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Apple and Fitbit should use all of the information they collect about you to protect public health.

The Apple Watch’s marketing says it all: “A healthy leap ahead.” “Here’s to a happier, healthier you.” “The future of health has never looked brighter.” The Apple Watch, like countless other digital health apps and wearables, sells itself with the implicit promise of individual empowerment: that we can use this health information to take specific actions that will lead to better health, such as exercising until your heart rate reaches a certain elevated threshold.

However, by pitching and promising health to individuals, companies that create apps and wearables, as well as the customers who buy them, are focusing on the trees – and missing the rest of the forest.

In reality, we have less control over our own health than we would like to believe. Indeed, our surroundings have a significant impact on our health, including the health of those around us. What this could mean for the ever-expanding wearables market is a shift in focus away from the individual and toward those in their household, friends, and even the entire community.

Exercises mapped – or diseases?
Digital health products could better capture what goes into public health by incorporating social and community factors. Some public health models already incorporate community-wide data from fitness trackers and other digital sources.

Fitbit publishes reports that show, for example, communities with the highest levels of physical activity. Location data from cellphones is sometimes used to map disease outbreaks. These models and their underlying datasets could be a boon to the people and organisations in charge of protecting and improving our collective health.

For centuries, aggregated health data reflecting communitywide factors has aided public health practitioners, allowing them to make seemingly simple improvements that have a significant impact on people’s lives.

When Florence Nightingale returned home from the Crimean War hospitals filled with sick and injured British soldiers, she sought to use the data she had meticulously collected to demonstrate the impact that community factors had on soldiers’ health. Nightingale famously used powerful graphics to persuade British leaders that sanitation improvements, such as cleaning the air, water, and sewer systems, could prevent disease and dramatically reduce deaths.

The world is still facing massive public health challenges, such as the ongoing COVID-19 pandemic. However, we have new cards to play, such as vast repositories of health sensor data and powerful artificial intelligence techniques that can assist us in making sense of it.

Using these tools to improve health devices and apps could provide a significant opportunity to address these enormous challenges.

Suicide early warning system
The teen mental health crisis is one of the most concerning recent public health issues. To combat rising suicide rates, a Harvard-led research project aggregates health data from many people and uses artificial intelligence to make it more useful, such as combining smartphone and Fitbit data to understand suicide indicators.

The hope is that combining AI and health sensor data will aid in the development of an early warning system for people at risk of suicide.

For the time being, this research is purely academic. Another significant challenge is that suicide is still uncommon, resulting in little data for algorithms to use in making predictions. However, suicide risk, like many other health events, does not occur in a vacuum.

Suicides may appear to be isolated acts, but they frequently occur in clusters, which researchers refer to as suicide contagion. What if suicide prediction algorithms, such as the one being developed at Harvard, could incorporate a social component derived from health sensor data, such as your friend network?

Digital health products could feed into social network algorithms that detect when people require mental health services. A wearable that monitors your mood and sleep patterns may intervene based on those metrics. A social-enabled algorithm, on the other hand, may be able to go a step further, recognising troubling trends among one’s close contacts – such as social media posts suggesting self-harm – and thus when someone may be at greater risk of suicide.

Concerns about personal privacy
There are many challenges to using social and community data in digital health products. Some users may be concerned about their personal information being shared with companies or public health agencies.

Overcoming these obstacles could entail creating open source standards for data transparency or ensuring that people have control over when and with whom their personal data is shared.

Nonetheless, the opportunity is too good to pass up. Although digital health products can and do benefit individuals, the greatest benefits may one day be realised when these products work in tandem with information about the community and beyond the individual.

Now, it is likely that those who purchase digital health products are wealthier and thus healthier. This follows a historical pattern in which new technologies, such as computers, initially cater to specific individuals before expanding to serve larger communities.

To credibly claim to improve people’s health – to truly become “the future of health,” as they promise – digital health companies may need to expand their willingness to look beyond an individualised notion of health and collaborate with public health agencies.

As a result, digital health products stand to benefit a much larger segment of society, including those who do not wear wearables.

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