2025
Lancaster, J., Brick, T., & Cleveland, H. (). The effects of dynamic recovery identity on lapse risk and the role of daily recovery meetings. International Journal of Drug Policy, 145. https://doi.org/10.1016/j.drugpo.2025.104941
Pohlmann, K., Tawil, N., Brick, T., Yaghoubi, E., & Kühn, S. (). Visualising and understanding human evaluation of house facades: GAN applied to environmental psychology. Journal of Environmental Psychology, 107. https://doi.org/10.1016/j.jenvp.2025.102803
Bai, S., Froidevaux, N., Chen, M., High, A., Ewing, K., DeFelice, J., Weaver, J., Ngigi, K., Riccio, M., Chiang, S., Bai, L., Lunkenheimer, E., & Brick, T. (). Families being supportive together: A multimethod and multi-informant intensive longitudinal study of family protective mechanisms for adolescent depression. Psychological Assessment, 37(10), 535-546. https://doi.org/10.1037/pas0001400
Oravecz, Z., Sliwinski, M., Kim, S., Williams, L., Katz, M., & Vandekerckhove, J. (). Partially Observable Predictor Models for Identifying Cognitive Markers. Computational Brain and Behavior, 8(3), 410-420. https://doi.org/10.1007/s42113-025-00238-8
Rosinger, A., McGrosky, A., Jacobson, H., Hinz, E., Sadhir, S., Wambua, F., Otube, T., Baker, L., Sherwood, A., Chrissy-Mbeng, T., Broyles, L., Musumeci, C., Meriwether, N., Bobbie, N., Farrar, Z., Todd, M., Nguyen, Z., Berger, G., Ford, L., Braun, D., et al (). Drinking Water NaCl Is Associated With Hypertension and Albuminuria: A Panel Study. Hypertension, 82(8), 1368-1378. https://doi.org/10.1161/HYPERTENSIONAHA.125.24751
Williams, L., Kim, S., Li, Y., Heshmati, S., Vandekerckhove, J., Roeser, R., & Oravecz, Z. (). How much we express love predicts how much we feel loved in daily life. PLoS One, 20(7). https://doi.org/10.1371/journal.pone.0323326
Coles, N., Perz, B., Behnke, M., Eichstaedt, J., Kim, S., Vu, T., Raman, C., Tejada, J., Huynh, V., Zhang, G., Cui, T., Podder, S., Chavda, R., Pandey, S., Upadhyay, A., Padilla-Buritica, J., Barrera Causil, C., Ji, L., Dollack, F., Kiyokawa, K., et al (). Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience. Royal Society Open Science, 12(6). https://doi.org/10.1098/rsos.241778
Heshmati, S., Muth, C., Li, Y., Roeser, R., Smyth, J., Vandekerckhove, J., Chow, S., & Oravecz, Z. (). Who benefits from mobile health interventions? A dynamical systems analysis of psychological well-being in early adults. Applied Psychology: Health and Well-Being, 17(3). https://doi.org/10.1111/aphw.70037
Liu, C., Chow, S., Aris, I., Dabelea, D., Neiderhiser, J., Leve, L., Blair, C., Catellier, D., Couzens, L., Braun, J., Ferrara, A., Aschner, J., Deoni, S., Dunlop, A., Gern, J., Rivera-Spoljaric, K., Hartert, T., Hershey, G., Karagas, M., Kennedy, E., et al (). Early-Life Factors and Body Mass Index Trajectories Among Children in the ECHO Cohort. JAMA network open, 8(5). https://doi.org/10.1001/jamanetworkopen.2025.11835
Hunter, M., Kirkpatrick, R., & Neale, M. (). Show Me Some ID: A Universal Identification Program for Structural Equation Models. Psychometrika, 90, 418-441. https://doi.org/10.1017/psy.2025.19
Drewelies, J., Fiedler, A., Brick, T., & Kühn, S. (). Investigating associations between the physical living environment and hippocampus in adulthood and older age. Environmental Research, 267. https://doi.org/10.1016/j.envres.2024.120728
Burt, S., Garrison, S., Lyu, X., Rodgers, J., Carroll, S., Smith, K., & Hunter, M. (). Inherited mtDNA contributes to longevity: Evidence from extended pedigrees with 176 million kinship pairs. eBioMedicine, 119(105911), 1-10. https://doi.org/10.1016/j.ebiom.2025.105911
