- Real-Time Influenza Surveillance Using DeepSeek-Augmented BERT Classification on Chinese Social Media: Infodemiology Study. [Journal Article]J Med Internet Res. 2026 Oct 27; 28:e98164.JM
- CONCLUSIONS: The results suggest that a DeepSeek-augmented lightweight BERT classifier can extract meaningful influenza-related signals from noisy Chinese social media data and produce a surveillance index that shows an approximate 1-week lead relative to official ILI reporting, with improved performance over keyword-based and TF-IDF approaches. This framework provides a proof-of-concept for cost-effective real-time influenza nowcasting using social media data.
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- Correction: Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data. [Journal Article]J Med Internet Res. 2026 Oct 30; 28:e110851.JM
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- Effects of Parent-Involved Digital Health Interventions on Adiposity-Related Outcomes and Behaviors in Children and Adolescents With Overweight or Obesity: Systematic Review and Meta-Analysis. [Systematic Review]J Med Internet Res. 2026 Oct 30; 28:e95757.JM
- CONCLUSIONS: Moderate-certainty evidence suggests that parent-involved DHIs may produce a small improvement in BMI-related outcomes among children and adolescents with overweight or obesity. However, evidence for improvements in adiposity-related behaviors remains limited, and neither the optimal digital delivery modality nor the most effective parent-involved strategy has been established. Future research should refine these interventions to identify optimal strategies and confirm their effectiveness.
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- Multimodal Analysis of Child-Horse Therapeutic Interactions Using Vision-Language Models in an Australian Aboriginal Equine-Assisted Learning Program for Social and Emotional Well-Being: Development and Evaluation Study. [Journal Article]J Med Internet Res. 2026 Oct 30; 28:e95887.JM
- CONCLUSIONS: Overall, the findings suggest that AI- and VLM-based approaches can provide complementary support for practitioner-led documentation and assessment of observable child-horse therapeutic interactions in EAL settings. These approaches may help reduce practitioner burden and strengthen the assessment of programs that promote children's social and emotional well-being.
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- Acceptability of Wearable Digital Devices for Remote Continuous Mobility Monitoring in People With Parkinson Disease and Carers: Qualitative Study. [Journal Article]J Med Internet Res. 2026 Oct 30; 28:e98987.JM
- CONCLUSIONS: Wearable mobility monitoring was generally acceptable to people with PD and carers when it offered clear clinical value, fit easily into everyday life, and provided reassurance that remote monitoring would complement rather than replace clinical care. As PD mobility was experienced as a shared concern within the care relationship, the value of monitoring depended on how well it supported the wider care dyad rather than the person with PD alone. These findings suggest that successful implementation will require co-design with people with PD and carers, alongside thoughtful clinical integration, to ensure that wearable technologies are both acceptable and effective in practice.
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- A Multistage Fine-Tuning and Empathy Alignment Approach for an Emotional Support Dialogue System: Development and Evaluation Study. [Journal Article]J Med Internet Res. 2026 Oct 29; 28:e95603.JM
- CONCLUSIONS: The findings indicate that fine-tuning on real-world counseling data and annotating models with structured emotional support labels improved context-sensitive empathic responding in LLMs. Without updating model parameters, E-URIAL further improved empathy identification and empathic expression across multiple models. Together, these methods provide a scalable, theory-informed approach to enhancing the emotional support capabilities of LLMs.
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- Nanorobots Are Using Light and Clever Engineering to Beat Biofilms and Tackle Antimicrobial-Resistant Infections. [Journal Article]J Med Internet Res. 2026 Oct 29; 28:e114831.JM
- The scope of the antimicrobial resistance issue is huge-one promising solution may be much, much smaller. In this News and Perspectives article, JMIR Correspondent Liam Critchley reports on advances in micro/nanorobotics, which may provide new avenues for treating biofilm infections.
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- The Current Roles of AI in Public Health Practice: Scoping Review. [Journal Article]
- CONCLUSIONS: The lack of evidence from public health practice, excluding outbreaks, makes it difficult to know which uses of AI, if any, would be useful to incorporate into practice in high-income countries. In the absence of evidence, new interventions, including those using AI, should be evaluated to ensure they are effective (and cost-effective). These evaluations should be widely and accessibly disseminated to create an evidence base that other public health teams could use to inform their own decisions around AI tools.
