e.g. mhealth
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Skip search results from other journals and go to results- 14 JMIR mHealth and uHealth
- 9 JMIR Formative Research
- 9 Journal of Medical Internet Research
- 4 JMIR Rehabilitation and Assistive Technologies
- 3 JMIR Research Protocols
- 2 Interactive Journal of Medical Research
- 2 JMIR Aging
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- 2 JMIR Mental Health
- 1 JMIR Human Factors
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To quantify physical activity, the number of steps, measured using an accelerometer, is widely used and is a valid and reliable variable for assessing physical activity in clinical rehabilitation settings [4,5]. Numerous studies have established a close association between the number of steps taken and health-related quality of life [6], self-efficacy [7], and the risk of stroke recurrence [8].
JMIR Form Res 2025;9:e63064
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Compared with researcher-focused accelerometer devices, consumer wearables (which also contain triaxial accelerometers) could yield more widely scalable, and clinician and user-friendly, systems for developing and testing potential applications. Our prior pilot study demonstrated that it is possible to collect 24-hour accelerometer data from the Apple Watch and generate standard 24-hour sleep/wake rhythm measures [6]. However, this prior study was limited to a convenience sample of young adults.
JMIR Aging 2025;8:e67294
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With the use of an accelerometer worn on the wrist, real-time information about physical activity and circadian rhythmicity patterns can be collected in a person’s natural environment. Such data may allow detection of variation in activity that may be missed during clinical visits [6,7].
JMIR Mhealth Uhealth 2025;13:e57599
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The increasing popularity of wearable devices has led to a surge in the collection of various physiological signals, including accelerometer data from wristbands, smartwatches, and other sensors [2]. While these wearables offer valuable insights into our daily activities, inertial measurement units (IMUs) stand out for their unique ability to capture 3-dimensional motion data, including acceleration, angular velocity, and orientation.
JMIR Mhealth Uhealth 2025;13:e60521
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Prior research has explored monitoring nighttime movement and identifying sleep-related disorders or sleep stages via the use of unobtrusive sensor systems equipped with accelerometer or pressure sensors, connected to beds [15-18]. However, only a limited number of researchers have directed the focus of nighttime movement monitoring with accelerometer sensors connected to the bed toward the exploration of detecting nighttime movement to support NH continence care.
JMIR Nursing 2024;7:e58094
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This is typically accomplished utilizing accelerometer (ACC)-based data from small, portable (wristwatch-sized) recording devices. Most studies report strong to relatively strong correlation with the gold standard PSG recordings, but with the added benefit of continuous, noninvasive monitoring [1,2,10-13,15-17,19,20].
JMIR Ment Health 2024;11:e62959
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Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review
Earlier studies commonly used uniaxial accelerometer devices that measure only vertical axis acceleration in g-units, corresponding to the acceleration due to gravity (9.81 m/s2) [10-13]. However, in recent decades, triaxial accelerometers operating in 3 orthogonal dimensions (ax, ay, and az) have gained preference due to their ability to offer a broad coverage, thus providing a more comprehensive understanding of overall human activity [14].
J Med Internet Res 2024;26:e59497
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Among the participants of the KNHANES VI (2014-2015), 1827 people participated in the accelerometer survey, but 59 people were excluded due to loss of the accelerometer (9 people), nonwearers (47 people), and mechanical errors (3 people).
JMIR Hum Factors 2024;11:e59659
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The signals collected by the accelerometer and gyroscope were used to train a 1 D convolutional neural network–based feature learning model, enabling the identification of 6 ADL. The results demonstrated high accuracy in both external and study data, validating the effectiveness of the proposed method.
The study by Huynh-The et al [41] introduces an innovative method for recognizing ADL- and sports-related activities using wearable sensors.
JMIR Med Inform 2024;12:e57097
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An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design
In this framework, both accelerometer and gyroscope data are used as input for the model. Then, we introduce a feature-generating method grounded in the CNN-SA architecture. Here, CNN is used to extract features from dual-stream data and capture spatial patterns, to preliminarily identify the pattern differences between fall and ADLs. The feature vector extracted by CNN subsequently passes through an SA layer, which assigns weights to accelerometer and gyroscope features.
J Med Internet Res 2024;26:e56750
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