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A framework for smartphone-enabled, patient-generated health data analysis.

Shreya S Gollamudi¹, Eric J Topol², Nathan E Wineinger¹

¹Scripps Translational Science Institute , La Jolla, California , United States.

|August 23, 2016

Summary

This summary is machine-generated.

A new statistical framework for analyzing smartphone health data detected a 2 mmHg blood pressure reduction in hypertension patients. This digital health monitoring approach enables timely health insights and supports the quantified self.

Keywords:

Digital medicine Mixed models Mobile blood pressure monitoring Quantified self Spatial power law Unequally spaced repeated measures

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Area of Science:

Digital medicine
Biostatistics
Health informatics

Background:

Smartphone-enabled health technologies offer novel human health data.
Analyzing complex, unstructured mobile health data remains challenging.
A prior trial monitored 38 hypertension patients using a smartphone health program, collecting 6,290 blood pressure readings.

Purpose of the Study:

To present a hypothesis testing framework for unstructured time series data from mobile health devices.
To analyze blood pressure data from a smartphone-based hypertension monitoring trial.

Main Methods:

Utilized a mixed-effects model for unequally spaced repeated measures.
Employed autoregressive and generalized autoregressive models.
Applied the framework to patient-generated blood pressure time series data.

Main Results:

Detected an approximate 2 mmHg decrease in both systolic and diastolic blood pressure over the trial period.
Observed the blood pressure reduction three months earlier than the study's official end using sequential analysis.
The results accounted for significant intra- and inter-individual variations in blood pressure readings.

Conclusions:

Smartphone-generated health data empowers individuals and researchers with actionable health insights.
The proposed hypothesis testing framework is applicable to future digital medicine studies.
This framework can be integrated into health technologies to advance the quantified self movement.