e.g. mhealth
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Skip search results from other journals and go to results- 66 JMIR mHealth and uHealth
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This tactile feedback can be delivered through wearable systems. This study is part of a broader effort to develop a haptic physiotherapy assistant, including a wearable device that provides vibrotactile feedback. Among the wearable formats, the shirt stands out as particularly promising due to its ability to cover larger areas of the body, allowing for precise and distributed feedback during rehabilitation exercises.
JMIR Form Res 2025;9:e62903
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The calculation was based on data reported by Rykov et al [30], in a study where patients were screened using wearable devices, and (MLMs were applied (Textbox 1).
JMIR Res Protoc 2025;14:e71374
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Wearable devices such as instrumented insoles have been shown to provide excellent objective measures for monitoring rehabilitation progress after surgery and injury, whereas responses obtained from questionnaires reflect subjective patient perspectives [1-3]. It is currently unknown whether and how the trajectories of these separate measures differ throughout recovery. This information, however, is crucial for drawing correct conclusions from such data in clinical practice and research.
JMIR Mhealth Uhealth 2025;13:e71022
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The Fitbit Sense (Fitbit Inc) is a wrist-worn wearable device designed for continuous monitoring of various physiological parameters, such as heart rate, resting heart rate, step counts, daily minutes of vigorous activity, and sleep patterns and architecture. This wearable device was chosen because of its long battery life (up to 6 d) and its durability, including waterproofing.
JMIR Form Res 2025;9:e54630
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Recommendations for Successful Development and Implementation of Digital Health Technology Tools
In the form of mobile health (m Health) apps, wearable devices, telemedicine, molecular technologies, enhanced electronic health records, and associated advanced analytics, these tools are being used to optimize and streamline clinical workflows [1]. Entirely new forms of health care are envisioned that are not only more participatory and personalized but also seamlessly integrated into patients’ daily routines and environments.
J Med Internet Res 2025;27:e56747
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For at-home measurements of naturalistic activity, we have recently developed a multisensor wearable suit for infants (MAIJU, motor ability assessment of infants with a jumpsuit [28-32]), using 4 IMU sensors that collect triaxial accelerometer and gyroscope signals at 52 Hz. The data analysis uses a validated, deep learning–based analytical pipeline to provide a human-equivalent level classification of postures (7 categories) and generic movements (9 categories) for each second of the recording [29].
JMIR Mhealth Uhealth 2025;13:e58078
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Previous works using passively sensed smartphone and wearable data to predict MS outcomes explored the feasibility of passive data collection and the preliminary association between sensed behaviors and standard rater-assessed clinical outcomes [6-14]. However, little is known regarding the clinical applicability of continuous longitudinal digital phenotyping to predict the severity of clinically relevant patient-reported symptoms in people with MS.
J Med Internet Res 2025;27:e70871
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After wearing the wearable for 5 consecutive days, children received a diploma and a first insight in the data was shown to their parents. The data were recorded locally on the device. By connecting the wearable to the computer, the accompanying software extracts and synchronizes all recorded data to the Empatica server (as is default). After downloading all data as a zip file containing several .csv files, the data were only locally stored at the UMCU and deleted from the server.
JMIR Form Res 2025;9:e65559
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Physical activity was measured using a wrist-worn wearable activity monitor (Fitbit Inspire 2). The active monitor estimates sedentary time (minutes), lightly active time (minutes), fairly active time (minutes), very active time (minutes), calories (kcal), steps (n), and distance moved (m). Steps are calculated by the 3-axis accelerometer, while distance moved is automatically calculated based on the number of steps and the participant’s stride length.
JMIR Hum Factors 2025;12:e66750
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Eligible studies involved participants diagnosed with UD, BD, or HC and used portable or wearable digital devices such as smartphone apps, wearable sensors, or audio or visual recordings. The studies were required to either compare digital phenotyping results with diagnostic outcomes from professional medical evaluations, compare UD with BD, or perform a classification task involving UD, BD, and HC.
J Med Internet Res 2025;27:e72229
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