Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
JMIR research protocols
A Health Promoting School Intervention in 10th Grade ('My Life - I Decide'): Protocol for a Pragmatic Controlled Trial
BACKGROUND: Health promoting school (HPS) interventions have the potential to improve adolescent health and well-being, but evidence regarding implementation and system-level impact in real-world school settings remains limited. "My Life - I Decide" (My Life) is a systems-oriented HPS intervention developed to strengthen positive mental and physical health, school well-being, and health-promoting school practices among 10th-grade students in Denmark. OBJECTIVE: This study aims to describe the intervention, study design, and evaluation framework of the "My Life" intervention and its pragmatic controlled trial, including effectiveness, process, and system-level evaluations. METHODS: The intervention is informed by the World Health Organization HPS framework and combines curriculum-based health education, action-oriented teaching, life-psychological approaches, and education outside the classroom. The intervention includes four phases: (1) preparation through cross-sectoral collaboration between health and education sectors; (2) planning and local adaptation; (3) delivery of a health education program; and (4) anchoring of health-promoting practices at the school level. Effectiveness is evaluated using a pragmatic controlled waiting-list trial design. Four intervention schools (11 classes) were matched with 4 control schools (15 classes), including approximately 416 students aged 15-17 years. Primary outcomes include social and emotional competences, self-efficacy, mental well-being, health literacy, school connectedness, and student interpersonal relations. Student survey data are collected at baseline, postintervention, and follow-up, and effectiveness will be analyzed using multilevel mixed models. System-level impacts are assessed using a mixed-methods design, including school staff surveys, interviews with school and municipal stakeholders, and student focus groups. A realist-informed multimethod process evaluation examines implementation fidelity, acceptability, contextual factors, and mechanisms of impact across intervention schools. Data sources include observations, interviews, student registration, and postsession surveys completed by health consultants after each teaching session. RESULTS: The study will generate quantitative and qualitative data on student outcomes, implementation processes, intersectoral collaboration, and development of health-promoting practices within schools. Findings from the effectiveness, process, and system-level evaluations will be triangulated to test and refine the initial program theory of the intervention. Recruitment of schools and student enrollment have been completed, and baseline data collection commenced in September 2025. Follow-up assessments are being conducted according to the study timeline. Qualitative data collection for the process and system-level evaluations were completed in June 2026. CONCLUSIONS: The "My Life" study will contribute knowledge on the implementation and evaluation of complex HPS interventions in real-world educational settings. The findings may inform future HPS initiatives and provide methodological insights into combining effectiveness, process, and system-level evaluations in adolescent health promotion research.
19 July 2026
Read appraisal →International journal of medical informatics
Examining user-AI interaction patterns in health-information queries
In this study, we examine how individuals utilize generative artificial intelligence (GAI) when seeking health-related information. Using a dataset of user-GAI chat logs available on Hugging Face, we analyzed real-world interactions in which users posed health-related questions to a generative model. We applied a combination of data and text-analytic methods to categorize these interactions, including supervised machine learning techniques such as Support Vector Machines (SVMs). SVMs were selected for their efficiency and strong performance in high-dimensional text classification tasks, and used to identify recurrent themes in user queries and interactions. We found that users frequently consult AI chatbots for symptom exploration, medical education, mental health support, and general health advice. The findings suggest that GAI tools may not only function as informational resources, but also as preliminary support tools that can shape users' health knowledge and encourage them to seek consultation with medical professionals.
16 July 2026
Read appraisal →JMIR aging
Smart Speaker-Based Applications to Support Social Connectedness in Older Adult Residents in Affordable Housing: User-Centered Design Study
BACKGROUND: Older adults in affordable housing face heightened risks of social isolation and loneliness due to limited social networks, transportation barriers, chronic conditions, and inadequate technology access. Smart speakers offer potential for enhancing social connectedness in this underserved population, yet technology interventions are rarely designed with meaningful input from older adults themselves. User-centered design (UCD) approaches can address this gap by engaging end users throughout the development process to ensure technology solutions align with their needs and living contexts. OBJECTIVE: This study aimed to engage older adults in affordable housing in an iterative UCD process to develop prototype scenarios for smart speaker-based applications that promote social connectedness while addressing safety, community-building, and wellness needs. METHODS: We conducted a 3-stage UCD study with 29 older adults (mean age 70, SD 6.8 years; 23/29, 79% African American; 20/29, 69% high school education or less) living alone in affordable housing between April 2021 and April 2022. Stage 1 included 5 focus groups (n=25) combining needs assessment discussions with rapid brainstorming activities. Stage 2 involved research team synthesis of focus group transcripts and brainstorming data to create a Design Strategies Map, development of initial prototype scenarios, 5 evaluation focus groups (n=18) to gather feedback, and iterative scenario refinement. Stage 3 comprised 4 validation focus groups (n=17) to assess refined scenarios and identify implementation recommendations. Participants included both smart speaker users (n=13) and nonusers (n=16). Data were analyzed using thematic analysis for needs assessment, content analysis for brainstorming ideas and feedback, and matrix analysis for systematic comparison across scenarios. RESULTS: Participants generated 153 ideas for smart speaker use, with Health and Safety and Daily Assistance being the most frequent categories. Analysis revealed that social connection needs were inseparable from safety concerns related to living alone. Through iterative co-design, we developed 7 prototype scenarios across 4 functional categories: Checking-In (peer and management safety verification with privacy controls), Social Companion (conversational artificial intelligence-based companionship and emotional support), Community Involvement (virtual bulletin boards and activity coordination), and Wellness Check (system-initiated monitoring of activity and behavioral patterns as health indicators with user-controlled interventions). Participants emphasized requirements for personalization, opt-in/opt-out controls, "Do-Not-Disturb" functionality, and safeguards preventing replacement of human connection. CONCLUSIONS: Older adults in affordable housing engaged in technology design and provided valuable insights that challenge assumptions about their needs and preferences. The prototype scenarios addressed the dual imperatives of social connection and safety while living alone, offering a foundation for developing technology-based applications tailored to underserved populations. Implementation should prioritize user control, privacy protection, and human-in-the-loop design ensuring that technology facilitates rather than replaces human connection and community programming, alongside consideration of user characteristics to build trust and ensure effective, sustained use of the intended technology platform.
9 July 2026
Read appraisal →Journal of medical Internet research
Ethical Considerations in Personal Health Large Language Models
Personal health large language models (PH-LLMs) have rapidly evolved from research prototypes into consumer-facing, data-linked systems that support symptom triage, medication questions, mental health check-ins, and longitudinal self-management. Their direct-to-consumer use without clinical oversight creates a distinct ethical risk profile that general artificial intelligence governance frameworks do not fully address. This viewpoint focuses on text-based, platform-mediated PH-LLMs and synthesizes PH-LLM-specific challenges across 6 domains: privacy, accuracy, equity, transparency, human-artificial intelligence interaction, and regulatory governance. These risks may be amplified by health literacy gaps, longitudinal data aggregation, persuasive conversational design, and fragmented oversight across the consumer-clinical boundary. Grounded in the 4 principles of biomedical ethics, we propose a governance framework that operationalizes beneficence, nonmaleficence, autonomy, and justice through design and deployment controls, including health literacy-aligned communication, crisis and pharmacological safeguards, hallucination mitigation, role disclosure, granular consent, fairness auditing, and accessible design. We further outline implementation mechanisms, including risk-tiered certification, tiered accountability, and postdeployment oversight through adverse-event reporting, transparency reporting, and independent safety evaluation. This framework is intended as an evidence-informed but partly anticipatory approach to governing PH-LLMs in personal health management.
19 June 2026
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