JMIR Biomedical Engineering
Engineering for health technologies, medical devices, and innovative medical treatments and procedures
Editor-in-Chief: Gunther Eysenbach, MD, MPH, FACMI, Adjunct Professor, School of Health Information Science, University of Victoria (Canada)
Gunther Eysenbach, MD, MPH, FACMI, Adjunct Professor, School of Health Information Science, University of Victoria (Canada)
JMIR Biomedical Engineering (JBME) is a new sister journal of JMIR (the leading open-access journal in health informatics), focusing on the application of engineering principles, technologies, and medical devices to medicine and biology.
As an open access journal, we are read by clinicians and patients alike and have (as are all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
JMIR Biomedical Engineering has been publishing since 2016 and features a rapid and thorough peer-review process. Articles are carefully copyedited and XML-tagged, ready for submission in PubMed Central. JMIR Biomedical Engineering is indexed in DOAJ.
A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping.
Applications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research.
Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO2) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra–low-cost smartphone-based optical sensing system that captures SpO2 and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO2 within 1% of the ground truth in levels as low as 75%.
Advances in mobile phone technologies coupled with the availability of modern wireless networks are beginning to have a marked impact on digital health through the growing array of apps and connected devices. That said, limited deployment outside of developed nations will require additional approaches to collectively reach the 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates, and a variety of user inputs, which can increase friction for the patient. An alternate, so-called Beyond the Mobile approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone-based systems, translating to improved engagement and adherence rates among patients. Furthermore, connecting these Internet of Medical Things instruments through next-generation networks offers the potential to reach patients with acute needs in nonurban regions of developing nations. Herein, we outline how deployment of Beyond the Mobile technologies through low-power wide-area networks could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally.
The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.
Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the “atmosome.” The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health.
Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them.
The majority of medications used in treating asthma and chronic obstructive pulmonary disease (COPD) are taken through metered-dose inhalers (MDIs). Studies have reported that most patients demonstrate poor inhaler technique, which has resulted in poor disease control. Digital Health applications have the potential to improve the technique and adherence of inhaled medications.
Mindfulness-based stress reduction has demonstrated some efficacy for chronic pain management. More recently, virtual reality (VR)–guided meditation has been used to assist mindfulness-based stress reduction. Although studies have also found electroencephalograph (EEG) changes in the brain during mindfulness meditation practices, such changes have not been demonstrated during VR-guided meditation.
Transcranial magnetic stimulation (TMS), when applied over the primary motor cortex, elicits a motor-evoked potential (MEP) in electromyograms measured from peripheral muscles. MEP amplitude has often been observed to fluctuate trial to trial, even with a constant stimulus. Many factors cause MEP fluctuations in TMS. One of the primary factors is the weak stationarity and instability of cortical activity in the brain, from which we assumed MEP fluctuations originate. We hypothesized that MEP fluctuations are suppressed when TMS is delivered to the primary motor cortex at a time when several electroencephalogram (EEG) channels measured on the scalp are highly similar in the frequency domain.
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