Digital sensors for (mental) health research
At Wellcome, we’re exploring how we can advance the utility of digital sensors for mental health research.
Read on to find out about a new opportunity to work with us on this, and the potential we see for digital sensors in health research more broadly.
Digital sensors are devices that can measure your behaviour, physiology, and environment as you go about your daily life. The GPS tracker in your phone can measure where you go, how you get there, and how long it takes you. The heart rate sensors and accelerometers in a smartwatch can measure how well you sleep. And thermostats can measure the air temperature in your home. Long story short: sensors can measure many different things, and this can be useful in many different ways.
Sensors are already proving useful in many different fields of health research. For example, sensors have been used to detect and monitor symptoms of Alzheimer’s disease, to measure cognitive impairment in people diagnosed with type 2 diabetes, and to measure post-operative physical activity levels in patients undergoing cancer surgery.
The opportunity for mental health research
The ability to measure and monitor mood, activity levels, sleep patterns and more provides clinicians and researchers with objective real-time data relevant to mental health symptoms. These data could be used to detect mental health conditions early on in their progression, to stratify populations according to their condition, and to deliver more effective and timely treatments. For example, see this review of using sensors to monitor depression.
At Wellcome, we want to support effective and trustworthy sensor use in mental health research, which we know requires looking at the technical and ethical challenges that this kind of data collection presents. And we’re not alone — others in the field are also considering this. In March 2023, we attended a workshop which brought together researchers, device manufacturers, and regulators to discuss our shared vision for sensor use in mental health and the challenges blocking that vision from coming to life.
Our first step to support work in this space is to commission research to understand how we might help solve the technical challenges facing researchers who use sensors in their work (click here to find out more). We already have an idea of what the challenges are — now, we want to know how we can solve them.
The challenges
First, the technical bar to using sensors in mental health research is high. Interested researchers must learn to interact with many different sensor devices — some people have Fitbits, some have Apple Watches, and others have no smartwatch but one of many different mobile phones. To collect data from all possible devices, researchers must wrestle with different operating systems, APIs, and standards for what data is available to them. Any solution they build must be updated regularly to keep up with the latest hardware and software. Researchers must do all of this without much support; there are some high-quality open-source platforms for sensor data collection (e.g., Beiwe, RADAR-Base), but support to make full use of these platforms is limited. In short, unless researchers happen to have access to a team of trained software engineers, collecting sensor data is difficult.
Even if researchers manage to collect any sensor data, the sensors available to them may not be the ideal sensors for robust mental health research. Many sensors used in mental health today were not developed for their value in mental health research and clinical practice. They were developed to sell phones and smartwatches. An Apple Watch, for example, comes with heart rate monitors, an accelerometer, a gyroscope, a barometer, and a blood oxygen monitor. How many of these sensors were developed primarily to track and help improve our mental health? The answer: likely none. Which begs the question: what would a purpose-built mental health sensor look like?
Second, there are also a bunch of thorny ethical issues to consider. The sheer amount of data sensors can collect about us is unlike many other data collection methods. Sensors can, in principle, measure where you go, who you meet, how long you stay there, and what you say. This level of data collection may well give researchers and clinicians the data they need to understand and improve our health, but at what cost? Despite their value, sensors may never reach their full potential unless we seriously consider how to minimise that cost and protect privacy.
These are not simple issues, but some solutions do already exist. Apple, for example, provides only abstracted navigation data centred around “landmarks” — I could find out how far you travelled away from home (the landmark) without actually knowing where your home is nor where you went. The bottom line is that acceptability from the participant’s point of view is vital for any attempt to advance sensor use in mental health.
Beyond mental health
At Wellcome, we’re focussed on driving progress in three areas: mental health, climate and health, and infectious diseases. While our initial focus is on mental health, it’s clear that sensors have the potential to advance our aims in all three.
In the climate and health field, researchers are often dependent on coarse population-level data on who has been exposed to the most recent climate-related event (e.g., a heatwave) and how their health has suffered as a result. For example, most studies investigating the health effects of extreme heat use morbidity and mortality data aggregated over an entire population — individual level data is rare (Campbell et al., 2018). Sensors, by contrast, can collect granular individual-level data at scale, providing detailed information on the experiences and health of a large number of individual people. We could then understand exactly how the health of individual people is affected by exposure to extreme heat, instead of making broad and imprecise statements about average trends in the entire population. These insights could demonstrate the effectiveness of adaptation and mitigation interventions, which could in turn inform action and protect health.
When it comes to infectious diseases, sensors can be used to support surveillance systems by monitoring factors that drive new and emerging diseases, like close human-animal interaction, and physiological and behavioural data. For example, some scientists believe that the origin of the COVID-19 pandemic can be traced back to the Huanan animal market. If true, a system collecting navigation and physiological data may have been able to identify that some people who visited the market in late 2019 went on to develop a common set of symptoms a few days later, alerting us to the outbreak of an emerging threat (COVID-19) much earlier.
So while we’re starting with mental health, we’re keeping an eye out for opportunities to advance the utility of sensors in research more broadly.
What next?
Wellcome is commissioning research to understand how we can solve the challenges faced by researchers using sensors in mental health. Click here to find out more.
If you’d like to hear more about this or other work we’ll be doing in the sensor space in the future, follow our Medium, or follow me on Twitter/X: @GwydionTW.
