Un esquema de decisiones para intervenciones adaptativas comportamentales de actividad física basado en control predictivo por modelo híbrido: ilustración con Just Walk

Autores/as

DOI:

https://doi.org/10.4995/riai.2022.16798

Palabras clave:

Control predictivo híbrido, control automático de variables fisiológicas y clínicas, identificación de sistemas y estimación de parámetros

Resumen

La inactividad física es uno de los principales factores que contribuyen a la morbilidad y la mortalidad en todo el mundo. Muchas intervenciones comportamentales de actividad física en la actualidad han mostrado un éxito limitado al abordar el problema desde una perspectiva a largo plazo que incluye el mantenimiento. Este artículo propone el diseño de un algoritmo de decisión para una intervención adaptativa de salud móvil e inalámbrica (mHealth) que se basa en conceptos de ingeniería de control. El proceso de diseño se basa en un modelo dinámico que representa el comportamiento basada en la Teoría Cognitiva Social (TCS), con una formulación de controlador fundamentada en el control predictivo por modelo híbrido (HMPC por sus siglas en inglés) la cual se utiliza para implementar el esquema de decisión. Las características discretas y lógicas del HMPC coinciden naturalmente con la naturaleza categórica de los componentes de la intervención y las decisiones lógicas que son propias de una intervención para actividad física. La intervención incorpora un modo de reconfiguración del controlador en línea que aplica cambios en los pesos de penalización para lograr la transición entre las etapas de entrenamiento de iniciación comportamental y mantenimiento. Resultados de simulación se presentan para ilustrar el desempeño del controlador utilizando un modelo ARX estimado de datos de un participante representativo de Just Walk, una intervención de actividad física diseñada usando principios de sistemas de control.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Daniel Cevallos, Escuela Superior Politécnica del Litoral (ESPOL)

Facultad de Ingeniería en Electricidad y Computación

César A. Martín, Escuela Superior Politécnica del Litoral (ESPOL)

Facultad de Ingeniería en Electricidad y Computación

Mohamed El Mistiri, Arizona State University

School for the Engineering of Matter, Transport, and Energy

Daniel E. Rivera, Arizona State University

School for the Engineering of Matter, Transport, and Energy

Eric Hekler, University of California - San Diego

Herbert Wertheim School of Public Health and Human Longevity Science

Citas

Adams, M. A., Sallis, J. F., Norman, G. J., Hovell, M. F., Hekler, E. B., Perata, E., 2013. An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PloS one 8 (12), e82901. https://doi.org/10.1371/journal.pone.0082901

Bandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall series in social learning theory.

Carver, C. S., Scheier, M. F., 1998. On the Self Regulation of Behavior. Cambridge University Press. https://doi.org/10.1017/CBO9781139174794

Chen, T., Ohlsson, H., Goodwin, G. C., Ljung, L., 2011. Kernel selection in linear system identification part ii: A classical perspective. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference. IEEE, pp. 4326-4331. https://doi.org/10.1109/CDC.2011.6160722

Clague, J., Bernstein, L., 2012. Physical activity and cancer. Current Oncology Reports 14 (6), 550-558. https://doi.org/10.1007/s11912-012-0265-5

Deshpande, S., Nandola, N. N., Rivera, D. E., Younger, J. W., 2014. Optimized treatment of fibromyalgia using system identification and hybrid model predictive control. Control Engineering Practice 33 (1), 161-173. https://doi.org/10.1016/j.conengprac.2014.09.011

Dong, Y., Deshpande, S., Rivera, D. E., Downs, D. S., Savage, J. S., 2014. Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. In: Proc. ACC. pp. 4198-4203. https://doi.org/10.1109/ACC.2014.6859462

El Mistiri, M., Rivera, D. E., Klasnja, P., Park, J., Hekler, E. B., 2022. Model predictive control strategies for optimized mhealth interventions for physical activity. In: 2022 American Control Conference (ACC). submitted.

