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Smart wearable body sensors for patient self-assessment and monitoring

Smart wearable body sensor
Photo: Smart wearable body sensor
Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices [1,2]. Expansion of health information technology and consumer e-health tools and services, such as telemonitoring platform and mobile health applications [3], have created new opportunities for individuals to participate actively in their healthcare, and provides the opportunity for remote monitoring of clinically relevant variables in non-clinical settings [4]. These devices can be integrated into routine care of acute and chronic diseases and provides essential information for management to both the healthcare providers and patients [5]. Studies show that a well-informed patient improves quality of life and patient outcome because they are more likely to participate in healthy behavioral changes [6,7]. Furthermore, the United States spends approximately 75% of their $2 trillion budget on chronic diseases per year, which make up 7 out of 10 deaths annually [8]. Chronic diseases also have debilitating effects, which lead the nation in causes of major disabilities and preventable illnesses [8].

The concept of remotely monitoring patients is not new but recently a lot of attention has been placed on smart wearable body sensors (SWS) [4,9]. Whereas other articles have focused primarily on devices which have been used for research or have needed a physician’s prescription, this article expands upon the opportunities and studies with devices that are available to all consumers. There is now more evidence to support the reliability of these devices and the technology is more easily accessed. These devices contain an assortment of different sensors which can be used to monitor variables and transmit data either to a personal device or to an online storage site. The variety of the sensors can be attributed to the types of stimuli that they respond to (e.g. physiological vital signs, body movements, and organic substances) and their placements (clothing, subcutaneous implant, body part accessory, etc.) These devices have the opportunity to meet the patients’ needs by administering information in real-time to the patient’s smartphone, computer or other wireless devices and has the potential to influence their behaviors [5,6]. Sensors allow patients to self-monitor, track, and assess human physiological data, while also providing interfaces and a dashboard for healthcare providers [7]. These sensors are easily managed and are becoming increasingly accurate and reliable for patient care [5,10,11]. The SWS’s can also be utilized as a diagnostic tool to aid in identifying and managing a myriad of diseases [7]. Current sensor technology for vital-sign monitoring promises great benefits for prevention, prediction, and management of diseases. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited.

Wearable body sensors
The SWS include a wide range of wearable devices and sensors such as accelerometers and gyroscopes, smart fabrics and actuators, wireless communication networks and power supplies, and data capture technology for processing and decision support [12]. Having a wearable device decreases the restrictions placed on their motility and daily activities which allows monitoring in the environment of the patients directly home but also at work.

The most used and well-known sensor accelerometers are electrochemical sensors that measure acceleration of objects in motion along reference axes and provide basic step and activity counts used as a quantitative assessment of physical activity [11,13]. This data can be used to obtain velocity and displacement by merging the data with respect to time [5]. Triaxial accelerometers, which monitor vibrations in three planes, can detect movement and posture, such as upright or lying down, according to the magnitude of acceleration signals along sensitive axes [14,15]. Gyroscopes are also another popular type of sensor. A gyroscope is a mechanical device that measures 3-D orientation based on the principles of angular momentum. A spinning rotor tends to maintain its orientation allowing the changes in orientation to be calculated by integrating the angular velocity [14].

Placement of SWS is versatile and provides flexibility and comfort for patients, which is one of the keys for patient acceptance. There are many devices already on the market for fitness and wellness that use consumer-facing applications which can be easily incorporated into clinical practice. Most sensors can either be worn or placed on clothes. Some wearable devices can be placed on the almost any part of the body: wrist, ankle, waist, chest, arm, legs, etc. These sensors can detect many different variables such as speed, distance, steps taken, floors climbed and calories burned [16]. Implementation of a real-time waist-mounted tri-axial accelerometer unit detects a range of basic daily activities, including walking and posture [17,18]. Other possibilities for wearable sensor placement include gloves, rings, necklace, brooches, pins, earrings, and even belt buckles. These models have been used to monitor blood oxygen saturation (SpO2), heart rates, and record hand posture while manipulating objects, such as eating or dressing [19,20]. A newly marketed device measures body temperature through the use of an ear probe which detects infrared radiation from the tympanic membrane [21]. Another approach which could be more convenient for patients is the placement of sensors in clothing, such as a vest or shoe. Smart Vest is a wearable physiological monitoring system for parameters such as, heart rate, blood pressure (BP), body temperature, galvanic skin responses, and can even perform electrocardiograms (ECG) [22]. There are also experimental designs, with promising preliminary results, demonstrating that sensors (heart rate, acceleration, and respiratory activity) can be incorporated into a regular t-shirt rather than a bulky vest, which adds another layer of convenience [23]. Placement in the shoe can provide a more convenient method to measure differences between mean foot extreme and gait stride time for healthy gait and those with physical disorders, as well as proved highly capable of detecting foot orientation and position [22].

Self-tracking and monitoring
Traditionally there have been three widely accepted approaches for outpatient monitoring: patient reported outcomes (PRO), telemonitoring and quantifying self-hybrid models (QSHM) [24]. PRO models encourage the patient to be proactive by allowing them to have more autonomy. This is accomplished by having the patient self-report a descriptive analysis of subjective data to their provider. Unfortunately, this data can be unreliable and inconsistent for objective measurements. Telemonitoring uses equipment to monitor physiological data passively, which is then transmitted to the patient and can also be sent to their provider. This monitoring could extend or replace routine outpatient care in dedicated hospital wards. Whereas PRO models report subjective data, telemonitoring can be used to report objective data but a limitation to this technology is that it is only capable of reporting quantifiable variables. Lastly, QSHMs have been developed in order integrate the previous methods by allowing the patient to be able to report their non-quantifiable variables while still having the ability to monitor quantifiable ones. This amalgamation mitigates the data that may be unreliable from the self-reporting coming from the patients and bypass the limitations of variables in the telemonitoring model. This model provides the ability for a patient to better understand their healthcare by integrating complement models that combine subjective symptoms with objective criteria. The majority of SWS fall under the telemonitoring model but a few possess the ability to allow the user to input subjective data as well, which then follows the QSHM model. Ideally, SWS would continue this trend towards QSHMs.

Many individuals with chronic diseases could benefit from having constant remote monitoring and the best way to monitor a patient is through understanding their interactions with their daily activities. Giving the patient the opportunity to depart from the hospital and continue to monitor themselves will allow for a more authentic representation and a more accurate assessment of physiological data. If patients could be monitored reliably away from the hospital, this could decrease the cost associated with the length of stay (LOS), which can greatly decrease healthcare costs and unintended consequences. Shifting the paradigm from a culture of treatment to one centered upon prevention.

Discussion
A major barrier for the implementation of SWS is the reliability and efficiency of sensor systems and data processing software [56]. Some of the studies reported in this review had authors who were also the system developers. This could lead to positive biases for their products and the dearth of randomized clinical trials, either for practical reasons or logistical ones, makes it difficult to truly scrutinize the results. However, many studies are being conducted to improve the reliability of sensors [56,57]. Smart wearable sensors, specifically accelerometer-based devices, have undergone many trials to determine their accuracy and precision. While accelerometers in a broad sense have been proven effective [58], individual studies and devices each require mean and variance determinations and adjustments to gain the most accurate results for the desired values. Some studies have even used multiple sensors on patients to combine data to achieve optimal results. A study by Olguin and Pentland compared the activity recognition accuracy of four configurations of accelerometers from three placements; the chest, wrist, and hip [59]. The mean and variance of the three axes were used as inputs to a Hidden Markov model. The classifier achieved an accuracy of 65% using only one accelerometer placed at the chest. By combining data from accelerometers placed on the wrist and hip, the accuracy increased to 87%. They also found that it is possible to obtain similar results using only two accelerometers placed on the chest and hip. Other chest-worn accelerometers are able to detect respiratory and snoring features for sleep apnea diagnosis [60].

