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Exploring Service Users’ Experiences of a Community-Based Intervention to Improve Follow-Up at Bharatpur Eye Hospital in Nepal: Qualitative Study

Exploring Service Users’ Experiences of a Community-Based Intervention to Improve Follow-Up at Bharatpur Eye Hospital in Nepal: Qualitative Study

This research showed no statistically significant difference in the proportion of follow-up between the intervention groups. Only 3% (8/264) of children completed all 3 follow-ups, but the overall compliance with follow-up was only 0.76%, with more follow-ups in the reminder with SMS text messaging and phone call groups [7].

Manisha Shrestha, Gopal Bhandari, Sadhan Bhandari, Gudlavalleti Venkata Satyanarayana Murthy, Ruchi Priya, Binod Pandey, Daya Shankar Chaudhary, Puspa Giri, Sureshkumar Kamalakannan, Operational Research Capacity Building Study Group

JMIR Pediatr Parent 2025;8:e65023

Sleep, Health Care–Seeking Behaviors, and Perceptions Associated With the Use of Sleep Wearables in Canada: Results From a Nationally Representative Survey

Sleep, Health Care–Seeking Behaviors, and Perceptions Associated With the Use of Sleep Wearables in Canada: Results From a Nationally Representative Survey

Data for this study were collected as part of a larger national sleep and mental health survey co-developed by the Mood Disorders Society of Canada, a nongovernmental organization advocating for mental health, as well as scientists and clinicians from the Canadian Sleep Research Consortium and the Canadian Sleep Society.

Karianne Dion, Meggan Porteous, Tetyana Kendzerska, Ashley Nixon, Elliott Lee, Massimiliano de Zambotti, Sheila N Garland, Mandeep Singh, Gino De Luca, Samuel Gillman, Andrée-Ann Baril, Dave Gallson, Rebecca Robillard, Canadian Sleep Research Consortium

J Med Internet Res 2025;27:e68816

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Therefore, this dataset is adequate to answer the research questions. Missing data analysis was conducted, and variables with more than 30% missing values were excluded to enhance model robustness. Missing data were imputed using Multivariate Imputation by Chained Equations. See Multimedia Appendix 1 for details on the percentage of missing data for each variable before imputation.

Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Research Dawadi, Takao Inoue, Jie Ting Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M Nakao, Yuka Kato, Masayuki Teramoto, Michihiro Araki

JMIR Cardio 2025;9:e68066

Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review

Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review

A literature review is a systematic way of collecting studies relevant to a research topic, assessing the methodologies and results of the studies, and making recommendations for improvements if necessary [11]. In the health care domain, the implementation of literature reviews has been considered important for conducting further research and developing guidelines for clinical practice [12].

Research Dawadi, Mai Inoue, Jie Ting Tay, Agustin Martin-Morales, Thien Vu, Michihiro Araki

JMIR AI 2025;4:e59094

Establishing Syndromic Surveillance of Acute Coronary Syndrome, Myocardial Infarction, and Stroke: Registry Study Based on Routine Data From German Emergency Departments

Establishing Syndromic Surveillance of Acute Coronary Syndrome, Myocardial Infarction, and Stroke: Registry Study Based on Routine Data From German Emergency Departments

The Robert Koch Institute (RKI), a federal agency and research institute responsible for disease control and prevention in Germany, has recognized the practical benefits of this approach. At the RKI, an ED syndromic surveillance system has been established in 2020, using daily routine data from the German Emergency Department Data Registry AKTIN [2]. Currently, 58 EDs in 12 German federal states voluntarily provide data for research and surveillance purposes.

Madlen Schranz, Mirjam Rupprecht, Annette Aigner, Leo Benning, Carmen Schlump, Nesrine Charfeddine, Michaela Diercke, Linus Grabenhenrich, Alexander Ullrich, Hannelore Neuhauser, Birga Maier, AKTIN Research Group, Felix Patricius Hans, Sabine Blaschke

JMIR Public Health Surveill 2025;11:e66218

Assessment of Environmental, Sociocultural, and Physiological Influences on Women’s Toileting Decisions and Behaviors Using “Where I Go”: Pilot Study of a Mobile App

Assessment of Environmental, Sociocultural, and Physiological Influences on Women’s Toileting Decisions and Behaviors Using “Where I Go”: Pilot Study of a Mobile App

To address this gap, the Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium developed a smartphone app called “Where I Go.” The Consortium’s goal was to produce a sophisticated data collection tool to explore potential relationships between factors influencing toileting decisions and bladder health.

Abigail R Smith, Elizabeth R Mueller, Cora E Lewis, Alayne Markland, Caroline Smerdon, Ariana L Smith, Siobhan Sutcliffe, Jean F Wyman, Lisa Kane Low, Janis M Miller, The Prevention of Lower Urinary Tract Symptoms (PLUS) Research C

JMIR Mhealth Uhealth 2025;13:e56533

Design of a Tool Capable of Assessing Environmental Sociocultural Physical Factors Influencing Women’s Decisions on When and Where to Toilet Within Real-World Settings: Protocol for the Build and Usability Testing of a Mobile App for Use by Community-Dwelling Women

Design of a Tool Capable of Assessing Environmental Sociocultural Physical Factors Influencing Women’s Decisions on When and Where to Toilet Within Real-World Settings: Protocol for the Build and Usability Testing of a Mobile App for Use by Community-Dwelling Women

The research protocol for usability testing was fully approved by the external expert panel for the PLUS Consortium. Multimedia Appendix 7 provides the full protocol for usability testing. We purposely excluded the University of Michigan PLUS Research Center in formal usability study data collection to remove potential bias since this team did the build and iterative informal process of debugging checks.

Janis M Miller, Jean F Wyman, Lawrence An, Haitao Chu, Cynthia S Fok, Missy Lavender, Cora Elizabeth Lewis, Alayne D Markland, Leslie M Rickey, Ying Sheng, Siobhan Sutcliffe, Lisa Kane Low, Elizabeth R Mueller, The Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium

JMIR Res Protoc 2024;13:e54046