Using Directed Acyclic Graphs (DAGs) to Determine if the Total Causal Effect of an Individual Randomized Physical Activity-Promoting Intervention is Identifiable

References

• Ainsworth, B. E., Bassett, D. R., Strath, S. J., Swartz, A. M., O’Brien, W. L., Thompson, R. W., Jones, D. A., Macera, C. A., & Kimsey, C. D. (2000). Comparison of three methods for measuring the time spent in physical activity. Medicine & Science in Sports & Exercise, 32(Supplement), S457–S464. https://doi.org/10.1097/00005768-200009001-00004
   PubMedGoogle Scholar
• Bandura, A. (1977). Self-efficacy: Towards a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295x.84.2.191
   PubMed Web of Science ®Google Scholar
• Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
   Google Scholar
• Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
   PubMed Web of Science ®Google Scholar
• Bauman, A. E., Reis, R. S., Sallis, J. F., Wells, J. C., Loos, R. J. F., & Martin, B. W. (2012). Correlates of physical activity: Why are some people physically active and others not? The Lancet, 380(9838), 258–271. https://doi.org/10.1016/S0140-6736(12)60735-1
   PubMed Web of Science ®Google Scholar
• Baumgartner, T. A. (1997). Editor’s note. Measurement in Physical Education and Exercise Science, 1(2), 103–104. https://doi.org/10.1207/s15327841mpee0102_1
   Google Scholar
• Baumgartner, T. A., & Safrit, M. J. (2003). A genealogy of measurement specialists in physical education and exercise science. Measurement in Physical Education and Exercise Science, 7(2), 121–127. https://doi.org/10.1207/S15327841MPEE0702_5
   Google Scholar
• Beauchamp, M. R., Crawford, K. L., & Jackson, B. (2019). Social cognitive theory and physical activity: Mechanisms of behavior change, critique, and legacy. Psychology of Sport and Exercise, 42, 110–117. https://doi.org/10.1016/j.psychsport.2018.11.009
   Web of Science ®Google Scholar
• Berkson, J. (1946). Limitations of the application of four-fold table analysis to hospital data. Biometrics Bulletin, 2(3), 47–53. https://doi.org/10.2307/3002000
   Google Scholar
• Blalock, H. (1964). Causal inferences in nonexperimental research. University of North Carolina Press.
   Google Scholar
• Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons. https://doi.org/10.1002/9781118619179
   Google Scholar
• Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 301–328). Springer. https://doi.org/10.1007/978-94-007-6094-3_15
   Google Scholar
• Christina, R. W. (1997). Concerns and issues in studying and assessing motor learning. Measurement in Physical Education and Exercise Science, 1(1), 19–38. https://doi.org/10.1207/s15327841mpee0101_2
   Google Scholar
• Curry, S. J., Krist, A. H., Owens, D. K., Barry, M. J., Caughey, A. B., Davidson, K. W., Doubeni, C. A., Epling, J. W., Grossman, D. C., Kemper, A. R., Kubik, M., Landefeld, C. S., Mangione, C. M., Phipps, M. G., Silverstein, M., Simon, M. A., Tseng, C.-W., Wong, J. B., & U.S. Preventive Services Task Force. (2018). Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: United States preventive services task force recommendations. Journal of the American Medical Association, 320(11), 1163–1171. https://doi.org/10.1001/jama.2018.13022
   PubMed Web of Science ®Google Scholar
• Duncan, O. (1975). Introduction to structural equation models. Academic Press.
   Google Scholar
• Elwert, F. (2013). Graphical causal models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 245–273). Springer. https://doi.org/10.1007/978-94-007-6094-3_13
   Google Scholar
• Elwert, F., & Winship, C. (2014). Endogenous selection bias: The problem of conditioning on a collider variable. Annual Review of Sociology, 40(3), 31–53. https://doi.org/10.1146/annurev-soc-071913-043455
   PubMedGoogle Scholar
• Feltz, D. L., Short, S. E., & Sullivan, P. J. (2008). Self-efficacy in sport: Research and strategies for working with athletes, teams, and coaches. Human Kinetics.
