for reducing the impacts of depression and associated healthcare costs. Few studies have used electronic health records (EHR) and machine learning (ML) with a longitudinal design to predict depression onset. We developed and validated ML algorithms using EHR to identify patients at high risk for the onset of diagnosis-based major depressive disorder (MDD) in primary care settings. METHODS: Using a prognostic modeling approach with retrospective cohort study design, we identified patient visits in primary care settings for individuals aged ≥18 years from the Accelerating Data Value Across a National Community Health Center Network Clinical Research Network 2015-2021 data. We measured 267 features at six-month intervals starting six months prior to the first encounter. We developed algorithms using Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and XGBoost with 10-fold cross validation. Using hold-out testing data, we measured prediction performance (e.g., C-statistics), stratified patients into decile risk subgroups, and assessed model biases. RESULTS: Among eligible 1,965,399 individuals (mean age = 43.52 ± 16.04 years; male = 35%; African American = 20%) with 4,985,280 person-periods, the MDD onset rate was 1% during the study period. XGBoost performed similarly to other models and had the fewest predictors, (C-statistic = 0.763, 95% CI = [0.760, 0.767]). XGBoost had a 66.78% sensitivity, 74.19% specificity, and 2.55% positive predictive value at the balanced threshold identified using Youdan Index. The top three risk decile subgroups captured ∼70% of MDD cases, without significant racial or sex biases. CONCLUSIONS: An ML algorithm using EHR data can effectively identify individuals at high risk of depression onset within the subsequent six months, without exacerbating racial or sex biases, providing a valuable tool for targeted early interventions.

BACKGROUND: Although mobile health apps integrated with Internet of Things-enabled devices are increasingly used to satisfy the growing needs for home-based older adult care resulting from rapid population aging, their effectiveness is constrained by 3 key challenges: a focus on specific functions rather than on holistic and integrated support, absence of a solid theoretical framework for development, and a lack of personalized, real-time feedback to address diverse care needs. To overcome these limitations, we developed a knowledge-based clinical decision support system using mobile health technology-an intelligent and integrated older adults care model (SMART system). OBJECTIVE: This study aims to systematically outline the development process and outcomes of a knowledge base and trigger rules for the SMART system. METHODS: Our study adopted a user-centered approach guided by the nursing process and intervention mapping (IM) framework. We first identified older adult care needs through semistructured, in-depth interviews. Guided by the nursing process and informed by guidance from the World Health Organization's Integrated Care for Older People and World Health Organization International Classification of Functioning, Disability, and Health, along with the North American Nursing Diagnosis Association-I nursing diagnosis, we then determined care problems along with their underlying causes and risk factors and diagnostic criteria. Building on these findings, we applied the first 3 steps of the intervention mapping framework to formulate corresponding long-term and short-term care objectives, select appropriate evidence-based interventions, and match practical implementation approaches, which were grounded in rigorous evidence derived from systematic literature reviews, clinical guidelines, and expert insights. We also developed a set of trigger rules to link abnormalities in older adults with corresponding care problems and interventions in the SMART knowledge base. RESULTS: The semistructured in-depth interviews identified 5 types of care needs-daily life care, health care, external support, social participation, and self-development-which formed the foundation of the SMART knowledge base. Based on this, we identified 138 care problems, each with associated causes and risk factors and diagnostic criteria. The objective matrix comprised 138 long-term and 195 short-term care objectives. Guided by 15 expert-defined selection criteria, we then selected 450 evidence-based interventions, each paired with at least 1 feasible and practical implementation approach. Additionally, we developed diagnostic rules to match the assessment data with relevant care problems and their causes and risk factors and intervention trigger rules to formulate personalized interventions based on individual characteristics, ensuring tailored care aligned with specific care objectives. CONCLUSIONS: This study outlines the development process and outcomes of the SMART knowledge base and trigger rules. The study methodology offers theoretical support for developing knowledge bases and trigger rules of similar clinical decision support systems for home-based older adult care.

BACKGROUND: The COVID-19 pandemic has accelerated the adoption of video consultations in mental health care, highlighting the importance of therapeutic alliances for successful treatment outcomes in both face-to-face and web-based settings. Telepresence, the sense of being present with the mental health specialist (MHS) rather than feeling remote, is a critical component of building a strong therapeutic alliance in video consultations. While patients often report high telepresence levels, MHSs express concerns about whether video consultations can replicate the quality of face-to-face interactions. Despite its importance, research on telepresence development in MHSs over time and the dyadic interplay between patients and MHSs remains limited. OBJECTIVE: This study aimed to evaluate the mutual influence within patient-MHS dyads on telepresence development during video consultations, using data from a randomized controlled trial assessing the feasibility of video consultations for depression and anxiety disorders in primary care. METHODS: The study included 22 patient-MHS dyads (22 patients, 4 MHSs). Telepresence was measured using the Telepresence in Videoconference Scale. Dyadic data were analyzed using the actor-partner interdependence model with a distinguishable dyad structural equation model. Actor effects refer to the impact of an individual's telepresence at time point 1 (T1) on their telepresence at time point 2 (T2), while partner effects represent the influence of one party's telepresence at T1 on the other's telepresence at T2. Sensitivity analyses excluded data from individual MHSs to account for their unique effects. RESULTS: A significant actor effect for MHSs (P<.001) indicated a high temporal stability of telepresence between T1 and T2. In contrast, the actor effect for patients was not statistically significant, suggesting a greater variability between T1 and T2. No significant partner effects for both patients and MHSs were observed, suggesting no mutual influence between dyad members. Age was a significant covariate for telepresence in both groups. CONCLUSIONS: Consistent with prior findings, MHSs experienced increased telepresence over time, whereas patients reported high telepresence levels from the start of therapy. The lack of dyadic influence highlights the need for further exploration into factors affecting telepresence development, such as age, technical proficiency, and prior treatment experience. Future studies with larger samples and more sessions are necessary to enhance the generalizability of these findings and to optimize the use of video consultations in mental health care.

OBJECTIVE: This paper outlines the design and implementation of iManage SCD, a self-management mobile health application for adolescents and young adults (AYA) with sickle cell disease (SCD) during transition from pediatric to adult health care. METHODS: The Integrate, Design, Assess, Share (IDEAS) framework, emphasizing user insights, iterative design, rigorous assessment, and knowledge sharing, guided the development process. The design team consisted of researchers, psychologists, physicians, social workers, AYA with SCD, and parents of AYA with SCD (n = 16) across three states. Qualitative focus groups and interviews were conducted and analyzed using thematic analysis across the integrate and design phases. Point of use feedback from AYA with SCD was used to assess feasibility and acceptability. RESULTS: The development process was centered around tenants of the Social-ecological Model of Adolescent and Young Adult Readiness to Transition. Development integrated multidisciplinary perspectives, fostering a person-centered approach. The iterative design process involved collaboration with a digital health firm, Agency39A. Health equity and implementation considerations were addressed at individual, community, and healthcare system levels. Themes that emerged from focus groups with AYA, clinicians, and researchers in the integrate and design phases of development included recommendations for content and user experience features. CONCLUSIONS: iManage SCD emerges as a comprehensive, user-friendly mobile health application, incorporating theoretical principles and direct user input. The development process demonstrated feasibility and acceptability, and the paper discusses dissemination strategies for the Community Health Workers and Mobile Health Programs to Help Young Adults with SCD Transition to Using Adult Healthcare Services (COMETS) study.