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Abstract Advances in information technology (IT) hardware in the last decade have led to the advent of small connected devices broadly referred to as the Internet of Things (IoT). The IoT and its subcategory of wearable devices (wearables) both have the potential to greatly impact biomedical research. This focused review covers recent biomedical research using the IoT and wearables in the area of neurological traits and disease. In addition, a look into the future of biomedical research using IoT devices and wearables as well as some areas requiring further consideration by the field will be discussed. Introduction The explosion of technology and rise of the internet in the last three decades has impacted the world (1). Within just the last decade, increasing processing power and connectivity of electronics, together with their miniaturization, has led to ‘smart’ devices permeating our lives (2). These devices include wearable devices (wearables), such as fitness trackers and electronic textiles, as well as devices comprising connected cars (3,4). The broad category for these devices is the internet of things (IoT). The market for IoT devices is projected to grow to from $5.8 billion in 2018 to $3.04 trillion in 2020 (3,5). Definition of the IoT and wearables The IoT refers to a network of connected devices that can collect and exchange data using embedded sensors and communication protocols (3). Example applications of the IoT include: wearables (e.g. Fitbit One), smart homes (e.g. Amazon Echo) and connected cars (e.g. AT&T’s connected car services; 4). Generally, any device that connects to the internet and is not considered a traditional information technology (IT) device (e.g. modem, computer, router etc.) is thought to be a part of the IoT (5). There are several classes of devices that fall under the umbrella of the IoT, which could be useful for scientific research. For example, sensor systems that continuously monitor the home environment could occupy a place in the detailed characterization of a study participant’s environment. Wearables, a subcategory of the IoT, are miniaturized electronics that can reside 24/7 with minimal invasiveness on one’s person. Wearables have the ability to connect to other devices and contain sensors for data collection (6). Wearables generally have a display, permitting access to information in real-time. Alternately, wearables may transmit data to another device or the cloud to offloading computationally intensive data analysis. Last, data-input ability and local storage options are often integrated into wearables; however, currently more devices are storing the majority of data in the cloud (5,6). The IoT and wearables in neurological traits and disease research Although the use of research-oriented wearables is nothing new to the research community (7); in the last decade, wearables have become commercially popular and spurned the interest of researchers (3). Widespread adoption of commercial IoT devices and wearables in the last decade presents an unprecedented opportunity. For example, heart rate, brain waves, respiration, diet, exercise, sleep, habits/behaviors and location data can be collected and stored from IoT devices and wearables end users. These data could be leveraged towards innovative research to beneficially impact public health. Specifically, gathered biometric data could be combined with diet and fitness data to identify individuals from a very large sample who are at high and low risk for cardiac and metabolic diseases. These individuals could then be contacted for consent to provide biospecimens for ‘multi-omic’ testing. Thus, potentially revealing therapeutic targets that would not have been discovered otherwise. One of the main promises of the integration of commercial IoT/wearables into scientific research is the ability to greatly enlarge the study cohort with minimal cost. The IoT The IoT in neurological traits and disease research IoT systems using both non-commercial and commercial devices were used to integrate context, patient behavior, environment factors, and health data to predict falls (8), and a pilot IoT system using bed sensors, which sent alerts to a nurse’s smart phone, was used to reduce patient bed fall risk (9). The IoT has been utilized in specific disease fields. Cohen, Bataille, and Martig (10) developed an IoT platform to provide population and patient multi-modal task analyses in Parkinson’s disease (PD). In the field of diabetes care, Onoue et al. (11), put together a randomized control study to evaluate IoT-dependent effects of intensive feedback from