Medical Informatics
Master Thesis
Rick Bolten
August 11, 2014
“Doctor, I Measured My Own Health”
A VIEW OF DUTCH GENERAL PRACTITIONERS ON
“Doctor, I Measured My Own Health”
A VIEW OF DUTCH GENERAL PRACTITIONERS ON COMMERCIALLY
AVAILABLE SELF-MEASUREMENT DEVICES
Key words:
Self-measurements
General practitioner
Student:
Rick Bolten, BSc
Student number: 6060358
E-mail: RickBolten@live.nl
Supervisor:
Dr. Floris Wiesman
Dept. of Medical Informatics
Academic Medical Center- University of Amsterdam
Mentor:
Johan Krijgsman, MSc
Henk Hutink, MSc
Nictiz (Dutch ICT Institute in Healthcare)
Location of scientific research project:
Nictiz (Dutch ICT Institute in Healthcare)
Oude middenweg 55
2491AC Den Haag
The Netherlands
Traineeship period:
ABSTRACT
Many apps and devices are available that allow consumers to measure their own health. The focus on self-management, the empowerment of patients and the advances in sensor technology that allow creation of inexpensive self-measurement devices will lead to consumers who measure their own health and bring these measurement to the gatekeepers of healthcare, namely the general practitioners (GPs).
The goal of the study was to find out how GPs assess whether a self-measurement can be used in the healthcare process when it is performed on the initiative of the patient with a commercially available personal health device (PHD).
The available PHDs for consumers were researched using methodological triangulation, by visiting conferences, studying literature and an online search on the websites of the manufacturers. Each PHD was classified by brand, type of device, location of device, goal of the measurement, whether it was on the market or in development, and connection capabilities.
After identifying the available PHDs, ten GPs in the Netherlands were interviewed about their perception of self-measurements performed on the initiative of consumers and their acceptance criteria for self-measurements. The acceptance criteria were mapped to the technology acceptance model. Furthermore, two medical technicians of the Dutch Gelre hospital were interviewed to find out the acceptance criteria for measurement devices in a hospital.
In total 870 PHDs were identified, of which 806 of the devices were available on the market and 496 had connection capabilities. The most used connectivity was Bluetooth. PHDs could be placed in the body, on the body as a wearable device, embedded in a house next to regular table top devices. The medical technicians stated that a correct measurement depends on three factors, namely the user, the surrounding and the device. Devices were checked on these three factors by the medical technicians. The medical technicians concluded that the deviation in the measurement, that occurs when a consumer performs a self-measurement, should be investigated and displayed by the device. The insight in the deviation would allow healthcare professionals to make better informed decisions. The GPs did not perceive benefits of self-measurements on initiative of consumers and believed that self-measurements would lead to anxiety and unnecessary medicalization of healthy persons. On the other hand, GPs did believe they could use measurements for patients with illnesses in some cases, because the measurements could provide them with additional information, and increase patient empowerment and adherence to treatment.
The GPs had few options of assessing self-measurement devices: they looked at the brand of the device, looked at quality labels if they were available, compared the output of the device with their own calibrated devices, performed Google or scientific literature searches to conclude if the measurements of a PHD could be used in the health care process.
ABSTRACT
Er zijn veel zelf-meetapparaten beschikbaar die consumenten in staat stellen om hun gezondheid te meten. Dit komt mede door de ontwikkelingen in sensor technologie en de focus op zelfmanagement en patiënt empowerment in de politiek. Er word verwacht dat consumenten die zelf-metingen verrichten, de metingen meenemen naar hun huisarts omdat huisartsen de poortwachters zijn voor de zorg.
Het doel van deze studie is om te achterhalen hoe huisartsen beoordelen of zelfmetingen die gedaan zijn op het initiatief van patiënten gebruikt kunnen worden in het zorgproces.
De beschikbare zelfmeetapparatuur is eerst geïnventariseerd, door conferenties te bezoeken, literatuur onderzoek te doen en de websites van fabrikanten te doorlopen. De zelfmeetapparatuur werd geclassificeerd op merk, naam, type apparaat, locatie van apparaat, reden van meting, connectiemogelijkheden en of het al te koop was of nog in ontwikkeling. Na het identificeren van de zelfmeetapparaten werden tien huisartsen geïnterviewd over wanneer ze een zelfmeting zouden gebruiken in het zorg proces. Het technologie acceptatie model werd gebruikt om te onderzoeken wanneer huisartsen zelfmetingen zouden accepteren en gebruiken. Daarnaast werden er twee medische technici van het Gelre ziekenhuis geïnterviewd over de acceptatie van meetapparatuur in ziekenhuizen. In totaal zijn 870 apparaten geïdentificeerd, waarvan er momenteel 806 beschikbaar zijn. 57% van de apparaten had connectiemogelijkheden waarvan Bluetooth het meest gebruikt was. Naast de gebruikelijke verplaatsbare apparaten, konden apparaten in en op het lichaam geplaatst worden, of ondergebracht in de woonomgeving.
Volgens de medische techneuten hangt een goede meting af van de gebruiker, de omgeving en het apparaat zelf. Zij controleerden de apparaten in het ziekenhuis dan ook op deze drie factoren. De medisch technici concludeerden dat voor zelfmetingen de grootte van de afwijking bij gebruik van de consument vastgesteld moet worden. Deze afwijking moet dan weergegeven worden door het apparaat zodat een zorgprofessional een weloverwogen beslissing kan nemen.
Huisartsen waren van mening dat zelfmetingen op initiatief van een consument zouden leiden tot ongerustheid en medicalisering van gezonde mensen. Aan de andere kant zagen huisartsen in sommige gevallen wel het nut van zelfmetingen bij zieke mensen, want daar zou het voor meer informatie kunnen zorgen, meer therapietrouw en patiënt empowerment.
De huisartsen hadden weinig manieren om te bepalen of een zelfmeetapparaat voor goede metingen zorgt. Ze maakten de keus door te kijken naar het merk, naar keurmerken als die beschikbaar waren, vergeleken de uitkomsten met hun eigen gekalibreerde apparaten, zochten de website van de fabrikant op Google of bekeken wetenschappelijke literatuur.
TABLE OF CONTENTS
1 Introduction ... 1
2 What is a sensor? ... 5
3 What PHDs are commercially available? ... 9
4 Classifications of personal health devices ... 29
5 healthcare providers acceptance criteria of PHDs ... 40
6 Thesis discussion ... 71
7 Conclusion thesis ... 75
References ... 76
Appendix A: Freemind diagrams ... 86
Appendix B: Interview invitation ... 93
Appendix C: Interview questions ... 94
1
1 Introduction
There is a dawning scarcity in healthcare in the Netherlands. The percentage of elderly is rising and will double in the next 27 years1, while the population at the working age will decrease2. Figure 1 illustrates the prospected scarcity, where more elderly depend on a shrinking work force3. Next to the scarcity, the healthcare expenditures are at an all-time high and rising4–7. Currently, already fifteen percent of the gross domestic product in the Netherlands is spent in healthcare 4.
eHealth is considered by many as a possible solution to the upcoming scarcity of healthcare professionals and increasing healthcare costs8, Despite there being no strong evidence for cost-effective eHealth9–12.
