Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

References

• Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on monte carlo simulation. Psychological Methods, 24(1), 1–19. https://doi.org/10.1037/met0000195
   PubMed Web of Science ®Google Scholar
• Ariens, S., Ceulemans, E., & Adolf, J. K. (2020). Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview. Journal of Psychosomatic Research, 137(July). https://doi.org/10.1016/j.jpsychores.2020.110191
   Google Scholar
• Asparouhov, T., Hamaker, E. L., & Muthén, B. O. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
   Web of Science ®Google Scholar
• Asutay, E., Genevsky, A., Barrett, L. F., Hamilton, J. P., Slovic, P., & Västfjäll, D. (2021). Affective calculus: The construction of affect through information integration over time. Emotion, 21(1), 159–174. https://doi.org/10.1037/emo0000681
   PubMed Web of Science ®Google Scholar
• Baldwin, S. A., Fellingham, G. W., & Baldwin, A. S. (2016). Statistical models for multilevel skewed physical activity data in health research and behavioral medicine. Health Psychology, 35(6), 552–562. https://doi.org/10.1037/hea0000292
   PubMed Web of Science ®Google Scholar
• Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458. https://doi.org/10.1177/1073191116648209
   PubMed Web of Science ®Google Scholar
• Boker, S. M., Staples, A. D., & Hu, Y. (2016). Dynamics of change and change in dynamics. Journal for Person-Oriented Research, 2(1–2), 34–55. https://doi.org/10.17505/jpor.2016.05
   PubMedGoogle Scholar
• Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.
   Google Scholar
• Bolger, N., & Zee, K. S. (2019). Heterogeneity in temporal processes: Implications for theories in health psychology. Applied Psychology: Health and Well-Being, 11(2), 198–201. https://doi.org/10.1111/aphw.12159
   PubMed Web of Science ®Google Scholar
• Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
   PubMed Web of Science ®Google Scholar
• Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Springer International Publishing. https://doi.org/10.1007/978-3-319-77219-6_11
   Google Scholar
• Brandt, P. T., & Williams, J. T. (2007). Introduction to multiple time series models. In Multiple time series models (pp. 2–14). SAGE Publications, Inc. https://doi.org/10.4135/9781412985215
   Google Scholar
• Bringmann, L. F., & Eronen, M. I. (2018). Don’t blame the model: Reconsidering the network approach to psychopathology. Psychological Review, 125(4), 606–615. https://doi.org/10.1037/rev0000108
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., Tuerlinckx, F., & Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23(4), 425–435. https://doi.org/10.1177/1073191116645909
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Borsboom, D., & Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8(4), Article e60188 https://doi.org/10.1371/journal.pone.0060188.
   PubMed Web of Science ®Google Scholar
• Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, Article 1540. https://doi.org/10.3389/fpsyg.2016.01540
   PubMed Web of Science ®Google Scholar
• Carpenter, R. W., Wycoff, A. M., & Trull, T. J. (2016). Ambulatory assessment: New adventures in characterizing dynamic processes. Assessment, 23(4), 414–424. https://doi.org/10.1177/1073191116632341
   PubMed Web of Science ®Google Scholar
• Clark, L. A., Watson, D., & Leeka, J. (1989). Diurnal variation in the positive affects. Motivation and Emotion, 13(3), 205–234. https://doi.org/10.1007/BF00995536
   Web of Science ®Google Scholar
• Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523
   PubMed Web of Science ®Google Scholar
• Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57(1), 505–528. https://doi.org/10.1146/annurev.psych.57.102904.190146
   PubMed Web of Science ®Google Scholar
• Conner, T. S., Tennen, H., Fleeson, W., & Barrett, L. F. (2009). Experience sampling methods: A modern idiographic approach to personality research. Social and Personality Psychology Compass, 3(3), 292–313. https://doi.org/10.1111/j.1751-9004.2009.00170.x
   PubMedGoogle Scholar
• Conroy, D. E., Lagoa, C. M., Hekler, E. B., & Rivera, D. E. (2020). Engineering person-specific behavioral interventions to promote physical activity. Exercise and Sport Sciences Reviews, 48(4), 170–179. https://doi.org/10.1249/JES.0000000000000232
   PubMed Web of Science ®Google Scholar
• Costantini, G., Richetin, J., Preti, E., Casini, E., Epskamp, S., & Perugini, M. (2019). Stability and variability of personality networks. A tutorial on recent developments in network psychometrics. Personality and Individual Differences, 136, 68–78. https://doi.org/10.1016/j.paid.2017.06.011
   Web of Science ®Google Scholar
• Cunningham, W. A., Dunfield, K. A., & Stillman, P. E. (2013). Emotional states from affective dynamics. Emotion Review, 5(4), 344–355. https://doi.org/10.1177/1754073913489749
   Web of Science ®Google Scholar
• Curtiss, J. E., Fulford, D., Hofmann, S. G., & Gershon, A. (2019). Network dynamics of positive and negative affect in bipolar disorder. Journal of Affective Disorders, 249(February), 270–277. https://doi.org/10.1016/j.jad.2019.02.017
   PubMedGoogle Scholar
• Deboeck, P. R. (2013). Dynamical systems and models of continuous time. In T. D. Little (Ed.), The Oxford handbook of quantitative methods (pp. 411–431). Oxford University Press.
