Despite the availability of vaccines for COVID-19, even in developed countries the vaccination rate for adults is still between 60% and 80%, as there are still quite a few people who are skeptical about the vaccine, whether it’s because of health fears or conspiracy theories. Could more data convince skeptics to take the vaccine by helping them better understand the impact of the doses? Maybe not. But more generally, could new technology, especially wearables, actually offer much more reliable data for healthcare purposes? Could it change how we develop vaccine trials, and help us better understand public health as a whole?
Current models are expensive
Clinical trials of drugs and vaccines take quite a bit of effort to put together. Aside from recruiting the actual participants, clear guidelines must be followed to develop and conduct these trials, which are regulated by local and international authorities. Clinical trials are typically also quite expensive, and the number of participants cannot be very high because it increases costs rapidly.
Health authorities collect data from the healthcare systems. For example, they can know when a vaccinated person comes to a doctor and tests positive for COVID-19, or whether a patient has experienced some side effects that require hospital treatment. People can also voluntarily report their side effects; however, self-reports are usually based on what people feel – they are not based on measured data. Feelings are indeed important, but it is also important not to mix different data points such as the feeling of a high temperature, higher heart rate, or low blood glucose, which are very different from actual measured numbers.
Clinical trials typically also include questionnaires where people need to give additional information. These answers might be biased, primarily when people evaluate certain things about themselves, or in some cases, when they don’t want to tell everything – for example, there have been cases where people hide symptoms they feel embarrassed about.
Nowadays, it would be easy to get data from many more people, especially from wearable device users. During the past year, there have been projects to utilize wearable data to enable early-warning apps to detect COVID symptoms. Some wearable apps also have functions to tag the date and time of the vaccine. All these could also help to study the efficacy of vaccines, and gather data about side effects.
Wearables make it possible to collect data from a large number of people, and could even provide additional insights from data such as a swift rise in temperature or a higher heart rate after receiving a vaccination. But it is statistically risky to interpret these data points, especially because health authorities need more information. You also need to know the people who give the data, how well they represent different types of people, and whether some external factors might impact results.
Some medical experts and health authorities remain skeptical of using data from wearable devices that are not certified medical devices. There are good reasons for this – the data in healthcare and clinical trials must be reliable. But the situation is probably not so black or white. Clinical trials, focus groups and measurements still have an essential role in research. Also, clinical trials often have only a few thousand people and thus a minimal amount of data to measure.
Also, hundreds of millions of people use specific wearables that measure data all the time – this data is not meaningless. Probably it cannot be used in a similar way as data from clinical trials and qualified medical devices. Still, it can provide relevant supplemental information and also be used together with other data sources. Especially for some rare diseases and symptoms, this kind of data mass can be very valuable. And aside from wearable data, there are also the tens of millions of people who have taken DNA tests.
There are several ways to better utilize wearable data in healthcare, treatments and clinical trials:
- Wearable devices can give additional data from people in clinical trials and help to understand potential effects and symptoms better. In addition, wearable devices can also help explain some external factors like sleeping, exercising, and stress.
- Sharing wearable data related to healthcare can help detect reasons for symptoms, reveal diseases, and help understand health conditions. Basic data like heart rate, movement and sleep data can help with this, but devices that measure things like blood pressure, ECG and blood glucose can give quite specific information. Many patients already refer to their own data – some doctors appreciate it, some not. The issue is really how to get consistent and relevant data.
- Collecting data from many people gives a more extensive understanding of public health. Besides, it can also help in specific cases, such as a better understanding of the impact of COVID vaccinations.
- Healthcare is naturally reactive, offering treatment when a person has health issues or displays symptoms. But more predictive and proactive utilization of data helps people to live healthier and prevent health issues from arising in the first place. New data sources and tools can really help with this.
Health data is very sensitive, and user privacy is paramount in all these use cases. It is also important to find models to increase the reliability of the data. This means people should have control over how to share data (to include not sharing any of it, if they wish) –and if they share it, third parties who access such data must not be able to modify it. In many cases, the data should also be anonymous, and people must be able to limit the use of it. It is also crucial to get intelligent data layers that can combine and synch data from different sources and filter out irrelevant or unreliable data.
It is time to think about how wearables data can be better utilized for clinical trials, public health and healthcare overall. This offers many opportunities to get more data that better covers different aspects of health and life. But this cannot be based on just random data collection from all devices. Instead, it requires proper models where individuals can control their own data, select which data to share, and have guarantees that the data will be unmodified and their privacy will be protected.
These are not small and simple steps, but their value is significant. Better data can really change healthcare and improve public health, and it can also shift the focus from treatment of health issues to prevention.