Lee, S., Fisher, Z., & Almeida, D. (). Daily reciprocal relationships between affect, physical activity, and sleep in middle and later life. Annals of Behavioral Medicine, 59(1). https://doi.org/10.1093/abm/kaae072
Noll, J., Felt, J., Russotti, J., Guastaferro, K., Day, S., & Fisher, Z. (). Rates of Population-Level Child Sexual Abuse After a Community-Wide Preventive Intervention. A.M.A. American journal of diseases of children. https://doi.org/10.1001/jamapediatrics.2024.6824
Kim, S., Hakun, J., Li, Y., Harrington, K., Elbich, D., Sliwinski, M., Vandekerckhove, J., & Oravecz, Z. (). Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment. JMIR Formative Research, 9. https://doi.org/10.2196/66300
Bhat, Y., Keller, K., Brick, T., & Pearce, A. (). ByteTrack: a deep learning approach for bite count and bite rate detection using meal videos in children. Frontiers in Nutrition, 12. https://doi.org/10.3389/fnut.2025.1610363
Das, J., Ji, L., Shen, Y., Kumara, S., Buxton, O., & Chow, S. (). Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting. Sleep Health, 11(2), 166-173. https://doi.org/10.1016/j.sleh.2024.10.003
Long, J., Cunningham, P., Maksi, S., Keller, K., Cheah, C., Boot, L., Klippel, A., Brick, T., Edwards, C., Kort, J., Grabusky, P., Rolls, B., & Masterson, T. (). Variety-seeking behavioral markers in an immersive virtual reality food buffet are associated with greater food and energy intake in laboratory meals. Appetite, 210(1), 107988. https://doi.org/10.1016/j.appet.2025.107988
Chen, M., Hunter, M., & Chow, S. (). Detecting critical change in dynamics through outlier detection with time-varying parameters in dynamic models. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.70010
Alexander, J., Duffy, K., Freis, S., Chow, S., Friedman, N., & Vrieze, S. (). Investigating the Magnitude and Persistence of COVID-19–Related Impacts on Affect and GPS-Derived Daily Mobility Patterns in Adolescence and Emerging Adulthood: Insights From a Smartphone-Based Intensive Longitudinal Study of Colorado-Based Youths From…. Journal of Medical Internet Research, 27, e64965. https://doi.org/10.2196/64965
Ringwald, W., Creswell, K., Low, C., Doryab, A., Chung, T., Oliva, J., Fisher, Z., Gates, K., & Wright, A. (). Common and Uncommon Risky Drinking Patterns in Young Adulthood Uncovered by Person-Specific Computational Modeling. Psychology of Addictive Behaviors. https://doi.org/10.1037/adb0001055
Skurka, C., Troy, C., Yang, Y., Smith, R., Tornello, S., Rosenberger, J., Brick, T., & Myrick, J. (). “It Is in the Air”: Seeking and Scanning for Information About Pre-Exposure Prophylaxis Among Young-Adult Men Who Have Sex with Men in the US. Health Communication. https://doi.org/10.1080/10410236.2025.2536314
Blahošová, J., Tancoš, M., Cho, Y., Šmahel, D., Elavsky, S., Chow, S., & Lebedíková, M. (). Examining the Reciprocal Relationship Between Social Media Use and Perceived Social Support Among Adolescents: A Smartphone Ecological Momentary Assessment Study. Media Psychology, 28(1), 70-101. https://doi.org/10.1080/15213269.2024.2310834
Lyu, X., Burt, S., Hunter, M., Good, R., Carroll, S., & Garrison, S. (). Detecting mtDNA Effects with an Extended Pedigree Model: An Analysis of Statistical Power and Estimation Bias. Behavior Genetics, 55(4), 320-337. https://doi.org/10.1007/s10519-025-10225-1
Knapp, K., Petrie, D., Brick, T., Deneke, E., Bunce, S., & Cleveland, H. (). Within-Person Affect Dynamics Among Individuals in Residential Treatment for Opioid Use Disorder: An Ecological Momentary Assessment Study. Journal of Psychopathology and Clinical Science, 134(2), 184-200. https://doi.org/10.1037/abn0000975