   Google Scholar
• Deboeck, P. R., & Boker, S. M. (2016). Analysis of dynamical systems: The modeling of change and variability. In S. J. Henly (Ed.), Routledge international handbook of advanced quantitative methods in nursing research (pp. 170–186). Routledge.
   Google Scholar
• Depaoli, S., Rus, H. M., Clifton, J. P., van de Schoot, R., & Tiemensma, J. (2017). An introduction to Bayesian statistics in health psychology. Health Psychology Review, 11(3), 248–264. https://doi.org/10.1080/17437199.2017.1343676
   PubMed Web of Science ®Google Scholar
• Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77(5), 1–35. https://doi.org/10.18637/jss.v077.i05.
   Web of Science ®Google Scholar
• Driver, C. C., & Voelkle, M. C. (2018a). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods, 23(4), 774–799. https://doi.org/10.1037/met0000168
   PubMed Web of Science ®Google Scholar
• Driver, C. C., & Voelkle, M. C. (2018b). Understanding the time course of interventions with continuous time dynamic models. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 79–109). Springer International Publishing. https://doi.org/10.1007/978-3-319-77219-6_4
   Google Scholar
• Dunton, G. F. (2018). Sustaining health-protective behaviors such as physical activity and healthy eating. JAMA, 320(7), 639–640. https://doi.org/10.1001/jama.2018.6621
   PubMed Web of Science ®Google Scholar
• Ekkekakis, P. (2003). Pleasure and displeasure from the body: Perspectives from exercise. Cognition and emotion, 17(2), 213–239. https://doi.org/10.1080/02699930302292
   PubMed Web of Science ®Google Scholar
• Emerson, J. A., Dunsiger, S. I., & Williams, D. M. (2018). Reciprocal within-day associations between incidental affect and exercise: An EMA study. Psychology & Health, 33(1), 130–143. https://doi.org/10.1080/08870446.2017.1341515
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Deserno, M. K., & Bringmann, L. F. (2019). mlVAR: multi-level vector autoregression (0.4.4.). https://cran.r-project.org/package=mlVAR.
