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Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Mental Health Issues and 24-Hour Movement Guidelines–Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach

Social network addiction (SNA) is an emerging behavioral addiction characterized by excessive dependence on social network platforms, which significantly impairing an individual’s daily social functioning. In recent years, with the rapid proliferation of smartphones and internet technologies, SNA has rapidly proliferated globally, particularly among university students.

Lin Luo, Junfeng Yuan, Chen Xu, Huilin Xu, Haojie Tan, Yinhao Shi, Haiping Zhang, Haijun Xi

J Med Internet Res 2025;27:e72260

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study

The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR; fasting glucose [mmol/L]×fasting insulin [μU/ml]/22.5) is regarded as an acceptable method for evaluating IR [7]. However, fasting insulin, one of its components, is not a routine test, making it difficult to obtain in community and grassroots settings.

Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

JMIR Med Inform 2025;13:e72238

Natural Language Processing Chatbot–Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review

Natural Language Processing Chatbot–Based Interventions for Improvement of Diet, Physical Activity, and Tobacco Smoking Behaviors: Systematic Review

We conducted an extensive electronic search to identify all randomized controlled trials (RCTs) that reported outcomes measured by changes in behaviors in diet, physical activity, or tobacco smoking after the NLP chatbot–based intervention.

Jing Chen, Run-Ze Hu, Yu-Xuan Zhuang, Jia-Qi Zhang, Rui Shan, Yang Yang, Zheng Liu

JMIR Mhealth Uhealth 2025;13:e66403

Economic Evaluation of the Next Generation Electronic Medical Records in Singapore: Cost-Utility Analysis

Economic Evaluation of the Next Generation Electronic Medical Records in Singapore: Cost-Utility Analysis

They face difficulties accessing more detailed information from different health care providers due to the lack of an integrated platform [5]. This can lead to, for example, duplication of tests from different health care facilities and unnecessary health care expenses, when patients are referred from primary to secondary or tertiary care. Care coordination failure can be costly and has been estimated to cost between US $27.2 billion and US $78.2 billion in the United States [6].

Cynthia Chen, Jarawee Sukmanee, Khai Wee Soon, Julian Lim, Jared Louis Andre D'Souza, Yot Teerawattananon

J Med Internet Res 2025;27:e70484

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation

(F) An integrated model was developed by fusing image features and clinicopathological features using an attention mechanism. AAD: acute aortic dissection; ACC: accuracy; AUC: area under the curve; Brier: Brier score; CTA: computed tomography angiography; DCA: decision curve analysis; NE: neutrophil; NRI: net reclassification index; ROC: receiver operating characteristic; Sen: sensitivity; Spe: specificity; WBC: white blood cell.

Zhechuan Jin, Jiale Dong, Chengxiang Li, Yi Jiang, Jian Yang, Lei Xu, Ping Li, Zhun Xie, Yulin Li, Dongjin Wang, Zhili Ji

J Med Internet Res 2025;27:e72649

Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

Current scoping reviews on LLMs are predominantly conceptual and focused mainly on the entire area of biomedical health [10], highlighting an urgent need for a targeted, domain-specific review to guide future research directions [4,11,12]. With this scoping review, we therefore intend to answer the following research question (RQ): What is the state of research regarding medical diagnosis based on LLMs?

Hankun Su, Yuanyuan Sun, Ruiting Li, Aozhe Zhang, Yuemeng Yang, Fen Xiao, Zhiying Duan, Jingjing Chen, Qin Hu, Tianli Yang, Bin Xu, Qiong Zhang, Jing Zhao, Yanping Li, Hui Li

J Med Internet Res 2025;27:e72062

Artificial Intelligence–Based Mobile Phone Apps for Child Mental Health: Comprehensive Review and Content Analysis

Artificial Intelligence–Based Mobile Phone Apps for Child Mental Health: Comprehensive Review and Content Analysis

This allows for a tailored intervention, optimizing the support offered based on an objective analysis of user data. Growing up in the digital era, children are inherently familiar with digital devices such as smartphones and tablets [12,13]. These tools offer an intuitive and user-friendly interface, eliminating complexities or abstract concepts and reducing potential barriers for younger users [13].

Fan Yang, Jianan Wei, Xuejun Zhao, Ruopeng An

JMIR Mhealth Uhealth 2025;13:e58597

Smart Technology Facilitated Patient-Centered Venous Thromboembolism Management (the SmaVTE Study): Protocol for a Randomized Controlled Trial

Smart Technology Facilitated Patient-Centered Venous Thromboembolism Management (the SmaVTE Study): Protocol for a Randomized Controlled Trial

Determining an optimal, individualized decision is therefore fundamental to improving VTE management. Patient-centered care, recommended by cardiovascular guidelines [15,16], emphasizes meeting patients’ unique needs and involving them in decision-making. Adoption of a patient-centered approach has demonstrated benefits in cardiovascular disease, improving patient knowledge, self-efficacy, and health outcomes [17,18].

Zhi-Geng Jin, Zhe-Qi Zhang, Bin-Bin Liu, Hao Wang, Ying Yang, Li-Na Ren, Hui Zhang, Wei Ji, Zhen-Guo Zhai, Yu-Tao Guo

JMIR Res Protoc 2025;14:e67254

Big Data–Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review

Big Data–Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review

Originally inspired by Alan Cooper's “User Personas” concept in 1998 and later adapted to health care by Liu et al [15], Cooper et al [16], and Pietilä et al [17], health portraits refer to an integrated, person-centered representation that synthesizes heterogeneous data, including symptoms, medical history, biochemical tests, and lifestyle factors, into a unified profile of an individual’s health status.

Haoyang Du, Jianing Yu, Dandan Chen, Jingjie Wu, Erxu Xue, Yufeng Zhou, Xiaohua Pan, Jing Shao, Zhihong Ye

J Med Internet Res 2025;27:e72636

Digital Health Interventions Targeting Psychological Health in Parents of Children With Autism Spectrum Disorder: Protocol for a Scoping Review

Digital Health Interventions Targeting Psychological Health in Parents of Children With Autism Spectrum Disorder: Protocol for a Scoping Review

Despite extensive research, ASD remains a complex neurodevelopmental disorder with an unclear etiology and no definitive cure [3]. While children with ASD generally have a normal life expectancy, they often require lifelong care, particularly from their parents, who typically serve as primary caregivers [4,5]. The demands of caregiving for a child with ASD are significant and can negatively affect the psychological health of parents.

Binbin Ji, Intan Maharani Sulistyawati Batubara, Janene Batten, Xinyi Peng, Sanmei Chen, Zhao Ni

JMIR Res Protoc 2025;14:e68677