Transportation research and practices often use male and female to define a person's gender and examine gender differences in travel behaviors and transportation needs. However, this overlooks that gender exists beyond a simple binary model of females and males and a person's gender identity may not conform to dominant gender norms. Hence, the project investigates whether gender and gender identity, in a broad sense, may lead to distinct activity-travel patterns using existing and new survey data collected in Minnesota. The new survey includes questions about gender identities and household responsibilities. And our analysis adopts the intersectional approach to investigate how gender, race, family types, and other social identities can interact and create a compounding impact on everyday behaviors and experiences.
Front-line health care workers (HCWs) have faced unpredictable and stressful working conditions during the COVID-19 pandemic. The purpose of this study was to get a better understanding of how HCWs behaviors, time use, and emotional state evolved during the pandemic (Dec 2020-Aug 2021). By combining data from regularly scheduled surveys that recorded stress levels and COVID-19 symptoms with time use information recorded by the Daynamica app, we identified interesting and at times surprising relationships between health status, time use, and stress among these vital workers.
For many people, Activity and travel patterns changed dramatically during the COVID-19 pandemic. The range and magnitude of the change in patterns of activity was not uniform, with certain subpopulations being more strongly affected. Of particular concern were individuals who had relevant pre-existing conditions and were at a higher risk of adverse consequences of a COVID-19 infection. This presentation will explore the causal effect of having a pre-existing condition on activity and time use behavior during the COVID-19 pandemic. Specific effects to be explored include time use patterns overall, the effect of time of day on time use, and subjective well-being measurements.
Population research on the health benefits of green/blue space largely delimits exposure to the residential environment. This myopic focus on natural environments at fixed neighborhood-level spatial units (e.g., census tracts) does not account for additional settings encountered in daily life that may influence pathways to health, including emotional experiences. Using Daynamica, this study collected data on participants’ travel episodes for up to two weeks in the Twin Cities, 2016-2017. Participants (n=362) completed real-time mobile surveys about emotional experiences associated with each travel episode (n=10,421). We linked environmental data from Open Street Map on greenspace and water bodies within a 50-meter buffer along travel routes to operationalize mobility-based green/blue space exposure. We specified longitudinal fixed-effects linear regression models to estimate within-person associations between green/blue space exposure and self-reported happiness. We further examined differences by gender, given that men and women show varying spatiotemporal mobility patterns, which may affect both environmental exposures and emotional experiences. Results show that mobility-based green/blue space exposure varies modestly with self-reported happiness, controlling for time-invariant confounders. Stratified and interaction models indicate stronger associations for women, relative to men. Findings support that exposure to green/blue space beyond the residential environment corresponds with greater self-reported happiness, particularly among women.
Comprehending and promoting well-being has long been considered key to the creation and maintenance of healthy, productive societies. As urban planners design and maintain living environments, it is critical for them to understand how the neighborhood built environment influences well-being through its physical characteristics, social environment, access to services, opportunities it affords to its residents, and the promotion of certain kinds of behavior. This presentation will highlight findings from a study conducted in the Twin Cities Metro Area to elucidate the relationship between the neighborhood built environment and emotional well-being. Data for the study was collected using the Daynamica smartphone application, through which 400 respondents reported episode-level activity data and affective response data over a 7-day period.
Dr. Huyen Le, Ohio State University
Previous studies have shown potential impacts of environmental exposure on subjective well-being, however, the relationship between subjective well-being and activity space is understudied, especially in the North American context. We aim to address these gaps by using smartphone-survey data from three U.S. metropolitan areas to quantify the impacts of activity space and exposure to nature and air pollution on momentary mood and overall subjective well-being. Our results show that the size of activity space and exposure to green and blue space are positively correlated with positive short-term subjective well-being but not long-term subjective well-being. Active travelers (e.g., cyclists and pedestrians) were more likely to experience positive affect after travel. Exposure to NO2 also negatively affected well-being.