   Google Scholar
• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823
   PubMed Web of Science ®Google Scholar
• Ernst, A. F., Albers, C. J., Jeronimus, B. F., & Timmerman, M. E. (2020). Inter-individual differences in multivariate time-series. European Journal of Psychological Assessment, 36(3), 482–491. https://doi.org/10.1027/1015-5759/a000578
   Web of Science ®Google Scholar
• Ernst, A. F., Timmerman, M. E., Jeronimus, B. F., & Albers, C. J. (2021). Insight into individual differences in emotion dynamics with clustering. Assessment, 28(4), 1186–1206. https://doi.org/10.1177/1073191119873714
   PubMed Web of Science ®Google Scholar
• Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences, 115(27), E6106–E6115. https://doi.org/10.1073/pnas.1711978115
   PubMed Web of Science ®Google Scholar
• Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126(7), 969–988. https://doi.org/10.1037/abn0000276
   PubMed Web of Science ®Google Scholar
• Fredrickson, B. L., Arizmendi, C., & Van Cappellen, P. (2021). Same-day, cross-day, and upward spiral relations between positive affect and positive health behaviours. Psychology and Health, 36(4), 444–460. https://doi.org/10.1080/08870446.2020.1778696
   PubMed Web of Science ®Google Scholar
• Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6), 999–1020. https://doi.org/10.1177/1745691617705892
   Web of Science ®Google Scholar
• Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2020). Latent variable GIMME using model implied instrumental variables (MIIVs). Psychological Methods, 25(2), 227–242. https://doi.org/10.1037/met0000229
   PubMed Web of Science ®Google Scholar
• Gelfand, A. E., & Wang, F. (2002). A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models. Statistical Science, 17(2), 193–208. https://doi.org/10.1214/ss/1030550861
   Web of Science ®Google Scholar
• Gelfand, L. A., & Engelhart, S. (2012). Dynamical systems theory in psychology: Assistance for the lay reader is required. Frontiers in Psychology, 3. Article 382.https://doi.org/10.3389/fpsyg.2012.00382.
   Google Scholar
• Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. Guilford Press.
   Google Scholar
• Groen, R. N., Ryan, O., Wigman, J. T. W., Riese, H., Penninx, B. W. J. H., Giltay, E. J., Wichers, M., & Hartman, C. A. (2020). Comorbidity between depression and anxiety: Assessing the role of bridge mental states in dynamic psychological networks. BMC Medicine, 18(1), 308. https://doi.org/10.1186/s12916-020-01738-z
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. O. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446819
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Asparouhov, T., & Muthén, B. O. (2021). Dynamic structural equation modeling as a combination of time series modeling, multilevel modeling, and structural equation modeling. In R. H. Hoyle (Ed.), The handbook of structural equation modeling (2nd ed.). Guilford Press.
   Google Scholar
• Hamaker, E. L., Ceulemans, E., Grasman, R. P. P. P., & Tuerlinckx, F. (2015a). Modeling affect dynamics: State of the art and future challenges. Emotion Review, 7(4), 316–322. https://doi.org/10.1177/1754073915590619
   Web of Science ®Google Scholar
• Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015b). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Mulder, J. D., & van IJzendoorn, M. H. (2020). Description, prediction and causation: Methodological challenges of studying child and adolescent development. Developmental Cognitive Neuroscience, 46(January), Article 100867. https://doi.org/10.1016/j.dcn.2020.100867.
   PubMedGoogle Scholar
• Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15. https://doi.org/10.1177/0963721416666518
   Web of Science ®Google Scholar
• Haslbeck, J. M. B., Bringmann, L. F., & Waldorp, L. J. (2020). A tutorial on estimating time-varying vector autoregressive models. Multivariate Behavioral Research, 56(1), 120–149. https://doi.org/10.1080/00273171.2020.1743630
   PubMed Web of Science ®Google Scholar
• Hecht, M., & Voelkle, M. C. (2021). Continuous-time modeling in prevention research: An illustration. International Journal of Behavioral Development, 45(1), 19–27. https://doi.org/10.1177/0165025419885026
   Web of Science ®Google Scholar
• Hecht, M., & Zitzmann, S. (2020). A computationally more efficient Bayesian approach for estimating continuous-time models. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 829–840. https://doi.org/10.1080/10705511.2020.1719107
   Web of Science ®Google Scholar
• Hecht, M., & Zitzmann, S. (2021a). Exploring the unfolding of dynamic effects with continuous-time models: Recommendations concerning statistical power to detect peak cross-lagged effects. Structural Equation Modeling: A Multidisciplinary Journal, 1–9. Advance online publication. https://doi.org/10.1080/10705511.2021.1914627
   Google Scholar
• Hecht, M., & Zitzmann, S. (2021b). Sample size recommendations for continuous-time models: Compensating shorter time series with larger numbers of persons and vice versa. Structural Equation Modeling: A Multidisciplinary Journal, 28(2), 229–236. https://doi.org/10.1080/10705511.2020.1779069
   Web of Science ®Google Scholar
• Heino, M. T. J., Vuorre, M., & Hankonen, N. (2018). Bayesian evaluation of behavior change interventions: A brief introduction and a practical example. Health Psychology and Behavioral Medicine, 6(1), 49–78. https://doi.org/10.1080/21642850.2018.1428102
   PubMed Web of Science ®Google Scholar
• Hofmann, S. G., & Curtiss, J. E. (2018). A complex network approach to clinical science. European Journal of Clinical Investigation, 48(8), e12986. https://doi.org/10.1111/eci.12986
   PubMed Web of Science ®Google Scholar
• Hofmann, S. G., Curtiss, J. E., & Hayes, S. C. (2020). Beyond linear mediation: Toward a dynamic network approach to study treatment processes. Clinical Psychology Review, 76(January), Article 101824.https://doi.org/10.1016/j.cpr.2020.101824.
