Resilience characterized and quantified from physical activity data
a tutorial in R
DOI:
https://doi.org/10.51224/SRXIV.168Keywords:
Resilience, Physical Activity, time series, R tutorial, AUC, wearable devicesAbstract
Objective: Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on daily physical activity. In this tutorial, we present a method to quantify the resilience process for physical activity data. We leverage the prior operationalization of resilience as area under the curve and expand it to suit the characteristics of physical activity time series. Methods: As use case to illustrate the methodology, we quantified resilience in step count for eight participants following the first COVID-19 lockdown as a stressor. Steps were collected daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. Results: For each person’s step count time series we fitted multiple growth models and identified the best one using the Bayesian information criterion (BIC). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. Conclusions: By developing a methodological guide with a step-by-step implementation in R, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.
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References
Baggio, J., Brown, K., & Hellebrandt, D. (2015). Boundary object or bridging concept? A citation network analysis of resilience. Ecology and Society, 20(2). https://doi.org/10.5751/ES-07484-200202
Bernard, P., Chevance, G., Kingsbury, C., Baillot, A., Romain, A.-J., Molinier, V., Gadais, T., & Dancause, K. N. (2021). Climate Change, Physical Activity and Sport: A Systematic Review. Sports Medicine (Auckland, N.Z.), 51(5), 1041–1059. https://doi.org/10.1007/s40279-021-01439-4
Blozis, S. A., & Cudeck, R. (1999). Conditionally Linear Mixed-Effects Models with Latent Variable Covariates. Journal of Educational and Behavioral Statistics, 24(3), 245–270. https://doi.org/10.2307/1165324
Bryan, C., O’Shea, D., & MacIntyre, T. (2019). Stressing the relevance of resilience: A systematic review of resilience across the domains of sport and work. International Review of Sport and Exercise Psychology, 12(1), 70–111. https://doi.org/10.1080/1750984X.2017.1381140
Carver, C. S. (1998). Resilience and Thriving: Issues, Models, and Linkages. Journal of Social Issues, 54(2), 245–266. https://doi.org/10.1111/j.1540-4560.1998.tb01217.x
Chakrabarti, A., & Ghosh, J. K. (2011). AIC, BIC and Recent Advances in Model Selection. In P. S. Bandyopadhyay & M. R. Forster (Eds.), Philosophy of Statistics (Vol. 7, pp. 583–605). North-Holland. https://doi.org/10.1016/B978-0-444-51862-0.50018-6
Chevance, G., Baretta, D., Golaszewski, N., Takemoto, M., Shrestha, S., Jain, S., Rivera, D. E., Klasnja, P., & Hekler, E. (2021). Goal Setting and Achievement for Walking: A Series of N-of-1 Digital Interventions. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association, 40(1), 30–39. https://doi.org/10.1037/hea0001044
Chevance, G., Baretta, D., Heino, M., Perski, O., Olthof, M., Klasnja, P., Hekler, E., & Godino, J. (2021). Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior. PLOS ONE, 16(5), e0251659. https://doi.org/10.1371/journal.pone.0251659
Chevance, G., Baretta, D., Romain, A. J., Godino, J. G., & Bernard, P. (2022). Day-to-day associations between sleep and physical activity: A set of person-specific analyses in adults with overweight and obesity. Journal of Behavioral Medicine, 45(1), 14–27. https://doi.org/10.1007/s10865-021-00254-6
Chevance, G., Perski, O., & Hekler, E. B. (2021). Innovative methods for observing and changing complex health behaviors: Four propositions. Translational Behavioral Medicine, 11(2), 676–685. https://doi.org/10.1093/tbm/ibaa026
Corder, K., Winpenny, E. M., Foubister, C., Guagliano, J. M., Hartwig, X. M., Love, R., Clifford Astbury, C., & van Sluijs, E. M. F. (2020). Becoming a parent: A systematic review and meta-analysis of changes in BMI, diet, and physical activity. Obesity Reviews, 21(4), e12959. https://doi.org/10.1111/obr.12959