Many SWS employ algorithms to transform data obtained and many times the results are only estimates of the physiological data. There are a myriad of variables that could influence the estimates and their generalizability needs to be confirmed by the physicians and patients. This review attempted to use articles that demonstrated real-world applications of these devices rather than studies which have only been used in laboratories. The optimal application of these devices would be to tailor each one to the individual using them and regularly calibrate whenever necessary. Although healthcare is always trying to increase patient’s autonomy and create a harmonious relationship between physicians and patients, endowed with this technology some patients could erroneously disregard the role of the physician. This could be circumvented through patient education and understanding of the limits of this technology. There is no “one size fits all solution”, and matching the right technology for a given patient population or desired clinical objective is key to ensuring sufficient perceived usefulness and uptake [61-63]. Furthermore, with sensors that operate on closed-loop systems, such as the aforementioned sensors for diabetic patients and AD, the adaptability of these SWS can be used as a method to provide personalized medicine to patients in novel ways that were not available before. Rather than strict monitoring, these devices have the ability to calculate idiosyncratic patterns that can be used to modulate treatment and tailor it to the specific needs of the individual. As access to these devices continues to increase, the feasibility of more direct comparisons of these devices will be available.

Additionally, SWSs can still be very expensive and, to the best of these authors’ knowledge, there have yet to be a designation of codes for reimbursement of these devices [64]. We believe that as the trend to utilize these devices by patients and physicians continues to rise, eventually these will be integrated into the coding systems that will allow for reimbursement of products for consumers. With the increase in physician shortages, some states, such as Maryland, have already started to expand coverage through telemedicine for delivery of health care services [65].

Legal and ethical issues such as privacy data protection and ownership are also major concerns of any Internet-based application. The balance between the patient as the owner of data and the documentation and use of the data must be properly managed, with patient confidentiality always at the forefront without impeding the development of innovative solutions. Moreover, researchers believe that SWSs introduce risks of social inclusion of users [66]. Lastly, elderly users strive for independence and any technology that seems to limit their independence will be met with opposition[67].

Conclusion
The evolution of SWS and their ability to track mobility, health indicators, and symptoms have great potential that can revolutionize the healthcare system and change patient behavior. Driven by the quantified self, emerging patient driven healthcare models are contributing to shaping a positive future for healthcare with the patient at the epicenter. Rather than a physician reacting to an event that occurred to a patient, the SWS distributes responsibility to the patients which can lead to more personalized medicine. There has already been a host of clinical applications involving SWS that have been analyzed, including but not limited to blood pressure, cardiac monitoring, respiratory rate, blood electrolyte and glucose concentration systems, neurological monitoring, and physical therapy and rehabilitation medicine. These technologies are continuously being improved upon and can extend into any field of medicine. However, the integration of wireless technologies requires an infrastructure of evidence regarding reliability, validity, and responsiveness for each application across a range of disease and injury related disorders while also contributing to preventative methods. Collaboration between physicians, patients, engineers, and the wireless industry is essential for the design and optimization of inexpensive wireless systems. Further studies and clinical trials are needed to further this research and provide better overall models for patients. The quantified self is being pioneered by the patients and is revolutionizing patient behavior as they adopt healthy behavioral changes into preventative measures. These changes will alter the way that countries utilize funds on healthcare, set guidelines for protocols regarding preventative and post-operative monitoring, and augment the physician-patient relationship. Incorporating these technologies now will facilitate the transition and increase favorable outcomes in the future.

This article is adapted from Geoff Appelboom, Elvis Camacho, Mickey E Abraham, Samuel S Bruce, Emmanuel LP Dumont, Brad E Zacharia, Randy D’Amico, Justin Slomian, Jean Yves Reginster, Olivier Bruyère and E Sander Connolly, Smart wearable body sensors for patient self-assessment and monitoring. Source article. This work is licensed under a Creative Commons Attribution 4.0 License.

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