Literature Collection

OBJECTIVE: To explore the relationships among anxiety, depression, and headache-related disability in a pediatric clinic-based retrospective cross-sectional study. BACKGROUND: Anxiety and depression are commonly considered comorbidities of headache disorders and are frequently seen in children and adolescents. These conditions can contribute to disability and may have a cumulative impact. In this study, we tested whether self-reported anxiety and/or depression in youth were associated with headache-related disability. METHODS: This was a retrospective cross-sectional study of children ages 6-17 years old who completed a headache intake questionnaire at the time of a new outpatient neurology visit. Those who reported on behavioral health symptoms, involvement of a behavioral health provider (i.e., yes/no), and the PedMIDAS (a validated metric of headache-related disability) were included. The relationship between anxiety and/or depression and headache-related disability was examined. RESULTS: Of the 12,660 questionnaires queried, 9118 met criteria for inclusion. Respondents were 64.0% female and had a median age of 13.5 years (interquartile range [IQR] 10.3, 15.7). Compared to patients without self-reported anxiety/depression, patients with anxiety and depression reported higher headache-related disability (M = 17.0, [IQR 6.0, 41.0]) even after accounting for covariates (estimated difference = 6.0, 95% confidence interval [CI: 4.4-7.5]). For participants with anxiety and/or depression, having a behavioral health provider was associated with greater headache-related disability (estimated difference = 7.0; 95% CI 4.6-9.3). CONCLUSIONS: Patients with self-reported anxiety and/or depression reported higher headache-related disability. Having a behavioral health provider was associated with greater headache-related disability, indicating the complexity and high level of need for this population. Further research is needed to understand the directionality of these results; however, patients with headache as well as depression and or anxiety are a vulnerable group who may benefit from an integrated care model.

BACKGROUND: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. OBJECTIVE: This study aimed to investigate, from an end user's (patient's) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. METHODS: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. RESULTS: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won't have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. CONCLUSIONS: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.

INTRODUCTION: Quantitative global or regional brain imaging measurements, known as imaging-specific or -derived phenotypes (IDPs), are commonly used in genotype-phenotype association studies to explore the genomic architecture of the brain and how it may be affected by neurological diseases (e.g., Alzheimer's disease), mental health (e.g., depression), and neurodevelopmental disorders (e.g., attention-deficit hyperactivity disorder [ADHD]). For this purpose, medical images have been used as IDPs using a voxel-wise or global approach via principal component analysis. However, these methods have limitations related to multiple testing or the inability to isolate high variation regions, respectively. METHODS: To address these limitations, this study investigates a localized, principal component analysis-like approach for dimensionality reduction of cross-sectional T1-weighted MRI datasets utilizing diffeomorphic morphometry. This approach can reduce the dimensionality of images while preserving spatial information and enables the inclusion of spatial locality in the analysis. In doing so, this method can be used to explore morphometric brain changes across specific components and spatial scales of interest and to identify associations with genome regions in a multivariate genome-wide association study. For a first clinical feasibility study, this method was applied to data from the Adolescent Brain Cognitive Development (ABCD) study, including adolescents with ADHD (n = 1,359), obsessive-compulsive disorder (n = 1,752), and depression (n = 1,766). RESULTS: Meaningful associations of specific morphometric features with genome regions were identified with the data and corresponded to previous found brain regions in the respective mental health and neurodevelopmental disorder cohorts. DISCUSSION: In summary, the localized, principal component analysis-like approach can reduce the dimensionality of medical images while still being able to identify meaningful local brain region alterations that are associated with genomic markers across multiple scales. The proposed method can be applied to various image types and can be easily integrated in many genotype-phenotype association study setups.

OBJECTIVE: Integrating pediatric obesity care into primary care remains challenging, especially in underserved areas. To address this, the REACH-KIDS (Reverse Consult Care Model for the Comprehensive Treatment of Pediatric Obesity in Primary Care) program, which aims to empower primary care clinician through education and resources was developed. This study describes the design and early evaluation of a quality improvement initiative aimed at building PCP capacity in pediatric obesity care using a collaborative care model. METHODS: The REACH-KIDS model was implemented across two sites, involving didactic mini-series sessions designed to educate primary care clinicians on obesity care. The program's feasibility was evaluated by tracking attendance, engagement, satisfaction, and knowledge gained. Between November 2023 and May 2024, two mini-series were conducted across the sites, and feedback was gathered to assess shifts in clinical practice, particularly in anti-obesity medication use. RESULTS: At Site one, 68, 67, and 67 attendees participated in September, October, and November 2023 sessions, respectively. At Site two, 42, 45, and 52 attendees participated in March, April, and May 2024. Most attendees (75%) reported a shift in clinical practice regarding obesity pharmacotherapy after attending the mini-series. Satisfaction with the sessions was high, and knowledge assessments significantly improved participants' understanding of obesity treatment options. Notably, participants reported increased confidence in prescribing anti-obesity medications, indicating a positive clinical impact of the educational intervention. CONCLUSIONS: The REACH-KIDS model demonstrated feasibility and effectiveness in improving primary care clinicians' knowledge of comprehensive obesity care. This quality improvement initiative highlights the potential of education and collaboration to enhance obesity care in primary care settings. Future studies should focus on expanding this model and assessing its impact on clinical outcomes in underserved populations. Limitations include the restricted geographic scope and reliance on self-reported changes in clinical practice; future work will require objective measures of patient outcomes and broader implementation.