Many definitions of eHealth exist, but a literature review by Pagliairi et al. concluded that the following definition of Eysenbach fits the best13:
e-health is an emerging field of medical informatics, referring to the organization and delivery of health services and information using the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a new way of working, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology. [Eysenbach, Journal of Medical internet research, 2001]14
eHealth could reduce the demand of care and make the work processes of healthcare professionals more efficient15. A successful example which demonstrates the efficiency gain is tele-dermatology16. A general practitioner (GP) is able to consult a dermatologist by sending (real time) images to the dermatologist. This removes the need for a separate appointment between the patient and the dermatologist. The dermatologist establishes the diagnosis based on these images and the referring GP is able to proceed with the treatment of the patient.
The Dutch minister of healthcare believes that substitution of the traditional health care by new eHealth care could help with the challenges of strengthening the independence of patients, offer solutions to the dawning personnel scarcity, improving patient safety and lower rising healthcare costs17. The minister’s aim for the coming 5 years is to allow 75% of chronic patients to monitor their own health and 100% to be able to contact a care professional via screen to screen contact and support by domotica18. Domotica is a part of ambient assisted living (AAL) which is a subcategory of eHealth aimed at creating a better quality of life for the older adults or patients in their domestic environment with information and communications technology (ICT)19.
In addition to the minister of healthcare, the royal Dutch society for the advancement of medicine (KNMG), the Dutch patient consumer federation (NPCF) and the Dutch healthcare insurers (ZN) share this vision and plan to promote eHealth in the coming years. This is described in the national implementation agenda20. The European Commission concludes in the eHealth action plan that stimulating eHealth leads to health improvements and patient empowerment15,21. The latter is “a continuous process through which patients (and patient groups) work in partnership with their
Figure 1-1 The old-age dependency ratio in the Netherlands in percentages. (population age 65+ divided by population age 15-64). Adapted from the united nations department of economic and social affairs3
2 healthcare system to enable patients to become more responsible for, and involved in their treatment and healthcare” [Loukanova et al., Expert review of pharmacoeconomics & outcomes research, 2007]22.
The empowered patient is supported by another subcategory of eHealth called mHealth (mobile health). mHealth is described by the world health organization as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices”23 [European Commision, Green paper on mobile health, 2014]. These devices can measure medical, physiological, lifestyle, daily activity, and environmental data24. mHealth allows patients to access and contribute to their health information giving patients information that allows them to become more responsible for their treatment and work in partnership with health care professionals.
The mHealth market is expected to grow rapidly in the coming years as is shown in Figure 1-225. GfK (a market research company) found an increase in sales of 42% for blood pressure monitors with connection possibilities and 88% rise for personal weight scales with connection possibilities in the year 2013 compared with 201226. Smartphone health apps are popular as well. The top 20 free health apps were installed 231 million times in 201324. There are an estimated 97,000 health apps and it is expected that 1,6 billion consumers will have a smart phone with health apps installed on them in 201727.
The Dutch and European initiatives are mostly focused on eHealth set in motion by the healthcare system. On the other hand, there are consumers who measure their health on their own initiative. They
belong to the quantified self (QS) movement. The QS was initiated by Gary Wolf and Kevin Kelly and received attention after Wolf’s TED talk on QS28. The QS followers
use sensors to quantify themselves, parameters of their own health. The existence of this movement is not unexpected. Patient empowerment is a major focus in healthcare. Furthermore, the
technology is widely available. mHealth technology allows quantification of many aspects, such as work-outs29, sleep30 and heartrate31. These mHealth devices which allows consumers to measure their own health will be called personal health devices (PHD) in this thesis. An increasing number of PHD are commercially available32. Examples include regular
thermometers, blood pressure monitors, step counters and sports heart rate monitors. Often PHDs are able to store the measurements and consumers could share this data with healthcare providers. Ambient assisted living devices do not always measure a consumer’s health, they can as well measure factors that influence health, for example a smoke detector or a fall monitor. These devices also fall under the definition of PHD in this thesis.
In the Netherlands, Nictiz (the Dutch ICT institute in healthcare) also identified this trend, which is displayed as arrow A in Figure 1-133. Arrow A shows the patients that measure their own health and bring the self-measurement data to their doctor who could use
this information during treatment. This happens on initiative of the patient. Arrow B depicts eHealth on initiative of the healthcare professional, who uses self-measurements performed by patients during treatment.
Figure 1-2 World-wide mHealth market in US dollars. Adapted from PWC report Touching lives through mobile health25
Figure 1-1 Nictiz self-measurement trend. Adapted from the whitepaper self-measurements and the Dutch healthcare 33
3 Patients can store their measurements performed with their PHDs in a personal health record (PHR). A PHR is “an electronic application through which individuals can access, manage and share their health information, and that of others for whom they are authorized, in a private, secure, and confidential environment”[Tang et al. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption, 2006]34. The PHR allows patients to store their measurements and then share it with healthcare providers, on own initiative (trend A) or initiative of the healthcare provider (trend B), where a patient asks a healthcare provider for his health information from the health care providers patient record, to fill their own PHR.
The growing mHealth market, the interest in health apps, and the focus on an empowered patient led Nictiz to expect that many people will use PHDs to assess their own health in the near future. PHRs and devices with connection possibilities allow the consumer to visit the doctor with self-gathered health information.
The GP is the gatekeeper of the healthcare system in the Netherlands35. Dutch people who require healthcare need to visit their GP first. The GP helps the patient at his office or refers the patient to a specialist. There is an exception in the case of an emergency: patients can go directly to the emergency department of a hospital. This means that a consumer, who brings his own measurements on his own initiative to the healthcare system, as identified in trend A in figure 1-3, will first encounter the GP. This leads to the goal of this study: finding out what the perception is of GPs of self-measurements performed by consumers. Research in the field of PHRs and GPs showed that GPs often do not see the benefits of PHRs, because GPs doubt the reliability of patient entered data and they believe it will lead to more unreimbursed work. on the other hand the GPs believed PHRs could help patients with patient empowerment and selfmanagement36.
The European commission questioned the reliability of mHealth in their green paper on mHealth. They state “given their variety, consumers, patients or healthcare professionals may find it difficult to choose the right mHealth solution or app”[European commission, Green paper on mHealth, 2014]24
. By answering the following research question, this study will find out how a GP decides if a self-measurement performed by consumers can be used in healthcare.
Research questions
The main research question is:
How do general practitioners in the Netherlands assess whether the measurements of commercially available personal health devices can be used in the healthcare process?
I used the following sub-questions to answer the main question: 1. What personal health devices are commercially available?
This sub-question will give insight into the scale of the market for personal health devices and the self-measurement possibilities consumers have. I will answer this sub-question by visiting conferences, doing a literature study and an online search.