   PubMedGoogle Scholar
• Kuiper, R. M., & Ryan, O. (2018). Drawing conclusions from cross-lagged relationships: Re-considering the role of the time-interval. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 809–823. https://doi.org/10.1080/10705511.2018.1431046
   Web of Science ®Google Scholar
• Lafit, G., Adolf, J. K., Dejonckheere, E., Myin-Germeys, I., Viechtbauer, W., & Ceulemans, E. (2021). Selection of the number of participants in intensive longitudinal studies: A user-friendly Shiny App and tutorial for performing power analysis in multilevel regression models that cccount for temporal dependencies. Advances in Methods and Practices in Psychological Science, 4(1). https://doi.org/10.1177/2515245920978738
   Google Scholar
• Lane, S. T., & Gates, K. M. (2017). Automated selection of robust individual-level structural equation models for time series data. Structural Equation Modeling: A Multidisciplinary Journal, 24(5), 768–782. https://doi.org/10.1080/10705511.2017.1309978
   Web of Science ®Google Scholar
• Lane, S. T., Gates, K. M., Fisher, Z. F., Arizmendi, C., Molenaar, P. C. M., Hallquist, M., Pike, H. K., Henry, T., Duffy, K., Luo, L., & Beltz, A. M. (2020). gimme: Group iterative multiple model estimation (Version 0.7-4.). R Package. https://cran.r-project.org/package=gimme.
   Google Scholar
• Lane, S. T., Gates, K. M., Pike, H. K., Beltz, A. M., & Wright, A. G. C. (2019). Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods, 24(1), 54–69. https://doi.org/10.1037/met0000192
   PubMed Web of Science ®Google Scholar
• Leventhal, A. M. (2012). Relations between anhedonia and physical activity. American Journal of Health Behavior, 36(6), 860–872. https://doi.org/10.5993/AJHB.36.6.12
   PubMed Web of Science ®Google Scholar
• Liao, Y., Chou, C., Huh, J., Leventhal, A. M., & Dunton, G. F. (2017b). Examining acute bi-directional relationships between affect, physical feeling states, and physical activity in free-living situations using electronic ecological momentary assessment. Journal of Behavioral Medicine, 40(3), 445–457. https://doi.org/10.1007/s10865-016-9808-9
   PubMed Web of Science ®Google Scholar
• Liao, Y., Chou, C.-P., Huh, J., Leventhal, A. M., & Dunton, G. F. (2017a). Associations of affective responses during free-living physical activity and future physical activity levels: An ecological momentary assessment study. International Journal of Behavioral Medicine, 24(4), 513–519. https://doi.org/10.1007/s12529-016-9626-z
   PubMed Web of Science ®Google Scholar
• Little, T. D., Wang, E. W., & Gorrall, B. K. (2017). VIII. The past, present, and future of developmental methodology. Monographs of the Society for Research in Child Development, 82(2), 122–139. https://doi.org/10.1111/mono.12302
   PubMed Web of Science ®Google Scholar
• Lyon, A. R., Connors, E., Jensen-Doss, A., Landes, S. J., Lewis, C. C., McLeod, B. D., Rutt, C., Stanick, C., & Weiner, B. J. (2017). Intentional research design in implementation science: Implications for the use of nomothetic and idiographic assessment. Translational Behavioral Medicine, 7(3), 567–580. https://doi.org/10.1007/s13142-017-0464-6
