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Stress Management: the mHealth Solution

Stress is a huge issue for everyone in the world today. It can affect every part of daily life, from your health to your mood. Often, stress is a cause of things like heart attacks. For such a big factor, it is widely unmanaged. There are many tips and articles on the web for managing stress, but often the only guaranteed solution is to remove people or situations from life completely. For some, this is not an option.They have to continue doing to work, interacting with stressful people, and generally living their life. How can we manage stress and overcome it without having to be put on bedrest, or otherwise shut down life? Sharecare, a “health and wellness engagement platform” has invested in technology that might begin this process.

The issue is that often people do not realize the level of their stress until it becomes a serious health risk. Because we operate every day overcoming certain amounts of stress, like traffic, or an difficult co-worker, we become used to feeling stressed. Some become very good at living with stress and overcoming it. Yet it continues to build, and eventually they are putting themselves in situations where the stress has escalated to dangerous heights. Similar to the way lobsters are boiled, people can find themselves in scaldingly stressful situations without even realizing it.

This is where Sharecare’s new app can help. They, with newly acquired German technology magnate Feingold, have developed an app that analyzes human behavior and offers a comprehensive health profile. They describe the app as being able to listen to the human voice and then offer a diagnoses of the patient’s mood and stress levels. The app collates data from every call the patient makes, providing charts that explain which calls left the patient more stressed, and which contacts gave the patient which levels. The app sounds a little bit like a mood ring at this stage. Skepticism is a valid first reaction. But the vision that Sharecare co-founder Jeff Arnold outlines sounds legitimately promising. “As part of our strategy to create the comprehensive health profile for every consumer regardless of where they are on their health journey, it is critical we find new ways to help people be mindful of their health every day without disrupting their daily routine,” mobihealthnews.com reports him saying.

This statement could be a motto for all mHealth development. The idea of being able to non-invasively provide health assistance to patients is one of the foundations of mHealth, and to have major players like Arnold and Sharecare pursuing this goal is exciting. If Sharecare’s app, which is in trials right now, pans out to be an effective stress management tool that will be a major advancement and effective help for all smartphone owners. But even if the app does not end up working correctly, we still have the idea permeating the app market. Stress relief and management, because of its intricate and powerful nature, will probably only be achieved by means of non-invasive mHealth solutions. We now have those solutions in development.

 
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HONY and mHealth – the Importance of Community

In case you missed it, a little boy and a roving photographer just raised one million dollars in less than a month. The photographer is Brandon Stanton, of Humans of New York, and the boy is Vidal, a middle school student at Mott Hall Bridges Academy in New York. Stanton has been taking pictures around New York City for over two years, portraits of the city’s inhabitants with captions that offer glimpses into their life. Stanton posted a picture in mid-January of Vidal, with a caption in which the boy described the impact his school’s principal had had on him. Stanton pursued this, visiting the school’s principal, Mrs. Lopez. He then learned that the school had many financial needs, and informed his followers. Stanton did not start a huge campaign or beg his followers for help. He just pursued a need, brought it to the attention of his established community, and then set up a way for them to help. Through these unobtrusive steps, over one million was raised by people all across the world. This is one of the clearest examples of the power of a social media community that has been seen in a while.

This is interesting news, certainly, but it seems fairly distant from the mHealth realm. An interesting charity strategy, perhaps, but the premise of the Humans of New York website is very different from that of most mobile healthcare companies. Much can be learned, however, from the community aspect of this story. Community impact is a large factor in every field, and in the mHealth field it is often overlooked. One of the reasons that Stanton’s campaign was so successful was because his community was already established. Stanton was not trying to find people and then give them information. He has over twelve million likes on Facebook and a steady following on Twitter and Tumblr as well. People expect his daily posts on their feed – when the post includes information on a way to help they are ready to jump in. This is a major asset, simply due to the nature of his work.

It would be fascinating to take this model and apply it to the healthcare world. A website that offered pictures of researchers all over the world and glimpses of their research and of their personalities. The same idea as Humans of New York, but specialized. Or an app integrated within community health centers that knit the community together, highlighting patients and doctors alike. The beauty of Stanton’s blog is that it is a fairly unbiased, varied glimpse into New York City life. Each post is different and offers a new perspective. This would be a interesting new aspect of mHealth; a way to weave the varied sides of healthcare together. From ultra-specialized researcher to low-income patient, a Humans of New York style community would be a good way to open the field, and to facilitate understanding of the many facets of healthcare.

