e.g. mhealth
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Skip search results from other journals and go to results- 32 JMIR mHealth and uHealth
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For example, EMA prompts could be used to deliver real-time feedback or motivational messages when a participant is detected to be sedentary or transitioning to PA, leveraging data from wearable devices synced with smartphones. This adaptability positions EMA as a promising tool for both preventive health strategies and rehabilitation programs aimed at improving PA behaviors.
J Med Internet Res 2025;27:e59878
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Positive and negative affect surveys were deployed to participants’ smartphones once every 2 weeks via an abbreviated form of the Positive and Negative Affect Schedule (PANAS) [41,42]. The abbreviated PANAS consisted of 5 items assessing negative affect and 5 items assessing positive affect. Example items include “indicate to what extent you have felt afraid over the past few days” (negative affect) and “indicate to what extent you have felt inspired over the past few days” (positive affect).
J Med Internet Res 2025;27:e64965
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Leveraging the ubiquity of smartphones, ECHAS aims to be a scientifically validated and regulatory-approved digital medicine technology. The app is modeled on the “history and examination” of a neurologist or cardiologist by asking a series of evidence-based questions about the user’s medical history and symptoms, as well as a finger-tapping test designed to detect unilateral weakness.
JMIR Form Res 2025;9:e60465
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Person-Specific Analyses of Smartphone Use and Mental Health: Intensive Longitudinal Study
Participants installed a custom study app on their smartphones, enabling an unobtrusive collection of smartphone screens. The app required no active user management and operated transparently, with a status bar icon indicating its activity. Screenshots, along with operating system metadata like phone battery state and foreground app were captured and recorded automatically every 5 seconds when the screen was active.
JMIR Form Res 2025;9:e59875
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While we know that mood disorders impact daily activity and sleep rhythms, the activity rhythms of patients with mood disorders inferred from passively collected data from their smartphones are not well studied [34]. This work investigates the different activity rhythms inferred from smartphones and evaluates the differences between patients and healthy controls.
JMIR Ment Health 2025;12:e63622
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These apps significantly enhance the accessibility and delivery of health services, especially with the increasing demand for smartphones and other digital devices driven by rapid technological advancements [3]. These apps empower individuals to participate in symptom control and identification, receive treatment, and obtain personal feedback and motivational support [2,4,5].
Asian Pac Isl Nurs J 2025;9:e66852
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We aimed to investigate digital traces passively collected from participants’ smartphones and wearable devices during their daily lives.
Graphical abstract showing study methodology and feature generation. A total of 20 participants with and 20 without ADHD were recruited in the United Kingdom between August and November 2020 and matched on age and gender.
JMIR Form Res 2025;9:e54531
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