Understanding the technology elements for a study utilizing wearable and mobile technologies will assist researchers during the study planning process in several ways.
Download the document provided on the right toolbar titled (Technology Elements for a study with mobile technologies.xls). The information below will guide you in completing the information in the document.
A Mobile Study Consultation guides investigators through these decisions and resources.
Mobile Data Measures
Study objectives will determine what mobile data measures are necessary and will drive device selection.
Resources:
Studies using mobile data require additional skills for managing and cleaning data before it can be analyzed. Many factors can impact the time needed to manage and clean data, including:
Data Sensitivity Classification
Investigators should know the classification of the data they are collecting to ensure they are handling the data appropriately.
U-M Data Classification Levels
Options for handling and storing data can be driven by the data classification level.
Wearable & Mobile Devices
Not all wearable & mobile devices are validated for use in research. Investigators should select devices that collect data necessary to meet their study objectives.
If purchasing computers, please consult with HITS.
It is recommended that devices be provided to study participants. The cost of devices should be part of the study budget.
Study, Survey, or Data Management Technology
U-M Approved Technology Options
Non-Mobile Data
Many studies need data in addition to data collected from mobile devices. This can include data from the following sources: Data Direct, Epic, and 3rd party mobile apps.
Adding additional data sources can increase the time necessary to merge, manage, and clean study data impacting staffing costs.
Study Data Storage
Data Architecture & Data Pipelines
It is recommended to store data in a study database so advanced data analysis and data visualization tools can be used.
Data Analysis & Visualization Tools
Tools of the Trade for Mobile Data
Location of Code for Automation & Analysis
Mobile Technologies Core GitHub Repository
Long-term Data Storage Locations
As the Mobile Technologies Core Manager at the University of Michigan’s Eisenberg Family Depression Center, Victoria Bennett helps investigators navigate the university’s robust resources while perpetually looking for opportunities to curate new resources. She aims to reduce friction for investigators who wish to utilize mobile technologies in health research. Drawing from her extensive experience with entrepreneurship, Victoria applies an entrepreneurial mindset to create efficient systems, improving operational effectiveness and impact.
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