January 1, 2017IEEE AccessOpen Access

Trend Analysis of Fragmented Time Series for mHealth Apps: Hypothesis Testing Based Adaptive Spline Filtering Method With Importance Weighting

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Population

Heart rate data sets, blood pressure data sets, and noisy sine data sets

Comparison

Hypothesis-testing-based adaptive spline… vs Straightforward approaches

Design

Other

Authors

XDXiangfeng DaiUniversity of North Carolina at Chapel Hill MBMarwan BikdashNorth Carolina Agricultural and Technical State University

Discussion

Member takes

Implication

May improve missing-data handling in mobile health time series; leaves open prospective clinical validation before adoption.

Structured PICO

Population

Heart rate data sets, blood pressure data sets, and noisy sine data sets

Intervention

Hypothesis-testing-based adaptive spline filtering (HASF) trend analysis algorithm with importance weighting

Comparator

Straightforward approaches (supplementing missing data with constant or zero values or with linear trends)

Outcome

Quality of trend analysis in fragmented time series

The proposed HASF algorithm provides a robust method for analyzing intermittent time series data from mHealth apps, such as heart rate and blood pressure.

Cite This Study

Dai et al. (2017) studied this question.

synapsesocial.com/papers/6a6f7ece2163a0a01bc3c86d https://doi.org/10.1109/access.2017.2696502

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Bookmark

Trend Analysis of Fragmented Time Series for mHealth Apps: Hypothesis Testing Based Adaptive Spline Filtering Method With Importance Weighting

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Trend Analysis of Fragmented Time Series for mHealth Apps: Hypothesis Testing Based Adaptive Spline Filtering Method With Importance Weighting

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider