2025
Cho, Y., Chow, S., Li, J., Wang, S., Wang, W., Ji, L., Chinchilli, V., Intille, S. S.,, , & Dunton, G. (). Within- and Between-Individual Compliance in Mobile Health: Joint Modeling Approach to Nonrandom Missingness in an Intensive Longitudinal Observational Study. JMIR mHealth and uHealth, 13, e65350. https://doi.org/10.2196/65350
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
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
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
Cho, Y., Huang, Y., Chow, S., & Martire, L. (). Couple synchrony in physical activity: Effects on individuals with knee osteoarthritis. Annals of Behavioral Medicine, 59(1). https://doi.org/10.1093/abm/kaaf092
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
Oh, H., Hunter, M., & Chow, S. (). Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations. Structural Equation Modeling, 32(3), 511--528. https://doi.org/10.1080/10705511.2025.2452884
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
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
Li, Y., Williams, L., Muth, C., Heshmati, S., Chow, S., & Oravecz, Z. (). A Growth of Hierarchical Autoregression Model for Capturing Individual Differences in Changes of Dynamic Characteristics of Psychological Processes. Structural Equation Modeling, 32(2), 237-250. https://doi.org/10.1080/10705511.2024.2402328
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
Oravecz, Z., Vandekerckhove, J., Hakun, J., Kim, S., Katz, M., Wang, C., Lipton, R., Derby, C., Roque, N., & Sliwinski, M. (). Computational Phenotyping of Cognitive Decline With Retest Learning. Journals of Gerontology - Series B Psychological Sciences and Social Sciences, 80(7). https://doi.org/10.1093/geronb/gbaf030
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
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
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
2024
Kim, Y., Fisher, Z., & Pipiras, V. (). Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity. Biometrische Zeitschrift, 66(8). https://doi.org/10.1002/bimj.202300370
Fisher, Z., Pipiras, V., & Kim, Y. (). Structured Estimation of Multiple-Subject Time Series. Multivariate Behavioral Research.