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Prerequisites for Cost-Effective Home Blood Pressure Telemonitoring: Early Health Economic Analysis

Prerequisites for Cost-Effective Home Blood Pressure Telemonitoring: Early Health Economic Analysis

Proactive monitoring in patients with off-target blood pressures could improve overall blood pressure control through adjustment of medical treatments or by improving adherence, in particular to drug therapy [4]. Besides its potential to improve clinical outcomes, HBPT could optimize health care delivery and resource use [5] by including patient-specific measurement schedules and monitoring algorithms, designed by the responsible health care providers.

Job van Steenkiste, Pim van Dorst, Daan Dohmen, Cornelis Boersma

JMIR Cardio 2025;9:e64386

Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

Real-World Effectiveness of Glucose-Guided Eating Using the Data-Driven Fasting App Among Adults Interested in Weight and Glucose Management: Observational Study

Self-monitoring glucose levels before eating can help people recognize their physiological cues to eat, improve glycemic control, and help modify eating behaviors. This method has previously been referred to as hunger recognition [1] and hunger training [2], and is now termed glucose-guided eating (GGE) [3,4]. Early clinical trials confirmed that self-monitoring of glucose could be a feasible and effective approach to promote weight loss and insulin sensitivity in adults without diabetes [2,5,6].

Michelle R Jospe, Martin Kendall, Susan M Schembre, Melyssa Roy

JMIR Form Res 2025;9:e65368

Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach

Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach

Such insights can contribute to enhanced risk monitoring and patient stratification and provide valuable support for clinicians in their decision-making processes, ultimately improving the quality patient care. By elucidating these critical factors and their associated risk factor patterns, we provided clinicians valuable insights through rigorous analysis for enhancing risk monitoring and patient care across various cancer types.

Xiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, Yun Jiang

JMIR Cancer 2025;11:e62833

Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study

Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study

In recent decades, there has been an increasing focus on self-monitoring apps in primary care, which, with the advent of new technologies, have become more convenient and accessible for patients [1-3]. The use of mobile health (m Health) apps is undeniably a valuable tool for enabling self-monitoring and health care interventions [4,5]. Specifically, the advancement of noncontact techniques for monitoring human vital signs holds significant potential to enhance patient care across various settings [6,7].

Gianvincenzo Zuccotti, Paolo Osvaldo Agnelli, Lucia Labati, Erika Cordaro, Davide Braghieri, Simone Balconi, Marco Xodo, Fabrizio Losurdo, Cesare Celeste Federico Berra, Roberto Franco Enrico Pedretti, Paolo Fiorina, Sergio Maria De Pasquale, Valeria Calcaterra

JMIR Res Protoc 2025;14:e65229

Preferences for Mobile App Features to Support People Living With Chronic Heart Diseases: Discrete Choice Study

Preferences for Mobile App Features to Support People Living With Chronic Heart Diseases: Discrete Choice Study

The discussions yielded themes centered on the user-friendly nature of the app, the capacity of the app to assist in self-monitoring of disease conditions, the need for personalized health education, concerns about data security, and considerations regarding subscription charges. Finally, 2 more attributes were added to the list generated from the stage 1 literature review, bringing the total to 40 attributes.

Sumudu Avanthi Hewage, Sameera Senanayake, David Brain, Michelle J Allen, Steven M McPhail, William Parsonage, Tomos Walters, Sanjeewa Kularatna

JMIR Mhealth Uhealth 2025;13:e58556

Cost-Effectiveness Analysis of a Machine Learning–Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study

Cost-Effectiveness Analysis of a Machine Learning–Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study

The objective of this study was to analyze the clinical and economic impacts of this e Health device in real life compared to the usual monitoring of frail older people living at home. In France, as this medical device is the first to predict ED use, there are, to our knowledge, few medico-economic studies available.

Charlotte Havreng-Théry, Arnaud Fouchard, Fabrice Denis, Jacques-Henri Veyron, Joël Belmin

JMIR Form Res 2025;9:e63700

Understanding the Relationship Between Ecological Momentary Assessment Methods, Sensed Behavior, and Responsiveness: Cross-Study Analysis

Understanding the Relationship Between Ecological Momentary Assessment Methods, Sensed Behavior, and Responsiveness: Cross-Study Analysis

Leveraging the convenience and ubiquity of mobile devices, EMA has been particularly effective in longitudinally monitoring conditions such as depression and mental well-being [1,2], mobility [3], physical activity [4], and fatigue [5]. The strength of EMA lies in its ability to minimize recall bias [6,7] and provide more fine-grained longitudinal data compared with traditional observation methods or retrospective reporting [8,9].

Diane Cook, Aiden Walker, Bryan Minor, Catherine Luna, Sarah Tomaszewski Farias, Lisa Wiese, Raven Weaver, Maureen Schmitter-Edgecombe

JMIR Mhealth Uhealth 2025;13:e57018

Clinical, Psychological, Physiological, and Technical Parameters and Their Relationship With Digital Tool Use During Cardiac Rehabilitation: Comparison and Correlation Study

Clinical, Psychological, Physiological, and Technical Parameters and Their Relationship With Digital Tool Use During Cardiac Rehabilitation: Comparison and Correlation Study

Patients who self-reportedly used either 1 or multiple of the following technologies during OUT-III were categorized as a patient using digital tools: phone-based assessments by the attending cardiac rehabilitation facility and digital training diaries (with and without adherence monitoring done by the cardiac rehabilitation facility and with and without wearables).

Fabian Wiesmüller, David Haag, Mahdi Sareban, Karl Mayr, Norbert Mürzl, Michael Porodko, Christoph Puelacher, Lisa-Marie Moser, Marco Philippi, Heimo Traninger, Stefan Höfer, Josef Niebauer, Günter Schreier, Dieter Hayn

JMIR Mhealth Uhealth 2025;13:e57413