A framework for smartphone-enabled, patient-generated health data analysis
- Published
- Accepted
- Subject Areas
- Evidence Based Medicine, Translational Medicine, Science and Medical Education, Human-Computer Interaction, Computational Science
- Keywords
- quantified self, unequally spaced repeated measures, digital medicine, spatial power law, mobile blood pressure monitoring, mixed models
- Copyright
- © 2016 Gollamudi et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. A framework for smartphone-enabled, patient-generated health data analysis. PeerJ Preprints 4:e1911v1 https://doi.org/10.7287/peerj.preprints.1911v1
Abstract
Background: Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial. Methods: In the present study we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial. Results: We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end – highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions. Conclusions: Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self. The study was registered at clinicaltrials.gov (NCT01975428).
Author Comment
This is a submission to Peerj for review.
Supplemental Information
Histogram of the number of blood pressure readings recorded relative to the time since study enrollment
Histogram of the number of blood pressure readings recorded relative to the time of day (PST)
Normalized systolic blood pressure readings
Each box is one study individual. Points are arranged along the x-axis which represents the time in days from the beginning of the study, and along the y-axis which represents the normalized diastolic blood pressure reading recorded at that time. The red line is the least squares regression line. Individuals are ordered left to right, top to bottom according to the number of readings recorded.
Parameter estimate and corresponding 95% confidence interval assessing change in systolic blood pressure over the course of the study
Study participant demographics at enrollment visit (n=38)
Values are in counts (%) unless otherwise noted.
Study participant self-assessment of health (n=38)
Values are in counts (%) unless otherwise noted. * = values in mean (standard deviation).