e.g. mhealth
Search Results (1 to 10 of 55 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 14 JMIR mHealth and uHealth
- 9 Journal of Medical Internet Research
- 8 JMIR Formative Research
- 4 JMIR Rehabilitation and Assistive Technologies
- 3 JMIR Research Protocols
- 2 Interactive Journal of Medical Research
- 2 JMIR Biomedical Engineering
- 2 JMIR Cancer
- 2 JMIR Diabetes
- 2 JMIR Mental Health
- 1 JMIR Aging
- 1 JMIR Human Factors
- 1 JMIR Medical Informatics
- 1 JMIR Nursing
- 1 JMIR Pediatrics and Parenting
- 1 JMIR Public Health and Surveillance
- 1 JMIR Serious Games
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Challenges
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Perioperative Medicine
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
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
Download Citation: END BibTex RIS
During a recent Framingham Heart Study (FHS) exam cycle, physical activity was measured using both a consumer or mobile health device (Apple Watch) and a research-grade accelerometer (Actical) at the same time in the same individuals. The purpose of this investigation was to assess the agreement between Apple Watch and Actical-derived daily step count in free-living environments.
JMIR Biomed Eng 2024;9:e54631
Download Citation: END BibTex RIS
To do so, we leveraged data from 2 previous studies of individuals who had completed cancer treatment to test whether an array of digital measures of real-world physical behavior, measured with a wearable accelerometer over a 1-week period, were related to self-reported and performance measures of physical function. First, we examined associations between real-world physical behavior and self-reported well-being and physical function.
JMIR Cancer 2024;10:e53180
Download Citation: END BibTex RIS