healthcare professionals. Lastly, the IoT has been leveraged in the field of childhood development. A project funded by the Spanish government designed and refined a smart toy based off IoT principles, aimed to automatically detect delays in psychomotor development (12). A common theme of these studies was linking existing IT devices (e.g. smart phones, tablets etc.) to IoT devices to achieve the aims of the study at a lower cost compared with integrating new systems and infrastructure. Further, the use of the IoT in this work permitted study of each respective population in a way that could not be achieved via traditional research methods. Wearables Wearables in neurological traits and disease research Sleep Recently, several sleep studies have employed commercially available wearables. One study set out to determine how well research-oriented and commercially available wearables stacked up against the ‘gold standard’ of sleep monitoring (i.e. polysomnography [PSG], 7). The validity of five wearable devices: Basis Health Tracker, Misfit Shine, Fitbit Flex, Withings Pulse O2 and a research-oriented Actiwatch Spectrum, were tested against PSG. Although all devices reported approximately the same total sleep time, no single wearable device was clearly better at matching other PSG sleep measures (e.g. sleep efficiency, light sleep time or deep sleep time). This study revealed the strengths and weaknesses of sleep measures generated by current wearables and illuminated areas were more development in necessary (7). Further, Kang and colleagues (13) found that comparisons between the Fitbit Flex and PSG were excellent between good sleepers and insomnia patients. However, agreement between the Fitbit Flex and PSG was higher in good sleepers and lower in insomnia patients. Two studies examined electroencephalogram (EEG) recordings from the ear to measure sleep. The first study established the feasibility of using an in-ear wearable to record EEG patterns, in order to inform the creation of a wearable for sleep stage monitoring (14). The second study found substantial agreement between recordings derived from a new ear-EEG sensor and conventional scalp electrodes during daytime naps (15). Parkinson’s disease With regard to recent PD research using wearables, the Parkinson@home study demonstrated feasibility of collecting unbiased data using several wearables in a large cohort of PD patients, as they went about their daily life (16). A second study found that wearables needed to be user-friendly, designed attractively, and demonstrate PD management efficacy (17). Furthermore, Ozanne et al. (17), indicated that engagement of end users, patients and healthcare professionals, is crucial in designing and developing wearable devices that facilitate neurological rehabilitation in PD. Taken together, these studies suggest that wearables are good option to aid in at both the bench and the bedside of PD, as long as the wearables meet certain well-thought-out design criteria including: easy-to-use, efficacy in PD, minimally invasive and esthetically pleasing. General physiological monitoring Wearables provide an exciting avenue for measuring and tracking the cardiovascular system in both the laboratory and the clinic. Kroll et al. (18) evaluated the Fitbit Charge as a tool to monitor patients after intensive care. The Fitbit Charge was well tolerated, and generated useable data, although further development of this platform was necessary. Further, two research-oriented wearables were compared with a hospital electrocardiography (ECG) in patients with temporal lobe epilepsy. Data from the Bittium Faros 360° were closer to the hospital ECG when compared with the Empatica E4 (19). Lastly, two recent studies used wearables to measure heart rate variability (HRV). One study used a wearable-based HRV detection to predict mood switches in bipolar disorder patients (20). A second study developed the PhysioCam, a color camera system, capable of obtaining unobtrusive HRV measurements during several challenges (21). Electroencephalogram Several current studies aim to develop a wearable that provides continuous real-time EEG recordings outside the clinic. Mahajan, Morshed, and Bidelman (22) designed a ‘Lego-like’ reconfigurable wearable EEG, facilitating reliable and valid EEG recordings in various real-world situations. Similarly, Billeci et al. (23) designed a wearable EEG to study children with autism spectrum disorders. More recently, studies have moved to less obtrusive wearable EEG with wireless capabilities. Specifically, Pinho et al. (24) developed a wearable wireless EEG, able to provide efficient long-term monitoring