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- Reporting Gaps and Challenges in Randomized Trials of Generative AI for Medical Use: Insights From a Systematic Review. [Systematic Review]
- CONCLUSIONS: This review provides the first empirical audit distinguishing general nonadherence from structural mismatches where deterministic CONSORT-AI items fail to capture GenAI mechanics. Differing from traditional AI evaluations, it reveals that key reproducibility drivers-system prompts, inference settings, and access modalities-are omitted from current standards. These empirical findings highlight critical reporting gaps in medical GenAI trials and identify domains for a tailored extension to current reporting standards, although the evidence is limited by the use of nonvalidated signaling questions. Establishing GenAI-specific trial guidelines will ensure that clinical decisions, regulatory approvals, and patient safety safeguards are grounded in reproducible and trustworthy evidence.
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- Experiences of Immersive Virtual Reality Among Older Adults With Physical Disabilities: Qualitative Process Evaluation of a Multicenter Randomized Controlled Trial. [Randomized Controlled Trial]J Med Internet Res. 2026 Oct 29; 28:e101815.JM
- CONCLUSIONS: Implementing an IVRTP in residential care settings, particularly for older adults with mobility limitations, is positively viewed as an accessible, enjoyable, and beneficial intervention to support psychosocial well-being. Therapeutically oriented content delivered through a structured protocol, coupled with the use of advanced IVR technology, was appreciated by both older adults and student interventionists.
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- Digital Health Applications for Mental Disorders in Germany Among Users and Nonusers on Actual and Intended Use, Self-Reported Completion, and Reasons for Discontinuation: Cross-Sectional Survey. [Journal Article]J Med Internet Res. 2026 Oct 29; 28:e99464.JM
- CONCLUSIONS: This study provides patient-reported real-world evidence on certified and reimbursable digital mental health interventions. Many users reported weeks of active use, but noncompletion remained common and was more frequent among younger users. Lack of motivation as a frequently reported reason for discontinuation suggests that supporting continued engagement may be important during DiGA use. Beyond Germany, these findings may support implementation of digital mental health interventions in other health care systems.
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- Nonpatient Stakeholder Perspectives on the Use of Gamification and Financial Incentives in Mobile Health for Medication Adherence: Modified Nominal Group Technique Study. [Journal Article]J Med Internet Res. 2026 Oct 29; 28:e101295.JM
- CONCLUSIONS: Nonpatient stakeholders largely reinforce patient priorities while extending them with critical perspectives on implementation, governance, and sustainability. Gamification and financial incentives are viewed as potentially effective but require careful, ethically grounded design to balance engagement with long-term motivation and trust. These findings support the development of stakeholder-informed guidelines for responsible mHealth innovation and highlight the importance of integrating patient and system-level perspectives in digital health design. Future research should prioritize co-designed longitudinal studies using apps with gamification and a range of incentive offers with clear redemption processes to evaluate the long-term impact on medication adherence across diverse patient populations.
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- Assessing Digital Determinants of Health and Their Impact as Part of Strategic Health Planning in Catalonia: Modified Delphi Study. [Journal Article]J Med Internet Res. 2026 Oct 29; 28:e93840.JM
- CONCLUSIONS: This study demonstrates the value of systematically contextualizing DDoH and other health determinants shaped by digitalization to inform strategic health planning in digitally mature public health systems. The findings show that international digital health agendas require regional reprioritization to reflect local infrastructure, governance arrangements and equity challenges. The results suggest that, beyond technological capacity, governance and equity considerations should become central to digital health planning. The proposed approach offers a replicable framework to support more anticipatory, coordinated and equity-oriented digital health strategies.
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- AI-Enabled Patient Profiling for Personalized Nursing Care and Health Support: Scoping Review. [Review]J Med Internet Res. 2026 Oct 29; 28:e103362.JM
- CONCLUSIONS: Existing evidence positions AI-enabled patient profiling as an emerging data-to-action mechanism for personalized nursing care and health support rather than as a mature clinical intervention. Future research should strengthen conceptual clarity, validate the profile, establish explicit pathways linking profiles to actions, incorporate nurse and patient involvement, assess equity and safety, and evaluate implementation in real-world nursing settings.
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- Identifying Burnout in Pediatric Clinicians Using Wearable Biometric Data: Development and Feasibility Study. [Journal Article]
- CONCLUSIONS: Our evidence of feasibility offers promising results for a passive method of data collection to be used for classifying burnout risk in other health care settings. Furthermore, the strong performance of our machine learning model provides a foundational step toward improving an organization's ability to respond and mitigate burnout through thoughtful intervention strategies.
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