Significant attention must be given to the collection, extraction, storage, processing, and display of the large amount of high-resolution data acquired by mobile technologies. 

The Mobile Technologies Core is working across the U-M mobile technologies ecosystem to build capacity, standardize processes and resources, and enable investigators to conduct research in a reproducible and rigorous manner. With our partners, we are developing expertise to support investigators at all stages of the research process. 

While each study is unique, the infrastructure required to successfully collect and manage mobile data has common elements. The Mobile Technologies Core is working to develop a collection of mobile data pipelines that will enable investigators to focus on their study instead of the infrastructure needed to support their work. The following mobile data pipelines are from current studies. They illustrate the complexity and variety of approaches that are currently being used. 

Data Pipeline Examples

Intern Health Study Data Pipeline:

Example of a Mobile Data Pipeline

MIPACT Study Data Pipeline: 

Example of a Mobile Data Pipeline

THRIVE Study Data Pipeline: 

Example of a Mobile Data Pipeline

The core, along with partners in the U-M mobile data ecosystem, is actively working toward standardization of mobile data formats and the data curation processes. In addition to developing best practices for information architecture and data flow processes, core team members will directly support study teams through training and assistance with extracting, processing, and maintaining mobile data. Investigators will be given the opportunity to have their data become part of larger integrated mobile datasets. Costs for maintaining these integrated datasets will be covered by the Mobile Technologies Core. 

The following health parameters are anticipated to be most frequently utilized by investigators. This list may expand based on investigator interest and technological advances.

Commonly Used Health Measures

Example parameters in research with mobile technologies

Domain Example Measure Constructed Measures


  • daily step count
  • daily exercise minutes
  • activity type (e.g., running, walking, biking, swimming)
Sleep Sleep status
  • daily Total Sleep Time (TST), minutes and variability
  • sleep onset time, mid-point, and wake time and variability of each
  • sleep efficiency ([TST/time between bedtime and wake time]*100%)
  Sleep stage
  • REM,light, deep stages (time in minutes, %age in each stage)
  • circadian phases (based on sleep and heart rate)
Cardiovascular Heart rate
  • beats per minute
  • heart rate variability
  • maximum heart rate
  • resting heart rate
Respiratory Apneic events
  • apnea hypopnea index (AHI)
  Oxygen saturation
  • SpO2 nadir
  • SpO2 mean
  Respiratory rate 
  • breaths per minute
Social Interactions Text messages, calls, app use
  • texts per day
  • time spent in calls per day
  • minutes of social media app use
Human-Phone Interaction Status of phone and specific apps (in use/not in use)
  • screen time per day
  • app use minutes per day
  • typing speed
Environmental Sound
  • decibel level
  GPS Coordinate
  • location
  • latitude/longitude

Mobile Data Library

Historical and current mobile technologies research cohorts may act as a source for secondary data analysis and can provide important input to hypothesis generation and testing. The following studies allow access to mobile technologies acquired data. Going forward, the core aims to grow this collection with our own (standardized) data repository.


MiPACT seeks to investigate the relationship between disease, daily quality of life, and healthcare services. Apple Watch and blood pressure cuffs were used in the study. Data Available: Blood Pressure; Activity goals: steps taken and distances, calories burned; Vitals: heart rate, heart rate variability, respiratory rate; Electronic Health Record Data: diagnoses, procedures, labs; Surveys: quality of life, behavioral measures, cognitive and physical function. Data can be accessed through DataDirect


The PROMPT Study aims to reduce the burden of depression by 1) increasing capacity in the mental health care system through expanding use of mobile technology-delivered interventions and 2) accelerating recovery from mental illness by better matching patients to pharmacological, psychological, and mobile-based treatments. Apple Watch and Fitbit were used in the study. Data Available: Activity data; Weight/BMI; Heart rate; Sleep; Survey responses. Data can be accessed through DataDirect

All of Us Research Hub »

The All of Us data set provides Fitbit data for thousands of FitBit users and includes the following measures: Heart Rate by Zone Summary, Heart Rate (Minute-Level), Activity (Daily Summary), and Activity Intraday Steps (Minute-Level).

Brno University of Technology Smartphone PPG Database(BUT PPG) »

The data consisted of 48 10-second recordings of PPGs and associated ECG signals used for determination of reference HR for the purpose of evaluating PPG quality and estimation of heart rate (HR).

Brno University of Technology ECG Quality Database (BUT QDB) »

The data consisted of 18 long-term recordings of single-lead ECGs and associated 3-axis accelerometer data, collected from 15 subjects (9 female, 6 male) aged between 21 to 83 years for the purpose of evaluating ECG quality.

Smartphone-Based Recognition of Human Activities and Postural Transitions Data … »

Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors. The Eisenberg Family Depression Center’s Data & Design Core has collated high-quality, publicly available data sources that collect information on depression and mental health that provide a cost and time-efficient means of answering research questions. View the Data Resources here.

Intern Health Study »

This multi-center, multi-year study investigates the interplay between genes and stress in the development of depression among medical school interns. FitBit and survey data are available for 2014 through 2020 with increasing data points by year. Please click here for the Mobile Data Dictionary.

TamNet Data Repository »

The TamNet Data Repository was established by the Prechter Program in October 2016 to safely store, process, and distribute mobile or electronically-captured health information, including anonymous speech data. This repository is a vital component of the Prechter Program's mobile health research project, PRIORI, which is currently in use in multiple studies at the University of Michigan and collaborating institutions. The Prechter Bipolar Research Program is dedicated to sharing its rich data sets with fellow researchers. If you are interested in using our data in your research project, please contact us at for further information. 

Eisenberg Family Depression Center’s Data Resources »

The Eisenberg Family Depression Center’s Data & Design Core has collated high-quality, publicly available data sources that collect information on depression and mental health that provide a cost and time-efficient means of answering research questions.