What a day looks like when you measure all of it

Measuring the time toxicity of cancer care

Cancer treatment consumes time in ways the medical record never captures: the waiting, the parking, the two-hour round trip for a twenty-minute infusion, the pharmacy calls made from a work desk. Because none of it is counted, care models get chosen without anyone weighing what they cost patients and families in hours and energy.

This project is building an app-assisted day reconstruction method that quantifies those burdens directly, so oncologists and hospital administrators have something concrete to design against.

• Vogel, R. I., Jewett, P., Parsons, H., et al. (2025). Time burden in patients with metastatic breast and ovarian cancer from clinic and home demands. JAMA Network Open, 8(12), e2549957.
• Rocque, G. B., Henderson, N. L., Parsons, H. M., et al. (2026). Characterizing the objective time spent on health care activities in clinic and at home for patients with cancer. Cancer Medicine, 15(9), e72277.
• Dona, A. C., Jewett, P. I., Hwee, S., et al. (2024). Logistic burdens of cancer care: A qualitative study. PLOS ONE, 19(4), e0300852.
• Vogel, R. I., Jewett, P., Parsons, H. M., et al. (2025). Beyond the clinic: Comprehensive assessment of time burdens in cancer care. Journal of Clinical Oncology (ASCO Annual Meeting abstract), 43(16_suppl), 11011.
• Vogel, R. I., Jewett, P., Parsons, H., et al. (2025). Time burden of cancer care among individuals with advanced ovarian cancer or metastatic breast cancer (dataset). Data Repository for the University of Minnesota.
• Trial registration: NCT06322992

Moving greenspace research from the census tract to the route

Greenspace exposure has conventionally been measured at the census-tract level around someone's home — a static, residence-anchored proxy that ignores the fact that people move. Two neighbors with identical tract-level greenness can have completely different days.

• Background on the approach, Center for Transportation Studies
• Peer-reviewed results in preparation.

Using pedestrians as sensors for street-level stress

Walkability is normally scored by an auditor with a checklist: is the sidewalk blocked, is the surface uneven, is there trash. It's a reasonable proxy, but it measures the street rather than the experience of walking down it, and it can't tell you which of those conditions actually register on the people passing through.

• Kim, J., Nirjhar, E. H., Lee, H., et al. (2023). Location-based collective distress using large-scale biosignals in real life for walkable built environments. Scientific Reports, 13, 5940.
• Yupanqui, R., Sohn, J., Kim, Y., et al. (2024). A multimodal analysis of environmental stress experienced by older adults during outdoor walking trips. Proceedings of the 26th International Conference on Multimodal Interaction, 302–311.

COVID-19 exposure and stress among front-line health care workers

Health care workers absorbed an extraordinary burden during the pandemic, and almost none of the available data could distinguish what happened to them at work from what happened in the rest of their lives. Exposure indices built from job title alone miss the commute, the grocery run, the second job.

• Results in preparation.

Daily movement and mood in breast cancer survivors

Survivorship guidelines recommend physical activity, but adherence is measured with recall questionnaires that ask people to reconstruct a typical week. For a population managing fatigue, that recall is exactly the thing least likely to be accurate.

• Ryu, S., Adams, K., Chen, Y., & Gao, Z. (2022). Breast cancer survivors' physical activity, psychosocial beliefs, daily trip behaviors, and subjective well-being: A descriptive study. Complementary Therapies in Clinical Practice, 49, 101688.
• Ryu, S., & Gao, Z. (2023). Breast cancer survivors' daily physical activity and subjective well-being: Differences by activity types and weight status. Medicine & Science in Sports & Exercise (abstract), 55(9S), 309.

Trip happiness across modes, purposes, and companions

Transportation planning has good data on how long trips take and almost none on what they feel like. Trip happiness — emotional well-being measured during travel rather than remembered afterward — turns out to vary systematically with mode, purpose, duration, time of day, and who you're with.