Whether in an app, a community heath center, a website, or any other multitude of things, mHealth innovation begins when we take inspiration from non-healthcare models and weave it into the established framework.

 
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All the Talk About Vaccines; We Are Asking the Wrong Questions

The issues and questions wrapped up in the “vaccinate or not” debate are reaching an unprecedented high this week. Outbreaks of previously ‘dead’ diseases are bringing these debates back to the bright lights of mainstream media. From Facebook to CNN, articles arguing for and against vaccination are rampant, and everyone who writes and shares these articles has a detailed, emotional argument. It is an issue easy to argue from the desk chair, typing away, secure in an idea of how things are. But out in the real world, for parents, the issue becomes much more messy. Nearly a year ago, we published a post on Vaccines, but in light of recent activity, the topic deserves revisitation.

The questions start in the early stages of parenthood. Option upon option, constant decisions, and each one has potential for extreme consequences. As a parent you are forced to make decisions for another human, decisions which impact their life forever. Being an early parent means constantly hunting for knowledge, for data and experience that can inform your decisions. There is plenty of opinion out in the internet, copious articles of parenting advice. But when it comes down to facts about the exact consequences of early childhood choices, very little can be found. So parents are left to wander around in the dark, hoping their choices are legitimate and solid. It is a brave and scary thing to be a parent.

Some of the questions these parents are asking are the ones which are today held in so much contention. “Should we vaccinate?” “What are the side-effects of vaccination?” “Are vaccines related to autism?” “How can I keep my child as safe and healthy as possible?” The problem is that these questions have no answers. No one can yet give accurate, authentic answers. This is from pure lack of data. We do not yet have the healthcare data needed to prove beyond doubt that vaccines are either harmful or safe. Nor do we have enough data to start figure out the exact nature of autism. As long as people continue to pursue answers to these questions they will find them in emotional articles and advice pieces. Instead, we should begin to ask the right questions, “How can we figure out the nature of vaccines?” “What does the data say about vaccination, and how can I assist in furthering the scope of that data?” “How can I use the technology I have to further our knowledge of the nature of vaccinations?”

We can continue to discuss the benefits and detriments of vaccines for another year, but so long as it remains an opinion driven conversation we will be able to write the same post in February 2016. Instead, lets pursue the data, the facts, and continue pushing our mobile healthcare technology to collect and analyze that data. Just with the data we’ve discussed on this blog, like the storage and analytical power of google, or potential information logs, we could certainly make advancements in our knowledge. Hopefully we can read an article a year from now describing the data gathered on vaccines, and the knowledge and action plans that are now available from that data. Instead of wandering in the dark, parents can begin to make fully informed decisions and rest in that confidence.

 
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mHealth and the Data Age

As we settle into the new year, a constant remains from 2014. We are still in a data-focused age, an epoch of innovation and information. We are focused on effective solutions and any method that will achieve that end. Television, radio, and especially the internet are all filled with advertisements and individuals claiming to have obtained new information, pushed a new limit, and achieved a new level of excellence. Sometimes this ingenuity is just jargon, but often it is the work of intelligent people unearthing creative answers. In the case of Dr. Patrick Soon-Shiong it is an interesting and optimistic foray into new realms of intelligence. Dr. Soon-Shiong is a multibillionaire, a surgeon, and a researcher at UCLA. He is well known for creating a drug for breast cancer called Abraxane, which greatly improved treatment prognoses. Dr. Soon-Shiong was recently interviewed on an episode of 60 Minutes, discussing his views on cancer research and treatment future. Dr. Soon-Shiong’s answers center around one aspect of research that is exponentially improving with time, that is, data.

