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Patient-Centered Risk Prediction, Prevention, and Intervention Platform (TIMELY) to Support the Continuum of Care in Coronary Artery Disease Using eHealth and Artificial Intelligence: Protocol for a Randomized Controlled Trial

Patient-Centered Risk Prediction, Prevention, and Intervention Platform (TIMELY) to Support the Continuum of Care in Coronary Artery Disease Using eHealth and Artificial Intelligence: Protocol for a Randomized Controlled Trial

The Coropredict score assesses cardiovascular risk using a combination of laboratory-based parameters (including hemoglobin A1c, N-terminal pro-B-type natriuretic peptide, high-sensitivity troponin I, cystatin C, and high-sensitivity C-reactive protein) and demographic information (age, sex, and smoking status) [35]. Increasing the 6-minute walking test distance (primary behavioral outcome indicating functional fitness level) from baseline to 6 months.

Mirela Habibovic, Emma Douma, Hendrik Schäfer, Manuela Sestayo-Fernandez, Tom Roovers, Xin Sun, Henrik Schmidt, Mona Kotewitsch, Jos Widdershoven, David Cantarero-Prieto, Frank Mooren, Carlos Pena-Gil, José Rámon González Juanatey, Martin Schmidt, Hagen Malberg, Vassilis Tsakanikas, Dimitrios Fotiadis, Dimitris Gatsios, Jos Bosch, Willem Johan Kop, Boris Schmitz

JMIR Res Protoc 2025;14:e66283

Comparison of Sleep Features Across Smartphone Sensors, Actigraphy, and Diaries Among Young Adults: Longitudinal Observational Study

Comparison of Sleep Features Across Smartphone Sensors, Actigraphy, and Diaries Among Young Adults: Longitudinal Observational Study

The gray dashed line in plots B and E indicates the time at which EARS truncated data collection at 639 minutes from midnight. EARS: Effortless Assessment Research System. This study tested the concordance among smartphone-based, diary, and wearable (ie, actigraphy) sources of data to measure common metrics of sleep health. We found that when comparing diary and EARS in-bed and out-of-bed periods across 24 hours, EARS yielded a high TPR and low FPR.

Jaclyn S Kirshenbaum, Ryann N Crowley, Melissa D Latham, David Pagliaccio, Randy P Auerbach, Nicholas B Allen

JMIR Form Res 2025;9:e67455

Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

Integrating Food Preference Profiling, Behavior Change Strategies, and Machine Learning for Cardiovascular Disease Prevention in a Personalized Nutrition Digital Health Intervention: Conceptual Pipeline Development and Proof-of-Principle Study

Target behaviors appropriate for CVD prevention were selected based on the capability, opportunity, motivation–behavior (COM-B) model, which provides a comprehensive framework for understanding behavior change [12]. This was further analyzed using the Theoretical Domains Framework (TDF) to identify specific barriers and facilitators related to dietary changes, particularly those involving fats, through a literature review [13-18].

Hana Fitria Navratilova, Anthony David Whetton, Nophar Geifman

J Med Internet Res 2025;27:e75106

Patient Perspectives on Open-Door Policies in Psychiatry: Mixed Methods Study

Patient Perspectives on Open-Door Policies in Psychiatry: Mixed Methods Study

Node centrality reflects the relative importance of terms within the networks (topics): (A) Restriction and Institutionalization and (B) Autonomy and Self-Determination. The final topic analysis revealed the following topics: (1) Restriction and Institutionalization and (2) Autonomy and Self-Determination.

Timur Liwinski, Robert Davidson, Jan Sarlon, Rainer Gaupp, Lukas Imfeld, Annette B Brühl, Marc Vogel, Christian G Huber, Undine E Lang

J Med Internet Res 2025;27:e73610

Planning for the Unexpected and Unintended Effects of mHealth Interventions: Systematic Review

Planning for the Unexpected and Unintended Effects of mHealth Interventions: Systematic Review

Characteristics of the studies. am Health: mobile health. b N/A: not applicable. c MR: menstrual regulation. d COM-B: capability, opportunity, motivation, and behavior. e OR: operating room. f TB: tuberculosis. g HEWs: health extension workers. RQ1 asked about what unintended effects have been identified in the studies. And one goal of the study is to develop a typology of the unintended effects based on the ecological model [25]. A total number of 26 unintended effects were identified.

Weidan Cao, Xiaohui Cao, Andrew David Sutherland

J Med Internet Res 2025;27:e68909