e.g. mhealth
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Skip search results from other journals and go to results- 9171 Journal of Medical Internet Research
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The data comprises discussions from a publicly accessible Nurse Forum [4]. Data come from posts aggregated over 28 2-week periods from March 2020 to April 2021. Our preprocessing approach ensures that the data is clean, standardized, and focused on the most relevant linguistic features, allowing for a clearer identification of the key aspects discussed in the nurse forum over time. Texts were tokenized using the Python library Gensim [31].
JMIR AI 2025;4:e64447
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However, most studies on DMHI engagement were directed at patients and the general public [6,16,17]. Little research has focused on HCPs, who represent a unique population with distinct working contexts and are experiencing increasing levels of distress and burnout [22,26,27]. The level of engagement with DMHIs among HCPs and the factors that impact their engagement remain unclear.
JMIR Ment Health 2025;12:e67190
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However, because nearly all of the same CIED-related data can be obtained via RM, an alternative would be to end in-person visits completely if patients were consistently and continuously connected to RM, with in-person evaluations only when needed for clinically actionable reasons, such as CIED reprogramming [2].
JMIR Cardio 2025;9:e66215
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Mobile Apps and Wearable Devices for Cardiovascular Health: Narrative Review
Privacy is comprehensively addressed, with 95% (19/20) of apps including a privacy policy and 75% (15/20) ensuring General Data Protection Regulation (GDPR) compliance. Only 20% lack explicit GDPR mention but still provide privacy policies. Finally, 20% (4/20) of the apps are medically certified, with 3, Preventicus Heartbeats Gmb H, Cardio Signal, and Fibri Check, being clinically tested and capable of detecting AFib.
JMIR Mhealth Uhealth 2025;13:e65782
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In AI-driven item generation, such as with Chat GPT, these issues often emerge due to the model’s reliance on its training data, which may not always align perfectly with the specific objectives intended by educators. For example, an AI might generate content that includes incorrect information, such as asserting that “the human heart only has two chambers” [24], or misinterpret the complexity level required for a medical education context.
JMIR Form Res 2025;9:e65726
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The M-CHAT—a parent-completed dichotomous questionnaire designed for children aged 16‐30 months—is an effective primary screening tool for ASD and other developmental concerns in the general population [3,5]. In our study, we aimed to identify children showing potential developmental concerns at an earlier stage, specifically at 12 months of age.
JMIR Pediatr Parent 2025;8:e58337
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Commercial VR systems, designed primarily for general consumer use, are often marketed for gaming and entertainment purposes rather than rehabilitation [17]. While these systems can be cost-effective and engaging, they frequently lack the specific therapeutic features needed for rehabilitation, resulting in inconsistent outcomes [18]. In contrast, customized VR systems are specifically developed for clinical or rehabilitative applications.
J Med Internet Res 2025;27:e69003
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Modernizing the Staging of Parkinson Disease Using Digital Health Technology
Screening assessment tools, which include the Mini-Mental State Examination [16] and the Montreal Cognitive Assessment [17], provide a quick general assessment of an individual with suspected neurocognitive impairment and identify areas needing further comprehensive evaluation. These assessments focus on a range of neurocognitive functions.
J Med Internet Res 2025;27:e63105
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The Role of AI in Nursing Education and Practice: Umbrella Review
This domain exhibited variability, with several high-quality reviews using standardized data extraction and appraisal frameworks. However, inconsistencies in reporting and appraisal depth were noted in certain studies.
Gonzalez-Garcia et al [45] demonstrated meticulous attention to detail, using dual independent data extraction and rigorous appraisal methods.
J Med Internet Res 2025;27:e69881
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CDSSs are valuable tools that assist clinicians in their decision-making processes by providing targeted clinical knowledge, patient information, and other relevant health data. CDSSs play a central role in managing large amounts of data [29] and ensuring the retrieval of relevant information. CDSSs do not make decisions autonomously. However, the integration of machine learning and AI, such as CNNs, has significantly enhanced their capabilities [30].
J Med Internet Res 2025;27:e58660
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