Across most produced economies, healthcare costs are rising faster than inflation. In the U.K., the National Health Service (NHS) faces an estimated funding gap of £30 billion by 2020. In the U.S., the situation looks more bleak, along with total annual healthcare spending surpassing $3.8 trillion, representing an astonishing 17.4 percent of the country’s total GDP.
A key cause of the rise in healthcare spending lies in the spiraling costs of treating preventable chronic diseases (such as obesity, heart disease, stroke and cancer), which account for 88 percent of total healthcare spending. This figure isn’t surprising when you think of that approximately half of all adults in the U.S. have actually one or more chronic conditions. More worryingly, seven of the top 10 causes of deaths occur as a result of preventable chronic diseases, along with cigarette smoking alone accounting for 480,000 deaths in the U.S. every year.
These facts suggest that several of the key healthcare challenges of the twenty-initial century lie in how we tackle chronic disease. This guide will certainly explore the role of mobile technologies in meeting these challenges, why they have actually failed to do so until now and exactly what a solution might look like in the future.
The paradox of mobile health
With almost five billion mobile phone users in the world, of which two billion are smartphones, mobile health (additionally known as m-health or connected health) has actually been lauded as an attractive solution to address the challenges of the rising costs of chronic morbidities.
Moreover, the ubiquity of smartphones has actually led to a burgeoning market for m-health apps and wearable devices, resulting in more health data being collected than ever before. This has actually given rise to a phenomenon known as “the quantified self,” the process of tracking everyday activities to learn more about yourself.
For example, it is now possible for people to know their standard time spent in REM sleep over three months and whether their sleep quality correlates along with bad weather. One can easily now check their blood pressure, oxygen saturations and ECG in a single device, receive a full genetic analysis for much less than $100 and soon be able to keep track of real-time glucose levels thanks to Bluetooth enabled contact lenses.
Simply knowing more won’t necessarily advice us live healthier lives.
A simple look at Apple’s Health app yields no much less than 79 different health records, spanning Vitamins A through E, variations in body temperature and caffeine levels.
A new mobile app takes the obsession to quantify in to the bedroom, by helping people track their sexual encounters on their smartphones. The app collects write-up on the sexual duration and noise levels in order to quantitatively assess the user’s performance, presenting the data in a collection of attractive graphs.
Proponents of “the quantified self” phenomenon argue that the data we collect is for self-knowledge and self-empowerment. Whilst there is definitely nothing inherently wrong along with the notion, insights from behavioral economics teach us that Just knowing more won’t necessarily advice us live healthier lives.
Numerous experiments have actually shown that we human beings are not “rational people that engage in maximising behaviour,” yet instead place little emphasis on long-term rewards, tend to prefer avoiding losses to acquiring gains and, in general, are motivated by cognitive biases of which we are largely unaware.
Failing to appreciate this, most health and wellness m-health apps have actually historically went through from fairly short life spans, along with engagement disintegrating soon after the novelty wears off — users are then free to return to risky health behaviors, negating any intended long-term health benefits.
It’s all about health behavior
As a senior medical student at Imperial College London, I locate myself exposed to a range of medical and surgical specialties during my clinical rotations — each of them presenting different problems, yet managed in the same three stages: conservative, medical and surgical management.
Despite the rapid pace of medical innovations over the past century, the initial line treatment for almost any insidious chronic disease involves taking a conservative approach comprising “lifestyle and risk-factor modification.” This typically involves the doctor reciting their well-rehearsed spiel; for example, instructing arthritic patients on the mobility exercises which, if completed regularly, could extend the life of their joints by 5-10 years, or young diabetic patients on the importance of good glucose control.
Yet research shows that patient recall of the medical write-up delivered in doctor consultations tends to be poor and inaccurate, along with most patients focusing on diagnosis-related write-up and therefore failing to register advice, ultimately affecting their ability to later act on it.
Mobile devices offer a promising solution as a conduit for behavioral intervention programs. By combining the “big data” generated by health apps and devices along with digital behavioral interventions, there is potential to pave the means for a brand-new generation of m-health apps.
The victory of such an approach requires a radical transformation — from Just collecting, storing and relaying write-up back to users to intelligently processing the data collected to advice recommend highly personalized, evidence-based techniques, designed to nudge behaviors in the right direction.
The future of healthcare lies in how we tackle chronic disease and stay away from health-risky behaviors.
For example, research shows that not all smokers confer the same benefits from smoking, nor have actually the same motivations for wanting to quit.
A simple illustration would certainly be that person A might smoke to advice manage their pressure and want to quit as a result of social pressures, whilst person B might take pleasure in the social aspect of smoking yet might be looking to quit due to health concerns.
The support needed to address their reasons for smoking and motivations for quitting will certainly vary drastically from person A to person B. To tackle this, we are developing our flagship product, Quit Genius, a behavioral intervention for smokers looking to quit, capable of intelligently identifying and adapting to all aspects of the user’s thoughts and feelings about smoking.
There are additionally cross-specialty implications for digital behavioral programs. A new coin-sized device produced at Imperial College now makes it possible for patients to accurately monitor their breathing and cardiac activity overnight at estate to detect sleep apnea, a devastating sleep disorder estimated to cost the U.S. economy as much as $165 billion annually.
A promising application of this data could involve a targeted digital behavioral program personal to the individual, matching their weight loss to measurable improvement in their quality of sleep. Linking these lifestyle modifications to the individual’s chronic condition and consequently providing a personalized and highly structured digital behavioral intervention could have actually a transformational effect on the individual’s life, enhancing their quality of sleep and, as a result, their ability to function.
Maintaining the evidence threshold
There are currently some 165,000 health-related apps available on the market. Even though that’s a highly impressive number, research has actually revealed several mental health apps, as well as existing digital behavior interventions, are plagued by a lack of underlying evidence and scientific credibility and, most worryingly, confer limited clinical effectiveness.
Theranos’ recent misadventures clearly illustrate exactly what can easily go wrong when healthtech startups believe their own hype and refuse to engage the scientific community. As a result, there is an ongoing debate within academic circles regarding the level of clinical validation needed for m-health apps. To tackle this, the Department of Health in the U.K. is looking to develop a brand-new endorsement model for evidence-based health apps.
Embracing an evidence-initial approach by building up a solid evidence-base through published clinical trials will certainly confer additional benefits. It will certainly offer healthcare practitioners much called for reassurance when recommending m-health apps to their patients, laying down the foundations for clinicians to “prescribe” digital behavioral interventions to advice tackle personal “lifestyle and risk-factor changes” in the future (similar to how a clinician might currently prescribe drugs).
However, for this to occur, more job is needed to advice establish a framework for conducting rigorous clinical trials. A great balance ought to be struck to make certain these apps can easily be clinically validated within a reasonable time frame, whilst maintaining validity in the scientific approach and the evidence-base that is generated.
Toward a much better future?
It is widely accepted that people can easily drastically reduce their risk of premature morbidity and mortality by avoiding risky health behavior, of which lack of exercise, poor nutrition, tobacco use and drinking too much alcohol are the four main culprits.
To truly make a dent on a global scale, digital health startups ought to recognize the importance of creating highly structured and personalized evidence-based behavioral therapies to tackle personal risky health behaviors. To achieve this, one approach is to develop a close cross-collaboration between clinicians, behavioral psychologists, designers and developers to conceptualize, build and clinically test behavioral interventions programs.
The future of healthcare lies in how we tackle chronic disease and stay away from health-risky behaviors. The opportunity is clear; the portion m-health interventions will certainly play in tackling this challenge has actually yet to be decided.
Featured Image: Bryce Durbin
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