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Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

The consortium’s objectives include (1) addressing ethical and legal requirements for the use of data from different sources for the CPM; (2) creating a CPM that incorporates both conventional and specific, attainable nonconventional risk factors; (3) cocreating with health care professionals and patients to realize a personalized artificial intelligence–driven e Health solution (e Coach) in the CVRM health care plan; and (4) evaluating the effectiveness of this e Health solution in primary care settings.

Rutger van Mierlo, Bart Scheenstra, Joost Verbeek, Anke Bruninx, Petros Kalendralis, Inigo Bermejo, Andre Dekker, Arnoud van 't Hof, Marieke Spreeuwenberg, Laura Hochstenbach

JMIR Res Protoc 2025;14:e66068

Effectiveness and Cost-Effectiveness of Using a Social Robot in Residential Care for Individuals With Challenges in Daily Structure and Planning: Protocol for a Multiple-Baseline Single Case Trial and Health Economic Evaluation

Effectiveness and Cost-Effectiveness of Using a Social Robot in Residential Care for Individuals With Challenges in Daily Structure and Planning: Protocol for a Multiple-Baseline Single Case Trial and Health Economic Evaluation

However, Brandt et al [13] conclude that further research on the effectiveness and cost-effectiveness of such technologies is warranted. In line with this, the Dutch Ministry of Health, Welfare, and Sport has emphasized the importance of evaluating the impact of innovative technologies in disability care settings to ensure that they are both effective and financially sustainable [14]. Effectiveness studies measure the level of beneficial effect of interventions in “real-world” settings [15,16].

Kirstin N van Dam, Marieke F M Gielissen, Nienke M Siebelink, Ghislaine A P G van Mastrigt, Wouter den Hollander, Brigitte Boon

JMIR Res Protoc 2025;14:e67841

Mobile-Based Cognitive Behavioral Therapy for Health Care Workers’ Mental Health in Ecuador: Quasi-Experimental Study

Mobile-Based Cognitive Behavioral Therapy for Health Care Workers’ Mental Health in Ecuador: Quasi-Experimental Study

Recent studies have assessed the effectiveness of m Health interventions targeting various mental health issues, including depression [6,7], suicide [8], schizophrenia [9], substance use disorders [10], and psychosis [11], among others [12].

Sandra Lorena Muñoz-Ortega, Rubén Vladimir Alvarado Muñoz, Daniela Santamaria Guayaquil, Jade Pluas-Borja, Marco Faytong-Haro

JMIR Hum Factors 2025;12:e58943

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

This exceptional performance underscores the potential of ML to transform diagnostic pathology, especially given the rapid advancements in technology, availability, and cost-effectiveness of MLaa S platforms such as Google’s Vertex AI. Previous AI applications in digital pathology, specifically in colorectal cancer screening, have shown great potential in increasing the efficiency and accuracy of diagnosis.

David Beyer, Evan Delancey, Logan McLeod

JMIR Form Res 2025;9:e67457

Usefulness of Interventions Using a Smartphone Cognitive Behavior Therapy Application for Children With Mental Health Disorders: Prospective, Single-Arm, Uncontrolled Clinical Trial

Usefulness of Interventions Using a Smartphone Cognitive Behavior Therapy Application for Children With Mental Health Disorders: Prospective, Single-Arm, Uncontrolled Clinical Trial

In recent years, mental health research has increased on CBT treatment provided via the internet and smartphones (ICBT) as an alternative to face-to-face CBT treatment [16,18-21], and the effectiveness of ICBT is reportedly no different from that of face-to-face CBT [22]. According to Aemissegger et al [23], the baseline characteristics of patients in internet-based and face-to-face intervention trials revealed no significant differences.

Shinichiro Nagamitsu, Ayumi Okada, Ryoichi Sakuta, Ryuta Ishii, Kenshi Koyanagi, Chizu Habukawa, Takashi Katayama, Masaya Ito, Ayako Kanie, Ryoko Otani, Takeshi Inoue, Tasuku Kitajima, Naoki Matsubara, Chie Tanaka, Chikako Fujii, Yoshie Shigeyasu, Michiko Matsuoka, Tatsuyuki Kakuma, Masaru Horikoshi

JMIR Form Res 2025;9:e60943

Minimal Clinically Important Difference of Average Daily Steps Measured Through a Consumer Smartwatch in People With Mild-to-Moderate Parkinson Disease: Cross-Sectional Study

Minimal Clinically Important Difference of Average Daily Steps Measured Through a Consumer Smartwatch in People With Mild-to-Moderate Parkinson Disease: Cross-Sectional Study

In this context, the minimum detectable change (MDC) and the minimal clinically important difference (MCID) are 2 key metrics and are pivotal for calculating clinical trial sample sizes and assessing the effectiveness of interventions [37,38]. On the one hand, MDC denotes the smallest change that can be confidently identified beyond the measurement error of the instrument used for a specific parameter [37].

