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Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Several systematic reviews concluded that rapid weight gain (RWG) from birth to the age of 1 year, defined as the upward centile crossing in a weight growth chart, was associated with a fourfold higher risk of later obesity than those without infant RWG [14,15]. As such, infant RWG is considered a sensitive proxy marker that denotes future obesity risk.

Miaobing Zheng, Yuxin Zhang, Rachel A Laws, Peter Vuillermin, Jodie Dodd, Li Ming Wen, Louise A Baur, Rachael Taylor, Rebecca Byrne, Anne-Louise Ponsonby, Kylie D Hesketh

JMIR Public Health Surveill 2025;11:e69220

Improving HIV Prevention Among Heterosexual Men Seeking Sexually Transmitted Infection Services in Malawi: Protocol for a Type I Effectiveness-Implementation Hybrid Randomized Controlled Trial of Systems Navigator–Delivered Integrated Prevention Package (HPTN 112-NJIRA Study)

Improving HIV Prevention Among Heterosexual Men Seeking Sexually Transmitted Infection Services in Malawi: Protocol for a Type I Effectiveness-Implementation Hybrid Randomized Controlled Trial of Systems Navigator–Delivered Integrated Prevention Package (HPTN 112-NJIRA Study)

Access to a systems navigator Intended to engage with participants at each preexposure prophylaxis (Pr EP) visit using a menu of “sessions” that examine items such as risk perception and address barriers to Pr EP adherence (Multimedia Appendix 1); provide reminders for upcoming Pr EP visits; and trace participants who have missed a scheduled Pr EP visit via phone or in person. 2.

Sarah E Rutstein, Laura Limarzi-Klyn, Jane S Chen, Yaw O Agyei, Shahnaz Ahmed, Ian Bell, Myron Cohen, Jessica M Fogel, Vivian Go, Dan Haines, Erica L Hamilton, Irving F Hoffman, Mina C Hosseinipour, Mark A Marzinke, William C Miller, Mathews Mukatipa, Julie Pulerwitz, Hans M L Spiegel, Ting Ye, Mitch Matoga

JMIR Res Protoc 2025;14:e72981

Evaluating Effectiveness of mHealth Apps for Older Adults With Diabetes: Meta-Analysis of Randomized Controlled Trials

Evaluating Effectiveness of mHealth Apps for Older Adults With Diabetes: Meta-Analysis of Randomized Controlled Trials

Meta-analyses are vital for evidence-based practice and health care decision-making because they use an objective qualitative approach for assembling, arranging, and assessing existing literature in a research domain, which also includes a quantitative approach and statistical analysis of a collection of results from its individual studies for the purpose of integrating findings [21-23].

Renato Ferreira Leitao Azevedo, Michael Varzino, Erika Steinman, Wendy A Rogers

J Med Internet Res 2025;27:e65855

Applying the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Adapt the CHAMP App for Pediatric Feeding Tube Weaning: Application and Case Report

Applying the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Adapt the CHAMP App for Pediatric Feeding Tube Weaning: Application and Case Report

The CHAMP App was developed for data transfer with a pediatric cardiac population and provides families and the clinical team with a new model of care leveraged by a mobile app integrated with a software platform [17]. Data options include feeding intake, output, vital signs, weights, videos, and concerns (Figure 1).

Dana M Bakula, Alexandra Zax, Sarah Edwards, Kristina Nash, April Escobar, Rachel Graham, Amy Ricketts, Ryan Thompson, Sarah Bullard, Julianne Brogren, Leah Shimmens, Lori A Erickson

JMIR Form Res 2025;9:e67398

Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions

Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions

The novelty lies in a parallel signal processing approach, deriving 3 physiological readings from a single bioimpedance feed, which streamlines the device. Initial testing on a single human participant demonstrated the potential for future integration into a smart telemonitoring health system. Agcayazi et al [41] designed a wearable bioimpedance analyzer for infant hydration monitoring.

Nazim A. Belabbaci, Raphael Anaadumba, Mohammad Arif Ul Alam

JMIR Mhealth Uhealth 2025;13:e60569

Temporal Dynamics of Subtle Cognitive Change: Validation of a User-Friendly Multidomain Digital Assessment Using an Alcohol Challenge

Temporal Dynamics of Subtle Cognitive Change: Validation of a User-Friendly Multidomain Digital Assessment Using an Alcohol Challenge

Moreover, the burden and cost of administering a full cognitive assessment would likely make such an approach unrealistic. One possible solution to this issue is the development of a digital platform with automated administration/scoring, designed for remote data collection—eliminating the need for participants to visit a clinic [18].

John Frederick Dyer, Florentine Marie Barbey, Ayan Ghoshal, Ann Marie Hake, Bryan J Hansen, Md Nurul Islam, Judith Jaeger, Rouba Kozak, Hugh Marston, Mark Moss, Viet Nguyen, Rebecca Louise Quinn, Leslie A Shinobu, Elizabeth Tunbridge, Brian Murphy, Niamh Kennedy

J Med Internet Res 2025;27:e55469

The Influence of Medical Culture and Key Factors on Mobile App Usage Frequency and Perceived Effectiveness in Physical Therapy: Cross-Sectional Study

The Influence of Medical Culture and Key Factors on Mobile App Usage Frequency and Perceived Effectiveness in Physical Therapy: Cross-Sectional Study

Conversely, a rigid or traditional medical culture can act as a barrier to the usage of new technologies and methods [4]. The role of medical culture extends beyond the attitudes of individual professionals, influencing how teams function and how institutions respond to emerging challenges in health care [5].

Khalid A Alahmari, Ravi Shankar Reddy

J Med Internet Res 2025;27:e68406

Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study

Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study

Overview of unstructured data processing for a single timestep. CUIs are extracted from clinical notes by c TAKES and assembled into a usable data structure that maps encounter ID to a list of CUIs associated with the timestep. A table of CUI-associated data is created, independent of patient data, containing preferred text, ICD codes, Sap BERT embedding, and cluster information.

Charles A Kotula, Jennie Martin, Kyle A Carey, Dana P Edelson, Dmitriy Dligach, Anoop Mayampurath, Majid Afshar, Matthew M Churpek

J Med Internet Res 2025;27:e75340

Relationship of Hair Cortisol Concentration With Perceived and Somatic Stress Indices: Cross-Sectional Pilot Study

Relationship of Hair Cortisol Concentration With Perceived and Somatic Stress Indices: Cross-Sectional Pilot Study

Since cortisol is incorporated into growing hair, hair is emerging as a novel matrix for measuring retrospective cortisol secretion over months [9,11,12]. Each 1 cm hair segment, beginning from the proximal end, approximates a month’s cortisol production.

Sharon H Bergquist, Danyang Wang, Brad Pearce, Alicia K Smith, Allison Hankus, David L Roberts, Miranda A Moore

JMIR Form Res 2025;9:e63811

Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

Nivel-PCD: All insurance claims codes as recorded by the GP are received, however, when a dataset has been requested by the researcher, the codes are filtered by the data processor to include a selection of codes relevant to the study (in agreement with the researchers), on a database zone level. Contacts Defined as moments of contact between a GP and a patient. Based on unique dates on which an insurance claim code was recorded by the GP, that is, the maximum number of contacts per patient per day is 1.

Melissa H J van Essen, Robin Twickler, Yvette M Weesie, Ilgin G Arslan, Feikje Groenhof, Lilian L Peters, Isabelle Bos, Robert A Verheij

J Med Internet Res 2025;27:e64628