   PubMed Web of Science ®Google Scholar
• McNeish, D. M. (2019). Two-level dynamic structural equation models with small samples. Structural Equation Modeling: A Multidisciplinary Journal, 26(6), 948–966. https://doi.org/10.1080/10705511.2019.1578657
   PubMed Web of Science ®Google Scholar
• McNeish, D. M., & Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in mplus. Psychological Methods, 25(5), 610–635. https://doi.org/10.1037/met0000250
   PubMed Web of Science ®Google Scholar
• McNeish, D. M., Mackinnon, D. P., Marsch, L. A., & Poldrack, R. A. (2021). Measurement in intensive longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 28(5), 807–822. Advance Online Publication. https://doi.org/10.1080/10705511.2021.1915788
   PubMed Web of Science ®Google Scholar
• Mkhitaryan, S., Crutzen, R., Steenaart, E., & De Vries, N. K. (2019). Network approach in health behavior research: How can we explore new questions? Health Psychology and Behavioral Medicine, 7(1), 362–384. https://doi.org/10.1080/21642850.2019.1682587
   PubMed Web of Science ®Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
   Google Scholar
• Molenaar, P. C. M. (2019). Granger causality testing with intensive longitudinal data. Prevention Science, 20(3), 442–451. https://doi.org/10.1007/s11121-018-0919-0
   PubMed Web of Science ®Google Scholar
• Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specifc paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x
   Web of Science ®Google Scholar
• Nezlek, J. B. (2017). A practical guide to understanding reliability in studies of within-person variability. Journal of Research in Personality, 69, 149–155. https://doi.org/10.1016/j.jrp.2016.06.020
   Web of Science ®Google Scholar
• Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Wiley.
   Google Scholar
• Piccirillo, M. L., & Rodebaugh, T. L. (2019). Foundations of idiographic methods in psychology and applications for psychotherapy. Clinical Psychology Review, 71, 90–100. https://doi.org/10.1016/j.cpr.2019.01.002
   PubMed Web of Science ®Google Scholar
• Ram, N., & Gerstorf, D. (2009). Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes. Psychology and Aging, 24(4), 778–791. https://doi.org/10.1037/a0017915
   PubMed Web of Science ®Google Scholar
• Raudenbush, S. W., Bloom, H., Spybrook, J., & Martinez, A. (2011). Optimal design software for multi-level and longitudinal research (Version 3.01).
   Google Scholar
• Ravindran, A. V., Balneaves, L. G., Faulkner, G., Ortiz, A., McIntosh, D., Morehouse, R. L., Ravindran, L., Yatham, L. N., Kennedy, S. H., Lam, R. W., MacQueen, G. M., Milev, R. V., & Parikh, S. V. (2016). Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder. The Canadian Journal of Psychiatry, 61(9), 576–587. https://doi.org/10.1177/0706743716660290
   PubMed Web of Science ®Google Scholar
• R Core Team. (2021). R: A language and environment for statistical computing. (4.0.5). https://www.r-project.org/.
