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Exploring 97 Years of Aedes aegypti as the Vector for Dengue, Yellow Fever, Zika, and Chikungunya (Diptera: Culicidae): Scientometric Analysis

Exploring 97 Years of Aedes aegypti as the Vector for Dengue, Yellow Fever, Zika, and Chikungunya (Diptera: Culicidae): Scientometric Analysis

Doubling time increased from 9.3 in 1978-1998 to 12.1 in 2000-2009, and reverted back to 9.3 in 2020-2023 (Figure 1 B). The RGR and DT varied across countries. Among the top 10 countries in total publications, France exhibited the longest doubling time, while China had the shortest doubling time. The Unites States has published since 1927 and had a short doubling time initially, but the numbers reduced in recent years (Figure 1 B).

Yoon Ling Cheong, Sumarni Mohd Ghazali, Mohd Hazilas Mat Hashim, Mohd Khairuddin Che Ibrahim, Afzufira Amran, Tsye Yih Tiunh, Hui Li Lim, Yong Kang Cheah, Balvinder Singh Gill, Kuang Hock Lim

Interact J Med Res 2025;14:e65844

Assessing Social Interaction and Loneliness and Their Association With Frailty Among Older Adults With Subjective Cognitive Decline or Mild Cognitive Impairment: Ecological Momentary Assessment Approach

Assessing Social Interaction and Loneliness and Their Association With Frailty Among Older Adults With Subjective Cognitive Decline or Mild Cognitive Impairment: Ecological Momentary Assessment Approach

The following is a detailed explanation of the variables considered in this study. We constructed the conceptual framework of our study, based on the social-ecological model for older adults [31] and the successful aging model proposed by Rowe and Kahn [32], as shown in Figure 1.

Bada Kang, Dahye Hong, Seolah Yoon, Chaeeun Kang, Jennifer Ivy Kim

JMIR Mhealth Uhealth 2025;13:e64853

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis

Eight databases labeled lung pathologies including 6 studies that labeled a single lung pathology (pneumonia in 2 studies, asthma in 2 studies, bronchitis in 1 study, and CF in 1 study) and 2 studies that labeled multiple lung pathologies.

Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

J Med Internet Res 2025;27:e66491

Developing the Digital Health Communication Maturity Model: Systematic Review

Developing the Digital Health Communication Maturity Model: Systematic Review

Advancements in digital technology have ushered in an era characterized by rapid digital transformations across various sectors. This progress has led to the emergence of digital health as a pivotal force in the public health arena, fundamentally reshaping the accessibility, delivery, and management of health care services [1].

Grace Jeonghyun Kim, Kang Namkoong

J Med Internet Res 2025;27:e68344

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Acute kidney injury (AKI) represents a critical challenge in postoperative care, significantly affecting patient outcomes and health care systems. It is a common complication that affects up to 5% to 7.5% of all hospitalized patients, with a markedly higher prevalence of 20% in intensive care units [1]. Among all AKI in hospitalized patients, 40% occur in postoperative patients [1].

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

J Med Internet Res 2025;27:e62853

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

These factors may modestly impact its effectiveness in predicting clinical outcomes following noncardiac surgeries in practical clinical environments [10]. Subsequent predictive tools, such as the American College of Surgeons, National Surgical Quality Improvement Project (NSQIP), and NSQIP Myocardial Infarction or Cardiac Arrest, developed after RCRI, also show strong performance in predicting postoperative MACE.

Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

J Med Internet Res 2025;27:e66366

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

In addition, real-time inference in clinical settings remains problematic. The difficulty in ensuring that variables reflect the current patient state, coupled with infrequent and inconsistent timing of data collection, impedes real-time monitoring and decision-making in fast-paced clinical environments [23]. Moreover, most existing models rely on static data points, failing to capture the dynamic nature of a patient’s condition.

Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

J Med Internet Res 2025;27:e59520

Effect of the Yon PD App on the Management of Self-Care in People With Parkinson Disease: Randomized Controlled Trial

Effect of the Yon PD App on the Management of Self-Care in People With Parkinson Disease: Randomized Controlled Trial

In previous studies, a total score of 70 or higher on the SC-CII was reported to indicate a high level of self-care [36,38]. The high validity and reliability of the SC-CII have been reported in previous research [38]. In this study, Cronbach α of the SC-CII was 0.580-0.673 in maintenance, 0.872-0.916 in monitoring, and 0.601-0.725 in management. To measure satisfaction with the Yon PD app, we developed a self-report questionnaire.

JuHee Lee, Subin Yoo, Yielin Kim, Eunyoung Kim, Hyeran Park, Young H Sohn, Yun Joong Kim, Seok Jong Chung, Kyoungwon Baik, Kiyeon Kim, Jee-Hye Yoo

J Med Internet Res 2025;27:e62822

Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial

Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial

At the same time, commercial smartphone apps have many limitations in research, especially in collecting physical activity data over a sufficient period [16]. Consequently, the practical application of the research results to patients with cancer in the real world is limited, although e Health tools can provide a potent resource to facilitate personalized and accessible care in daily life [11,12].

Yae Won Tak, Junetae Kim, Haekwon Chung, Sae Byul Lee, In Ja Park, Sei Won Lee, Min-Woo Jo, Jong Won Lee, Seunghee Baek, Yura Lee

J Med Internet Res 2025;27:e52694

Authors’ Reply: Balancing Challenges and Opportunities When Evaluating Remote Rehabilitation for Sarcopenia in Older Adults

Authors’ Reply: Balancing Challenges and Opportunities When Evaluating Remote Rehabilitation for Sarcopenia in Older Adults

Thank you for your reading of our article “A 4-Week Mobile App–Based Telerehabilitation Program vs Conventional In-Person Rehabilitation in Older Adults With Sarcopenia: Randomized Controlled Trial” [1]. We are truly gratified that our study has garnered your attention and interest and has sparked meaningful discussion. In response to the points raised by the authors [2], our answers are as follows.

Lu Zhang, Ying Ge, Wowa Zhao, Xuan Shu, Lin Kang, Qiumei Wang, Ying Liu

J Med Internet Res 2025;27:e73174