e.g. mhealth
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Skip search results from other journals and go to results- 17 JMIR mHealth and uHealth
- 12 Journal of Medical Internet Research
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The Fitbit Sense is equipped with a range of sensors, including a photoplethysmogram sensor, an optical heart rate sensor, an electrodermal activity sensor, an accelerometer, a gyroscope, and an ambient light sensor. The photoplethysmogram sensor uses green and red light-emitting diodes along with a photodiode to measure blood volume changes in the microvasculature of the skin, allowing for the estimation of heart rate and blood oxygen saturation.
JMIR Form Res 2025;9:e54630
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To organize the selected literature and facilitate a comprehensive analysis, a classification taxonomy was developed based on the types of sensors used for hydration monitoring in wearable technologies. This taxonomy categorized the papers into the following groups: electrical sensors, optical sensors, thermal sensors, microwave sensors, multimodal sensors, and commercial products (Figure 2).
JMIR Mhealth Uhealth 2025;13:e60569
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A suite of in-home monitoring sensors, developed at the Center to Stream Healthcare In-Place, and commercially licensed by Foresite Healthcare, LLC [14,15], were installed within the study participants’ homes. Data were collected from 3 types of in-home sensors: a bed mat containing 4 hydraulic transducers that captured ballistocardiogram signals, a thermal depth sensor for capturing gait and detecting falls, and passive infrared (PIR) motion sensors [16-19].
JMIR Mhealth Uhealth 2025;13:e56678
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Furthermore, the use of wearable or embedded sensors allows many different aspects of functional ability to be characterized and objectively quantified [12,13,17,18]. Thus, they provide more granular and more detailed information than captured with the single scores of traditional standard clinical assessments such as the Nine-Hole Peg Test, oral Symbol Digit Modalities Test, or the Timed 25-Foot Walk [19-21].
J Med Internet Res 2025;27:e63090
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The recent development of digital and wearable technologies has made it possible to continuously track PA in real life through sensors embedded in digital devices. This expansion provides researchers with a broader range of choices, as both research-grade and consumer-grade wearables, with varying costs and capacities to measure health conditions, are now available in the market.
J Med Internet Res 2025;27:e59878
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Toward Unsupervised Capacity Assessments for Gait in Neurorehabilitation: Validation Study
Moreover, combining the 10-MWT with wearable sensors, such as inertial measurement units, allows for the extraction of additional spatiotemporal gait parameters. These parameters are not only robust, but they enhance the interpretation of clinical assessment outcomes and aid in detecting motor recovery poststroke as well as predicting prognosis after stroke [24-26].
J Med Internet Res 2025;27:e66123
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This study focuses on the proximal effects of the JITAI on targeted outcomes collected via EMA among adherent participants, previously defined as those who wore the sensors for more than 70% of the time during the 2-week period and completed a majority of the strategies [16]. Our rationale for examining adherent participants is 2-fold.
JMIR Mhealth Uhealth 2025;13:e55379
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To accomplish this, TATRC will (1) identify which commercial off-the-shelf (COTS) sensors are most suitable to collect TCCC data elements required to populate the DD Form 1380; (2) conduct human subjects research using participants who perform TCCC skills in controlled, simulated environments; and (3) annotate all sensor suite data collected to build a TCCC dataset for current and future ML and AI algorithms to leverage.
JMIR Res Protoc 2025;14:e67673
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As such, this study makes use of integrated, environmentally deployed sensors to enrich this dataset in a nonintrusive manner. This setup allows for the formation of a technology, which can deliver circadian-aligned lighting and simultaneously monitor any resultant changes to well-being. Additionally, validated questionnaires are used to collect information on certain behavioral symptoms, which the sensors cannot themselves generate.
JMIR Mhealth Uhealth 2024;12:e56951
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Sensor-based digital health technologies (s DHTs), defined as connected digital medicine products that process data captured by mobile sensors using algorithms to generate measures of behavioral and/or physiological function [1], have been increasingly adopted in both research and health care in recent years [2,3]. s DHTs include products designed to capture data passively (such as continuous glucose monitors and wearables for monitoring sleep) or during active tasks (such as mobile spirometry or smartphone-based
J Med Internet Res 2024;26:e57628
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