Can smartphones predict mortality risk?

Passive smartphone monitoring of people’s walking activity could be used to construct population-level models of health and death risks, according to a new study published Oct.ein the open access journal PLOS Digital Health by Bruce Schatz of the University of Illinois at Urbana-Champaign, USA, and colleagues.

Previous studies have used measures of physical fitness, including walking tests and self-reported walking pace, to predict individual mortality risk. These statistics focus on quality rather than quantity of movement; for example, measuring a person’s walking speed has become standard practice for certain clinical settings. The emergence of passive smartphone activity monitoring opens up the possibility for population-level analytics with comparable metrics.

In the new study, researchers studied 100,000 participants in the UK Biobank’s national cohort who wore activity monitors with motion sensors for 1 week. While the pulse sensor is worn differently than how smartphone sensors are worn, their motion sensors can both be used to extract information about walking intensity from short bursts of walking — a daily version of a walking test.

The team was able to successfully validate predictive models of mortality risk using just 6 minutes a day of steady walking, collected by the sensor, combined with traditional demographics. The equivalent of walking speed calculated from these passively collected data was a predictor of 5-year mortality independent of age and sex (pooled C-index 0.72). The predictive models only used walking intensity to simulate smartphone monitors.

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“Our results show that passive measurements with motion sensors can achieve comparable accuracy to active measurements of walking speed and walking pace,” say the authors. “Our scalable methods provide a viable path to national health risk screening.”

Schatz added, “I’ve used low-cost phones for a decade for clinical models of health status. These have now been tested on the largest national cohort to predict life expectancy at a population scale.”

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Materials supplied by PLOS. Note: Content is editable for style and length.


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