of epileptic patients in both clinical and non-clinical settings. In another study, a portable, wireless and wearable cap combining functional near-infrared spectroscopy (fNIRS) with EEG was developed (25). This was the first reported wearable fNIRS/EEG able to monitor neurological states. To summarize, a common thread, tying together most of the current research using wearables in neurological traits and disease research is feasibility; that is, wearables need to be tolerated by participants, useable, and perform/collect data in line with older, similar, ‘gold standard’, non-wearable, and laboratory/clinic-based technology. Although further refinement and smart design choices are needed to make wearables widely accepted throughout the neurological traits and disease research community, the overall picture is that the number of wearables used in research is on the rise. In addition, the unobtrusive and long-term ability of wearables to collect data in real-time and in more native environments (e.g. in a person’s home instead of a research laboratory), has the potential to greatly impact science. The Future Looks Bright for the IoT and Wearables in Research The IoT and wearables in clinical trials As of 2015 there were 299 clinical trials reported to be utilizing wearables (26). Although more recent numbers are not available, it is hard to imagine that the number of clinical trials using IoT and/or wearables has not increased. Notably, in terms of the IoT and clinical trials, Pfizer has teamed up with IBM to build a connected house as a proof-of concept ahead of its planned use in a Phase III PD trial projected to begin in 2019 (27). In addition, in terms of wearables and clinical trials, AstraZeneca conducted a human factors study evaluating the usability of six different body sensors and wearables over the course of a month (28). A major reason pharmaceutical companies are turning to IoT devices and wearables in their clinical trials is to increase accuracy while reducing the overall cost to conduct the trial (28). Potential new avenues for research using the IoT and wearables It is hard to predict how the IoT and wearables will benefit future research; however, some trends do emerge from the literature. The IoT will be a powerful tool to facilitate longitudinal studies. For example, records made and updated automatically via IoT devices and wearables, will make more complete and accurate records compared to manual data entry (29). This notion will soon be put to the test as the National Institutes of Health’s ‘All of Us’ Research Program will be giving out 10 000 Fitbit Charge 2 and Alta HR devices to a representative sample of participants (30). Another trend emerging is IoT devices and wearables with multi-modal sensors; that is, sensors able to track multiple parameters at once. On this topic, Goverdovsky et al. (31) developed an inconspicuous earpiece that can measures brain, cardiac, and respiratory functions. Moreover, Strangman, Ivkovic, and Zhang (32) developed an easy-to-use, wearable, near-infrared scanning device able to collect coordinated functional brain activation, cerebral perfusion, cerebral oxygenation, evoked electrical responses, and electrical activity. One potential novel and exciting avenue of research opened by the IoT and wearables is reactive biospecimen acquisition and phenotyping. It is now possible to remotely monitor a participant’s activity and sleep patterns. Next the participant is asked to perform a task (e.g. complete a test of cognition) or collect a biospecimen (e.g. a saliva or finger prick blood sample) based on their activity or sleep. For example, a study could be conducted to identify blood metabolites that are altered following a particularly poor night’s sleep. IoT devices and wearables could automate aspects of rapid analysis of sleep data at first wake and coupling that with a push notification to the study participant’s smart phone requesting them to collect a finger prick blood sample (Fig. 1). Figure 1. View largeDownload slide A diagram depicting the steps in reactive biospecimen acquisition and phenotyping. 1) A physiological state of interest occurs in a consented participant (e.g. a poor night’s sleep), and is recoded by an IoT or wearable device. 2) The IoT or wearable device transmits the data concerning the physiological state to the cloud for archiving. 3) These data are sent from the cloud to the researcher's laboratory, alerting them that this participant is in a physiological state of interest. 4) The research team then sends an alert to the participant (e.g. a push notification to the participants smart phone upon awakening). 