2. What groups of commercially available personal health devices are there? Nictiz
Nictiz is a center of expertise on standardization and eHealth. Nictiz’s mission is to improve healthcare by improving information. Nictiz does this by developing standards, stimulating and checking quality of ICT in healthcare, and by providing knowledge and expertise in this field172. Nictiz will focus the coming years on four themes173. The first theme is efficient and effective chronic care by a good transfer of information between healthcare providers. The second theme is patient empowerment. The third theme is transparency, by making quality of healthcare measurable. The fourth theme is patient safety, because incidents occur due to incorrect, incomplete or unclear information. eHealth is important to all four themes.
4 Grouping the sensors will lead to further insights about what kinds sensors are available. It also allows me to focus on a particular group of sensors. The grouping will be based on a literature study and the sensors I found as a result of sub-question 1.
3. What are the laws, standards, and GPs’ criteria, for acceptance of commercially available personal health devices, in one of the sensor groups resulting from sub-question 2?
The answer to this question tells me what is required before a PHD and the measurements are accepted by the GP. I will find the answer by interviewing the healthcare providers and studying literature.
Thesis outline
Chapter 2 gives a short introduction to sensors in general.
In chapter 3 I present which PHDs are available. I will present the PHDs I found during conferences, in literature and during an online search.
In chapter 4 I answer sub-question 2 where I create a grouping of the available sensors. In this chapter a grouping based on literature is presented and a grouping based on the identified sensors from chapter one. By combining these two groupings a final grouping of sensors is created.
In chapter 5 sub-question three is answered by discussing the standards, laws and acceptance of consumer sensors by GPs, based on interviews performed during a conference, interviews with medical technicians and interviews with GPs.
5
2 WHAT IS A SENSOR?
Sensors are everywhere in our lives. Almost every electronic device in our house has a sensor. Some everyday examples are the light switch in a refrigerator, the infrared sensor of a television and the thermometer in a thermostat. In this chapter an overview of sensors is presented. Sensors are the components that are used in PHDs which allow consumers to perform self-measurements. In this chapter is defined what a sensor is, followed by the types of sensors and important aspects of sensors. The last section describes the trend of sensors becoming less expensive and smaller.
2.1 SENSOR DEFINITION
I present two definitions of sensors, because the combination of both definitions describes sensors well.
1. “A sensor is an electronic device that produces electrical, optical or digital data derived from a physical condition or event.”[IEEE, Draft standard for a smart transducer interface for sensors and actuators, IEEE 1451]37.
2. “A sensor converts a physical parameter to an electric output” [Webster and Peura, Basic sensors and principles. In Medical Instrumentation Application and Design, 2010]38.
The first definition describes a sensor as an electronic device, but this is not always the case, because as is later described in this chapter, a sensor may also consist of biomass.
The second definition describes that a sensor may only convert to an electric output, although as the second definition describes it could also be optical or digital data as output. The most important part of the output is that a person or an object is able to read the output.
Combining the two definitions leads to my definition of a sensor: A sensor converts a physical parameter to an output signal that is readable for a human or an object.
The international bureau of weights and measures (BIPM), has the task of world-wide unification of physical measurements, the unification is captured in The International System of Units (SI). The SI is comprised of seven base quantities (Table 2-1). For each of these base quantities the BIPM standardized the unit, for example ‘The metre is the length of the path travelled by light in vacuum during a time interval of 1/299 792 458 of a second.’[BIPM, The international system of units 8th edition, 2006]
Table 2-1 the 7 base quantities and their SI unit
Base quantity SI base unit
Name Symbol Name Symbol
Length l, x, r, etc Meter m
Mass m Kilogram kg
Time, duration t Second s
Electric current I, i Ampere A
Thermodynamic temperature T Kelvin K
Amount of substance N Mole mol
Luminous intensity Iv Candela cd
From these seven base quantities numerous derived quantities can be created, that can be measured with sensors. An example is speed, which is distance (meters) per time (seconds).
6
Table 2-2 Example of derived quantities and their SI unit
Derived quantity SI derived unit
Name Symbol Name Symbol
Area A Square meter m2
Volume V Cubic meter m3
Speed, velocity C Meter per second m/s
Acceleration a Meter per second squared m/s2
Density, mass density ρ Kilogram per cubic metre Kg/m3
Surface density ρA Kilogram per square meter Kg/m2
Luminance Lv Candela per square metre Cd/m2
2.2 TYPES OF SENSORS
Sensors are able to measure base and derived quantities and convert these measurements to a signal which can be interpreted by humans or instruments. In this section the nine different fields of measurands are discussed, namely: acoustic, biological, chemical, electric, magnetic, mechanical, optical, radiation and thermal39.
Acoustic sensor
Acoustic sensors measure the wave amplitude, phase, polarization and spectrum of sound39. This can be useful for position sensing and, surface characterization, as well as studying the sounds of structures under stress, which can be useful for assessing structural integrity. Furthermore, by measuring the frequency properties it is possible to identify biological substances, vapours, pressure, electric and magnetic fields and acceleration40.
Biological sensor
Biological sensors (biosensors) are able to measure the identities, concentrations and states of biomass39. Biosensors are able to convert bio-recognition processes into measurable signals, for example a fluorescent protein that is able to bind to a biomass41. The fluorescence is the signal that can be measured and informs about the quantity or concentration of the biomass.
Biosensors can be classified by the different types of recognition of biomass, namely enzyme-based, immunological or DNA41.
Chemical sensor
A chemical sensor is a device that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytically useful signal42.
There are three types of chemical sensors: chemical (where a readable signal arises from a chemical reaction), physical (based on physical principles e.g conductivity, reflection of light, temperature or mass change) and the already described biosensors (biological process is the source for the analytical signal)42,43. This means that biosensors are a subgroup of chemical sensors
Electric sensor
Electric sensors are able to measure the charge, current, potential, potential difference, the amplitude, phase, polarization and spectrum of an electric field. Furthermore these sensors can measure the conductivity and permittivity of materials39.
Magnetic sensor
Magnetic sensors measure the amplitude, phase, polarisation and spectrum of a magnetic field39. The magnetic sensors can be classified in three categories according to their measuring sensitivity44. The first category consists of sensors that measure fields that are stronger than the earth’s magnetic field (e.g. non-contact switches, current measurement), the second category measures the perturbations of
7 the earth’s magnetic field (e.g. magnetic compass) and the third category measures small generated magnetic fields (e.g. brain function mapping).
Mechanical sensor
Mechanical sensors are able to perform a large variety of measurements and are used in countless applications45. Such as position (linear, angular), velocity, acceleration, force, stress, pressure, strain, mass, density, moment, torque, speed of flow, rate of mass transport, shape, roughness, orientation, stiffness, compliance, viscosity, crystallinity and structural integrity39. The sensor for the light in the refrigerator is an example of a mechanical sensor.
Optical sensor
Optical sensors measure light and turn that into a readable signal. An optical sensor measures the amplitude, phase polarization, spectrum or velocity of a wave39.