Costello, V. L., Chevance, G., Wing, D., Mansour-Assi, S. J., Sharp, S., Golaszewski, N. M., Young, E. A., Higgins, M., Ibarra, A., Larsen, B., & Godino, J. G. (2021). Impact of the COVID-19 Pandemic on Objectively Measured Physical Activity and Sedentary Behavior Among Overweight Young Adults: Yearlong Longitudinal Analysis. JMIR Public Health and Surveillance, 7(11), e28317. https://doi.org/10.2196/28317
Den Hartigh, R. J. R., & Hill, Y. (2022). Conceptualizing and measuring psychological resilience: What can we learn from physics? New Ideas in Psychology, 66, 100934. https://doi.org/10.1016/j.newideapsych.2022.100934
Devoogdt, N., Van Kampen, M., Geraerts, I., Coremans, T., Fieuws, S., Lefevre, J., Philippaerts, R., Truijen, S., Neven, P., & Christiaens, M.-R. (2010). Physical activity levels after treatment for breast cancer: One-year follow-up. Breast Cancer Research and Treatment, 123(2), 417–425. https://doi.org/10.1007/s10549-010-0997-6
Engberg, E., Alen, M., Kukkonen-Harjula, K., Peltonen, J. E., Tikkanen, H. O., & Pekkarinen, H. (2012). Life Events and Change in Leisure Time Physical Activity. Sports Medicine, 42(5), 433–447. https://doi.org/10.2165/11597610-000000000-00000
Estrada, A. X., Severt, J. B., & Jiménez-Rodríguez, M. (2016). Elaborating on the Conceptual Underpinnings of Resilience. Industrial and Organizational Psychology, 9(2), 497–502. https://doi.org/10.1017/iop.2016.46
Gerber, M., Ludyga, S., Mücke, M., Colledge, F., Brand, S., & Pühse, U. (2017). Low vigorous physical activity is associated with increased adrenocortical reactivity to psychosocial stress in students with high stress perceptions. Psychoneuroendocrinology, 80, 104–113. https://doi.org/10.1016/j.psyneuen.2017.03.004
Grimm, K. J., & Ram, N. (2009). Non-linear Growth Models in Mplus and SAS. Structural Equation Modeling : A Multidisciplinary Journal, 16(4), 676–701. https://doi.org/10.1080/10705510903206055
Hill, Y., Den Hartigh, R. J. R., Meijer, R. R., De Jonge, P., & Van Yperen, N. W. (2018). Resilience in sports from a dynamical perspective. Sport, Exercise, and Performance Psychology, 7(4), 333–341. https://doi.org/10.1037/spy0000118
Hill, Y., Van Yperen, N. W., & Den Hartigh, R. J. R. (2021). Facing Repeated Stressors in a Motor Task: Does it Enhance or Diminish Resilience? Journal of Motor Behavior, 53(6), 717–726. https://doi.org/10.1080/00222895.2020.1852155
Hooker, S. A., Oswald, L. B., Reid, K. J., & Baron, K. G. (2020). Do Physical Activity, Caloric Intake, and Sleep Vary Together Day to Day? Exploration of Intraindividual Variability in 3 Key Health Behaviors. Journal of Physical Activity and Health, 17(1), 45–51. https://doi.org/10.1123/jpah.2019-0207
Kogevinas, M., Castaño-Vinyals, G., Karachaliou, M., Espinosa, A., de Cid, R., Garcia-Aymerich, J., Carreras, A., Cortés, B., Pleguezuelos, V., Jiménez, A., Vidal, M., O’Callaghan-Gordo, C., Cirach, M., Santano, R., Barrios, D., Puyol, L., Rubio, R., Izquierdo, L., Nieuwenhuijsen, M., … Tonne, C. (2021). Ambient Air Pollution in Relation to SARS-CoV-2 Infection, Antibody Response, and COVID-19 Disease: A Cohort Study in Catalonia, Spain (COVICAT Study). Environmental Health Perspectives, 129(11), 117003. https://doi.org/10.1289/EHP9726
Kuranova, A., Booij, S. H., Menne-Lothmann, C., Decoster, J., van Winkel, R., Delespaul, P., De Hert, M., Derom, C., Thiery, E., Rutten, B. P. F., Jacobs, N., van Os, J., Wigman, J. T. W., & Wichers, M. (2020). Measuring resilience prospectively as the speed of affect recovery in daily life: A complex systems perspective on mental health. BMC Medicine, 18(1), 36. https://doi.org/10.1186/s12916-020-1500-9