Data is now more available than ever. It can be collected, stored, and examined in new and more effective ways every day. Previously, we posted about Google and the human genome project, and how improvements in storage space are opening many doors for researchers. This is the direction that Dr. Soon-Shiong’s research has taken him, and his predictions are enticing. Dr. Soon-Shiong believes that the cure for cancer will come from pure data. He and his team are inspecting the genomes of cancer cells, the makeup of every single cancer cell that they can obtain. They are meticulously cataloging and analyzing these cells, putting the genome into sequencing software and searching for connections, weaknesses, or anything enlightening. Dr. Soon-Shiong hopes that at some point in the near future it will be easy to disseminate this information through cancer cell genome databases. With these databases, doctors can more quickly identify the mutated cells and get rid of them precisely.

“We have been treating cancer all wrong,” Dr. Soon-Shiong proclaimed in his 60 Minutes interview. Instead of trying to specify treatment, trying to figure out the precise type of cancer and individualizing the treatment used on that area of the body with that type of cancer, Dr. Soon-Shiong believes that cancer should be looked at more cohesively. A treatment that works on one type of cancer has a high chance of working elsewhere, he believes, and he’s acting on it, putting his breast-cancer drug Abraxane through trials to be approved to fight lung, melanoma, gastric, and pancreatic cancer. Cancer should be looked at on a large scale and attacked from every possible angle, and Dr. Soon-Shiong outlined his plans for each angle of attack. Some are still lacking technology or data necessary for implementation, but it is not a creativity or innovation shortage which inhibits cancer’s demise. Instead, it is only data which we need to begin the process of controlling and then eliminating cancer completely.

Most of today’s adults were born, raised, and educated in the information age. As a society we are firmly planted in a mindset of technology and innovation. In the healthcare realm, this is invaluable. In attacking and eliminating disease, especially one as prevalent and aggressive as cancer, technology and innovation are research necessities. But neither can do very much if they don’t have data to work with, to process, and to use as a launch pad for innovation. As the ability to process and store data grows, researchers like Dr. Soon-Shiong will be able to reach unfathomable conclusions. The realm of mobile healthcare is a vital area, consistently perpetuating, legitimizing, and motivating the advance of data.

 
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Predictive Analysis in mHealth

In the last blog, we discussed Walmart’s successes with predictive analysis using their data-gathering RetailLink software. The company is able to dominate sales because they have the information necessary to predict needs, and market accordingly. Walmart takes external factors like the weather, internal factors from their collected data, and combine them with demographic knowledge to cultivate a sales triumph. This could be applied to healthcare, especially in the mobile realm, with great outcome. An app that recorded the daily health data of individuals, combined with EHR and external factor data, could achieve the beginning stages of analysis. This information could then be applied to every facet of the healthcare realm.

A simple example would be dietary data. Daily dietary data was recorded that revealed that in a certain time period, people drank more sweet drinks than usual. This was compared to external data which said that this was when it was cold outside and coffee companies with sugary, warm drinks were running sales. Also, this time coincided with the holiday season, which gave people more opportunity and motivation to drink sugary sodas or alcoholic drinks. This data collection is given to dentists, who are then able to send teeth cleaning reminders to their current patients, or even run deals or special marketing for dental work near the end of this time. These reminders come right when the patient is beginning to feel the effects of all the sugar on his teeth, and thus is motivated to take care of them. This cause and effect is already highly visible in the holiday season, as it is a known fact that the season often produces weight gain. So after the holidays, many more weight loss commercials are released and sales are rampant.

This analysis can get more precise though, in dietary and other fields, such as if a fast food chain were to push a certain special meal, for example a McRib. They obviously have done the analysis with their marketing team to determine that this is the right time to bring back and advertise this meal, and they are expecting big sales. The next step would be for pharmaceutical companies to push out their advertisements for blood pressure and cholesterol medicine, or other such medicine. The correlation is not huge, but it is proven, and small connections can add up. These small connections are when the demographic data and external factors align to create an obvious consequence. This information could be utilized in a community health center setting as well. If the CHC were to gather data on their patients they would begin to understand the nature of their surrounding demographic. Dietary and fitness data could illuminate the basic needs of their patients. They could then combine this with external factors: a park nearby, or a lot of young sports leagues, a retirement community or a smattering of nightclubs. Each of these comes with its own unique set of health needs. A health clinic near a youth sports hotspot, which has patients whose diet contains little dairy, would perhaps stock up more in cast and splint items than cholesterol and blood pressure regulators.