An example of an optical sensor in the medical practice is the pulse-oximeter, which measures oxygen saturation. In blood, haemoglobin that is saturated with oxygen absorbs light from specific wavelengths, while unsaturated blood does not. By shining light with two wave-lengths through blood the intensity difference measured by the optical sensor between the radiated and received light at the optical sensor make it possible to calculate the oxygen concentration of saturated blood46.
Radiation sensors
Radiation sensors measure the type (e.g. alpha or beta radiation), energy and intensity of radiation39. A well-known example of a radiation sensor is the Geiger counter, that counts radiation particles by converting incoming radiation into a measurable electric signal47.
Thermal sensors
The thermal sensors measure the temperature, thermal flux (transfer of heat energy through a surface), specific heat (the amount of heat needed to raise the temperature per unit of mass one degree celcius) and thermal conductivity39.
2.3 TECHNOLOGICAL ASPECTS OF SENSORS
Technological aspects of sensors are displayed in Table 2-3 where the first column is the aspect and the second column the description.
Table 2-3 Aspects of sensors with description
The sensitivity of a sensor is the change in signal of the sensor for every measured unit. A chemical sensor that measures a concentration may give smaller output signals for higher concentrations than lower concentrations, which in this example would show that for high concentration the sensor is not sensitive, but for low concentrations it is39,48,49. Furthermore, this example shows that the sensor has non-linear sensitivity.
A sensor has a measurand range, this is a limitation for the input of the sensor. Using for measurements outside the measurand range might lead to inaccurate readings or even damage to the
Aspect Description
Sensitivity39,48,49 Change in signal of the sensor for every measured unit
Resolution39,48 The smallest change of the measured input that the sensor can detect. Measurand
range39,48
Range of the measurand that can be measured by the sensor. Selectivity39,48,50 The measurands that can be measured by the sensor
Speed of response39,48
Time that is needed to create a signal after it received the input Noise39,48,51 The root mean square of the fluctuations in the signal
8 sensor39,48. An extreme example of measuring outside the range of a sensor would be using a personal weight scale to weigh a car.
Selectivity of a sensor describes that a sensor is able to measure one or more parameters. Electronic noses are able to detect dangerous gasses. Since sensors are selective and only able to measure one gas a set of different sensors are used to measure the different gasses50.
Sensors always suffer from noise, which is defined as the average fluctuation in the signal in both positive and negative direction (i.e. the root mean square fluctuations in the signal). These fluctuations have a range of causes which will not be discussed in this thesis48. One example is Johnson noise, where temperature causes random voltage fluctuations51.
Other practical aspects are important for sensors as well, namely operating life, ambient conditions (which might influence the measurement), output format, cost, size and weight of the sensor39.
2.4 S
IZE AND PRICEThe sales price, cost of production, size and weight of sensors have all reduced over the years. In the 1950’s researchers started investigating the possibility to create smaller sensors using silicone, which revolutionized the electronics industry with the creation of transistors52. In 1962 the first micro pressure sensor was developed, which would currently be called a MEMS (micro-electromechanical systems) sensor53. Even though the sensors maybe neither systems nor electromechanical, this is the term used to describe miniaturized devices53.
In the years that followed the advances in MEMS fabrication allowed the creation of smaller and different sensors54. Currently, MEMS devices consist of parts of only a few micrometer53 and are still becoming smaller as is depicted in Figure 2-1.
Micro fabrications methods did not only allow sensors to become smaller, but the prices also dropped. An example in the medical field is disposable blood pressure sensors, which now cost a few dollars. Before bulk micromachining fabrication these sensors cost over 600 dollars55. The MEMS sensors continue to become less expensive, the price of a MEMS accelerometer was $3.00 in 2007 but dropped to $0,65 in 201056.
The MEMS market is growing rapidly, especially in the automotive, consumer and medical applications54. The MEMS market is expected to grow to over 20 billion US dollars in 201757. The International Electronics Manufacturing Initiative identified a new area of application for MEMS in their roadmap for the coming 10 years, namely the PHDs58. These devices are medical but are aimed at consumers, which means that they do not require government approval to be sold, allowing rapid development and deployment to consumers58.
2.5 S
UMMARYA sensor is a device that can measure a physical quantity and convert it in a readable signal. Nine different types of sensors were described and their aspects were described. Developments in sensors technology have made sensors smaller and less expensive over the years, and this trend continues. These developments allowed sensors to become mainstream products found in almost every motor vehicle, smart phone or laptop54. The application area of PHDs is a recently identified field for MEMS applications, which also explains the growth of the mHealth market discussed in the introduction.
Figure 2-1 MEMS sensors becoming smaller over the years (2005-2013) Copied from: MEMS for automotive and consumer electronics54
9
3
WHICH PHDS ARE COMMERCIALLY AVAILABLE?
In the medical field sensors play an important role in objectively measuring the patient’s health. Advances in technology make it possible that these sensors are now small and affordable enough for domestic use. To provide insight to the current state of available PHDs, I created a list of PHDs using methodological triangulation. The methods I used were: visiting conferences focussed on PHDs in healthcare (section 3.1), a literature search (section 3.3), and an online search (section 3.4). This chapter answers the research question: Which commercial personal health monitor devices are available?
3.1 C
ONFERENCE SURVEYI visited three conferences, which allowed me to get a quick overview of companies that make PHDs. The companies I identified during the conference survey were used in the online search described in section 3.4, where I thoroughly searched through the websites of these companies to identify PHDs. The question I answer in this section is: What companies make PHDs?
3.1.1 Conferences Medica
The Medica conference in Germany is the largest medical trade show in the world59. Medica is combined with Compamed which is a trade show for medical technology manufacturing. Over three days there were 132,000 attendees from 120 countries looking at the stands of 4641 exhibitors from 66 countries59. I visited the trade show to identify the available and upcoming PHDs.
Health tech event
The health tech event in the Netherlands was small in comparison with the Medica conference. There were approximately a dozen exhibitors in the exhibition hall. In the main hall presentations were given about eHealth. After the presentations there were two parallel workshop sessions where visitors had to choose which session to attend. Although, the exhibition hall was small, the presentations gave a good in-depth view on eHealth related topics.
Med tech meeting
The Med tech meetings in the Netherlands were aimed at startup companies. Startups get the chance to present their product for an audience of invited guests, coming from academic medical centers, universities, medical companies, financial institutions and governments. After the presentations the host would couple these guests with the presenters, allowing them to share thoughts, experiences or create investment opportunities. The Med tech meeting was valuable, because the ideas that are presented are currently not on the market, thereby providing a glimpse of the future of PHDs.
3.1.2 Methods
The methods for the conferences are presented per conference. For the three conferences companies were included if their PHDs measured health electronically and are accessible now or in the near future for consumers.
Medica
Before I visited the Medica trade fair, I searched the online catalogues of the Medica and Compamed congresses for exhibitors using the term “sensor”60. I created a list of exhibitors I would visit during the conference, by looking at the website of every exhibitor I found in the online catalogues.
I visited the Medica conference for two days. During the conference I used the list that was created beforehand to visit exhibitors. I found other PHD companies by looking at the exhibitors in the same hall as the visitors on the list. The Medica congress was also home to different forums about specific topics. I visited the “Wireless Health Pavilion” and the “wearable technologies show”, because they presented wearable devices that could be used as PHDs.
