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Effects of Integrating Wearable Activity Trackers With a Home-Based Multicomponent Exercise Intervention on Fall-Related Parameters and Physical Function in Older Adults: Randomized Controlled Trial

Effects of Integrating Wearable Activity Trackers With a Home-Based Multicomponent Exercise Intervention on Fall-Related Parameters and Physical Function in Older Adults: Randomized Controlled Trial

According to the Centers for Disease Control and Prevention, 27.6% of individuals aged >65 years in the United States reported falling during the previous year, and 38,742 (78.0 per 100,000) died from unintentional falls in 2020 [4]. In Korea, the 2017 National Survey of Korean Elderly reported that 15.9% of older adults experienced falls in the past year, with an average of 2.1 falls per person [5].

Yejin Kim, Kyung Hee Park, Hye-Mi Noh

JMIR Mhealth Uhealth 2025;13:e64458

Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Despite advances in the methodologies for each process in clinical trials, a systematic approach for enhancing the efficacy of participant enrollment is lacking. Artificial intelligence (AI) has various applications in every section of the industry, and a machine learning (ML)–based optimal design of clinical trials has entered the sphere of pharmaceuticals [11].

Byungeun Shon, Sook Jin Seong, Eun Jung Choi, Mi-Ri Gwon, Hae Won Lee, Jaechan Park, Ho-Young Chung, Sungmoon Jeong, Young-Ran Yoon

JMIR AI 2025;4:e64845

Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study

Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study

Alternatively, patients living far away from the pediatric cancer center, especially those in remote or rural areas, might use less care, therefore contributing excess zeros in our data. This approach addresses our overdispersion problem by accounting for an excess 0 ED visits and an excess number of patients with no hospitalizations in our data. We considered findings statistically significant at the α=.05 level. Statistical analyses were conducted in Stata SE (version 18; Stata Corp LLC).

Yvette H Tran, Seho Park, Scott L Coven, Eneida A Mendonca

JMIR Public Health Surveill 2025;11:e66834

Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study

Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study

Numerous studies have established a strong link between metabolic syndrome and CVD [2], indicating that the early identification and management of metabolic syndrome may play a critical role in preventing CVD [7]. In recent years, there has been growing interest in using machine learning and deep learning models to predict metabolic syndrome and related chronic diseases, such as CVD [8,9].

Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

J Med Internet Res 2025;27:e67525

Association Between Prevention Focus and Sedentary Behavior in Older Adults: Cross-Sectional Study

Association Between Prevention Focus and Sedentary Behavior in Older Adults: Cross-Sectional Study

Negative self-perceptions of aging in older adults have also been found to have a negative relationship with self-reported physical activity (PA) and performance in daily activities [13-15]. This is likely due to a belief that PA and overall movement are not beneficial in the long term and could increase their risk of falling.

Jethro Raphael Suarez, Amber Blount, Kworweinski Lafontant, Joon-Hyuk Park, Rui Xie, Nichole Lighthall, Ladda Thiamwong

Interact J Med Res 2025;14:e63280

Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study

Designing Health Care Provider–Centered Emergency Department Interventions: Participatory Design Study

Emergency departments (EDs) are dynamic, challenging, and time-critical medical environments in a hospital. In recent years, the increasing need for emergency care services has led to overcrowding [1-3], resulting in extended wait times for patients, diminished patient satisfaction [4], and suboptimal patient outcomes [5,6]. With the high volume of patients and staff shortages [7], health care providers in the ED face significant challenges in their workflow [3,8].

Woosuk Seo, Jiaqi Li, Zhan Zhang, Chuxuan Zheng, Hardeep Singh, Kalyan Pasupathy, Prashant Mahajan, Sun Young Park

JMIR Form Res 2025;9:e68891

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

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

Adolescent Self-Reflection Process Through Self-Recording on Multiple Health Metrics: Qualitative Study

Adolescent Self-Reflection Process Through Self-Recording on Multiple Health Metrics: Qualitative Study

With the significance of early intervention in addressing mental health challenges, particularly as adolescents develop coping mechanisms for daily stressors and negative emotions, studies have demonstrated the efficacy of digital self-recording in emotional well-being [38-42]. In a case study, a self-recording system increased positive mood and coping strategies in the adult population under stress [43].

Minseo Cho, Doeun Park, Myounglee Choo, Doug Hyun Han, Jinwoo Kim

J Med Internet Res 2025;27:e62962

COVID-19–Related Racism and Mental Health Among Asian Americans: Integrative Review

COVID-19–Related Racism and Mental Health Among Asian Americans: Integrative Review

In June 2021, the National Commission to Address Racism in Nursing defined racism as “assaults on the human spirit in the form of actions, biases, prejudices, and an ideology of superiority based on race that persistently causes moral suffering and physical harm to individuals and perpetuates systemic injustices and inequities” [1].

Tania Von Visger, Amy Lyons, Yanjun Zhou, Kayla Wardlaw, Eunhee Park, Yu-Ping Chang

Asian Pac Isl Nurs J 2025;9:e63769