The potential of a predictive analysis system for healthcare goes off in many directions. Fitness opportunities would be numerous, and the data gathered from chronic disease patients could help tailor suggested treatment options in each individual hospital, just to start. The problem is that the data has not been gathered yet. The strength of Walmart’s system is their data. RetailLink, their data collector, holds petabytes of information, larger than most of the world’s server collections. Because of their years of sales data, Walmart can predict based on past trends. For healthcare to step into these predictive analysis possibilities, data must be available. With the app variety today, it is hard to imagine that large-scale data collection is far off. This should be the next step in mHealth innovation; health data collected for purposes of predictive analysis.

 
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The Walmart Method and Healthcare.

Walmart has successfully figured out the processes of predictive analysis. It begins with data collection. Thanks to their RetailLink software, Walmart and its suppliers are able to access information about their products on a wide scale. In depth information about sales is all at their fingertips. RetailLink records every sale in every Walmart in the world. It holds information on such a microscopic level that there are companies who specialize in educating Walmart’s suppliers. Courses and consultants abound for the RetailLink software, helping the suppliers figure out how to find and interpret meaningful data from the massive RetailLink database. Data collection on this level is considered one of Walmart’s major points of success, and a key factor in their consistent market triumph.

The importance of the RetailLink software is not the impressive amount of data, but the way it equips Walmart and its suppliers to predict sales. RetailLink records each sale, the other products that were purchased in concurrence, and much more. It knows the demographics around each store, and can then explain the buying patterns of those demographics. This enables analysis of the correlation of products, the frequency of purchases in correlation to certain seasons, holidays, large events, and any other factor. Here, Walmart’s elaborate method shines, a method best explained by example. In the spring, there is a first warm break, the first warm snap that speaks of a healthy season. People that work for Walmart, specifically in the predictive analysis department, are watching the weather. When this warm snap is forecasted, they pay even closer attention. Then comes the first spring rain, warm and nourishing to the snow-mutilated grass. When this first big rain is forecasted, the predictive analysis team at Walmart sends out the green light. Walmart runs adds for lawn equipment during the rainstorm and the weekend afterwards. They’ve already notified their suppliers, so they’ve restocked their lawn care section, with deliveries coming right before the weekend. They’ve also notified their store managers to prepare, to place the lawn care and especially lawn mowers in prime position. End-cap displays are changed, sales vignettes placed around the store. On cue, the customer walks out of his house on Friday morning and the grass is looking ragged, shooting up. Unfortunately, the lawn mower which sat in the shed all winter is not looking too great. Why even try fixing it, when he saw a huge add for cheap lawnmowers at Walmart this morning. He stops in on his way home and picks one up; by the end of the weekend, Walmart has sold out of lawn mowers.

This seems almost too good. Certainly the system does not always work out perfectly, but at least 95% of the time, Walmart analysis professionals have a hearty understanding of the market and its future. This is because they have spent hours studying the data from RetailLink. They know that for the past ten years, lawnmower sales have skyrocketed after the first warm spring rain. They know that they should send more lawnmowers to one Walmart, which is surrounded by suburban homeowners who have a history of high lawn care sales; obviously possessing large lawns and the will to take care of them. Less lawnmowers are sent to the inner city Walmarts, surrounded by apartment buildings whose residents have no lawn to mow. Dozens of factors go into every item; how it is stocked, and how it is displayed. Nothing is done blindly.

This seems like business 101, but Walmart’s intricate method could also revolutionize healthcare. The realm of healthcare has minimal predictive analysis currently. Flu shots are highly stocked and advertised in the months of flu season, but otherwise the data collection and predictive analysis fields in healthcare are strangely empty. Stay tuned as we go in depth into the implementation of predictive analysis in healthcare, especially in the mHealth field, in our next post.

 
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MHealth and Information; the Future of Healthcare

There is a multitude of factors that make up the healthcare system. Information comes from hundreds of sources and combines to make up one person’s medical data. The more of this information the doctor knows, the better they can diagnose and treat their patients. Electronic Health Records are usually the doctor’s first resource in diagnostics and treatment plans. A patient’s EHR holds their medical history; information about previous diagnoses and treatments. But the information in an EHR is just the tip of the iceberg. Doctors usually need more than just a medical history to understand the issue the patient is facing. And often, the EHR is not complete, or unaccessible to a new doctor. Doctors need more external and internal information about patients, to improve care and reduce costs.

