Mental wellbeing and health data needs better solutions to be useful

mental wellness health
Image by melitas | Bigstockphoto

Health data is becoming more important not only because we get more data from many sources – including wearables and DNA tests – but also because digital health services can better utilize it to interpret the data and make diagnoses or offer recommendations. But is the same true for mental health data? After all, mental wellbeing and mental health are essential parts of health, but data and interpretations of it have their own special issues. Can data help in this area, or can it make things worse?

Recently I participated in a discussion about wearable and wellness data with the coaches and managers of a top-level sports team. We talked about how they could better utilize wearable data in their coaching to get better results from their athletes – not only for their main team but also for their youth teams and programs. Interestingly, however, their primary interest or worry was not how hard players exercise or their physical fitness values. They were more concerned about stress levels and sleep. – two factors that have the most significant impact on training and performance in games.

The importance of mental health in sports has made headlines in recent months, such as the decision by US athlete Simone Biles to quit the Olympic Games in Tokyo last year due to mental health issues. Most recently, F1 driver Lewis Hamilton posted on Instagram about his own challenges.

However, one doesn’t have to be a top-level sports athlete to experience mental health challenges –  mental wellbeing is a very important part of everyone’s total wellbeing and health, and  everyone has mental health challenges to some degree in their lives. For example, sleep and mental wellbeing are often linked to each other, as stress often impacts your sleeping pattern.

The challenge is that mental wellbeing is a holistic multi-leveled concept, and different people are affected in different ways – which means using data to measure and assess it isn’t so straightforward.

Can we measure it?

For example, sleep data and HRV (heart rate variation) are probably the most obvious metrics people follow when talking about mental wellbeing. HRV indicates stress levels, and if you don’t feel well, you sleep badly. But these metrics are not straightforward. There are a lot of differences between people in terms their HRV values and how they sleep. You cannot say that a certain range is simply good or bad.

Another critical aspect is the reliability of data. Different devices can also give quite different values for things like whether your sleep is deep, light, or REM-level sleep. It’s the same with HRV; there are surprisingly big differences between devices. An expert of health data recently commented that devices like Apple Watch and Health have such a ‘simplified’ model to measure HRV that it may be impossible to get a reliable HRV reading that way. Personally, I test and use many such devices, and I see differences all the time. For example, when it comes to HRV, my Apple Watch, Oura ring, and Fitbit often give different values.

Of course, these two data points alone cannot fully summarize your mental health. However, if data is reliable and used correctly, it can help people in certain situations spot problems early and encourage them to investigate. For example, if your sleeping scores get significantly worse, or your HRV drops much lower than usual, it may be time to reflect about your situation, and think about whether you have some stress and what might be causing it. In a way, it follows the old saying, “Listen to your body.”

Expertise is crucial

Interpretations of data can also cause additional problems. For example, suppose your sleep numbers and HRV are worse than your family members or friends. This could create extra stress and worry, even though there are natural explanations and normal variations between people. Consequently, the interpretation of metrics always needs inpout from experts, or at least smart software that includes enough expert knowledge to be able to learn a person’s normal data ranges, and establish reasonable guidelines for users.

People are already drawing many conclusions from their wearables and DNA data. That’s why reliability must be taken seriously. Moreover, this is why – along with more reliable sensors – better software is becoming crucial for wellness and health data to both analyze the raw data and accurately interpret the results, Without knowledge software that can explain results to users and advise them to seek help if something appears abnormal, it makes no sense to draw some conclusions from the data. It could even be dangerous if the conclusions are wrong.

So while mental wellbeing involves much more than what we can measure with a few devices, data can help identify some problems and make some changes to your life when needed. But the data must be reliable, and software must be able to give some relevant, helpful conclusions and guidelines. And that’s why personal health data services must move from providing some entertaining numbers to offering proper and professional health services that take user’s needs seriously.

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