2026
Albers, C., & Hunter, M. (). Editorial: Special Issue on Multiple multivariate multimodal time series data analysis in psychological research. British Journal of Statistical Psychology, 79(2), 233-236. https://doi.org/10.1111/bmsp.70046
Hunter, M., Garrison, S., Lyu, X., Good, R., Carroll, S., & Burt, S. (). Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness. Behavior Genetics. https://doi.org/10.1007/s10519-026-10259-z
Whitney, J., Suhy, V., Pauley, A., Shao, D., Khan, O., Moore, A., Chow, S., Rivera, D., & Downs, D. (). Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity. Journal of Human Stress. https://doi.org/10.1080/08964289.2026.2641440
Li, Y., Xiong, X., Oravecz, Z., & Chow, S. (). Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures. British Journal of Statistical Psychology. https://doi.org/10.1111/bmsp.70029
2025
Singh, M., Hunter, M., Assary, E., Verhulst, B., Peterson, R., Maes, H., Dolan, C., Eley, T., & Neale, M. (). Causation Between Smoking Quantity and Depressive Symptoms in Young Adults: Evidence From Novel Cross-Lagged Twin Models. medArXiv, 1-40. https://doi.org/10.1101/2025.11.18.25340516
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
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
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), e2511835. 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(2), 418-441. https://doi.org/10.1017/psy.2025.19
Burt, S., Garrison, S., Lyu, X., Rodgers, J., Carroll, S., Smith, K., & Hunter, M. (). Contributions of inherited mtDNA 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
Xiong, X., Hunter, M., & Chow, S. (). Integrated Trend and Lagged Modeling of Multi-Subject, Multilevel, and Short Time Series. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2587286
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
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. JAMA Pediatrics. https://doi.org/10.1001/jamapediatrics.2024.6824
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
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(1), e66300. https://doi.org/10.2196/66300
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
Williams, L., Heshmati, S., Vandekerckhove, J., & Oravecz, Z. (). Current Methodological Approaches for Studying the Association Between Love and Psychological Well-Being in Daily Life. , 1611-1626. https://doi.org/10.1007/978-3-031-94512-0_75
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
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
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