Search Articles

View query in Help articles search

Search Results (1 to 10 of 230 Results)

Download search results: CSV END BibTex RIS


Remote Patient Monitoring System for Polypathological Older Adults at High Risk for Hospitalization: Retrospective Cohort Study

Remote Patient Monitoring System for Polypathological Older Adults at High Risk for Hospitalization: Retrospective Cohort Study

The outcome measures for this study were the total number of unplanned hospital admissions, including ED visits, the hospitalization rate per patient, and the cumulative length of hospital stay in days for both the total population and per patient. Outcomes were compared between the year preceding the patient’s enrollment in the RPM system (Y–1) and after 1 year of follow-up (Y).

Damien Testa, Israa Salma, Vincent Iborra, Victoire Roussel, Mireille Dutech, Etienne Minvielle, Elise Cabanes

J Med Internet Res 2025;27:e71527

Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach

Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach

To maintain some disease homogeneity, studies focusing on hematological patients alone were excluded, although those with mixed patient populations, solid tumors, or hematological cancers were admissible. Given that this review focuses on symptoms, articles evaluating Qo L, coping strategies, or symptom-targeted interventions alone were excluded. Reviews or meta-analyses were also excluded. Eligible articles had to be written in English. This systematic review was not registered.

Vincent Richard, Allison Gilbert, Emanuela Pizzolla, Giovanni Briganti

JMIR Cancer 2025;11:e66087

Spread and Scale-Up of a Region-Wide Telehealth Navigation Service in a Rural Context: Qualitative Process Evaluation

Spread and Scale-Up of a Region-Wide Telehealth Navigation Service in a Rural Context: Qualitative Process Evaluation

Candidates for Patient Watch were reviewed by the health coach (registered nurse) for appropriateness (eg, candidate was excluded if they were inpatients or living in a residential care facility). A lay tele-navigator support (TNS) worker contacted eligible candidates and provided information about the program. Interested candidates were then contacted by the health coach; consent was obtained; and their current medical, emotional, and social situation was assessed via telehealth.

Mary Malakellis, Anna Wong Shee, Laura Alston, Vincent L Versace, Pheona Griffith, Jade Odgers, Kevin Mc Namara

J Med Internet Res 2025;27:e64734

Insight inTo Stress and POOping on Work TIME (ITS POO TIME): Protocol for a Web-Based, Cross-Sectional Study

Insight inTo Stress and POOping on Work TIME (ITS POO TIME): Protocol for a Web-Based, Cross-Sectional Study

Inclusion criteria were individuals aged ≥18 years who self-identify as having paid employment in a part-time or full-time capacity in any work role in any organization, or self-employed, or undertaking paid training (eg, apprentice and internist).

Phillip John Tully, Suzanne Cosh, Gary Wittert, Sean Martin, Andrew Vincent, Antonina Mikocka-Walus, Deborah Turnbull

JMIR Res Protoc 2025;14:e58655

Multimodal Web-Based Telerehabilitation for Patients With Post–COVID-19 Condition: Protocol for a Randomized Controlled Trial

Multimodal Web-Based Telerehabilitation for Patients With Post–COVID-19 Condition: Protocol for a Randomized Controlled Trial

Participants were informed about the study purpose, procedures, potential risks and benefits, the voluntary nature of their participation, and their right to withdraw from the study at any time without consequences. Regarding any secondary analyses using existing data, the original consent and ethics approval explicitly covered the possibility of secondary analysis without requiring additional consent from participants. All collected data were fully anonymized before analysis.

Aleksandar Tomaskovic, Vincent Weber, David T Ochmann, Elmo Wanja Neuberger, Ella Lachtermann, Alexandra Brahmer, Nils Haller, Barlo Hillen, Kira Enders, Viktoria Eggert, Peter Zeier, Klaus Lieb, Perikles Simon

JMIR Res Protoc 2025;14:e65044

Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial

Effect of Uncertainty-Aware AI Models on Pharmacists’ Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial

Eligible participants were licensed pharmacists in the United States who were at least 18 years old and had access to a laptop or desktop computer with a webcam. Pharmacists were excluded if they required assistive technology to use the computer, wore eyeglasses with more than 1 power, had uncorrected cataracts, intraocular implants, glaucoma, or permanently dilated pupils, or had eye movement or alignment abnormalities (eg, lazy eye, strabismus, and nystagmus).

Corey Lester, Brigid Rowell, Yifan Zheng, Zoe Co, Vincent Marshall, Jin Yong Kim, Qiyuan Chen, Raed Kontar, X Jessie Yang

JMIR Med Inform 2025;13:e64902

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study

Training hyperparameters were set to 1000 epochs, a batch size of 32, and a learning rate of 0.001. Two datasets of 5000 and 10,000 artificial patients were generated, representing a data increase rate of 10 and 20 artificial patients, respectively, for 1 real patient. The next step involved assessing the consistency (fidelity scores) and confidentiality (filter similarity scores and degree of anonymization) of artificial data.

Fabrice Ferré, Stéphanie Allassonnière, Clément Chadebec, Vincent Minville

J Med Internet Res 2025;27:e63130

Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis

Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis

Search terms were selected following the population, prediction factors, and outcome format for conducting systematic reviews for prognostic or prediction studies [33]. Our target population was social media users, prediction factors were prediction terms (ie, machine learning, algorithms, text mining, and language style), and the outcome was depression.

Doreen Phiri, Frank Makowa, Vivi Leona Amelia, Yohane Vincent Abero Phiri, Lindelwa Portia Dlamini, Min-Huey Chung

J Med Internet Res 2025;27:e59002

Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis

Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis

In addition, inactive individuals were reported to have lower well-being scores and higher levels of depression and anxiety than moderately active and active individuals. A large-scale meta-analysis of data for 1,853,610 adults revealed that the rates of severe COVID-19 were 34% lower, the risk of hospitalization was 36% lower, and COVID-related mortality was 43% lower in participants regularly engaging in PA than in their inactive peers [10].

Bastien Wyatt, Nicolas Forstmann, Nolwenn Badier, Anne-Sophie Hamy, Quentin De Larochelambert, Juliana Antero, Arthur Danino, Vincent Vercamer, Paul De Villele, Benjamin Vittrant, Thomas Lanz, Fabien Reyal, Jean-François Toussaint, Lidia Delrieu

J Med Internet Res 2025;27:e68199