   Google Scholar
• Refinetti, R., Sani, M., Jean-Louis, G., Pandi-Perumal, S. R., Durazo-Arvizu, R. A., Dugas, L. R., Kafensztok, R., Bovet, P., Forrester, T. E., Lambert, E. V., Plange-Rhule, J., & Luke, A. (2015). Evidence for daily and weekly rhythmicity but not lunar or seasonal rhythmicity of physical activity in a large cohort of individuals from five different countries. Annals of Medicine, 47(7), 530–537. https://doi.org/10.3109/07853890.2015.1085125
   PubMed Web of Science ®Google Scholar
• Reis, R. S., Salvo, D., Ogilvie, D., Lambert, E. V., Goenka, S., & Brownson, R. C. (2016). Scaling up physical activity interventions worldwide: Stepping up to larger and smarter approaches to get people moving. The Lancet, 388(10051), 1337–1348. https://doi.org/10.1016/S0140-6736(16)30728-0
   PubMed Web of Science ®Google Scholar
• Rhodes, R. E., Boudreau, P., Josefsson, K. W., & Ivarsson, A. (2021). Mediators of physical activity behaviour change interventions among adults: A systematic review and meta-analysis. Health Psychology Review, 15(2), 272–286. https://doi.org/10.1080/17437199.2019.1706614
   PubMed Web of Science ®Google Scholar
• Rhodes, R. E., Janssen, I., Bredin, S. S. D., Warburton, D. E. R., & Bauman, A. E. (2017). Physical activity: Health impact, prevalence, correlates and interventions. Psychology & Health, 32(8), 942–975. https://doi.org/10.1080/08870446.2017.1325486
   PubMed Web of Science ®Google Scholar
• Rhodes, R. E., & Kates, A. (2015). Can the affective response to exercise predict future motives and physical activity behavior? A systematic review of published evidence. Annals of Behavioral Medicine, 49(5), 715–731. https://doi.org/10.1007/s12160-015-9704-5
   PubMed Web of Science ®Google Scholar
• Rhodes, R. E., McEwan, D., & Rebar, A. L. (2019). Theories of physical activity behaviour change: A history and synthesis of approaches. Psychology of Sport and Exercise, 42, 100–109. https://doi.org/10.1016/j.psychsport.2018.11.010
   Web of Science ®Google Scholar
• Ridenour, T. A. (2019). Precision strategies as a timely and unifying framework for ongoing prevention science advances. Prevention Science, 20(1), 110–114. https://doi.org/10.1007/s11121-019-0988-8
   PubMed Web of Science ®Google Scholar
• Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357. https://doi.org/10.2307/2087176
   Web of Science ®Google Scholar
• Russell, J. A. (2003). Core affect annd the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/0033-295X.110.1.145
   PubMed Web of Science ®Google Scholar
• Ryan, O., & Hamaker, E. L. (2021). Time to intervene: A continuous-time approach to network analysis and centrality. Psychometrika. https://doi.org/10.1007/s11336-021-09767-0
   Google Scholar
• Ryan, O., Kuiper, R. M., & Hamaker, E. L. (2018). A continuous time approach to intensive longitudinal data: What, why and how? In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 27–54). Springer International Publishing.
   Google Scholar
• Sallis, J. F., & Owen, N. (2015). Ecological models of health behavior. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior: Theory, research, and practice. (5th ed., pp. 43–64). Jossey-Bass.
   Google Scholar
• Scholz, U. (2019). It’s time to think about time in health psychology. Applied Psychology: Health and Well-Being, 11(2), 173–186. https://doi.org/10.1111/aphw.12156
   PubMed Web of Science ®Google Scholar
• Schöndube, A., Kanning, M. K., & Fuchs, R. (2016). The bidirectional effect between momentary affective states and exercise duration on a day level. Frontiers in Psychology, 7, Article 1414. https://doi.org/10.3389/fpsyg.2016.01414.
   PubMed Web of Science ®Google Scholar
• Schultzberg, M., & Muthén, B. O. (2018). Number of subjects and time points needed for multilevel time-series analysis: A simulation study of dynamic structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 495–515. https://doi.org/10.1080/10705511.2017.1392862
   Web of Science ®Google Scholar
• Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21(2), 206–221. https://doi.org/10.1037/met0000062
   PubMed Web of Science ®Google Scholar
• Schuurman, N. K., & Hamaker, E. L. (2019). Measurement error and person-specific reliability in multilevel autoregressive modeling. Psychological Methods, 24(1), 70–91. https://doi.org/10.1037/met0000188
   PubMed Web of Science ®Google Scholar
• Shephard, R. J., & Aoyagi, Y. (2009). Seasonal variations in physical activity and implications for human health. European Journal of Applied Physiology, 107(3), 251–271. https://doi.org/10.1007/s00421-009-1127-1
   PubMed Web of Science ®Google Scholar
• Shrout, P. E., & Lane, S. P. (2012). Psychometrics. In M. R. Mehl, & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 302–320). Guilford Press.