10 Health tech event
At the health tech event I attended presentations concerning eHealth. Lucien Engelen of the Radboud University presented about Google glass and its uses in healthcare. The presentation was followed by “Smarter living 2020”1
which was a project that used telemedicine to monitor the elderly. Philips presented their eHealth solutions. A student discussed the security of implantable devices. IMEC presented their research on wearable sensors. Proxible showed their patient tracking system and the final presentation was ART presenting their touchless respiration monitoring technology.
Med tech meeting
I witnessed the following presentations: Assistobot, a robot that can assist Alzheimer patients. Neutromodulator; a device that sends an electromagnetic signal that boosts the immune system. Microvision medical; a scope that allows physicians to check the microcirculation. Meminder; which is a pillbox that keeps track of medicine usage by the patient, E-nurse; a pressure mat used for bed monitoring and; Hyperhat a hyperthermia device for cancer therapy.
3.1.3 Results
The first search query in the online catalogues of Medica and Compamed resulted in 186 exhibitors. After examining their websites I narrowed the list to 18 exhibitors (Table 3-1).
Table 3-1 Medica catelogue
These were the first exhibitors I visited and in these halls I looked for more exhibitors. After two days I found 105 companies in total at the Medica conference.
Table 3-2 displays the companies at the Medica conference that made sensors as a component. An example of a sensor as a component is the accelerometer, which is used in PHDs such as activity monitors. I found 80 companies that make PHDs during the Medica conference.
Table 3-3 displays all the companies that make PHDs that were found at the three conferences.
Table 3-2 Companies making sensors as component
# Company # Company # Company
1 2e-mechatronic 10 HJK 19 Optoi
2 Aceos 11 HSG-IMIT 20 Sensirion
3 All sensors 12 IC haus 21 SETI
4 Alps 13 lmtb 22 Sonotec 5 Awaiba 14 Measurements specialites 23 ST
6 ESS 15 Metallux 24 Statek
7
First sensor 16 Morgan advanced
materials
25
Variohm
8 Fisba 17 Murate
9 Hamamatsu 18 Numerik jeha
1
In Dutch: Slimmer leven 2020170
# Company # Company # Company
1 Aceos 7 Ic-haus 13 Screentec
2 All sensors 8 Measurement
specialities
14 Sensirion
3 First sensor 9 Metallux 15 SETI
4 Fisba 10 Microstone 16 Sonotec
5 Hamamatsu 11 Murata 17 Statek
11
Table 3-3 Companies making PHDs. The first 80 companies were found at the Medica conference. Company 81 was found at the healthtech event. Company 82 was found at the med tech event.
# Company # Company # Company
1 4d force 29 Fitmefit 57 Ok biotech
2 9solutions 30 Fora 58 Philips
3 Actismile 31 Fraunhofer 59 Phyode
4 Adidas 32 Galvanic 60 Picsolution
5 Aeon 33 Glab 61 Polar
6 AIQ smart clothing 34 Gloreha 62 Propeller health
7 Alliance international co ltd 35 Google 63 Radiant tek
8 Alphafit
36 Healthwatch technologies ltd
64
Raiing
9 Berry med 37 Heapsylon 65 Sense core
10 Beurer 38 Heartmath 66 Sinohero
11 Biovotion 39 Hline online 67 SISS
12 Biozoom 40 Isotech 68 Smart lab
13 Bodytel 41 Jawbone 69 Smartcardia
14 Boso 42 Lumafit 70 Stanley healthcare
15 Boston scientific 43 Lumo 71 Stiegelmeyer
16 BreathResearch 44 Maestros 72 Telbios
17 Choicemmed 45 MD diagnostics 73 Tensiomed
18 CIS 46 Medisana 74 Thimble
19 CN systems 47 Meditech 75 Winmedical
20 Corehab 48 Microstone 76 Withings
21 Corscience 49 Mio global 77 Yahorng 22 CSEM 50 Misfit 78 Zydacron
23 Delta 51 Moticon 79 Wahoo
24 Dutch domotics 52 My glucose health 80 Nike
25 Epi 53 NEAT 81 ART1
26 EWA 54 Neurosky 82 Meminder 2
27 EXEL 55 Nissei 1 from Health tech event
2 From Med tech event
28 Famidoc 56 Obereg
The Health tech event introduced me to one new PHD, namely the radar from ART. This radar is used to measure the respiration of people in bed, which I added to table 3-3 as item 81. I found the Meminder pillbox at the med tech event which I added to table 3-3 as item 82.
I identified six different goals where PHDs are used for. These goals are displayed in Table 3-4 with an illustrating example per goal. I also identified that some PHDs are already on the market, whilst others are still in development. At the conferences I noted that there are two types of devices, the first type are devices aimed at healthcare professionals. These devices require training to use the device and knowledge to interpret the results (e.g. ultrasonagraphy). The second type are devices aimed at consumers. These devices require (almost) no medical knowledge and are easy to use.
12
Table 3-4 Goals of PHDs
3.1.4 Discussion
Visiting the conferences gave me a quick overview of the PHDs that were available on the market. Nevertheless, there were limitations. First of all, the size of the Medica conference made it physically impossible to visit all the exhibits, so I might have missed some companies that were presenting PHDs. I mitigated this problem by searching the digital catalogue beforehand. Secondly, not all existing sensor companies had an
exhibit one of the conferences, which led to excluded PHD companies. That is why I also performed a literature search and an online search on. These results can be found in sections 3.3 and 3.4.
This study provides an overview of companies that make sensors as a component for healthcare and companies that make PHDs. Developers who are interested in making a PHD could use this information to find sensor companies that have experience with healthcare products. Also the list of PHD companies provides a list of possible competitors. This study only provides an overview of the companies. To provide insight into the products of these companies I performed an online search, where I researched the PHDs found on the websites of these companies. These results are displayed in section 3.4.
3.1.5 Conclusion
During the three conferences I found 107 companies that develop sensors for healthcare. Furthermore, distinction needs to be made between sensors as a component and sensors as a finished product. 25 companies made sensors as a component and 82 companies made sensors as a finished product. Distinction can also be made between devices for healthcare providers and consumers. Furthermore, I identified six goals that PHDs can be used for: adherence, chronic disease management, detection, fitness, monitoring and rehabilitation.
Sensors as a component per company are numerous, therefore an overview of these sensors was not presented. The companies I found during the conference survey were used in my web search described in section 2.4.