Everyone cannot keep a minute by minute journal of their activities and other information. This level of data collection is nearly impossible. Yet that is the level of information that would revolutionize the healthcare system. There are, however, many systems in place that collect this data. Pharmacies collect data about prescriptions, insurance companies know the procedures and diagnoses of their clients, medical devices such as pacemakers or oxygen tanks often collect data. There is also  the data that comes from fitness apps, which can reveal even more about the patient’s health than past medical records. If you include the food bought and eaten, a fairly informed picture of a patients current health can begin to come together. If doctors were able to peruse all this information, of the patients fitness, diet, past medication and procedures, and more, they would be able to offer quicker diagnoses, more complete treatment plans, and overall better care.

This information could also be invaluable to research companies. The information from clinical trials, from insurance claims, and even from online medical websites like webmd, could provide key information to researchers. Medical research is often a matter of case studies, comparing cases to expand understanding of the common ways in which disease works, and the treatments which seem to help. Research cannot function without a constant influx of new information, and the data that is floating around would provide this influx in an excellent way. The need is some sort of system to gather this information securely and make it available to researchers and doctors. Mobility is the key to a solution. With mobile collection, data can easily be gathered and shared in cloud-based systems. Mobility offers security, with encryption abilities and limited access opportunity. If a mobile system could be developed to collect all the information of a patient and provide that information to the patient’s doctor, the doctor would be able to provide better patient care because they would have a fuller understanding of the patient’s lifestyle. The cost of patient care would go down, because diagnoses and treatment could be handled more efficiently. And if this information was made available to researchers, medical knowledge could expand by leaps and bounds because of the expansion of the field of knowledge and the foundation from which to work. This cloud based information system should be the focus of any developer wanting to work in the Mhealth world, because its consequences could be momentous.

 
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Google & Genomes – the mHealth connection

There are many disorders today that are not infections or viruses, but genome issues. Treatment and prevention of these diseases is limited by the fact that genome research is such a massive field. Disorders such as Autism, Alzheimers, and Down Syndrome are a result of DNA structuring. As such, treatment and prevention cannot be approached through the normal pharmaceutical routes; genome research is necessary to discover the approach that will work.  The human genome is the DNA code that makes up a person. Over 25000 genes make up the genome, and each one makes a difference. Just a slight mutation in any one gene can cause a disorder such as Autism. This makes research difficult, because the entirety of the genome has to be studied. Because of the size of the DNA, it is extremely difficult to fit even one person’s genome on a computer, much less fit enough for cross-referencing. In the last sixty plus years since autism was discovered, advancement towards treatment has hardly advanced, simply because it was nearly impossible to investigate the human genome in pursuit of answers

This is where google comes in. The company is known for its amazing computer feats, including its sheer computing size abilities. Google has discovered the way to research genetic disorders – with a ton of computing. With the company’s servers, the genomes can be uploaded and inspected, and research can finally leap forward. The company was interested in pairing their data collection and analyzing software with a large-scale life-science project. Genomics is the perfect fit. With these genetic diseases, often each case is different. Each person who has autism has a different genetic mutation causing a different strain of autism, it isn’t necessarily an exact case duplicated each time. With the ability to compare genomes and do case studies and comparison, different strains and effects can be compared, leading to more knowledge than researchers ever before dreamed.

This new pairing, of AutismSpeaks researches and Google software shows a promising future of technology in healthcare. The advancements of computing abilities are finally beginning to meet needs in healthcare, especially healthcare research. As has happened in the past, as needs are met, more will arise, but the way solutions are being devised now is exciting. Connections between fields are more electric than ever, and lives are being changed and even saved by the cooperation of technology, medicine, and research. This promising development in Autism and genomic research is just the beginning of the grand scale technological impact we will see on our health.

