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• W4361764024 abstract "The rise of wearable technology has enabled users to collect data about food, exercise, sleep, bio-markers, and other lifestyle parameters continuously and almost unobtrusively. However, there is untapped potential in developing personal models due to challenges in collecting longitudinal data. Therefore, we collect N-of-1 dense multimodal data for an individual over three years that encompasses their food intake, physical activity, sleep, and other physiological parameters. We formulate hypotheses to examine relationships between these parameters and test their validity through a combination of correlation, network mapping, and causality techniques. While we use correlation analysis and GIMME (Group Iterative Multiple Model Estimation) network plots to investigate the association between parameters, we use causal inference to estimate causal effects and check the robustness of causal estimates by performing refutation analysis. Through our experiments, we achieve statistical significance for the causal estimate thereby validating our hypotheses. We hope to motivate individuals to collect and share their long-term multimodal data for building personal models thereby revolutionizing future health approaches." @default.
• W4361764024 created "2023-04-04" @default.
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• W4361764024 date "2023-01-01" @default.
• W4361764024 modified "2023-09-27" @default.
• W4361764024 title "Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data" @default.
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• W4361764024 doi "https://doi.org/10.1007/978-3-031-27077-2_46" @default.
• W4361764024 hasPublicationYear "2023" @default.
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