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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

For offline participants, written consent was obtained after they fully understood the relevant information, while online participants were required to check the option “I have read the above information and agree to participate in this study” before proceeding to the questionnaire page. The study protocol was approved by the Academic Ethics Committee of Guizhou Normal University (Approval No.: 20230300005), ensuring ethical compliance throughout the research process.

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

circumference, BMI, blood pressure, and blood biochemical indicators, and a comparison of the means and SDs between the insulin resistance and non–insulin resistance groups, along with the corresponding P values to evaluate the association of each characteristic with insulin resistance. a IR: Insulin resistance. b HOMA: Homeostasis Model Assessment. c WC: waist circumference. d BMI: body mass index. e SBP: systolic blood pressure. f DBP: diastolic blood pressure. g BUN: blood urea nitrogen. h Sr: serum creatinine. i

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

JMIR Med Inform 2025;13:e72238

Lessons Learned From the Integration of Ambient Assisted Living Technologies in Older Adults’ Care: Longitudinal Mixed Methods Study

Lessons Learned From the Integration of Ambient Assisted Living Technologies in Older Adults’ Care: Longitudinal Mixed Methods Study

The following quotes illustrate these perceptions: I feel safer... I feel that it [Ubismart] is very good for its sense of safety. If anything happens to me, [the caregivers] would know...If nothing is happening for the time being, then all is good...While we sleep at night, we feel safer.

Oteng Ntsweng, Martin Kodyš, Zhi Quan Ong, Fang Zhou, Antoine de Marassé-Enouf, Ibrahim Sadek, Hamdi Aloulou, Sharon Swee-Lin Tan, Mounir Mokhtari

JMIR Rehabil Assist Technol 2025;12:e57989

Effectiveness of Telemedicine Interventions on Motor and Nonmotor Outcomes in Parkinson Disease: Systematic Review and Network Meta-Analysis

Effectiveness of Telemedicine Interventions on Motor and Nonmotor Outcomes in Parkinson Disease: Systematic Review and Network Meta-Analysis

Heterogeneity analysis indicated a moderate level (Section S6 in Multimedia Appendix 2: I²=49%, τ²=0.1000, P=.06). The design-by-treatment interaction test showed no significant global inconsistency (Section S6 in Multimedia Appendix 2: P=.54). A total of 16 studies involving 947 participants assessed quality of life. Section S5.2 in Multimedia Appendix 2 presents the direct comparisons and sample size distribution among telemedicine types for quality of life.

Jiejie Dou, Junyu Wang, Xianqi Gao, Guotuan Wang, Ying Bai, Yixin Liang, Kunyi Yang, Yong Yang, Lin Zhang

J Med Internet Res 2025;27:e71169