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Supervised Learning of Physical Activity Features From Functional Accelerometer Data.

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IEEE Journal of Biomedical and Health Informatics

|September 22, 2023

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We introduce a novel health informatics framework using Occupation-Time curves to analyze physical activity (PA) from accelerometers. This data-driven approach overcomes limitations of fixed cutoffs, offering adaptable insights into PA and biological aging.

Area of Science:

Health Informatics
Bioinformatics
Biostatistics

Background:

Accelerometer data is crucial for precision health but current analysis methods using fixed cutoffs lack generalizability across populations, devices, and studies.
Existing physical activity (PA) analysis relies on discretizing data, limiting its application in diverse real-world scenarios.

Purpose of the Study:

To propose a novel health informatics framework for analyzing physical activity (PA) data from accelerometer devices.
To develop a data-driven approach that overcomes the limitations of fixed cutoffs in PA data analysis.
To assess the influence of PA on biological aging using a new analytical methodology.

Main Methods:

Developed Occupation-Time curves (OTCs) to holistically summarize activity profiles.

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Employed multi-step adaptive learning algorithms with a scalar-on-function model for supervised learning.

Utilized a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection.

Main Results:

Demonstrated the performance of the proposed learning analytic through simulations and real-world data.
Identified a nuanced relationship between biological age and physical activity, dependent on the specific outcome.
The methodology proved adaptive to specific data, populations, and health outcomes.

Conclusions:

The proposed bioinformatics methodology offers an adaptive framework for analyzing physical activity data.
This approach enhances precision health decision-making by providing more generalizable insights from accelerometer data.
The findings highlight the complex interplay between physical activity and biological aging.