Recently, we have been working with researchers from Texas A&M University to create a mobile platform for measuring pedestrian experiences as they navigate areas that are under construction. As part of this project, we developed a custom integration that enables the Daynamica app to read data from the Empatica E4 wristband.
This integration allows us to link physiological data from the E4 wristband to the behavioral pattern data collected by Daynamica on the device in real-time, and store the linked data in the cloud. The context provided by the Daynamica data will allow our research partners to make sense of their sensor data much more quickly.
Do you use a wearable device in your research that you would like to integrate with the Daynamica app? Get in touch with us!
We are hard at work putting the finishing touches on the Android version of Daynamica Lite, which will be available shortly in the Google Play Store.
Daynamica Lite allows anyone to capture and view their daily activity information. App data is stored entirely on your device, and not sent to Daynamica servers. It is an ideal way for potential users to get a taste of the app’s core features.
Activities and trips detected by the Daynamica smartphone application can be easily be annotated by users, allowing researchers to collect data about the emotions associated with those. The following screenshot shows an example survey for collecting companionship and emotion data associated with a completed activity.
Understanding trip happiness—a measurement of people’s emotional well-being during trips—is an essential aspect of people-oriented transportation planning. We use data collected via smartphones from 350 residents in the Minneapolis-St. Paul region to examine trip- and person-level factors associated with trip happiness. Trip mode, purpose, duration, distance, companionship, activities during the trip, and temporal characteristics of the trip are significantly associated with trip happiness. Mode and companionship are the strongest predictors of trip happiness. Among personal factors, age is the strongest predictor, followed by general happiness of the person. Race, gender, and neighborhood have modest effects on trip happiness.
It’s a simple question, but gathering reliable data on how people spend their time is challenging. Traditional self-report based methods are burdensome to study participants and prone to recall bias. If this were an infomercial, we’d say (or yell): “THERE HAS TO BE A BETTER WAY!”
That’s where Daynamica comes in. We provide cutting-edge tools and infrastructure for collecting, processing, and understanding human activity and travel behavior data, including:
Daynamica, a smartphone application that captures detailed daily activity and trip data with minimal user burden.
StudyMap, A suite of study management tools to ensure compliance and data quality.
Co-founded by faculty members at the University of Minnesota and based on more than a half-decade of research, Daynamica was developed by researchers, for research and non-research settings. Our aim is to provide a platform that research teams and organizations can use to collect high-resolution activity and behavior data on study participants in a transparent, secure, and ethical manner while protecting individual privacy and confidentiality.
It seems like almost everyone is interested in mobile health (or, to be hipper, mHealth) these days. There are a ton of devices out there that can measure physical activity, heart rate, and a number of other physiological parameters. For the most part, these devices provide only a partial picture of human activity during the day. Daily habits and behavior patterns have a substantial impact on health, but until recently, our ability to uncover links between lifestyle and health outcomes has been limited by the inherent difficulty of accurately measuring individual human activity and behavior patterns.
For example, a mountain of research has shown that high levels of air pollution are associated with increased mortality. However, these findings are based primarily on pollution levels measured at fixed measuring stations; much less is known about how individual exposures vary over time, and hence how they impact health. Since the Daynamica app captures both movement patterns (i.e., location) and activity types (indoor/outdoor), it could be used either in conjunction with fixed or personal air pollution monitors to obtain much more precise, personalized measures of air pollution exposure. Indeed, Daynamica data could be paired with data from a wide variety of medical devices such as continuous glucose monitors or cardiac monitoring devices.
Another potential use of Daynamica is for developing and optimizing behavioral interventions. There is currently great interest in developing interventions that encourage behavior changes to improve health. But these interventions have mostly had only a modest degree of success. One key barrier to increasing the success of these interventions is that it is difficult to obtain accurate information on compliance, particularly since the behavior changes involved tend to be over-reported due to social desirability bias. Daynamica provides a platform for obtaining objective information about intervention compliance so that the reasons for intervention success or failure can be understood. The Daynamica app can also be used to deliver “just-in-time” interventions that adapt to each user’s past and current locations and activities. For instance, an intervention to decrease sedentary behavior could potentially be much more effective if it delivered reminders to users to increase their activity level at times when, based on past data, they have typically been sedentary.
March 19, 2018—Transportation agencies need travel behavior data to plan changes to their networks, systems, and policies. They’ll soon be able to purchase a new smartphone application called Daynamica, developed and patented by a University of Minnesota research team, to collect that important information more easily and for less cost than traditional methods.
The researchers, led by Humphrey School Associate Professor Yingling Fan, are in the final stages of creating a startup company and a licensing agreement with the University to sell Daynamica and its services.
This is the first patent and first startup company based on research completed at the Humphrey School, according to Associate Dean Carissa Slotterback.
“The work of Dr. Fan and her colleagues is a perfect example of the opportunities to use research to create products and outcomes that support practitioners in building healthy communities,” Slotterback says. “Her work has also connected to classes, creating opportunities for students to work at the forefront of transportation data and technology.”
Fan says Daynamica is a more efficient way to collect and process detailed data on how people get from place to place—driving or walking, biking or taking transit. It combines smartphone GPS sensing with advanced statistical and machine-learning techniques to automatically detect, identify, and summarize attributes of daily activity and travel periods. The app also allows users to view and add notes to the information at their convenience.
Fan says traditional travel survey methods like paper diaries are impractical, and GPS sensing tools can’t collect key information such as the purpose of a given trip, the traveler’s experience, and whether people are traveling alone or with companions.
“All of these factors are critical for understanding people’s travel choices,” Fan says. “Daynamica gives us the best of both worlds: It captures many more dimensions of travel behavior data than either GPS sensing or travel surveys can do alone.”
Daynamica places a much lower burden on users to recall and record their activities compared with traditional surveys, which means the data is more accurate and detailed. That ease of use could also allow agencies to study longer periods of time.
“Many traditional surveys track only a single day,” Fan says. “It would be better to collect a whole week of data to see how travel varies between weekdays and weekends. We could also look at seasonal variations and other factors. The more data, the better.”
Unlike other apps, Daynamica hosts both the data obtained from sensors and the data entered by users in a single device, and the two data sources interact with each other in real time. This in turn allows for data calibration and processing refinements over time.
“The algorithm learns from past mistakes,” Fan explains. “As it gets smarter, users need to make fewer corrections. After about a week of use, most data collection is automated. It learns what your location is—home, job, day care, or grocery store, for example—and remembers it.”
Daynamica offers several other advantages, Fan says. It’s easier to distribute and manage than other technologies because it doesn’t require an additional device; respondents can simply download the app and run it on their smartphones. In addition, Daynamica collects data in a way that reduces the need for processing, she says, which saves time and expense for the agencies using it.
Fan, who is the CEO of the new Daynamica, Inc., says the app will be marketed to businesses and government agencies interested in understanding and shaping transportation patterns, and to individuals interested in understanding and changing their own behavior for better economic, environmental, and health outcomes.
Daynamica was developed by a multidisciplinary team including Fan, Assistant Professor Julian Wolfson of the School of Public Health, Professor Gediminas Adomavicius of the Carlson School of Management, and computer science students Yash Khandelwal and Jie Kang.
Daynamica expands on the previous SmarTrAC app developed by the team under contract with the Volpe Center at the U.S. Department of Transportation in support of the Intelligent Transportation Systems Joint Program Office. Funding was also provided by the Center for Transportation Studies.