   Google Scholar
• Stenling, A., Ivarsson, A., & Lindwall, M. (2017). The only constant is change: Analysing and understanding change in sport and exercise psychology research. International Review of Sport and Exercise Psychology, 10(1), 230–251. https://doi.org/10.1080/1750984X.2016.1216150
   Web of Science ®Google Scholar
• Stone, A. A., Schneider, S., & Harter, J. K. (2012). Day-of-week mood patterns in the United States: On the existence of ‘Blue Monday’, ‘Thank God it’s Friday’ and weekend effects. The Journal of Positive Psychology, 7(4), 306–314. https://doi.org/10.1080/17439760.2012.691980
   Web of Science ®Google Scholar
• Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17(1), 61–77. https://doi.org/10.1037/a0025814
   PubMed Web of Science ®Google Scholar
• Trull, T. J., & Ebner-Priemer, U. W. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9(1), 151–176. https://doi.org/10.1146/annurev-clinpsy-050212-185510
   PubMed Web of Science ®Google Scholar
• van Bork, R., van Borkulo, C. D., Waldorp, L. J., Cramer, A. O. J., & Borsboom, D. (2018). Network models for clinical psychology. In E.-J. Wagenmakers (Ed.), Stevens’ Handbook of experimental psychology and cognitive neuroscience. John Wiley & Sons, Inc. https://doi.org/10.1002/9781119170174.epcn518
   Google Scholar
• Van Cappellen, P., Rice, E. L., Catalino, L. I., & Fredrickson, B. L. (2018). Positive affective processes underlie positive health behaviour change. Psychology & Health, 33(1), 77–97. https://doi.org/10.1080/08870446.2017.1320798
   PubMed Web of Science ®Google Scholar
• van Montfort, K., Oud, J. H. L., & Voelkle, M. C. (2018). Continuous Time Modeling in the Behavioral and Related sciences. Springer International Publishing. https://doi.org/10.1007/978-3-319-77219-6
   Google Scholar
• Vieira, R., McDonald, S., Araújo-Soares, V., Sniehotta, F. F., & Henderson, R. (2017). Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. Health Psychology Review, 11(3), 222–234. https://doi.org/10.1080/17437199.2017.1343680
   PubMed Web of Science ®Google Scholar
• Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2018). The role of time in the quest for understanding psychological mechanisms. Multivariate Behavioral Research, 53(6), 782–805. https://doi.org/10.1080/00273171.2018.1496813
   PubMed Web of Science ®Google Scholar
• Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
   PubMed Web of Science ®Google Scholar
• Williams, D. M., Rhodes, R. E., & Conner, M. T. (2018). Overview of affective determinants of health behavior. In D. M. Williams, R. E. Rhodes, & M. T. Conner (Eds.), Affective demterminants of health behavior (pp. 1–18). Oxford University Press. https://doi.org/10.1093/oso/9780190499037.003.0001
   Google Scholar
• Wright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S. T., Woods, W. C., & Edershile, E. A. (2019). Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology. Psychological Assessment, 31(4), 502–515. https://doi.org/10.1037/pas0000617
   PubMed Web of Science ®Google Scholar
• Wright, A. G. C., & Woods, W. C. (2020). Personalized models of psychopathology. Annual Review of Clinical Psychology, 16(1), 49–74. https://doi.org/10.1146/annurev-clinpsy-102419-125032
   PubMedGoogle Scholar
• Wright, A. G. C., & Zimmermann, J. (2019). Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement. Psychological Assessment, 31(12), 1467–1480. https://doi.org/10.1037/pas0000685
   PubMed Web of Science ®Google Scholar
• Wu, A. D., & Zumbo, B. D. (2008). Understanding and using mediators and moderators. Social Indicators Research, 87(3), 367–392. https://doi.org/10.1007/s11205-007-9143-1
   Web of Science ®Google Scholar
• Xu, R., DeShon, R. P., & Dishop, C. R. (2020). Challenges and opportunities in the estimation of dynamic models. Organizational Research Methods, 23(4), 595–619. https://doi.org/10.1177/1094428119842638
   Web of Science ®Google Scholar
• Zhou, L., Wang, M., & Zhang, Z. (2021). Intensive longitudinal data analyses with dynamic structural equation modeling. Organizational Research Methods, 24(2), 219–250. https://doi.org/10.1177/1094428119833164
   Web of Science ®Google Scholar