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How a smartphone could predict your risk of dying within 5 years

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Your smartphone likely vibrates multiple times a day with a variety of different notifications – messages from friends, bank account payments, weather warnings. But in the future there may be another kind of notification coming from your smartphone, a message from an app predicting your risk of death over the forthcoming years.

A new study published in the journal PLOS Digital Health has found passively tracking motion data through smartphone sensors can effectively predict a person’s five-year mortality risk with around 70% accuracy.

The research is based on a large body of evidence showing correlations between walking speed and general health. To attain accurate health and mortality predictions, prior studies have generally required participants to either wear specialized fitness trackers 24/7 or complete “gait analysis tests” in lab conditions. However, this new study wondered whether motion data gathered through sensors in common smartphones could be enough to deliver accurate predictions.

The researchers looked at a large dataset encompassing 100,000 participants from the UK Biobank. The cohort wore wrist activity monitors for one week and were followed for at least five years.

This is the largest motion sensor dataset currently available. According to the researchers, short stretches of motion intensity data gathered by these fitness trackers is analogous to data that can be captured by a smartphone in a person’s pocket.

“Although this data was gathered from activity monitors, our sensor models use only the inputs that would be feasible to gather using inexpensive, currently available, phones,” the researchers explained in the study. “This is possible because of our extensive clinical experiments with cheap phones, developing highly accurate predictive models for health status for cardiopulmonary patients.”

Using just six minutes per day of data tracking walking intensity, the predictive algorithm could offer five-year mortality risk estimates that were as accurate as those gathered by 24/7 wearables or more complex clinical gait testing.

The findings add to a growing body of research looking at various ways to estimate a person’s mortality risk. Simple eye scans, blood screening, or short balance tests have all been proposed as ways to screen people for risk of early death.

Larger trials are currently being planned to focus more specifically on data directly gathered from smartphones. And, the researchers are working to make study cohorts as diverse as possible in order to make the predictive models accurate across a variety of populations.

“This is particularly important for health equity purposes, given populations at highest health risk are often the least resourced – so persons most likely to have cheap phones rather than wearable devices would benefit most from easy assessment,” the researchers concluded in the study. “Phone apps could record six minutes of consecutive walking during daily living, then compute predictive models for risk stratification via population analysis.”

The new study was published in PLOS Digital Health.

Source: PLOS




Your smartphone likely vibrates multiple times a day with a variety of different notifications – messages from friends, bank account payments, weather warnings. But in the future there may be another kind of notification coming from your smartphone, a message from an app predicting your risk of death over the forthcoming years.

A new study published in the journal PLOS Digital Health has found passively tracking motion data through smartphone sensors can effectively predict a person’s five-year mortality risk with around 70% accuracy.

The research is based on a large body of evidence showing correlations between walking speed and general health. To attain accurate health and mortality predictions, prior studies have generally required participants to either wear specialized fitness trackers 24/7 or complete “gait analysis tests” in lab conditions. However, this new study wondered whether motion data gathered through sensors in common smartphones could be enough to deliver accurate predictions.

The researchers looked at a large dataset encompassing 100,000 participants from the UK Biobank. The cohort wore wrist activity monitors for one week and were followed for at least five years.

This is the largest motion sensor dataset currently available. According to the researchers, short stretches of motion intensity data gathered by these fitness trackers is analogous to data that can be captured by a smartphone in a person’s pocket.

“Although this data was gathered from activity monitors, our sensor models use only the inputs that would be feasible to gather using inexpensive, currently available, phones,” the researchers explained in the study. “This is possible because of our extensive clinical experiments with cheap phones, developing highly accurate predictive models for health status for cardiopulmonary patients.”

Using just six minutes per day of data tracking walking intensity, the predictive algorithm could offer five-year mortality risk estimates that were as accurate as those gathered by 24/7 wearables or more complex clinical gait testing.

The findings add to a growing body of research looking at various ways to estimate a person’s mortality risk. Simple eye scans, blood screening, or short balance tests have all been proposed as ways to screen people for risk of early death.

Larger trials are currently being planned to focus more specifically on data directly gathered from smartphones. And, the researchers are working to make study cohorts as diverse as possible in order to make the predictive models accurate across a variety of populations.

“This is particularly important for health equity purposes, given populations at highest health risk are often the least resourced – so persons most likely to have cheap phones rather than wearable devices would benefit most from easy assessment,” the researchers concluded in the study. “Phone apps could record six minutes of consecutive walking during daily living, then compute predictive models for risk stratification via population analysis.”

The new study was published in PLOS Digital Health.

Source: PLOS

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