e.g. mhealth
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- 38 JMIR Formative Research
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We assessed acceptability through semistructured interviews with patients and physicians focusing on aspects of the tool they found useful, what could be improved and added to the tool, and what should or could be removed from the tool. Ethnographic field observations were collected during patient encounters to supplement data on acceptability.
JMIR Form Res 2025;9:e66932
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Deconstructing Participant Behaviors in Virtual Reality Simulation: Ethnographic Analysis
This codebook served as a standardized tool for subsequent research phases (Phase 2: Description and Analysis and Phase 3: Pattern Explanation). Following the consolidation of the codebook, the research team independently applied it to each VR simulation session video recording. After coding each simulation session, the team reconvened to discuss and refine the codebook further as needed. This iterative process continued until data saturation [27] was achieved.
JMIR Med Educ 2025;11:e65886
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Two researchers independently reviewed the online content of each tool, comparing their analyses and discussing any disagreements to reach a consensus. Importantly, we only analyzed primary information available directly on the websites of the tools and did not use secondary data about these tools (eg, articles published about such tools).
JMIR Form Res 2025;9:e54472
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The objective of this study was to develop a simple, accessible NLP tool for identifying VTE symptoms in primary care EHRs, suitable for both high- and low-resource settings and aligned with the national quality payment program. The tool was tested on external datasets to evaluate its performance compared with deep learning and machine learning models. This main aim is to use narrative EHR data for clinical quality reporting to identify missed or delayed diagnoses of VTE after a primary care visit.
JMIR Med Inform 2025;13:e63720
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The tool comprised a pretraining stage of watching short educational videos on obesity science, weight-related communication, and weight stigma, then using the simulation tool (Multimedia Appendices 1 and 2).
This study used purposive and snowball sampling strategies to obtain a sample of 31 participants. This sample size aligns with recommendations for feasibility trials, which suggest total sample sizes ranging from N=24 to 50 [39].
JMIR Med Educ 2025;11:e65949
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By conducting a quasi-experimental, nonrandomized controlled trial among health care workers in a public hospital in Ecuador, this research seeks to evaluate the impact of the “Psicovida” mobile app [16], a digital tool designed to support mental health through cognitive behavioral therapy (CBT)–based activities, on depression and emotional distress symptoms.
JMIR Hum Factors 2025;12:e58943
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Exclusion criteria consisted of already using an e-MH tool, cognitive impairment, substance use, and unstable mental or physical health problems (eg, marked suicidality and active psychosis).
Participants were recruited between January 2021 and March 2022. The care manager performed a routine initial mental health evaluation for patients referred to the Co CM by their PCP.
J Particip Med 2025;17:e55349
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The aim of this study was to understand the tailoring variables and co-design the intervention options of a JITAI with individuals who have experienced trauma and problem anger, alongside experts in digital and mental health, by understanding:
What is the preferred language, visual features, and content of a digital mental health tool to help individuals manage their problem anger?
When do individuals need support to manage their anger differently?
JMIR Hum Factors 2025;12:e62960
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This is mainly due to the absence of structured schemas for these portals, making it difficult for the MDS team members to agree on a common mapping approach or tool to identify matching items. As a result, the mappings presented in this paper are based on older versions of the MDS and need to be revised. Detailed updated mappings, also containing optional items and value sets of the MDS, will be published elsewhere.
JMIR Med Inform 2025;13:e63906
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However, there are currently no studies assessing outcomes for a digital tool specifically created for a specialist NHS WMS that is fully incorporated into the patient pathway.
In September 2022, an innovative digital support tool, Gro Health W8 Buddy, was launched in the specialist WMS at University Hospital Coventry and Warwickshire Trust (UHCW). W8 Buddy is a bespoke version of the NHS-certified app “Gro Health.”
J Med Internet Res 2025;27:e62661
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