5) The alert on the device prompts the user to collect a biospecimen. 6) The participant uses an on-hand kit sent from the laboratory to immediately collect a biospecimen. 7) The participant places the biospecimen into prepaid shipping included in the kit. 8) The biospecimen is mailed to the laboratory. 9) The biospecimen is assayed by the laboratory to determine the biological parameters altered via the physiological state of interest (e.g. blood metabolites altered by a poor night’s sleep). Figure 1. View largeDownload slide A diagram depicting the steps in reactive biospecimen acquisition and phenotyping. 1) A physiological state of interest occurs in a consented participant (e.g. a poor night’s sleep), and is recoded by an IoT or wearable device. 2) The IoT or wearable device transmits the data concerning the physiological state to the cloud for archiving. 3) These data are sent from the cloud to the researcher's laboratory, alerting them that this participant is in a physiological state of interest. 4) The research team then sends an alert to the participant (e.g. a push notification to the participants smart phone upon awakening). 5) The alert on the device prompts the user to collect a biospecimen. 6) The participant uses an on-hand kit sent from the laboratory to immediately collect a biospecimen. 7) The participant places the biospecimen into prepaid shipping included in the kit. 8) The biospecimen is mailed to the laboratory. 9) The biospecimen is assayed by the laboratory to determine the biological parameters altered via the physiological state of interest (e.g. blood metabolites altered by a poor night’s sleep). Challenges and concerns facing scientists utilizing the IoT and wearables Data Data collected by IoT devices and wearables are kept on the device or more commonly uploaded to the cloud. This can make accessing the data, especially for large studies with a lot of data, difficult (5). This can be further complicated by the use of commercially available devices where the data are held by a private company that requires the use of application program interface and coding experience to access these data (33). One solution is for researchers to partner with private industry to standardize the routine upload of data to a secure and easy-to-use open database (e.g. data.gov; 5). Another concern in terms of data for the IoT and wearables, is how to make sense of all the data. Indeed, many commercial devices, each made differently, do not use the same validated parameters to measure things like physical activity or sedentary behavior. Thus, a metric to estimate these parameters for wearables is necessary (34). Further, the integration of data from multiple devices into an efficient platform that enhances the aims of the study is an important concern. To that end, the National Institute of Mental Health's Research Domain Criteria framework provided a roadmap to organize, guide and lead new digital phenotyping of IoT data towards basic and clinical discoveries (35). Last, Brinton et al. (36) established a protocol to link remote survey responses to wearables data, and Lanzola et al. (37) discussed the large-scale integration of sensors and actuators. Privacy issues and security Hacks of personal information are now an unfortunate fact of everyday life. IoT devices by their nature are vulnerable targets, due to their flexible connectivity options and oftentimes small and easy-to-lose nature (5). This fact is of greater concern when the device deals with sensitive participant/patient health information. Hence, IoT devices and wearables used in research should use advanced encryption standards if necessitated by the study. Further, this encryption needs to be in place at both the level of the data stored on the device as well as the communication protocol(s) it uses (e.g. Bluetooth Smart; 3,5). Lastly, devices should be designed so that: (i) data is only stored on the device for a short period of time before being securely uploaded to the cloud, and (ii) the device can use other devices to notify approved users its location if lost (e.g. Tile). Discussion The implications and uses of the IoT and wearables in research are far reaching. The goal of IoT devices and wearables in the field of science should be to incorporate reliable, accurate, valid and low-cost portable monitors easily into a study design. As the potential uses for the IoT and wearables in research grow, the potential positive impact of these technologies on how we conduct human research should not be underestimated (6). The future use of IoT devices and wearables in research is best leveraged when it can be coupled with