Smartphones can be used to predict mortality rates

The new research saw scientists follow 100,000 participants in the UK Biobank national cohort who wore activity monitors with motion sensors for one week. Despite the fact that the tested wrist sensors were worn differently than how smartphone sensors are carried, both their motion sensors were used to extract information on intensity from short bursts of walking.

The researchers were able to successfully deduce predictive models of mortality risk using only six minutes per day of steady walking collected by the sensors. They further combined this data with traditional demographic characteristics. The measurements acquired from this passively collected data was a predictor of 5-year mortality independent of age and sex. The predictive models used only walking intensity to simulate smartphone monitors.

“Our results show passive measures with motion sensors can achieve similar accuracy to active measures of gait speed and walk pace,” the authors say. “Our scalable methods offer a feasible pathway towards national screening for health risk.”

Schatz adds, "I have spent a decade using cheap phones for clinical models of health status. These have now been tested on the largest national cohort to predict life expectancy at population scale.”

Other sensors and monitoring devices

Sensors to monitor users’ health have been around for a while. In 2018, researchers from the Tufts University School of Engineering developed a 2mm x 2mm sensor that tracked nutrition data in real-time. Information about everything from salt, glucose, and even alcohol consumption could be easily and efficiently collected by the device. Furthermore, it was able to transmit the data wirelessly thanks to the use of radiofrequency technology.

Meanwhile, just last month, MIT invented an in-home device that can monitor a patient’s movement and gait speed, which can be used to evaluate Parkinson’s severity, the progression of the disease, and the patient’s response to medication.

The device is about the size of a Wi-Fi router, which can be found in every home, and collects data passively using radio signals that reflect off the patient’s body without the need for him or her to wear a gadget.

One example showed that this type of device could be used to detect Parkinson’s from a person’s breathing patterns while sleeping.