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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

Hence, interviewing them at their residence was identified to be a feasible and effective option. The study duration was 1 month, from January 15 to February 16, 2024. Participants were purposively selected for this research study. Participants who were part of a quasi-experimental study conducted to improve follow-up services in BEH and their service catchment area were chosen.

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

In 2021 alone, the global market for sleep wearables was valued at 1.8 billion and is expected to reach 3.1 billion by 2028, with North America being the largest market for these devices [1]. Sleep technology is advancing rapidly, with a wide range of wearables now available to consumers, with common examples being wrist-worn devices, rings, and headbands.

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

Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions

Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions

Compared with clinical research, which uses patient records at health care facilities, population-based research relies on surveys of large, diverse, and representative community samples, often requiring researchers to locate and interview participants in the field. Poor quality data collection undermines population data usefulness and analysis validity, with quality issues arising at every stage of the research process [7].

Ha Thanh Le, Dung Viet Tien Vu, Thi Ngoc Anh Nguyen, Hang Tran Thi, Tan Viet Nguyen, Thao Phuong Tran, Aria Kekalih, Samita Rijal, Dewi Friska, Raph L. Hamers, Abhilasha Karkey, Mary Chambers, Jennifer Ilo Van Nuil, SPEAR and CoAct Team, Sonia Lewycka

J Med Internet Res 2025;27:e65377

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

Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide, responsible for approximately 9.14 million deaths in 2019 [1,2]. Early identification of individuals at high risk is crucial, as timely interventions can significantly reduce the likelihood of severe outcomes like heart attacks and strokes.

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

Pain Assessment Tools for Infants, Children, and Adolescents With Cancer: Protocol for a Scoping Review

Pain Assessment Tools for Infants, Children, and Adolescents With Cancer: Protocol for a Scoping Review

Based on the findings, the need for future research on pain management in pediatric cancer will be discussed. Thus, our primary goal is to synthesize existing literature on cancer-related pain assessment tools for infants, children, and adolescents aged We will conduct a scoping review following the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis [17]. Research team members discussed and revised the drafted protocol.

Mika Hirata, Noyuri Yamaji, Shotaro Iwamoto, Ayaka Hasegawa, Mitsuru Miyachi, Takashi Yamaguchi, Daisuke Hasegawa, Erika Ota, Nobuyuki Yotani, Japanese Society for Palliative Medicine

JMIR Res Protoc 2025;14:e66614

Feasibility Testing a Meditation App for Professionals Working With Youth in the Legal System: Protocol for a Hybrid Type 2 Effectiveness-Implementation Pilot Randomized Controlled Trial

Feasibility Testing a Meditation App for Professionals Working With Youth in the Legal System: Protocol for a Hybrid Type 2 Effectiveness-Implementation Pilot Randomized Controlled Trial

Throughout our pilot trial of the meditation app for youth, officers approached our team requesting their own version of the app both (1) as a stress reduction tool for themselves during the workday, and (2) as a resource to support mindfulness skills–building among youth on their caseloads. In response to these requests, we developed a grant proposal for two main phases of research activity. The grant was funded by the National Center for Complementary and Integrative Health (NCCIH; grant R34 AT012078).

Ashley D Kendall, Emily Pela, Danielle Amonica, Erin Jaworski, Brenikki Floyd, The AIM+ Community Advisory Board

JMIR Res Protoc 2025;14:e71867

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management

Although AI-driven tools have already shown promise in improving diagnosis, triage, and decision-making in emergency department settings [4], in the context of prehospital emergency response, most research has remained at the proof-of-concept stage, highlighting the need for prospective validation to support real-world implementation [6].

The NIGHTINGALE Consortium, Marta Caviglia

J Med Internet Res 2025;27:e67318

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

With that in mind, in this paper, we aim to conduct a scoping review by assessing research papers from repositories, such as Pub Med and IEEE Xplore, which have used machine learning methods with smartphone-derived data to predict diseases related to the eyes, skin, and voice, and from databases available for public use. We aim to answer the following important research questions: What are the databases available for eye-, skin-, and voice-related diseases?

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

JMIR AI 2025;4:e59094