 
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Football and MHealth – an Important Connection

Healthcare is not something that is restricted to a doctor’s office or hospital. Medical issues happen everywhere, and illness does not wait for a convenient time to attack. Emergency response time is a constant factor in life or death situations. Often, even in cases of non-emergent illness, rapid diagnosis can lead to better treatment options. Time is important for healthcare, and so is accessibility. The sooner the diagnosis and treatment, the healthier the patient, and the better the situation is overall. This fact in and of itself explains the importance of mobile healthcare. A structure based on accessibility and speed, a format that is simple and ever-present. Mobile healthcare research should be focusing in part on emergent situations, on the situations in which response time is key. By equipping normal citizens with the tools to help someone in an emergency situation, or by assisting EMS officers in their response, mHealth can save lives.
We’ve discussed a few examples of this before, such as the app PulsePoint, and the use of TeleHealth consultation to provide detailed response in emergency situations. But one thing that is now coming to the forefront is the way in which mobil healthcare can help in situations with consistent medical needs, primarily sporting events. Since the advent of public sporting, medical staff has been on hand to provide assistance and care. Sports can be dangerous, and often injuries incurred in athletic activity are of a serious nature. Continuing to play with certain injuries can be life threatning, yet often the players do not realize how sever their injuries are. Mainstream diagnostic equipment is too large and expensive for every sporting occasion to have, thus players suffer because their injuries go undiagnosed. This is the thinking behind a new development in the NFL, reported on by EMRandEHR.com. The league has dispersed iPads to medical staff, equipped with concussion assessment software. This has been an ongoing development, however now the NFL is keeping their players records electronically, allowing the onsite medical staff to have access to the players’ history, immediately recognizing whether this is a repeat injury, and thus more serious. On the sideline, available to any player, is a professional with all the access and a growing amount of technological assistance ready to keep the player as healthy and ready for action as possible.

This sideline action happens in all sports, and should be an interesting avenue of mobile healthcare to keep an eye on. There is a lot of money ready to be put towards keeping players as safe as possible, and mobile healthcare is set up to provide safety.  If the NFL, and other sporting leagues, continue to pursue accessible sideline care, mHealth will certainly benefit from the attention.

 
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Fitness Band Craze – Helping or Hurting the MHealth World?

 

Health bands such as Fitbit and Jawbone are becoming the forefront of the technological scene. Nearly every major company is exploring the fitness band craze; Apple and Microsoft both have fitness watch components coming next year, and other companies either already have bands out or are quickly following suit. Slim wristbands are appearing on everyone, from your teenage neighbor to the CEO of your company. These bands started out as digital pedometers, measuring steps and setting goals. Now, they encompass everything from heart rate to run distance, suggesting workout plans and even noticing when you stand up. The focus on fitness has grown in the past decade, and continues to boom. The demand for increased fitness technology is the cause for these bands’ inception and success, and motivates further innovation. Healthy living requires perseverance and a modicum of focus. These fitness bands provide information and motivation in a readily accessible format, creating a bridge to exercise. Once you step out of a sedentary lifestyle it grows easier to maintain an exercise regime.

These fitness bands are inexorably connected to the mHealth world. Fitness is the side of healthcare to which every consumer is able to connect. Everyone needs to maintain a fit life, no matter if they are healthy or not, and mobile solutions can make this easier. Someone who has no other connection or interest to mobile healthcare might be greatly interested in fitness apps and technology, intrigued by the idea of monitoring healthy living on a mobile level. The boom of health bands is a great victory for the proponents of mobile healthcare technology. With fitness bands, the average citizen gets introduced to mobile healthcare, and has a stake in wanting it to advance and succeed. The more money and interest is poured into fitness bands the more research can be done. The same technology that allows wristbands to monitor heart rate and step count can assist in technology that monitors chronic disease. Diabetes sufferers could benefit from technology that helped monitor insulin and other levels easily. The research and focus that is now being directed towards monitoring technology is a signifigant step forward for mobile healthcare. The booming popularity of fitness bands seems like a consumer craze, but its repercussions are widespread and beneficial. Research in the mHealth field is encouraging and should be advocated, especially research into a field as important as monitoring technology. Focus on technology that assists chronic patients could revolutionize the world of healthcare and bring comfort and simplicity to a group of people who struggle with complex situations. That is why the popularity of fitness bands should be celebrated by mHealth advocates everywhere.

 
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