Literature Collection

Data on the chronicity of mental disorder in children with chronic physical illness (CPI) are limited. We examined the prevalence and predictors of homotypic and heterotypic continuity of mental disorder in children with CPI. A sample of 263 children aged 2-16 years with physician-diagnosed CPI were recruited from outpatient clinics (e.g., dermatology, respiratory) at a Canadian pediatric academic hospital and followed for 24 months. Parent and child-reported mental disorders (mood, anxiety, behavioral, attention-deficit hyperactivity disorder [ADHD]) were assessed using the Mini International Neuropsychiatric Interview for Children and Adolescents at baseline, 6, 12, and 24 months. Marginal regression models were computed to identify clinical, parent, and demographic factors associated with mental comorbidity over time. Mental disorder was observed in 24-27% of children with CPI based on child reports and 35-39% based on parent reports. Parent-reported models revealed significant homotypic continuity for all mental disorders (ORs = 4.2-9.5), and heterotypic continuity between mood and anxiety disorders (OR = 2.2), ADHD and behavioral disorders (OR = 5.1), and behavioral and each mental disorder (ORs = 6.7-8.4). Child-reported models revealed significant homotypic continuity for mood (OR = 8.8) and anxiety disorder (OR = 6.0), and heterotypic continuity between anxiety and mood disorders (OR = 12.4). Child disability (ORs = 1.3-1.5) and parent psychopathology (ORs = 1.2-1.8) were the most consistent predictors of both child- and parent-reported mental disorder over time. Mental comorbidity was prevalent and persistent in children with CPI with homotypic and heterotypic continuity common across informants. Child disability and parent psychopathology may be priority targets within integrated family-centered models of care to prevent mental comorbidity in children with CPI.

PURPOSE: Reducing potentially preventable hospitalization (PPH), also known as ambulatory care-senstive conditions, is a global concern. This study linked data from Sax Institute's 45 and Up Study on individuals aged 45 years and older from New South Wales, Australia, with Australian Medicare claims data to establish a causal relationship between continuity of care and acute PPH using a double machine learning model. METHODS: We utilized 11 years of linked data (2007-2017) to analyze the impact of continuity of care on acute PPH, controlling for key patient characteristics (ie, age, multimorbidity status, cultural diversity, sex, education level, psychological status, physical limitation, smoking status, socioeconomic deciles). Estimation was done using a double machine learning technique with 4 algorithms (ie, least absolute shrinkage and selection operator, random forest, extreme gradient boosting, artificial neural network) to ensure robustness. RESULTS: Among 54,376 participants, 27,634 individuals (50.8%) experienced at least 1 acute PPH episode during the 11-year study period. Our findings indicate that even a slight improvement in continuity of care can reduce the incidence of acute PPH compared with non-acute PPH. For example, the reduction in the probability of acute PPH compared with non-acute PPH ranges from 9.8% (95% CI, 1.1%-17.8%) to 23.5% (95% CI, 14.1%-32.4%) across 4 models when continuity of care increases from the 45th percentile (0.274) to the 50th percentile (0.301). CONCLUSION: Continuity of care at the primary level plays a key role in reducing acute PPH. Policies focused on person-centered or integrated care should include initiatives to promote continuity of care and support general practitioners in improving continuity of care.The authors of this article have provided Hindi and Vietnamese translations of the abstract.

BACKGROUND: Conversational agents (CAs) are increasingly used as a promising tool for scalable, accessible, and personalized self-management support of people with a chronic disease. Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context. To ensure their effectiveness, structured evaluations and descriptions of the interventions, integrating biomedical, behavioral, and technological perspectives, are essential. OBJECTIVE: We aimed to examine the design and evaluation of CAs for self-management support of chronic diseases, focusing on their characteristics, integration of behavioral change techniques, and evaluation methods. The findings will guide future research and inform intervention design. METHODS: We conducted a systematic search in the PubMed and Embase databases to identify studies that investigated CAs for chronic disease self-management, published from January 1, 2018, to April 15, 2024. Full-text journal articles, published in English, studying the efficacy or effectiveness of a CA in the context of self-management for chronic diseases in adults were included. Data extraction was guided by conceptual frameworks to ensure comprehensive reporting of intervention and methodologies: the behavioral intervention technology model and the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Risk of bias was assessed using the Risk of Bias 2 tool and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool (version 2). RESULTS: In total, 25 studies were included, primarily focusing on text-based, rule-based CAs delivered via a mobile apps. The chronic diseases predominantly targeted were diabetes and cancer. Commonly identified clusters of behavior change techniques were "shaping knowledge," "feedback and monitoring," "natural consequences," and "associations." However, reporting of behavior change techniques and their delivery was lacking, and intervention descriptions were limited. Studies were mostly in the early phase, with a great variety in intervention descriptions, study methods, and outcome measures. CONCLUSIONS: Advancing the field of CA-based interventions requires transparent intervention descriptions, rigorous methodologies, consistent use of validated scales, standardized taxonomy, and reporting aligned with standardized frameworks. Enhanced integration of artificial intelligence-driven personalization and a focus on implementation in health care settings are critical for future research.