Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach.
Date
2024-03
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Abstract
Background
Intensive longitudinal data (ILD) collected in near real time by mobile health devices provide a new opportunity for monitoring chronic diseases, early disease risk prediction, and disease prevention in health research. Functional data analysis, specifically functional principal component analysis, has great potential to abstract trends in ILD but has not been used extensively in mobile health research.Objective
To introduce functional principal component analysis (fPCA) and demonstrate its potential applicability in estimating trends in ILD collected by mobile heath devices, assessing longitudinal association between ILD and health outcomes, and predicting health outcomes.Methods
fPCA and scalar-to-function regression models were reviewed. A case study was used to illustrate the process of abstracting trends in intensively self-measured blood glucose using functional principal component analysis and then predicting future HbA1c values in patients with type 2 diabetes using a scalar-to-function regression model.Results
Based on the scalar-to-function regression model results, there was a slightly increasing trend between daily blood glucose measures and HbA1c. 61% of variation in HbA1c could be predicted by the three preceding months' blood glucose values measured before breakfast (Pā<ā0.0001, [Formula: see text]).Conclusions
Functional data analysis, specifically fPCA, offers a unique tool to capture patterns in ILD collected by mobile health devices. It is particularly useful in assessing longitudinal dynamic association between repeated measures and outcomes, and can be easily integrated in prediction models to improve prediction precision.Type
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Yang, Qing, Meilin Jiang, Cai Li, Sheng Luo, Matthew J Crowley and Ryan J Shaw (2024). Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach. BMC medical research methodology, 24(1). p. 69. 10.1186/s12874-024-02193-7 Retrieved from https://hdl.handle.net/10161/30417.
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Scholars@Duke
Qing Yang
Dr. Qing Yang is Associate Professor and Biostatistician at Duke School of Nursing. She received her PhD in Biostatistics from University of California, Los Angeles. Dr. Yangās statistical expertise is longitudinal data analysis and time-to-event data analysis. As a biostatistician, she has extensive experience collaborating with researchers in different therapeutic areas, including diabetes, cancer, cardiovascular disease and mental health. Her current research interests are advanced latent variable models that are widely used in symptom cluster research and intensive longitudinal data analysis that arise from mobile health research.
Sheng Luo
Matthew Janik Crowley
Diabetes, Hypertension, Health Services Research
Ryan Shaw
I lead teams that are shaping the future of healthcare through digital transformation. By applying a digital equity lens, we discover how to translate emerging technologies into innovative patient care models. My work has been funded by the US National Institutes of Health (NIH) and the National Science Foundation (NSF), among others.
In addition to research, I teach classes in health informatics and research methods, and mentor students and trainees to become the next generation of health scientists and clinicians.
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