Goals Description Example (brand)
Adherence PHDs aimed to increase the adherence to a treatment
A pill box that reminds the user to take their medication (Meminder) Chronic disease management PHDs especially for chronic diseases
A glucose meter used for diabetes (Beurer)
Detection PHDs used for detecting diseases or diagnosing the user
A sleep monitor that detects sleep apnea (Withings)
Fitness Sensor specifically for fitness / wellness purposes
A step counter (Nike)
Monitoring PHDs aimed at monitoring physical parameters
A radar that measures respiration rate (APT) Rehabilitation PHDs used for
rehabilitation
Belts with accelerometers that measure body position (Corehab)
13
3.2 P
RE SEARCH:
C
ASE REPORT FORMIn order to list the sensors in a standardized way I created a case report form (CRF). This form was created using HTML, PHP and Javascript and hosted on a webserver (www.rickbolten.nl)61. The data is sent from the form to a MySQL database hosted on the same server. Table 3-5 presents the main elements of the CRF. These choices for these items were based on my conclusions from the conference survey described in section 3.1.
For every device basic information was recorded, namely the brand name, product name, website of the manufacturer, source (which led me to the device) and a short description. The six goals of measurement (adherence, rehabilitation, monitoring, detection, chronic disease management and fitness), that were identified during the conference survey, were included to the form as well. I added a dropdown list with a textbox that allowed me to identify types of devices and more goals of measurement.
I made a similar dropdown list for connectivity. I recorded the connectivity, because this would allow consumers to share the measurements of the PHD with the GP.
During the conferences I noticed that some devices were on the market, while some are still in development. The checkbox development was used to indicate if it was still in development or on the market.
Table 3-5 Main elements of CRF
Brand Type Description
Product brand Textbox the brand name of the PHD Product name Textbox the name of the PHD
Product URL Textbox the URL of the website where this PHD can be found Source Textbox the webpage or article that showed me this PHD Product
description
Textbox A free text field which allowed me to fill in additional information and describe the product
Product type Dropdown + optional text box
A dropdown menu that allows the selection of a product type or inserting a new product type. The new product type is added to the dropdown menu Connectivity Dropdown +
optional text box
a dropdown menu that works similar to product type. I can select a
connection in the list or create a new connection which is then added to the list
Development Checkbox Checked: the PHD is still in development Unchecked: The PHD is on the market
Adherence Checkbox PHDs aimed to increase the adherence to a treatment Rehab Checkbox PHDs used for rehabilitation
Monitoring Checkbox PHDs aimed at monitoring physical parameters
Detection Checkbox PHDs used for detecting diseases or diagnosing the user Chronic Checkbox PHDs especially for chronic diseases
14
3.3
L
ITERATURE STUDYThis section describes a literature search for PHDs. I used the CRF to record the PHDs in a standardized way.
3.3.1 Introduction
The Medica congress gave me insight into available PHDs for healthcare, but to be more comprehensive I also performed a literature search. The literature study had two goals. The first goal was to find PHDs used in healthcare. The second goal was to find classifications of sensors, because I used those results to create a grouping of different sensors to answer sub-question 2.
3.3.2 Methods
I used a broad search query, because I wanted to identify as many different types of PHDs as possible. The search query I used was:
((Sensor OR sensors ) AND ("Telemedicine"[Mesh] OR telemedicine OR ehealth OR e-health)) The first part of the search query “(Sensor OR sensors )” focused on the main aim of my search, finding the different sensing devices in healthcare. To narrow it to sensors for domestic use I added the second part: “AND ("Telemedicine"[Mesh] OR telemedicine OR ehealth OR e-health)”.
I used the search query in Pubmed, Web of Science, IEEE Xplore digital library and ACM digital library. Even though Pubmed and Web of Science search through the databases of IEEE Xplore, I found different results when I used IEEE Xplore’s own search engine and decided to also search the IEEE Xplore separately.
I read the abstracts of articles that were returned by the search engines and excluded articles that were not focused on healthcare and sensors used by the patient. I also excluded articles that were not written in English or Dutch, because my skill in other languages is not sufficient.
Articles published before 2007 were also excluded because the devices in these articles are already outdated. I chose 2007 as a cutoff point for the following reasons. The rise in popularity and the drop in prices of micro-electro-mechanical-systems (MEMS) in 2007 allowed for the creation of a new range of smaller devices available for consumer use56. The price of an accelerometer was $3.00 in 2007 but dropped to $0,65 in 201056. Google Trends mentioned 2009 as the year MEMS was propelled to the mainstream62. Furthermore, because this was the year the first Apple iPhone was introduced, which could be seen as a hallmark for consumer technology. Also, the articles from 2007 and before,
showed bulky devices that are not comparable with the current small devices (see figure 3-1)63. Next, I read the full text of the remaining articles and included articles that met the following inclusion criteria:
It mentioned a PHD that is available on the market or could be in the near future.
The research led to a spin-off company selling a product that is on the market now or in the near future. To check whether a PHD was on the market, I used the Google search engine to check if there were websites selling the PHD.
It gave me insight in a new group of PHDs. I created a mind map using the Freemind software, where I included every new group of PHDs.
I had not found the PHD already.
15 I used the CRF described in section 0 to store all included sensors in a MySQL database. Using the method described in Figure 3-2. If the article did not describe one of the parts of the CRF looked up the specifications of the device on the manufacturers website by searching on the name of the device using the google search engine.
Figure 3-2 Activity diagram with the workflow of adding PHDs to the database
3.3.3 Results
The results of the search strategy are presented for the used sources, namely web of knowledge, IEEE, ACM digital library and Pubmed. Figure 3-3 depicts the results of the search strategy.
Web of Science
The literature search resulted in 263 articles. After reading the titles and abstracts I excluded 136 because they did not match the inclusion criteria. Two articles were excluded because they were not written in Dutch or English and 13 because there was no full text available. I read the full text of 112 articles and excluded 76 of them resulting in 36 articles. I also checked the references for articles and included 5 from 29 articles I had chosen to read the full text of.
IEEE Xplore
I found 116 hits, of which 59 were out of scope, for 1 article was no full text available and 1 article was not written in English or Dutch. As a result, I read the full text of 55 articles. Also I read the full text of 12 references. I identified 7 doubles before reading the full texts. I included 19 articles from the full texts. 8 of the included articles where found in the references.
16 ACM digital library
I found three results and after reading the full text I included 1 article. Pubmed
Out of a total of 404 articles 2 were not written in English or Dutch. 390 articles were discarded because they did not match the inclusion criteria. For 1 article was no full text available. 5 articles were discarded because they were doubles. I read the full text of 12 articles, 6 of those articles were found in the references. 7 articles were included after reading the full text.
Standard model
The upcoming scarcity of medical professionals and rising costs of health care causes research to focus on eHealth solutions, that shift care away from the expensive hospitals to the patients at home. Figure 3-4 depicts the model that is often used in literature. The model consists of a patient in his own residence (e.g. house, nursing home) with one or more measurement devices. The measurement devices can be positioned inside the body (insidables2), inside clothing or inside accessories (wearables), as a portable device (carriables) or embedded in a house (domotica)64. These wearables and insideables can be used during the day and night allowing doctors to monitor the patient in real-time for longer periods than during a hospital stay or a visit to a physician’s office64. The measurements are sent from the measurement devices to the sensor hub. The sensor hub is able to forward the measurements to the doctor.
Personal health monitoring devices
Table 3-6 displays the 90 PHDs that were available for consumers that were found during the literature search. The first column describes the brand of the PHDs, the second column is the product name, the third column is my description, the fourth column presents the connectivity of the device, The fifth column is the type of device based on my judgement and the sixth column refers to the corresponding article.