   Google Scholar
• Gentle, J. E. (2003). Random number generation and Monte Carlo methods (2nd ed.). Springer.
   Google Scholar
• Gill, D. L. (1997). Measurement, statistics, and research design issues in sport and exercise psychology. Measurement in Physical Education and Exercise Science, 1(1), 39–53. https://doi.org/10.1207/s15327841mpee0101_3
   Google Scholar
• Gourlan, M., Bernard, P., Bortholon, C., Romain, A. J., Lareyre, O., Carayol, M., Ninot, G., & Bioché, J. (2016). Efficacy of theory-based interventions to promote physical activity. A meta-analysis of randomized controlled trials. Health Psychology Review, 10(1), 50–66. https://doi.org/10.1080/17437199.2014.981777
   PubMed Web of Science ®Google Scholar
• Gourlan, M. J., Trouilloud, D. O., & Sarrazin, P. G. (2011). Interventions promoting physical activity among obese populations: A meta-analysis considering global effect, long-term maintenance, physical activity indicators and dose characteristics. Obesity Reviews, 12(7), e633–e645. https://doi.org/10.1111/j.1467-789X.2011.00874.x
   PubMed Web of Science ®Google Scholar
• Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37–48. https://doi.org/10.1097/00001648-199901000-00008
   PubMed Web of Science ®Google Scholar
• Haavelmo, T. (1943). The statistical implications of a system of simultaneous equations. Econometrica, 11(1), 1–12. https://doi.org/10.2307/1905714
   Google Scholar
• Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945–960. https://doi.org/10.1080/01621459.1986.10478354
   Web of Science ®Google Scholar
• Hollis, S., & Campbell, F. (1999). What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ: British Medical Journal, 319(7211), 670–674. https://doi.org/10.1136/bmj.319.7211.670
   Google Scholar
• Jackson, B., Beauchamp, M. R., & Dimmock, J. A. (2020). Efficacy beliefs in physical activity settings: Contemporary debate and unanswered questions. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (4th ed., pp. 57–80). Wiley.
   Google Scholar
• James, C. R., & Bates, B. T. (1997). Experimental and statistical design issues in human movement research. Measurement in Physical Education and Exercise Science, 1(1), 55–69. https://doi.org/10.1207/s15327841mpee0101_4
   Google Scholar
• Jensen, B., Martin, K., & Arthur, D. (2000). Mentoring students in the process of writing a research journal manuscript. Measurement in Physical Education and Exercise Science, 4(2), 117–122. https://doi.org/10.1207/S15327841Mpee0402_6
   Google Scholar
• Kim, H., & Kang, M. (2021). A tailored domain-specific intervention using contextual information about sedentary behavior to reduce sedentary time: A bayesian approach. Measurement in Physical Education and Exercise Science, 25(2), 171–179. https://doi.org/10.1080/1091367X.2020.1862123
   Web of Science ®Google Scholar
• Koopmans, T. (1953). Identification problems in econometric model construction. In W. Hood & T. Koopmans (Eds.), Studies in econometric method (pp. 27–48). Wiley.
   Google Scholar
• Lee, S., McMahon, A., Prilleltensky, I., Myers, N. D., Dietz, S., Prilleltensky, O., Pfeiffer, K. A., Bateman, A. G., & Brincks, A. M. (2021). Effectiveness of the fun for wellness online behavioral intervention to promote well-being actions in adults with obesity: A randomized controlled trial. Journal of Sport & Exercise Psychology, 43(1), 83–96. https://doi.org/10.1123/jsep.2020-0049
   Web of Science ®Google Scholar
• Lee, S., Patel, P., Myers, N. D., Pfeiffer, K. A., Smith, A. L., & Kelly, K. S. (2023). A systematic review of eHealth interventions to promote physical activity in adults with obesity or overweight. Behavioral Medicine, 49(3), 213–230. https://doi.org/10.1080/08964289.2022.2065239
   PubMed Web of Science ®Google Scholar
• Little, R. J., & Lewis, R. J. (2021). Estimands, Estimators, and Estimates. Journal of the American Medical Association, 326(10), 967–968. https://doi.org/10.1001/jama.2021.2886
   Web of Science ®Google Scholar
• Looney, M. A. (1997). ‘Home’ improvement: The task for measurement specialists. Measurement in Physical Education and Exercise Science, 1(2), 105–116. https://doi.org/10.1207/s15327841mpee0102_2
   Google Scholar
• MacKinnon, D. (2008). Introduction to statistical mediation analysis. Erlbaum.