INTRODUCTION: Integrated care is increasingly adopted to address the needs of patients with multimorbidity, but cost-effective configuration of integrated healthcare pathways remains unclear. This study reviewed decision analytic models (DAMs) used in economic evaluations of integrated care interventions for cardiometabolic multimorbidity. METHODS: A systematic search of eight electronic databases was conducted to identify peer-reviewed articles published in English until November 2024. Studies using DAMs to evaluate integrated care interventions for patients at risk or having cardiometabolic multimorbidity were included. Data on DAMs characteristics, integrated care models evaluated, and diseases were summarised. The quality of reporting was assessed using Philips (2006) checklist. RESULTS: Sixteen studies met inclusion criteria. Most studies (81%) were cost utility analyses, focused on hypertension and/or diabetes concordant multimorbidity (69%). High-income countries accounted for 69% of the studies. Markov models were used the most (63%), with only three studies employing individual patient simulation (microsimulation) models. Few studies were explicit about data validation and reporting uncertainty. CONCLUSION: Economic evaluations of integrated care cardiometabolic multimorbidity should adopt microsimulation to better capture patient-level interactions and health outcomes. Better reporting of validation and uncertainty is needed. There is limited application of DAM-based economic evaluations of integrated care in low- and middle-income countries.

INTRODUCTION: As the world's population ages, health problems among older rehabilitation patients show complicated features characterized by multimorbidity, functional decline, and interwoven dietary hazards. METHODS: This study uses a narrative review methodology to systematically examine the theoretical framework, key components, and current practice of the integrated rehabilitation-clinical-nutrition management model among older rehabilitation patients. RESULTS: The integrated management model demonstrates potential benefits in terms of functional recovery, nutritional improvement, prevention and control of complications, and mental health. The paper also critically examines the limitations of current research, evidence heterogeneity, and research gaps. DISCUSSION: The collaborative integration-based integrated rehabilitation-clinical-nutrition management approach has emerged as a crucial tactic for boosting quality of life, optimizing healthcare resource distribution, and increasing functional outcomes among older patients. Suggestions for improvement are offered from the standpoints of public health service systems, human resource allocation, and policy support. This review aims to provide recommendations for clinical practice, health management, and policy-making, while encouraging more consistent and long-lasting use of integrated care models in geriatric rehabilitation.

Men's health, particularly in the domain of urology, faces significant challenges in access to care, patient outcomes, and cost efficiency. Despite advances in medical treatment, conditions such as prostate cancer remain a leading cause of cancer-related death among men, with African American men disproportionately affected at twice the mortality rate of other groups. Compounding these challenges is a critical shortage of urologists, with 62% of US counties lacking a practicing urologist and only 1 new urologist entering the field for every 10 retiring. This shortage results in delayed diagnoses, increased rates of advanced-stage conditions, and significant health disparities. To address these pressing issues, telehealth and technology-based integrated care models present a promising solution. Telehealth expands access to specialized urological care by overcoming geographical barriers and offering virtual consultations, at-home diagnostics, and continuous patient engagement. Artificial intelligence-driven tools further enhance the efficiency and accuracy of care delivery, improving provider experience by automating administrative tasks and facilitating early intervention through predictive analytics. Furthermore, remote patient monitoring devices provide accurate, cost-effective, and highly accessible alternatives. These innovations reduce provider burnout, lower health care costs, and, critically, improve patient outcomes. This paper explores the potential of telehealth and integrated care in men's health urology as a practical pathway to bridging access gaps, enhancing care quality, and achieving cost savings. By leveraging digital health solutions, health care systems and employers can promote health equity, increase engagement, and ensure that all men receive timely and effective urological care.

OBJECTIVE: Shared Decision Making (SDM) is heralded as a standard for patient-centered care, but implementation of SDM in routine mental health practice has proven difficult to achieve. Human-centered design (HCD) may hold promise for improving SDM implementation in busy clinical settings. This study describes applying HCD to develop an SDM documentation support tool intended to encourage successful use of SDM by mental health clinicians. METHODS: This descriptive, proof-of-concept study utilized the Discover-Design-Build-Test HCD framework to simplify a comprehensive SDM protocol for mental health decision making. Implementation was piloted within multiple primary care clinics. The study consisted of three phases: information gathering (interviewing clinicians), solution generation and prototyping, and testing a final prototype in routine care settings. RESULTS: Our project proceeded through eight cycles of user design and feedback. Clinicians pilot tested the final product, a documentation note template incorporating SDM prompts and explanations. It is currently available for clinical use. CONCLUSIONS: Clinicians were able to use the HCD-redesigned SDM documentation note template intuitively, i.e., without explicit instruction. Leveraging buy-in from users throughout the entirety of the process (from problem investigation to solution discussions) created opportunities to tailor implementation strategies and may support ownership of the end-product by primary stakeholders. PRACTICE IMPLICATIONS: HCD may be a promising methodology for streamlining the adoption of complex clinical tasks like SDM.