Can Smartphones Predict Mortality Risk?

Passive smartphone monitoring of people’s walking activity can be used to construct population-level models of health and mortality risk, according to a new study publishing October 20th in the open access journal PLOS Digital Health by Bruce Schatz of University of Illinois at Urbana-Champaign, USA, and colleagues.

Previous studies have used measures of physical fitness, including walk tests and self-reported walk pace, to predict individual mortality risk. These metrics focus on quality rather than quantity of movement; measuring an individual’s gait speed has become a standard practice for certain clinical settings, for example. The rise of passive smartphone activity monitoring opens the possibility for population-level analyses using similar metrics.

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

The team was able to successfully validate predictive models of mortality risk using only 6 minutes per day of steady walking collected by the sensor, combined with traditional demographic characteristics. The equivalent of gait speed calculated from this passively collected data was a predictor of 5-year mortality independent of age and sex (pooled C-index 0.72). 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."

Zhou H, Zhu R, Ung A, Schatz B.
Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants.
PLOS Digit Health 1(10): e0000045, 2022. doi: 10.1371/journal.pdig.0000045

Most Popular Now

West Midlands to Digitally Transform Can…

NHS patients throughout the West Midlands are to benefit from a digital pathology programme, designed to help reduce cancer backlogs, transform services, and improve the speed and accuracy of cancer...

AI Approach may Help Identify Melanoma S…

Most deaths from melanoma - the most lethal form of skin cancer - occur in patients who were initially diagnosed with early-stage melanoma and then later experienced a recurrence that...

Siemens Healthineers and University of M…

Siemens Healthineers and UHealth - University of Miami Health System - announced a Value Partnership(1) agreement. This strategic relationship will further technological advancement and standardization of equipment at the health...

Siemens Healthineers Splits Fast-Growing…

Siemens Healthineers is splitting its Asia Pacific operations into two to allow both China and the rest of the region to achieve their full potential. China, now its own region...

Philips Advances MR Radiotherapy Imaging…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, announced two new advances in MR-only workflows to advance head and neck cancer radiotherapy imaging and simulation. The...

AI Transforms Smartwatch ECG Signals int…

A study published in Nature Medicine reports the ability of a smartwatch ECG to accurately detect heart failure in nonclinical environments. Researchers at Mayo Clinic applied artificial intelligence (AI) to...

3D Protein Structure Predictions Made by…

In a living being, proteins make up roughly everything: from the molecular machines running every cell's metabolism, to the tip of your hair. Encoded in the DNA, a protein may...

Siemens Healthineers Presents Two Revolu…

7 Tesla (T) Magnetom Terra.X(1) will offer excellent imaging of even the smallest structures 3T Magnetom Cima.X(2) more than doubles the gradient amplitude(3) AI algorithms which can reduce scanning...

Integrating Digital Twins and Deep Learn…

Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and technologies. Digital twins...

Willingness to Use Video Telehealth Incr…

Americans' use and willingness to use video telehealth has increased since the beginning of the COVID-19 pandemic, rising most sharply among Black Americans and people with less education, according to...

New Group to Advance Digital Twins in He…

EDITH (Ecosystem for Digital Twins in Healthcare) Coordination and Support Action (CSA) - a group made up of numerous internationally renowned research institutions, professional associations, companies, and hospitals of excellence...

DMEA Call for Papers: Supporting Digital…

25 - 27 April 2023, Berlin, Germany. Health meets digitalisation: from 25 to 27 April 2023 at DMEA - Connecting Digital Health, all actors aiming to promote health IT will be...