   Google Scholar
• Morgan, S. L. (Ed.). (2013). Handbook of causal analysis for social research. Springer Dordrecht. https://doi.org/10.1007/978-94-007-6094-3
   Google Scholar
• Muthén, B., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. https://www.statmodel.com/download/Article_075.pdf
   Google Scholar
• Muthén, L. K., & Muthén, B. O. (1998-2017). Mplus User’s Guide (8th ed.). Muthén & Muthén.
   Google Scholar
• Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
   Web of Science ®Google Scholar
• Myers, N. D., Lee, S., Bateman, A. G., Prilleltensky, I., Clevenger, K. A., Pfeiffer, K. A., Dietz, S., Prilleltensky, O., McMahon, A., & Brincks, A. M. (2019). Accelerometer-based assessment of physical activity within the fun for wellness online behavioral intervention: Protocol for a feasibility study. Pilot and Feasibility Studies, 5(1), 73. https://doi.org/10.1186/s40814-019-0455-0
   PubMedGoogle Scholar
• Myers, N. D., Lee, S., & Kostelis, K. T. (2018). Measurement in physical education and exercise science (MPEES): A brief report on 2017. Measurement in Physical Education and Exercise Science, 22(1), 1–10. https://doi.org/10.1080/1091367X.2017.1391817
   Web of Science ®Google Scholar
• Myers, N. D., McMahon, A., Prilleltensky, I., Lee, S., Dietz, S., Prilleltensky, O., Pfeiffer, K. A., Bateman, A. G., & Brincks, A. M. (2020). Effectiveness of the fun for wellness online behavioral intervention to promote physical activity in adults with obesity (or overweight): A randomized controlled trial. Journal of Medical Internet Research Formative Research, 4(2), e15919. Article e15919. https://doi.org/10.2196/15919
   Google Scholar
• Myers, N. D., Ntoumanis, N., Gunnell, K. E., Gucciardi, D. F., & Lee, S. (2018). A review of some emergent quantitative analyses in sport and exercise psychology. International Review of Sport and Exercise Psychology, 11(1), 70–100. https://doi.org/10.1080/1750984X.2017.1317356
   Web of Science ®Google Scholar
• Myers, N. D., Pacewicz, C. E., Hill, C. R., & Chun, H. (2023). Factor analysis with orderedcategorical indicators and measurement of self-efficacy in physical activity contexts: A substantive-methodological synergy. Measurement in Physical Education and Exercise Science, 27(4), 332–351. https://doi.org/10.1080/1091367X.2023.2186789
   Web of Science ®Google Scholar
• Myers, N. D., Prilleltensky, I., Lee, S., Dietz, S., Prilleltensky, O., McMahon, A., Pfeiffer, K. A., Ellithorpe, M. E., & Brincks, A. M. (2019). Effectiveness of the fun for wellness online behavioral intervention to promote well-being and physical activity: Protocol for a randomized controlled trial. BMC Public Health, 19(1), 737. https://doi.org/10.1186/s12889-019-7089-2
   PubMedGoogle Scholar
• Myers, N. D., Prilleltensky, I., McMahon, A., Lee, S., Dietz, S., Prilleltensky, O., Pfeiffer, K. A., Bateman, A. G., & Brincks, A. M. (2021). Effectiveness of the fun for wellness online behavioral intervention to promote subjective well-being in adults with obesity: A randomized controlled trial. Journal of Happiness Studies, 22(4), 1905–1923. https://doi.org/10.1007/s10902-020-00301-0