biospecimen collection and analysis. For this to be convenient for a participant and affordable for the researcher: (i) biospecimen collecting must happen without visiting a clinic, and (ii) the biospecimen collection approach must be amenable to standard shipment back to the laboratory. This likely means research will move away from the use of venous blood tube collections into biospecimens like dried blood spots collected via finger prick, saliva and dried urine cards. These biospecimen types can all be self-collected by the participant and can be returned to the lab using standard shipping approaches. The ability to combine minimally invasive longitudinal phenotyping via IoT devices and wearables with reactive biospecimen sampling is a unique promise for the field (Fig. 1). Conflict of Interest statement. None declared. References 1 Edberg M. ( 2014 ) How the internet has changed the world. First Published on October 14, 2014. 2 Presser M. ( 2016 ) The rise of IoT – why today? First Published on January 12, 2016. 3 Haghi M. , Thurow K. , Stoll R. ( 2017 ) Wearable devices in medical internet of things, scientific research and commercially available devices . Healthc. Inform. Res ., 23 , 4 – 15 . Google Scholar CrossRef Search ADS PubMed 4 Meola A. ( 2016 ) Internet of Things devices, applications & examples. First Published on December 19, 2016. 5 Wei J. ( 2014 ) How Wearables Intersect with the Cloud and the Internet of Things, Considerations for the developers of wearables . IEEE Cons. Elect. Magn ., 3 , 53 – 56 . Google Scholar CrossRef Search ADS 6 Kiana T. , Michael A. ( 2014 ) Wearable technology and wearable devices, everything you need to know. First Published on March 26, 2014. 7 Mantua J. , Gravel N. , Spencer R.M. ( 2016 ) Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography . Sensors (Basel) , 16 , 646. Google Scholar CrossRef Search ADS 8 Rajagopalan R. , Litvan I. , Jung T.P. ( 2017 ) Fall prediction and prevention systems, recent trends, challenges, and future research directions . Sensors (Basel) , 17 , 2509. Google Scholar CrossRef Search ADS 9 Balaguera H.U. , Wise D. , Ng C.Y. , Tso H.W. , Chiang W.L. , Hutchinson A.M. , Galvin T. , Hilborne L. , Hoffman C. , Huang C.C. et al. ( 2017 ) Using a medical intranet of things system to prevent bed falls in an acute care hospital, a pilot study . J. Med. Internet. Res ., 19 , e150 . Google Scholar CrossRef Search ADS PubMed 10 Cohen S. , Bataille L.R. , Martig A.K. ( 2016 ) Enabling breakthroughs in Parkinson’s disease with wearable technologies and big data analytics . Mhealth , 2 , 20 . Google Scholar CrossRef Search ADS PubMed 11 Onoue T. , Goto M. , Kobayashi T. , Tominaga T. , Ando M. , Honda H. , Yoshida Y. , Tosaki T. , Yokoi H. , Kato S. et al. ( 2017 ) Randomized controlled trial for assessment of Internet of Things system to guide intensive glucose control in diabetes outpatients, Nagoya Health Navigator Study protocol . Nagoya. J. Med. Sci ., 79 , 323 – 329 . Google Scholar PubMed 12 Gutierrez Garcia M.A. , Martin Ruiz M.L. , Rivera D. , Vadillo L. , Valero Duboy M.A. ( 2017 ) A smart toy to enhance the decision-making process at children’s psychomotor delay screenings, a pilot study . J. Med. Internet. Res ., 19 , 1 – 14 . Google Scholar CrossRef Search ADS 13 Kang S.G. , Kang J.M. , Ko K.P. , Park S.C. , Mariani S. , Weng J. ( 2017 ) Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers . J. Psychosom. Res ., 97 , 38 – 44 . Google Scholar CrossRef Search ADS PubMed 14 Nakamura T. , Goverdovsky V. , Morrell M.J. , Mandic D.P. ( 2017 ) Automatic sleep monitoring using Ear-EEG . IEEE J. Transl. Eng. Health. Med ., 5 , 1. Google Scholar CrossRef Search ADS 15 Looney D. , Goverdovsky V. , Rosenzweig I. , Morrell M.J. , Mandic D.P. ( 2016 ) Wearable in-ear encephalography sensor for monitoring sleep. preliminary observations from nap studies . Ann. Am. Thorac. Soc ., 13 , 2229 – 2233 . Google Scholar CrossRef Search ADS PubMed 16 Silva de Lima A.L. , Hahn T. , Evers L.J.W. , de Vries N.M. , Cohen E. , Afek M. , Bataille L. , Daeschler M. , Claes K. , Boroojerdi B. et al. ( 2017 ) Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease . PLoS One , 12 , 1 – 15 . Google Scholar CrossRef Search ADS 17 Ozanne A. , Johansson D. , Hallgren Graneheim U. , Malmgren K. , Bergquist F. , Alt Murphy M. ( 2018 ) Wearables in epilepsy and Parkinson’s disease-A focus group study . Acta. Neurol. Scand ., 137 , 188 – 194 . Google Scholar CrossRef Search ADS PubMed 18 Kroll R.R. , McKenzie E.D. , Boyd J.G. , Sheth P. , Howes D. , Wood M. , Maslove D.M. and group, W.E.I.T.f.h.I.s. ( 2017 ) Use of wearable devices for post-discharge