2 Insideables is a term introduced by Lucien Engelen of the Radboud University to describe devices that are
positioned in the body171.
Figure 3-3 Search results
17
Table 3-6 PHDs found in literature
Brand Name Description
Conn-ection Type ref
A&D UA-767PBT-C Continua certified blood pressure with Bluetooth Bluetooth Blood
pressure 65
Abbot diabetes
care Freestyle navigator Wearable continuous blood glucose monitoring
Wireless
unknown Blood glucose 66
Actibelt Actibelt Wearable movement sensor in a belt 3 axis accelerometer USB Movement
sensor 67
Alert-it Bed movement sensor Senses movement in a bed, and warns for possible epilepsy Cable un-known
Movement
sensor 68
Alert-it Bed occupancy sensor Senses if a person is in or out of a bed Cable
un-known Pressure mat 68
Alert-it Sound sensor Microphone that senses grunts and might point to epilepsy Cable
un-known Microphone 68
Alert-it Enuresis Sensor Blanket that senses urine or vomit and might point to epilepsy Cable un-known
Incontinence
sensor 68
Alert-it Fall detection Device that is carried and monitors falls Cable
un-known Fall monitor 68
Alive Heart and Activity
Monitor Device with ECG leads and an accelerometer.
Blue-tooth, SD card
ECG
69
Alive Pulse Oximeter Wearable Pulse Oximeter Bluetooth Pulse
oximeter 69
AliveCor AliveCor Connects with smartphone allows for a single lead ECG
Ultra-sound ECG 70
Ambu Sleepmate Airflow
pressure sensor Airflow sensor for sleep monitoring
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate Airflow
thermal sensor Thermal airflow sensor for sleep monitoring
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate body
position sensor A position sensor used in sleep monitoring
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate
CannuTherm Thermal pressure airflow sensor
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate Limb
Movement Sensor Limb movement sensor used in sleep monitoring
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate Piezo
Effort Sensors Thorax belt respiration monitor used for sleep monitoring
Cable un-known
Sleep
monitoring 71
Ambu Sleepmate RIPmate
Inductance Belts Thorax belt respiration monitor used for sleep monitoring
Cable un-known Sleep monitoring 71 Ambu Sleepmate Snoring Microphones and Sensors
Microphone that detects snoring during sleep monitoring
Cable
un-known Sleep monitoring 71
AMTI AccuGait A pressure plate used to measure gait RS232/US
B Pressure mat 72
AMTI BP400600 A pressure plate used to measure gait RS232/
USB Pressure mat 72
AMTI BP600600 A pressure plate used to measure gait RS232/
USB Pressure mat 72
AMTI OPT400600 A pressure plate used to measure gait RS232/US
B Pressure mat 72
AMTI OPT464508 A pressure plate used to measure gait RS232/US
B Pressure mat 72
AMTI OR6-7 A pressure plate used to measure gait RS232/
USB Pressure mat 72
APDM Sapphire Wearable movement sensors attached to the wrist sternum and
ankles to monitor gait and activity
USB Movement
sensor 67
APDM Emerald wearable movement sensors attached to the wrist sternum and
ankles to monitor gait and activity
Wireless un-known
Movement
sensor 67
APDM Opal wearable movement sensors attached to the wrist sternum and
ankles to monitor gait and activity
Wireless un-known
Movement
sensor 67
BioSensics BalanSens Belt for balance sensing wearable Blue-tooth Movement
sensor 73
18
monitor gait sensor
BioSensics PAMS ys Wearable ECG device with movement sensors for gait USB Movement
sensor 73
Braun ThermoScan PRO
4000 Ear thermometer
0
Thermometer 74
Cardionet Wireless Event Wearable ECG leads connect to the small device which transmits the data via cellular network
GSM
ECG 68
Cardionet Dual Alert Afib Wearable ECG leads connect to the small device which transmits the data via cellular network
GSM
ECG 68
Cardionet Looping Multi-Event Monitor
Wearable ECG leads connect to the small device which transmits the data via cellular network
GSM
ECG 68
Cardionet Non-Looping Chest Plate
patients holds ECG against its chest for a short measurement. transmits the data via cellular network
GSM
ECG 68
Cardionet CardioNet Holter Monitoring
Wearable ECG leads connect to the small device which transmits the data via cellular network
SD /GSM
ECG 68
Clevemed Sleepview Wearable sleep monitor SD/ USB Sleep
monitoring 74
Clevemed Sleepscout Wearable sleep monitor
USB / Wireless unknown
Sleep
monitoring 74
Clevemed Sapphire PSG Sleep monitor
USB / Wireless unknown
Sleep
monitoring 74
Corventis NUVANT Wearable ECG a sticks like a band aid to the chest Wireless
unknown ECG 68
Gaitrite Gaitrite Pressure mat that is used to check the gait of a person USB Pressure mat 75
Health Guardian PMP4 Self Check
ECG ECG provides 1-and 12-lead ECG
Bluetooth
ECG 76
Health Guardian CG-7000DX BT 12-Lead ECG Recorder Bluetooth ECG 76
Health Guardian CG-6108 ACT 1 and 3-channel ECG via radio it is send to the smartphone Radio ECG 76 Health Guardian PMP4 SelfCheck
Weight Scale Weight scale with bluetooth
Bluetooth
Weight scale 76
Health Guardian PMP4 Easy2Check Blood glucose and blood pressure monitor 2 in 1 Bluetooth Blood glucose 76
Health Guardian PMP4 Oxy Pro Wearable pulse oximeter Bluetooth Pulse
oximeter 76
Health Guardian CG-6106 Wearable ECG 1-lead sends the data via the telephone landline
Tele-phone line ECG 76
Health Guardian CG-7100 12-lead ECG wearable data sent via the telephone landline
Tele-phone line ECG 76
Health Guardian CG-2206 Personal 1-lead ECG wearable send data via the telephone landline
Tele-phone line ECG 76
Health Guardian PMP4 Spiro Pro Spirometer Bluetooth Lungfunction 76
Health Guardian PMP4 BP Pro Blood pressure monitor with Bluetooth Bluetooth Blood
pressure 76
Health Guardian CG-900P Fetal
Maternal Monitor Measures heart rate of the baby in the belly of the mother.
USB
ECG 76
Health Guardian HealthPod USB Wearable ECG USB Wearable 76
Health Guardian HealthPod Bluetooth Wearable ECG Bluetooth Wearable 76
Intelesense Lifeguard Vital signs monitor Wireless
un-known Vital signs 64
McRoberts Dynaport Wearable movement sensor SD /USB Movement
sensor 73
McRoberts Hybrid Wearable movement sensor SD/ USB Movement
sensor 73
Microlife WatchBP Office Dual-cuff in-office blood pressure monitor Bluetooth Blood
pressure 77
Microlife WatchBP Office
Target Blood pressure monitor
0 Blood
pressure 77
Microlife WatchBP Office Afib Simultaneous double-arm blood pressure monitor Bluetooth Blood
pressure 77
Microlife WatchBP O3 Blood pressure monitor USB Blood
pressure 77
Microlife WatchBP Home Blood pressure monitor USB Blood
19
Microlife WatchBP Home S Blood pressure monitor 0 Blood
pressure 77
Microlife WatchBP Home N Night blood pressure monitoring
three measurements at night will be performed.