Urban built environments often include many negative stimuli (e.g., dead animals, broken houses, graffiti, abandoned vehicles) that are linked with stress symptomatology among urban populations. Bio-signals (electrodermal activity, gait patterns, and blood volume pulse) can help assess pedestrian distress levels induced by negative environmental stimuli by overcoming the measurement limitations of traditional self-reporting methods and field observations. This presentation introduces how the spatial analysis of bio-signals can be leveraged to capture the environmental distress of pedestrians. In particular, it describes how Daynamica applications have been used in collecting bio-signals by integrating a wristband-type wearable sensor.
This study empirically examines how and to what extent the environment satisfaction of travelers is induced by built environment, travel, and personal factors. We develop a random-effects ordered logistic model on trip and activity episodes of 355 residents of Minneapolis-St. Paul Metropolitan Area collected over a one-week cycle between October 17, 2016 and October 25, 2017. Results indicate that population density, road network density, and distance to transit station at the block group geographical level are negatively correlated with environment satisfaction. Pedestrian-oriented network density and destination access, however, are found positively correlated with environment satisfaction. Results also indicate that physical activities and companionship increase the likelihood of environment satisfaction, while trips purposes are negatively associated with environment satisfaction except for leisure and recreation. We finally illustrate that African Americans, Asians, and younger participants reported a lower level of satisfaction with the environment when conducting trip and activities. Identifying the determinants of positive or negative effects of environment satisfaction has the potential to facilitate delivering quality and measurable improvements in day-to-day activities of people.
In the past, Daynamica allowed for 4 distinct types of surveys. They were the trip/activity based surveys, an End of Day survey, as well as intake and exit surveys. Recently, we have completed a new survey framework more in line with traditional Ecological Momentary Assessment research. This framework will allow for researchers to administer time based surveys throughout the day as well as allow for self reported surveys. Using these new survey formats, researchers will be able to more effectively collect data and design their study to fit their needs.
Over the past year, the Daynamica team has been working on redesigning the App User Interface. We want to make the app easier and more intuitive for first time users while also ensuring that we continue to support the core features that makes Daynamica a powerful data collection tool. We will continue to work to improve the user experience, streamlining processes and making it easier for participants to collect high quality data.
We know how overwhelming it can be to use a new data collection platform for research. With every new system, there is a learning curve to understanding the nuances of the product. In order to make this process easier, the Daynamia team is committed to improving its documentation, providing you with the necessary resources to understand how data collection works, what data is being collected, and what the data means.
In addition to the initiatives listed above, the Daynamica team is actively working on a range of other projects. Many of these projects are in the planning stage and we want to hear from you about what features matter to you most. This will help us prioritize features that are most important to our customers. Some of the features we are considering include:
Easier integration of External Data Sources
Whether it be smartwatches, step counters, or something else, we want to know if you are using these devices for research and how helpful it would be to get these data sources integrated into the Daynamica Pipeline.
New Survey Questions Types
Do you feel limited by the current Question Type formats in Daynamica. We want to know if you need Image Upload Features or Ranked Ordering Questions
Customizable Activity and Trip Types
Do you feel that the trips and activity labels Daynamica uses is too restrictive or too broad? We are considering making the list of activities and trips available in the app customizable by the study manager.
Send Messages via the Dashboard
Have you ever wanted a more direct way of communicating with participants, specifically through the app. We want to know how helpful it would be for us to provide ways for you to communicate with participants within the app.
Processed Daynamica Data
Our current focus is to provide accurate data products with no customization or processing of the data. We are interested in hearing whether study teams would be interested in having access to data manipulation tools allowing you to process the data automatically. Examples include deriving trip purpose or origin activities for trips, splitting calendar items at midnight, and identifying which trip segments belong to the same trip.
Analysis and Visualization Tools
Should Daynamica work on providing dashboards and tools to help you visualize and analyze Daynamica data during data collection and following study completion?