• Fan, Y., Brown, R., Das, K., & Wolfson, J. (2019). Understanding trip happiness using smartphone-based data: The effects of trip- and person-level characteristics. Findings.
• Erinne, J., Ermagun, A., & Fan, Y. (2022). Just because I'm a woman: Gender happiness gap in human mobility. Travel Behaviour and Society, 29, 224–235.
• Ermagun, A., Erinne, J., & Fan, Y. (2022). Travel duration tolerance: Examining the sensitivity of emotional well-being to trip duration. Transportation Research Part D: Transport and Environment, 102, 103137.
• Ermagun, A., Erinne, J., & De Vos, J. (2025). Daily activity duration tolerance: A sensitivity analysis of emotional well-being to activity duration. Transportation, 52(3), 741–765.
• Brown, R., Fan, Y., Das, K., & Wolfson, J. (2021). Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data. Biometrics, 77(2), 401–412.
• Fan, Y., Ormsby, T., Wiringa, P., Liao, C.-F., & Wolfson, J. (2020). Visualizing transportation happiness in the Minneapolis–St. Paul region. Center for Transportation Studies, University of Minnesota.
• Brown, R. (2020). A statistical framework for harnessing human activity data to understand behavior, health, and well-being. Doctoral dissertation, University of Minnesota.
• Erinne, J. O. (2022). Travel behavior and subjective well-being: Effects of travel, activity, and personal factors. Mississippi State University.

Testing a mode-shift nudge on committed drivers

The Travel Options Project targeted the hardest group to move: people who had already bought a parking contract and drove every day. Understanding their commutes was the first half of the problem. The second was finding out whether a well-timed, personally relevant alternative would land.

• Fan, Y., Becker, A., Ryan, G., Wolfson, J., Guthrie, A., & Liao, C.-F. (2020). Smartphone-based interventions for sustainable travel behavior: The University of Minnesota parking contract holder study. Center for Transportation Studies, University of Minnesota.
• Ryan, G., Fan, Y., Becker, A., & Douma, F. (2022). Parking FlexPass at ABC Ramps: Integrating parking and transit options for sustainable mobility. Center for Transportation Studies, University of Minnesota.

How built and natural environments along a route shape mood

Most travel-satisfaction research captures a single retrospective rating per person, which makes within-person variation invisible and leaves no way to connect mood to the specific places someone passed through.

• Glasgow, T. E., Le, H. T. K., Geller, E. S., Fan, Y., & Hankey, S. (2019). How transport modes, the built and natural environments, and activities influence mood: A GPS smartphone app study. Journal of Environmental Psychology.
• Le, H. T. K., Buehler, R., Fan, Y., & Hankey, S. (2020). Expanding the positive utility of travel through weeklong tracking: Within-person and multi-environment variability of ideal travel time. Journal of Transport Geography, 84, 102679.
• Glasgow, T. E., Geller, E. S., Le, H. T. K., & Hankey, S. (2018). Travel mood scale: Development and validation of a survey to measure mood during transportation. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 318–329.
• Glasgow, T., Le, H., Geller, E. S., Fan, Y., & Hankey, S. (2017). Assessing the impact of transport mode, trip purpose, and the built environment on mood using a smartphone app. ISEE Conference Abstracts.
• Le, H. T. K. (2019). Emotional agents: Modeling travel satisfaction, affinity, and travel demand using a smartphone travel survey. Doctoral dissertation, Virginia Tech.

Urban infrastructure and everyday well-being

Cities invest in parks, gardens, transit, and cleaner air partly on the promise that they make life better, but the evidence usually rests on a single survey question about life satisfaction. That says little about which parts of the day an investment actually touches.

Working from the same Twin Cities sample as the trip happiness study, this group linked how people felt during specific activities to the infrastructure around them: gardening at home and in community plots, local air quality, and the street and land-use features that encourage walking and biking or drive up travel emissions.