Ferster, C. B., 1970. Schedules of reinforcement with Skinner. In: Dews, P. B. (Ed.), Festschrift for B. F. Skinner. Century psychology series. New York, Appleton-Century-Crofts, pp. 37-46.

Freigoun, M. T., Mart ́ın, C. A., Magann, A. B., Rivera, D. E., Phatak, S. S., Korinek, E. V., Hekler, E. B., 2017. System identification of just walk: A behavioral mhealth intervention for promoting physical activity. In: 2017 American Control Conference (ACC). pp. 116-121. https://doi.org/10.23919/ACC.2017.7962940

Guillaume, P., Schoukens, J., Pintelon, R., Kollar, I., 1991. Crest-factor minimization using nonlinear Chebyshev approximation methods. IEEE Transactions on Instrumentation and Measurement 40 (6), 982-989. https://doi.org/10.1109/19.119778

Hekler, E., 2015. Just Walk Study. http://justwalkstudy.weebly.com/, [Online; accessed September-23-2015].

Hekler, E., Rivera, D. E., 2021. Optimizing individualized and adaptive mhealth interventions via control systems engineering methods, R01CA244777: National Institute of Health, National Cancer Institute.

Hekler, E. B., Rivera, D. E., Martin, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E., Klasnja, P., Adams, M. A., Buman, M. P., 2018. Tutorial for using control systems engineering to optimize adaptive mobile health interventions. Journal of medical Internet research 20 (6), e8622. https://doi.org/10.2196/jmir.8622

Kha, R., Rivera, D. E., Klasjna, P., Hekler, E., 2022. Model personalization in behavioral interventions using Model-on-Demand estimation and discrete simultaneous perturbation stochastic approximation. In: 2022 American Control Conference (ACC). p. submitted.

King, A. C., Hekler, E. B., Grieco, L. A., Winter, S. J., Sheats, J.L., Buman, M. P., Banerjee, B., Robinson, T. N., Cirimele, J., 2013. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS ONE 8 (4), e62613. https://doi.org/10.1371/journal.pone.0062613

Korinek, E. V., Phatak, S. S., Martin, C. A., Freigoun, M. T., Rivera, D. E., Adams, M. A., Klasnja, P., Buman, M. P., Hekler, E. B., 2018. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. Journal of behavioral medicine 41 (1), 74-86. https://doi.org/10.1007/s10865-017-9878-3

Ljung, L., Mar 1994. From Data to Model: A Guided Tour. In: International IEE Conference on Control. Warwick, England, pp. 422-430. https://doi.org/10.1049/cp:19940169

Ljung, L., 1999. System Identification: Theory for the User, 2nd Edition. Upper Saddle River, NJ: Prentice Hall PTR.

Ljung, L., Singh, R., Chen, T., 2015. Regularization features in the system identification toolbox. IFAC-PapersOnLine 48 (28), 745-750, 17th IFAC Symposium on System Identification SYSID 2015. https://doi.org/10.1016/j.ifacol.2015.12.219

Martín, C. A., 2016. A system identification and control engineering approachfor optimizing mhealth behavioral interventions based on social cognitivetheory. Ph.D. thesis, Electrical Engineering, Arizona State University

Martín, C. A., Deshpande, S., Hekler, E. B., Rivera, D. E., 2015a. A system identification approach for improving behavioral interventions based on Social Cognitive Theory. In: Proc. ACC. pp. 5878-5883. https://doi.org/10.1109/ACC.2015.7172261

Martín, C. A., Rivera, D. E., Hekler, E. B., 2015b. Design of informative identification experiments for behavioral interventions. In: Proceedings of the 17th IFAC Symposium on System Identification. pp. 1325-1330. https://doi.org/10.1016/j.ifacol.2015.12.315

Martín, C. A., Rivera, D. E., Hekler, E. B., 2015c. An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. In: Proc. 54th IEEE CDC. pp. 2719-2724. https://doi.org/10.1109/CDC.2015.7402627