USB Blood
pressure 77
Microlife WatchBP Home A Screening for AFIB with high accuracy while measuring blood pressure
USB Blood
pressure 77
MiniSun IDEEA Wearable movement sensor USB /
rs232
Movement
sensor 73
MobiHealth BP@Home €100 subscription per year for blood pressure monitoring USB Sensor hub 77
Nuubo nECG A smart shirt, a device clicks on the shirt and transmits the ECG data.
Bluetooth
Wearable 78
Orthocare
innovations StepWatch Wearable a activity step counter as an ankle bracelet
Infrared /USB
Movement
sensor 67
Peregrine Peregrine Glove measures movement of the hand USB
Rehab-ilitation 79
Philips Stardust II Sleep monitoring Rs232 Sleep
monitoring 80
PositiveID Glucosechip A chip in vivo that measures the blood glucose RFID Blood glucose 64
Schiller AR12plus ECG Bluetooth ECG 74
Schiller FD12plus ECG Bluetooth ECG 74
Sensimed Triggerfish
A wearable lens which measures the eyeball pressure. used with glaucoma. patch around the eye which receives RFID signal from the lens and transmits it to personal device.
RFID
Eye lens
81
SensiumVitals SensiumVitals Wearable patch like band aid heart rate, temperature, and respiration
Wireless
unknown Vital signs 64
Shimmer Shimmer3 Wearable movement sensor SD /
blue-tooth
Movement
sensor 82
Stresseraser StressEraser Small ECG device used to battle stress 0 ECG 83
TMSi Mobita EMG EEG ECG wearable compact physiologic signal
amplifier system 32 channels
Wifi
EMG 77
Variable Node
Accelerometer gyroscope and magnetometer possibility to attach different kind of sensors: thermometer, gas sensors and barcode sensors
Bluetooth
Movement
sensor 84
Vicon T-series Gait and posture motion capture LEMO
cable
Motion
capture 85
VRI Medical Alert System Alarm button Wireless
unknown Elderly help 86 VRI Vitals Monitoring Sensor hub connect via internet to caregivers. uses Bluetooth
peripherals.
Bluetooth
Sensor hub 86
WelchAllyn Micropaq Wearable ECG Wireless
unknown Wearable 64
WelchAllyn Home Blood Pressure
Systems Blood pressure monitor upper arm
0 Blood
pressure 74
WelchAllyn Welch Allyn
PC-Based SpiroPerfect Spiro monitor connects with PC
USB
Lung function 74
Zydacron Box Sensor hub which connect with different measurement devices Infrared /
20 Table 3-7 displays the identified measurement types, a small description is given per type and how often the type was identified. Devices can have more than one type, which explains the total of 108 types for 90 PHDs.
Table 3-7 Identified types
Type Description n Type Description n
ECG Heart monitoring,
electrocardiography (ECG) 26 EMG Electromyography 3
Movement sensor
Accelerometer, especially in
activity sensors 17 Sensor hub
Device which connects to different sensors and stores the data
3 Blood pressure Blood pressure measurements 12 Lung function Flow measurements 2 Sleep
monitoring Measures sleep duration 12
Incontinence
sensor Detects incontinence 1
Pressure mat A mat that measures pressure 8 Microphone Microphone 1
Pulse oximeter Percentage oxygen in blood 6 Motion capture Captures movement and gait 1 Vital signs A sensor that measures
multiple parameters 6 Fall monitor Detects falls 1
Thermometer Thermometer 5 Weight scale Weight scale 1
Blood glucose Blood glucose measurements 3 Total n = 108
Besides the types displayed in table 3-7,I found 29 wearable devices, of which 1 device was worn as a contact lens. One device was specifically aimed at elderly and 1 device was specifically aimed at rehabilitation.
The CRF allowed me to create a list of connection types. The different types of connectivity I found are displayed in Table 3-8. The total of 112 connections for 90 PHDs is explained by the fact that a device can have more than one connection possibility.
Table 3-8 Overview connections
Connection group
Identified
connection Description n
Cable USB USB port for connectivity 28
Cable Rs232 Uses a rs232 cable 8
Cable Telephone line Uses the telephone line 3
Cable LEMO Optical cable from the LEMO company 1
Cable Cable unknown Uses an unknown cable 13
Portable
data carrier SD card
Uses a SD memory card (commonly used in digital cameras or mobile
phones) 9
Wireless Bluetooth Uses a version of Bluetooth 22
Wireless GSM Uses GSM also known as 2G 5
Wireless Infrared Uses infrared (e.g. TV remote) 2
Wireless RFID Radio-frequency identification 2
Wireless Ultrasound Uses ultrasound (sending sounds that are captured by a microphone in the
cell phone) 1
Wireless Wifi WIFI connection 1
Wireless Wireless unknown Uses an unknown radio signal 11
No
connectivity
No connectivity
(=0) No connection possibility 5
21 Connectivity can be divided in three groups: via cable, via portable data carrier or via wireless transmission. The largest group is the wireless group (n=51), followed by cables (n=45), and portable data carrier (n=9). The smallest group has no connectivity (n=5).
3.3.4 Discussion
ECG was most often identified as a type, followed by movement sensor and blood pressure measurements. The devices could share their data via cables, wirelessly and via portable data carriers. This means that whenever a GP wants to retrieve the information from a device the GP will most likely encounter a Bluetooth or an USB interface, although other connections are used as well. In order to reach operability between the GPs system and the PHDs the interface is only a small part.
The following limitations are identified. Firstly, I only included English or Dutch articles, which can lead to a language bias, where I might have missed articles and PHDs because I did not include articles in other languages.
Secondly, in my search strategy I only searched from the year 2007 until now. Articles published before 2007 might have introduced new sensors. However, during the literature search I noticed that articles from before the year 2007 introduced only PHDs that were outdated devices.
Thirdly, during my search I used Google to check if a study describing a novel sensor device made it to the consumer market. Commercialization is often undertaken by a spin-off company. This introduces a problem because a spin-off company may use a different name for the device than the name that was used in the article, resulting in possibly not included PHDs.
Fourthly, I was not able to identify the precise connections for some PHDs. I introduced the connections cable unknown and wireless unknown for these connections.
Fifthly, developers of new sensors might not have published their research yet, which would not be included in my literature search.
3.3.5 Conclusion
I found 90 PHDs. Most devices were used for heart monitoring. Almost all had connection possibilities allowing data to be shared. The most common means of connectivity were USB and Bluetooth. Only 6 PHDs had no connectivity possibilities. Other connectivity options were: GSM, infrared, LEMO cable, rs232 cable, SD card, telephone line, ultrasound and WIFI.
