Article

Predicting age from 100,000 one week-long 100Hz wrist accelerometer records of physical activity

2021-06-25

Abstract excerpt

Physical activity improves quality of life, physical health and mental health, and is also an important protective factor against highly prevalent age-related diseases such as cardiovascular diseases, diabetes, cancer and mental health. With age, physical activity tends to decrease, leading down a vicious cycle that increases vulnerability to disease in the elderly. In the following, we trained neural network arch...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 918fedec-1133-5d97-ac14-7ef8154ef6d9
DOI: 10.1101/2021.06.21.21259265

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Predicting age from 100,000 one week-long 100Hz wrist accelerometer records of physical activityDOI 10.1101/2021.06.21.21259265Read

Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictorsSemantic proximity Read

Using deep learning to analyze the compositeness of musculoskeletal aging reveals that spine, hip and knee age at different rates, and are associated with different genetic and non-genetic factorsSemantic proximity Read

Dual foundation models for accelerometry predict future healthSemantic proximity Read

Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience.Semantic proximity Read

GWAS identifies 10 loci for objectively-measured physical activity and sleep with causal roles in cardiometabolic disease.Semantic proximity Read

Phenome-Wide Association Study of Actigraphy in the UK BiobankSemantic proximity Read

Wearable-ome meets epigenome: A novel approach to measuring biological age with wearable devicesSemantic proximity Read

Predicting physiological aging rates from a range of quantitative traits using machine learning.Semantic proximity Read

Investigating the comparability of wearable accelerometer methods in the association between physical activity and cardiovascular disease: a cohort study using UK BiobankSemantic proximity Read

11 million days of longitudinal wearable data reveal novel future health insightsSemantic proximity Read

High-Resolution Digital Phenotypes from Consumer Wearables Enhance Prediction of Cardiometabolic Risk MarkersSemantic proximity Read

DNAmFitAge: Biological Age Indicator Incorporating Physical FitnessSemantic proximity Read