   Web of Science ®Google Scholar
• Myers, N. D., Prilleltensky, I., McMahon, A., Lee, S., Prilleltensky, O., Pfeiffer, K. A., Bateman, A. G., & Brincks, A. M. (2023). Mechanisms by which the fun for wellness intervention may promote subjective well-being in adults with obesity: A reanalysis using baseline target moderation. Prevention Science, 24(2), 286–298. https://doi.org/10.1007/s11121-021-01274-z
   PubMed Web of Science ®Google Scholar
• Myers, N. D., Prilleltensky, I., Prilleltensky, O., McMahon, A., Dietz, S., & Rubenstein, C. L. (2017). Efficacy of the fun for wellness online intervention to promote multidimensional well-being: A randomized controlled trial. Prevention Science, 18(8), 984–994. https://doi.org/10.1007/s11121-017-0779-z
   PubMed Web of Science ®Google Scholar
• Neyman, J. (1923). Sur les applications de la thar des probabilities aux experiences agaricales: Essay des principle. English translation of excerpts (1990) by D. Dabrowska and T. Speed. Statistical Science, 5, 463–472.
   Google Scholar
• Ntoumanis, N., & Myers, N. D. (Eds.). (2016). An introduction to intermediate and advanced statistical analyses for sport and exercise scientists. John Wiley & Sons.
   Google Scholar
• Pacewicz, C. E., & Myers, N. D. (2021). Latent growth curve modeling in exercise science. Measurement in Physical Education and Exercise Science, 25(1), 53–65. https://doi.org/10.1080/1091367X.2020.1803331
   Web of Science ®Google Scholar
• Palmer, K. K., Stodden, D. F., Ulrich, D. A., & Robinson, L. E. (2021). Using process- and product-oriented measures to evaluate changes in motor skills across an intervention. Measurement in Physical Education and Exercise Science, 25(3), 273–282. https://doi.org/10.1080/1091367X.2021.1876069
   PubMed Web of Science ®Google Scholar
• Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 29(3), 241–288. https://doi.org/10.1016/0004-3702(86)90072-x
   Web of Science ®Google Scholar
• Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Morgan Kaufmann.
   Google Scholar
• Pearl, J. (1993). Comment: Graphical models, causality, and intervention. Statistical Science, 8(3), 266–269. https://doi.org/10.1214/ss/1177010894
   Google Scholar
• Pearl, J. (1995). Causal diagrams for empirical research. Biometrika, 82(4), 669–688. https://doi.org/10.1093/biomet/82.4.669
   Web of Science ®Google Scholar
• Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research, 27(2), 226–284. https://doi.org/10.1177/0049124198027002004
   Web of Science ®Google Scholar
• Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
   Google Scholar
• Pearl, J. (2001). Direct and indirect effects. In J. Breese & D. Koller (Eds.), Uncertainty in artificial intelligence: Proceedings of the seventeenth conference (pp. 411–420). Morgan Kaufmann.