• Das, K., Ramaswami, A., Fan, Y., & Cao, J. (2026). Unpacking the association of multiple urban infrastructures with subjective well-being: A comparison of three measures. Journal of Planning Education and Research.
• Ambrose, G., Das, K., Fan, Y., & Ramaswami, A. (2023). Comparing happiness associated with household and community gardening: Implications for food action planning. Landscape and Urban Planning, 230, 104593.
• Tao, T., Wu, X., Cao, J., Fan, Y., Das, K., & Ramaswami, A. (2023). Exploring the nonlinear relationship between the built environment and active travel in the Twin Cities. Journal of Planning Education and Research, 43(3), 637–652.
• Ambrose, G., Das, K., Fan, Y., & Ramaswami, A. (2020). Is gardening associated with greater happiness of urban residents? A multi-activity, dynamic assessment in the Twin-Cities region, USA. Landscape and Urban Planning, 198, 103776.
• Lal, R. M., Das, K., Fan, Y., et al. (2020). Connecting air quality with emotional well-being and neighborhood infrastructure in a US city. Environmental Health Insights, 14, 1178630220915488.
• Wu, X., Tao, T., Cao, J., Fan, Y., & Ramaswami, A. (2019). Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions. Transportation Research Part D: Transport and Environment, 75, 1–12.
• Flanegin, R. K. (2018). Unpacking the role of household and neighborhood infrastructure in shaping human wellbeing: Method development to inform sustainability. Master's professional paper, University of Minnesota.

Whose trips count: equity, identity, and pandemic travel

Transportation plans are built on travel surveys that undercount the people for whom getting around is hardest, and that record where people went but not what the trip was like. Those gaps fall hardest on underserved communities, on women and gender minorities, and on the transit riders whose options shifted during the pandemic.

• Zhang, Y. (2024). Using GPS-enabled activity-travel surveys to understand individuals' spatiotemporal behaviors and promote equitable mobility. Doctoral dissertation, University of Minnesota.
• Fan, Y., Greenberg, G., Panchal, N., et al. (2023). Centering the margins: The transportation experience of underserved communities. University of Minnesota.
• Fan, Y., Song, Y., Zhang, Y., & McDonnell, A. (2023). Advancing equity in accessibility and travel experiences: The role of gender and identity.
• Fan, Y., Becker, A., Ryan, G., & Wolfson, J. (2022). COVID-19 implications on public transportation: Understanding post-pandemic transportation needs, behaviors, and experiences. Center for Transportation Studies, University of Minnesota.

Statistical methods built for Daynamica-shaped data

A Daynamica day is a sequence of categorical states at minute resolution — a data structure that most off-the-shelf analysis tools handle badly. Sequence-alignment approaches to clustering become expensive fast and produce results that are hard to interpret.

Biostatisticians at the University of Minnesota have built a set of methods for exactly this kind of data: fast, interpretable clustering of activity days; synthetic sequences that mimic real participants for sharing and simulation; dimension reduction that finds the times of day that separate one pattern from another; and Bayesian borrowing that sharpens estimates for a single participant by learning from similar ones.

• Barnard, M., Fan, Y., & Wolfson, J. (2025). Adjacency matrix decomposition clustering for human activity data. Journal of the American Statistical Association, 120(552), 2053–2064.
• Barnard et al. (2025), arXiv preprint
• Huang, Z., Wolfson, J., Fulkerson, J. A., Demmer, R., & Chen, H. N. (2025). A flexible framework for synthesizing categorical sequences with application to human activity patterns. Journal of Computational and Graphical Statistics, 34(4), 1410–1418.
• Huang, Z., Fan, Y., Hodges, J., & Wolfson, J. (2025). Multinomial thresholded LASSO for interpretable dimension reduction of human activity sequences. arXiv:2507.17900.
• Ji, Z., & Wolfson, J. (2024). Reinforced borrowing framework: Leveraging auxiliary data for individualized inference. Statistics in Medicine, 43(30), 5837–5848.
• Ji, Z., & Wolfson, J. (2023). A flexible Bayesian framework for individualized inference via adaptive borrowing. Biostatistics, 24(3), 669–685.
• Barnard, M. (2026). Statistical methods for mobile health data and causal effect estimation under positivity violations. Doctoral dissertation, University of Minnesota.
• Huang, Z. (2026). Statistical methods for sequential categorical data. Doctoral dissertation, University of Minnesota.
• Ji, Z. (2023). Bayesian dynamic data borrowing methodologies for source-specific inference. Doctoral dissertation, University of Minnesota.