Some technological revolutions take years to unfold. Think of how many years we’ve heard about the coming of AI, VR or Quantum Computing. However, the changes come fast when some of these prominent tech trends mature. Just think of AI.

Amidst these emerging tech frontiers, wearable biosensors have steadily gained momentum. Transitioning from rudimentary step counters to intricate devices, these sensors now monitor a wide array of health metrics – heart rate, body temperature, blood oxygen levels, and more – providing invaluable insights into our physical well-being.

It’s likely that as you read this, a smartwatch or similar gadget on your wrist silently tracks countless health biomarkers. A good guess is that all this valuable data is stored in Apple Health or in Google Fit on your smartphone. Unfortunately, it’s also likely that no health professionals have ever used this data, making it feel less valuable.

However, the landscape is shifting. At Shortcut, we are increasingly involved in projects leveraging, refining, and analyzing health data from smart devices like Apple Watch, Galaxy Watch or Garmin smartwatches. Recognizing the significance of this domain, we’ve intensified our focus here.

‍

‍

The State of Healthtech: Transformative Insights from Stockholm
A testament to our focus was ‘The State of Healthtech Apps’ conference we co-hosted with our sister company, Shortcut MedTech, in Stockholm last week. The event spotlighted how cutting-edge technology is reshaping healthcare, from enhancing diagnostic processes to safeguarding privacy in app development. And how to secure privacy and easy-to-use apps when building health solutions.
At the conference we had talks from companies like AsthmaTuner and Cognes, who build apps for handling problems as different as respiratory diseases and dementia detection. We had presentations from app developers about how gamification, solid regulatory roadmaps, and good old UX can make the difference between a great app and wasted money. And we had talks from doctors and other health professionals.

A recurring theme at the conference was how enormous the potential benefit was for the health sector – but at the same time, how much we need to change our ways.
During the COVID pandemic, Fitbit ran an experiment where, using sensors on a smartwatch, it tried to predict COVID-19 infections based solely on readily available data. After only 8 weeks of trials, they discovered a COVID-19 infection in 50% of patients a full day before the onset of the first symptoms. Analyzing patterns in heart rate, respiration rate, and heart rate variability enabled the researchers to create a machine-learning model that identified COVID-19 patients solely based on these three data points.

‍

Changing the way we see early detection
Back to your pocket, where you probably have a trove of insights about your private health condition. If something is wrong today, the current model for initial diagnosis is that you show up at the general practitioner’s office. Based on a few measurement points—current blood pressure, body temperature, pain level, and blood samples—combined with your demographic profile, like age and gender, the doctor will give a diagnosis based on her expertise and a good dose of common sense.
In a few years, we will consider this approach as uncertain and imprecise as a chequebook or driving a car without seatbelts. Why aren’t these rich data reservoirs being utilized for more precise diagnoses?

The answer could lie in the current need for more tools for practical data analysis within a traditional sector where change comes slowly.

But the potential is huge. Super-simplified, adults in the West die from four types of diseases: Cancer, Cardiovascular diseases, metabolic syndrome and neurodegenerative disease. Our best bet at living long and healthy lives is avoiding these diseases.
Cardiovascular diseases can largely be predicted based on readings your smartwatch already takes. The challenge is that the traditional health system often only knows a patient’s risk for different cardiovascular diseases when the individual has developed a heart disease, and solutions are much harder to implement. Better biosensors and the combination of AI and biosensors are changing this. Now, doctors also need to change their ways.

Metabolic syndrome (conditions like diabetes and fatty liver disease) can largely be overcome with exercise and better nutrition. Detecting cancer early requires efficient screening. Neurodegenerative diseases like Alzheimer’s and Parkinson’s can be detected early with technology, as demonstrated by Cognes at our event, using a smartphone camera.

‍

Security and Privacy by design
Data in health apps should by definition be private and secure. Both Apple and Google have created an architecture where data is saved only on the user’s phone. When making apps it is absolutely key that both privacy and security is part of the technical architecture. Security is not something you can add to an app or think about later. Security and privacy must be part of the core design.

‍

Wearables, AI, Gamification and the path to better habits
Making a successful health app or wearable device requires many of the same skills needed to make a great B2C app for more traditional industries. Often, the health app will combine the handling of sensor data, analysis, and some kind of habit-building. Regarding habit-building, gamification is one of the most common mechanisms that can make a huge difference.

Therefore, let us embrace our role as tech-optimists. We believe our smartphones, watches, health wearables, and AI, combined with new approaches in the health sector, are keys to unlocking a more efficient, personal, and human-centred healthcare system.

‍