Martín, C. A., Rivera, D. E., Hekler, E. B., 2016. A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control. In: Proceedings of the American Control Conference. pp. 3576-3581. https://doi.org/10.1109/ACC.2016.7525468

Martín, C. A., Rivera, D. E., Hekler, E. B., Riley, W. T., Buman, M. P., Adams, M. A., Magann, A. B., 2020. Development of a control-oriented model of Social Cognitive Theory for optimized mHealth behavioral interventions. IEEE Transactions on Control Systems Technology 28 (2), 331-346. https://doi.org/10.1109/TCST.2018.2873538

McGinnis, J. M., Williams-Russo, P., Knickman, J. R., 2002. The case for more active policy attention to health promotion. Health Affairs 21 (2), 78-93. https://doi.org/10.1377/hlthaff.21.2.78

Morari, M., Zafiriou, E., 1989. Robust Process Control. Prentice-Hall International.

Nandola, N. N., Rivera, D. E., 2013. An improved formulation of hybrid model predictive control with application to production-inventory systems. IEEE Trans. on Control Systems Technology 21 (1), 121-135. https://doi.org/10.1109/TCST.2011.2177525

Navarro-Barrientos, J. E., Rivera, D. E., Collins, L. M., 2011. A dynamical model for describing behavioural interventions for weight loss and body composition change. Mathematical and Computer Modelling of Dynamical Systems 17 (2), 183-203. https://doi.org/10.1080/13873954.2010.520409

Payne, H. E., Lister, C., West, J. H., Bernhardt, J. M., 2015. Behavioral functionality of mobile apps in health interventions: A systematic review of the literature. JMIR mHealth and uHealth 3 (1), e20. https://doi.org/10.2196/mhealth.3335

Phatak, S. S., Freigoun, M. T., Martín, C. A., Rivera, D. E., Korinek, E. V., Adams, M. A., Buman, M. P., Klasnja, P., Hekler, E. B., 2018. Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention. Journal of Biomedical Informatics 79, 82-97. https://doi.org/10.1016/j.jbi.2018.01.010

Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L., 2014. Kernel methods in system identification, machine learning and function estimation: A survey. Automatica 50 (3), 657-682. https://doi.org/10.1016/j.automatica.2014.01.001

Rivera, D. E., Lee, H., Mittelmann, H. D., Braun, M. W., 2009. Constrained multisine input signals for plant-friendly identification of chemical process systems. Journal of Process Control 19 (4), 623-635. https://doi.org/10.1016/j.jprocont.2008.08.006

Shiffman, S., Stone, A. A., Hufford, M. R., 2008. Ecological momentary assessment. Clinical Psychology 4 (1), 1-32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415

Timms, K. P., Rivera, D. E., Collins, L. M., Piper, M. E., 2014a. Continuous-time system identification of a smoking cessation intervention,. International Journal of Control 87 (7), 1423-1437. https://doi.org/10.1080/00207179.2013.874080

Timms, K. P., Rivera, D. E., Piper, M. E., Collins, L. M., 2014b. A hybrid model predictive control strategy for optimizing a smoking cessation intervention. In: Proc. ACC. pp. 2389 - 2394. https://doi.org/10.1109/ACC.2014.6859466

Descargas

Publicado

27-04-2022

Cómo citar

Cevallos, D., Martín, C. A., El Mistiri, M., Rivera, D. E. y Hekler, E. (2022) «Un esquema de decisiones para intervenciones adaptativas comportamentales de actividad física basado en control predictivo por modelo híbrido: ilustración con Just Walk», Revista Iberoamericana de Automática e Informática industrial, 19(3), pp. 297–308. doi: 10.4995/riai.2022.16798.

Número

Sección

Sección especial: “Técnicas de control y optimización como solución a problemas de la sociedad”

Datos de los fondos