Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants

Haowen Zhou, Ruoqing Zhu, Anita Ung and Bruce Schatz

PLOS Digital Health, 2022, vol. 1, issue 10, 1-22

Abstract: Many studies have utilized physical activity for predicting mortality risk, using measures such as participant walk tests and self-reported walking pace. The rise of passive monitors to measure participant activity without requiring specific actions opens the possibility for population level analysis. We have developed novel technology for this predictive health monitoring, using limited sensor inputs. In previous studies, we validated these models in clinical experiments with carried smartphones, using only their embedded accelerometers as motion sensors. Using smartphones as passive monitors for population measurement is critically important for health equity, since they are already ubiquitous in high-income countries and increasingly common in low-income countries. Our current study simulates smartphone data by extracting walking window inputs from wrist worn sensors. To analyze a population at national scale, we studied 100,000 participants in the UK Biobank who wore activity monitors with motion sensors for 1 week. This national cohort is demographically representative of the UK population, and this dataset represents the largest such available sensor record. We characterized participant motion during normal activities, including daily living equivalent of timed walk tests. We then compute walking intensity from sensor data, as input to survival analysis. Simulating passive smartphone monitoring, we validated predictive models using only sensors and demographics. This resulted in C-index of 0.76 for 1-year risk decreasing to 0.73 for 5-year. A minimum set of sensor features achieves C-index of 0.72 for 5-year risk, which is similar accuracy to other studies using methods not achievable with smartphone sensors. The smallest minimum model uses average acceleration, which has predictive value independent of demographics of age and sex, similar to physical measures of gait speed. Our results show passive measures with motion sensors can achieve similar accuracy to active measures of gait speed and walk pace, which utilize physical walk tests and self-reported questionnaires.Author summary: Healthcare infrastructure implementation could benefit tremendously from national scale screening with passive monitors. Large scale population data could delineate health risks without intruding into daily living. Digital health offers potential solutions if sensor devices of adequate accuracy for predictive models could be widely deployed. The only such current devices are cheap phones, smartphone devices with embedded accelerometers. This limits measures to motion sensor data collected when the phones are carried during normal activities. So measuring walking intensity is possible, but the total activity measure that is possible with 24-hour wearable devices is not. Our study simulates the use of smartphone sensors to predict mortality risk in the largest national cohort with sensor records, the demographically representative UK Biobank. Mortality is the most definitive outcome, with accurate death records for five years available for the 100,000 participants who wore sensor devices. We analyzed this dataset to extract walking sessions during daily living, then used characteristic motions of these walking sessions to predict mortality risk. The accuracy achieved was similar to activity monitors measuring total activity and even similar to physical measures such as gait speed during observed walks. Our scalable methods offer a feasible pathway towards national screening for health risk.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000045

DOI: 10.1371/journal.pdig.0000045

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