Edoardo Bianchini, Marika Alborghetti, Silvia Galli, Clint Hansen, Alessandro Zampogna, Antonio Suppa, Marco Salvetti, Francesco Ernesto Pontieri, Domiziana Rinaldi, Nicolas Vuillerme

JMIR Mhealth Uhealth 2025;13:e64213

Feasibility of a Randomized Controlled Trial of Large AI-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students: Pilot Randomized Parallel-Group Study

Feasibility of a Randomized Controlled Trial of Large AI-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students: Pilot Randomized Parallel-Group Study

Therefore, the aim of the study is to explore the main barriers and facilitators that could be encountered in conducting a randomized clinical trial to study the effectiveness of the implementation of LLM models as tools to work on the clinical reasoning of physical therapy students. This study was approved by the local ethics committee of the La Salle University Center for Advanced Studies, Madrid, Spain (CSEULS-PI-002/2025). This trial was registered in clinicaltrials.org (NCT06809634).

Raúl Ferrer-Peña, Silvia Di-Bonaventura, Alberto Pérez-González, Alfredo Lerín-Calvo

JMIR Form Res 2025;9:e66126

Implementation Outcomes of Reusable Learning Objects in Health Care Education Across Three Malaysian Universities: Evaluation Using the RE-AIM Framework

Implementation Outcomes of Reusable Learning Objects in Health Care Education Across Three Malaysian Universities: Evaluation Using the RE-AIM Framework

To date, several models have been used to evaluate the effectiveness and implementation of e-learning resources. The Kirkpatrick model, an outcome-focused model, has been widely employed to evaluate the effectiveness of an educational program across 4 levels: reaction, learning, behavior, and results [9]; however, it does not evaluate the implementation outcomes.

Hooi Min Lim, Chin Hai Teo, Yew Kong Lee, Ping Yein Lee, Kuhan Krishnan, Zahiruddin Fitri Abu Hassan, Phelim Voon Chen Yong, Wei Hsum Yap, Renukha Sellappans, Enna Ayub, Nurhanim Hassan, Sazlina Shariff Ghazali, Nurul Amelina Nasharuddin, Puteri Shanaz Jahn Kassim, Faridah Idris, Klas Karlgren, Natalia Stathakarou, Petter Mordt, Stathis Konstantinidis, Michael Taylor, Cherry Poussa, Heather Wharrad, Chirk Jenn Ng

JMIR Med Educ 2025;11:e63882

Examining the Influence of Demographic and Socioeconomic Factors on Disparities in Health Care App Usage: Protocol for a Systematic Scoping Review

Examining the Influence of Demographic and Socioeconomic Factors on Disparities in Health Care App Usage: Protocol for a Systematic Scoping Review

The review seeks to inform stakeholders about the demographic and socioeconomic factors influencing app usage, providing insights to improve app accessibility and effectiveness. This review addresses the following research questions (RQs). RQ1: What are the key demographic and socioeconomic factors associated with health care app usage disparities? RQ2: How do these factors influence the adoption and utilization of health care apps?

Fahad Aljuaid, Emily Reed, Sara Imanpour, Daniel J Mallinson

JMIR Res Protoc 2025;14:e63596

Assessing Postoperative Pain in Patients Who Underwent Total Knee Arthroplasty Using an Automated Self-Logging Patient-Reported Outcome Measure Collection Device: Retrospective Cohort Study

Assessing Postoperative Pain in Patients Who Underwent Total Knee Arthroplasty Using an Automated Self-Logging Patient-Reported Outcome Measure Collection Device: Retrospective Cohort Study

Thus, to showcase the clinical utility and effectiveness of assessing postoperative pain, the purpose of this study was to use the Pain Pad device to determine if tourniquet use during TKA has a detrimental effect on postoperative pain levels in the context of a modern multimodal pain pathway and early mobilization. This research was approved under Integrated Research Application System (229503) and the Open University Human Research Ethics Committee (17/LO/1404).

Prabjit Ajrawat, Blaine Price, Daniel Gooch, Rudolf Serban, Ruqaiya Al-Habsi, Oliver Pearce

JMIR Hum Factors 2025;12:e65271