Larson, E. A., Bader-Larsen, K. S., & Magkos, F. (2021). The Effect of COVID-19-related Lockdowns on Diet and Physical Activity in Older Adults: A Systematic Review. Aging and Disease, 12(8), 1935–1947. https://doi.org/10.14336/AD.2021.0606
Lee, I.-M., Shiroma, E. J., Lobelo, F., Puska, P., Blair, S. N., & Katzmarzyk, P. T. (2012). Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. The Lancet, 380(9838), 219–229. https://doi.org/10.1016/S0140-6736(12)61031-9
Liu, Z., Maneekul, P., Pendergrast, C., Doubleday, A., Miles, S. B., Errett, N. A., & Choe, Y. (2022). Physical activity monitoring data following disasters. Sustainable Cities and Society, 81, 103814. https://doi.org/10.1016/j.scs.2022.103814
McKee, G., Mooney, M., O’Donnell, S., O’Brien, F., Biddle, M. J., & Moser, D. K. (2019). A cohort study examining the factors influencing changes in physical activity levels following an acute coronary syndrome event. European Journal of Cardiovascular Nursing, 18(1), 57–66. https://doi.org/10.1177/1474515118786203
Namazi, H. (2021). COMPLEXITY AND INFORMATION-BASED ANALYSIS OF THE HEART RATE VARIABILITY (HRV) WHILE SITTING, HAND BIKING, WALKING, AND RUNNING. Fractals, 29(05), 2150201. https://doi.org/10.1142/S0218348X21502017
Obón-Santacana, M., Vilardell, M., Carreras, A., Duran, X., Velasco, J., Galván-Femenía, I., Alonso, T., Puig, L., Sumoy, L., Duell, E. J., Perucho, M., Moreno, V., & Cid, R. de. (2018). GCAT|Genomes for life: A prospective cohort study of the genomes of Catalonia. BMJ Open, 8(3), e018324. https://doi.org/10.1136/bmjopen-2017-018324
Olthof, M., Hasselman, F., Strunk, G., van Rooij, M., Aas, B., Helmich, M. A., Schiepek, G., & Lichtwarck-Aschoff, A. (2020). Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clinical Psychological Science, 8(1), 25–35. https://doi.org/10.1177/2167702619865969
Oud, J. H. L., & Voelkle, M. C. (2014). Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling. Quality & Quantity, 48(6), 3271–3288. https://doi.org/10.1007/s11135-013-9955-9
Pruessner, J. C., Kirschbaum, C., Meinlschmid, G., & Hellhammer, D. H. (2003). Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology, 28(7), 916–931. https://doi.org/10.1016/S0306-4530(02)00108-7
R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ram, N., & Grimm, K. (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31(4), 303–316. https://doi.org/10.1177/0165025407077751
Scheffer, M., Bolhuis, J. E., Borsboom, D., Buchman, T. G., Gijzel, S. M. W., Goulson, D., Kammenga, J. E., Kemp, B., van de Leemput, I. A., Levin, S., Martin, C. M., Melis, R. J. F., van Nes, E. H., Romero, L. M., & Olde Rikkert, M. G. M. (2018). Quantifying resilience of humans and other animals. Proceedings of the National Academy of Sciences, 115(47), 11883–11890. https://doi.org/10.1073/pnas.1810630115
Stadnitski, T., & Wild, B. (2019). How to Deal With Temporal Relationships Between Biopsychosocial Variables: A Practical Guide to Time Series Analysis. Psychosomatic Medicine, 81(3), 289–304. https://doi.org/10.1097/PSY.0000000000000680
Wagner, A. L., Keusch, F., Yan, T., & Clarke, P. J. (2019). The impact of weather on summer and winter exercise behaviors. Journal of Sport and Health Science, 8(1), 39–45. https://doi.org/10.1016/j.jshs.2016.07.007
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Winter, B., & Wieling, M. (2016). How to analyze linguistic change using mixed models, Growth Curve Analysis and Generalized Additive Modeling. Journal of Language Evolution, 1(1), 7–18. https://doi.org/10.1093/jole/lzv003
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(1), 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279
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Copyright (c) 2022 Dario Baretta, Sarah Koch, Ines Cobo, Gemma Castaño-Vinyals, Rafael de Cid, Anna Carreras, Joren Buekers, Judith Garcia-Aymerich, Jennifer Inauen, Guillaume Chevance
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