e.g. mhealth
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Skip search results from other journals and go to results- 2 Journal of Medical Internet Research
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The patient bots were modeled to represent 3 distinct emotional personas—anxious, depressed, and frustrated—and were designed to simulate real-life patient interactions. Each patient bot was assigned the role of a 40-year-old male patient with lung cancer undergoing treatment. Detailed persona-specific instructions were included to guide their interactions:
Persona-specific emotional states:
Anxious persona: Expresses uncertainty and seeks detailed explanations.
JMIR Nursing 2025;8:e63058
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To enable more student-patient interactions without increasing costs, staff’s workload, or the burden on patients, virtual simulated patients have emerged as an adjunctive approach [10,11]. For communication skills in particular, web-based chatbots have been developed to offer an additional learning format [12], and recent advances in artificial intelligence (AI) such as large language models (LLMs) have helped those tools to achieve a new level of realism [13-15].
JMIR Med Educ 2024;10:e59213
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Notably, younger patients with more severe health conditions and limited health care system interactions have shown a propensity for OHI use and may tend to be early adopters of Chat GPT for health purposes [22]. Identifying the characteristics of early Chat GPT adopters may provide insight into who may benefit most from tailored guidance on appropriate use and potential risks of Chat GPT OHI.
J Med Internet Res 2024;26:e55138
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Questions probed satisfaction with the social interactions, web-based group play, and how classes were conducted. The questions were scored on a 5-point Likert scale, with a score of 1 indicating “very dissatisfied” and a score of 5 indicating “very satisfied.” Similarly, enjoyment of the program was measured using a single-question score that pertained to the overall enjoyment of the program.
JMIR Form Res 2023;7:e47630
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This study aimed to uncover the patterns of behaviors and interactions of users with ADHD on Twitter. Specifically, we investigated the difference between Twitter users with ADHD and Twitter users without ADHD based on the following three aspects: (1) the pattern of talking about different topics; (2) the pattern of expressing emotions; and (3) the interactions of time, tweet type, followers, and followings.
J Med Internet Res 2023;25:e43439
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During a 6-hour observation period, Willemse et al [12] reported, on average, 1.5 meaningful interactions, with one-third of participants experiencing zero meaningful interaction during the observation. Care home interactions are not only infrequent but can also be short, be fragmented, sometimes consist of “Elder speak,” and be task orientated in nature [13,14].
JMIR Res Protoc 2023;12:e43408
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