monitoring of ICU patients, a feasibility study . J. Intensive Care , 5 , 64. Google Scholar CrossRef Search ADS PubMed 19 Vandecasteele K. , De Cooman T. , Gu Y. , Cleeren E. , Claes K. , Paesschen W.V. , Huffel S.V. , Hunyadi B. ( 2017 ) Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment . Sensors (Basel) , 17 , 2338. Google Scholar CrossRef Search ADS 20 Gentili C. , Valenza G. , Nardelli M. , Lanata A. , Bertschy G. , Weiner L. , Mauri M. , Scilingo E.P. , Pietrini P. ( 2017 ) Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients, A pilot study . J. Affect. Disord ., 209 , 30 – 38 . Google Scholar CrossRef Search ADS PubMed 21 Davila M.I. , Lewis G.F. , Porges S.W. ( 2017 ) The PhysioCam, a novel non-contact sensor to measure heart rate variability in clinical and field applications . Front. Public Health , 5 , 300 . Google Scholar CrossRef Search ADS PubMed 22 Mahajan R. , Morshed B.I. , Bidelman G.M. ( 2016 ) Design and validation of a wearable “DRL-less” EEG using a novel fully-reconfigurable architecture . Conf. Proc. IEEE Eng. Med. Biol. Soc ., 2016 , 4999 – 5002 . Google Scholar PubMed 23 Billeci L. , Tonacci A. , Tartarisco G. , Narzisi A. , Di Palma S. , Corda D. , Baldus G. , Cruciani F. , Anzalone S.M. , Calderoni S. et al. ( 2016 ) An integrated approach for the monitoring of brain and autonomic response of children with autism spectrum disorders during treatment by wearable technologies . Front. Neurosci ., 10 , 276. Google Scholar PubMed 24 Pinho F. , Cerqueira J. , Correia J. , Sousa N. , Dias N. ( 2017 ) myBrain, a novel EEG embedded system for epilepsy monitoring . J. Med. Eng. Technol ., 41 , 564 – 585 . Google Scholar CrossRef Search ADS PubMed 25 Kassab A. , Le Lan J. , Tremblay J. , Vannasing P. , Dehbozorgi M. , Pouliot P. , Gallagher A. , Lesage F. , Sawan M. , Nguyen D.K. ( 2018 ) Multichannel wearable fNIRS-EEG system for long-term clinical monitoring . Hum. Brain. Mapp ., 39 , 7 – 23 . Google Scholar CrossRef Search ADS PubMed 26 A, E. and C, C. ( 2015 ) Big pharma hands out fitbits to collect better personal data. First Published on September 14 2015. 27 Taylor N.P. ( 2016 ) Pfizer enlists IBM for IoT-enabled clinical trials by 2019. First Published on April 8th 2016. 28 Hale C. ( 2018 ) Sensors and wearables transform clinical trials but challenges remain, experts say. First Published on February 19th 2018. 29 Lopez E.A. , Costa O.A. , Ferrari D.V. ( 2016 ) Development and technical validation of the mobile based assistive listening system, a smartphone-based remote microphone . Am. J. Audiol ., 25 , 288 – 294 . Google Scholar CrossRef Search ADS PubMed 30 Muoio D. ( 2017 ) Fitbit wearables will help power NIH’s All of Us Research Program. First Published on November 7th 2017. 31 Goverdovsky V. , von Rosenberg W. , Nakamura T. , Looney D. , Sharp D.J. , Papavassiliou C. , Morrell M.J. , Mandic D.P. ( 2017 ) Hearables, Multimodal physiological in-ear sensing . Sci. Rep ., 7 , 6948. Google Scholar CrossRef Search ADS PubMed 32 Strangman G.E. , Ivkovic V. , Zhang Q. ( 2017 ) Wearable brain imaging with multi-modal physiological recording . J. Appl. Physiol ., 124 , 564 – 572 . Google Scholar CrossRef Search ADS PubMed 33 Cadmus-Bertram L. ( 2017 ) Using fitness trackers in clinical research, what nurse practitioners need to know . J. Nurse. Pract ., 13 , 34 – 40 . Google Scholar CrossRef Search ADS PubMed 34 Hickey A. , Newham J. , Slawinska M.M. , Kwasnicka D. , McDonald S. , Del Din S. , Sniehotta F.F. , Davis P.A. , Godfrey A. ( 2016 ) Estimating cut points, A simple method for new wearables . Maturitas , 83 , 78 – 82 . Google Scholar CrossRef Search ADS PubMed 35 Torous J. , Onnela J.P. , Keshavan M. ( 2017 ) New dimensions and new tools to realize the potential of RDoC, digital phenotyping via smartphones and connected devices . Transl. Psychiatry , 7 , e1053. Google Scholar CrossRef Search ADS PubMed 36 Brinton J.E. , Keating M.D. , Ortiz A.M. , Evenson K.R. , Furberg R.D. ( 2017 ) Establishing linkages between distributed survey responses and consumer wearable device datasets, a pilot protocol . JMIR Res. Protoc ., 6 , e66. Google Scholar CrossRef Search ADS PubMed 37 Lanzola G. , Losiouk E. , Del Favero S. , Facchinetti A. , Galderisi A. , Quaglini S. , Magni L. , Cobelli C. ( 2016 ) Remote Blood glucose monitoring in mhealth scenarios, a review . Sensors (Basel) , 16 , 1983. Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: firstname.lastname@example.org This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
Human Molecular Genetics – Oxford University Press
Published: Mar 19, 2018
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