| Title: | Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants |
| Journal: | PLOS Digital Health |
| Published: | 20 Oct 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36812566/ |
| DOI: | https://doi.org/10.1371/journal.pdig.0000045 |
| URL: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000045&type=printable |
Publication 8114
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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.4 Authors
- Haowen Zhou
- Ruoqing Zhu
- Anita Ung
- Bruce Schatz
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