   Google Scholar
• Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
   Google Scholar
• Pearl, J. (2012). The causal foundations of structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 68–91). Guilford Press. https://doi.org/10.21236/ada557445
   Google Scholar
• Pearl, J. (2013). Linear models: A useful “microscope” for causal analysis. Journal of Causal Inference, 1(1), 155–170. https://doi.org/10.1515/jci-2013-0003
   Google Scholar
• Pearl, J. (2017). A linear “microscope” for interventions and counterfactuals. Journal of Causal Inference, 5(1), 155–170. Article 2017003. https://doi.org/10.1515/jci-2017-0003
   Web of Science ®Google Scholar
• Pearl, J. (2023). The causal foundations of structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed., pp. 49–75). Guilford Press. https://doi.org/10.21236/ada557445
   Google Scholar
• Pfeiffer, K. A., Lisee, C., Westgate, B. S., Kalfsbeek, C., Kuenze, C., Bell, D., Cadmus-Bertram, L., & Montoye, A. H. K. (2023). Using accelerometers to detect activity type in a sport setting: Challenges with using multiple types of conventional machine learning approaches. Measurement in Physical Education and Exercise Science, 27(1), 60–72. https://doi.org/10.1080/1091367X.2022.2069467
   Web of Science ®Google Scholar
• Rikli, R. E. (1997). Measurement challenges in assessing special populations: Implications for behavioral research in adapted physical activity. Measurement in Physical Education and Exercise Science, 1(2), 117–126. https://doi.org/10.1207/s15327841mpee0102_3
   Google Scholar
• Robins, J., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. https://doi.org/10.1097/00001648-199203000-00013
   PubMed Web of Science ®Google Scholar
• Rosenbaum, P. R. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society Series A: Statistics in Society, 147(5), 656–666. https://doi.org/10.2307/2981697
   Web of Science ®Google Scholar
• Rubenstein, C. L., Duff, J., Prilleltensky, I., Jin, Y., Dietz, S., Myers, N. D., & Prilleltensky, O. (2016). Demographic group differences in domain-specific well-being. Journal of Community Psychology, 44(4), 499–515. https://doi.org/10.1002/jcop.21784
   Web of Science ®Google Scholar
• Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. https://doi.org/10.1037/h0037350
   Web of Science ®Google Scholar
• Scarpa, M. P., Prilleltensky, I., McMahon, A., Myers, N. D., Prilleltensky, O., Lee, S., Pfeiffer, K. A., Bateman, A. G., & Brincks, A. M. (2021). Is fun for wellness engaging? Evaluation of user experience of an online intervention to promote well-being and physical activity. Frontiers in Computer Science, 3. Article 690389. https://doi.org/10.3389/fcomp.2021.690389
   Web of Science ®Google Scholar
• Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. Springer. https://doi.org/10.1007/978-1-4612-2748-9
   Google Scholar
• Steiger, J. H. (2001). Driving fast in reverse: The relationship between software development, theory, and education in structural equation modeling. Journal of the American Statistical Association, 96(453), 331–338. https://doi.org/10.1198/016214501750332893
   Web of Science ®Google Scholar
• Textor, J., Hardt, J., & Knüppel, S. (2011). A graphical tool for analyzing causal diagrams. Epidemiology, 22(5), 745. https://doi.org/10.1097/EDE.0b013e318225c2be
   PubMed Web of Science ®Google Scholar
• Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H. (2016). Robust causal inference using directed acyclic graphs: The R package ‘dagitty. International Journal of Epidemiology, 45(6), 1887–1894. https://doi.org/10.1093/ije/dyw341
   PubMed Web of Science ®Google Scholar
• U.S. Department of Health and Human Services. (2013). Managing Overweight and Obesity in Adults: Systematic Evidence Review from the Obesity Expert Panel. https://www.nhlbi.nih.gov/sites/default/files/media/docs/obesity-evidence-review.pdf
   Google Scholar
• U.S. Department of Health and Human Services: 2018 Physical activity guidelines advisory committee. (2018). 2018 physical activity guidelines advisory committee scientific report. https://health.gov/paguidelines/second-edition/report/
   Google Scholar
• VanderWeele, T., & Robins, J. (2007). Four types of effect modification: A classification based on directed acyclic graphs. Epidemiology, 18(5), 561–568. https://doi.org/10.1097/ede.0b013e318127181b
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
   Google Scholar
• World Health Organization. (2018). Obesity and Overweight Fact Sheet. http://www.who.int/mediacentre/factsheets/fs311/en/
   Google Scholar
• World Health Organization. (2020). Guidelines on Physical Activity and Sedentary Behaviour. https://www.who.int/publications/i/item/9789240015128
   Google Scholar
• Wright, S. S. (1921). Correlation and causation. Journal of Agricultural Research, 20(3), 557–585.
   Google Scholar