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

dc.contributor.authorCevallos, Danieles_ES
dc.contributor.authorMartín, César A.es_ES
dc.contributor.authorEl Mistiri, Mohamedes_ES
dc.contributor.authorRivera, Daniel E.es_ES
dc.contributor.authorHekler, Erices_ES
dc.contributor.funderNational Science Foundation, EEUUes_ES
dc.contributor.funderNational Institutes of Health, EEUUes_ES
dc.date.accessioned2022-10-04T12:52:02Z
dc.date.available2022-10-04T12:52:02Z
dc.date.issued2022-06-29
dc.description.abstract[EN] Physical inactivity is a major contributor to morbidity and mortality worldwide. Many current physical activity behavioral interventions have shown limited success addressing the problem from a long-term perspective that includes maintenance. This paper proposes the design of a decision algorithm for a mobile and wireless health (mHealth) adaptive intervention that is based on control engineering concepts. The design process relies on a behavioral dynamical model based on Social Cognitive Theory (SCT), with a controller formulation based on hybrid model predictive control (HMPC) being used to implement the decision scheme. The discrete and logical features of HMPC coincide naturally with the categorical nature of the intervention components and the logical decisions that are particular to an intervention for physical activity. The intervention incorporates an online controller reconfiguration mode that applies changes in the penalty weights to accomplish the transition between the behavioral initiation and maintenance training stages. Controller performance is illustrated using an ARX model estimated from system identification data of a representative participant for Just Walk, a physical activity intervention designed on the basis of control systems  principles.en_EN
dc.description.abstract[ES] 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.es_ES
dc.description.accrualMethodOJSes_ES
dc.description.bibliographicCitationCevallos, D.; Martín, CA.; El Mistiri, M.; Rivera, DE.; 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):297-308. https://doi.org/10.4995/riai.2022.16798es_ES
dc.description.issue3es_ES
dc.description.referencesAdams, 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.0082901es_ES
dc.description.referencesBandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall series in social learning theory.es_ES
dc.description.referencesCarver, C. S., Scheier, M. F., 1998. On the Self Regulation of Behavior. Cambridge University Press. https://doi.org/10.1017/CBO9781139174794es_ES
dc.description.referencesChen, 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.2011.6160722es_ES
dc.description.referencesClague, J., Bernstein, L., 2012. Physical activity and cancer. Current Oncology Reports 14 (6), 550-558. https://doi.org/10.1007/s11912-012-0265-5es_ES
dc.description.referencesDeshpande, 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.2014.09.011es_ES
dc.description.referencesDong, 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.2014.6859462es_ES
dc.description.referencesEl 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.es_ES
dc.description.referencesFerster, 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.es_ES
dc.description.referencesFreigoun, 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.2017.7962940es_ES
dc.description.referencesGuillaume, 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/10.1109/19.119778es_ES
dc.description.referencesHekler, E., 2015. Just Walk Study. http://justwalkstudy.weebly.com/, [Online; accessed September-23-2015].es_ES
dc.description.referencesHekler, E., Rivera, D. E., 2021. Optimizing individualized and adaptive mhealth interventions via control systems engineering methods, R01CA244777: National Institute of Health, National Cancer Institute.es_ES
dc.description.referencesHekler, 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.8622es_ES
dc.description.referencesKha, 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.es_ES
dc.description.referencesKing, 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.0062613es_ES
dc.description.referencesKorinek, 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/s10865-017-9878-3es_ES
dc.description.referencesLjung, 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:19940169es_ES
dc.description.referencesLjung, L., 1999. System Identification: Theory for the User, 2nd Edition. Upper Saddle River, NJ: Prentice Hall PTR.es_ES
dc.description.referencesLjung, 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.2015.12.219es_ES
dc.description.referencesMartín, 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 Universityes_ES
dc.description.referencesMartín, 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.2015.7172261es_ES
dc.description.referencesMartín, 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.2015.12.315es_ES
dc.description.referencesMartín, 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.2015.7402627es_ES
dc.description.referencesMartín, 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.2016.7525468es_ES
dc.description.referencesMartín, 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.2018.2873538es_ES
dc.description.referencesMcGinnis, 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.21.2.78es_ES
dc.description.referencesMorari, M., Zafiriou, E., 1989. Robust Process Control. Prentice-Hall International.es_ES
dc.description.referencesNandola, 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.2011.2177525es_ES
dc.description.referencesNavarro-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/10.1080/13873954.2010.520409es_ES
dc.description.referencesPayne, 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.3335es_ES
dc.description.referencesPhatak, 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.2018.01.010es_ES
dc.description.referencesPillonetto, 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.2014.01.001es_ES
dc.description.referencesRivera, 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.2008.08.006es_ES
dc.description.referencesShiffman, S., Stone, A. A., Hufford, M. R., 2008. Ecological momentary assessment. Clinical Psychology 4 (1), 1-32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415es_ES
dc.description.referencesTimms, 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/10.1080/00207179.2013.874080es_ES
dc.description.referencesTimms, 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.2014.6859466es_ES
dc.description.sponsorshipEl apoyo para este trabajo ha sido proporcionado por la Fundación Nacional de Ciencias (NSF por sus siglas en inglés) a través de la subvención IIS-449751, y el Instituto Nacional de la Salud (NIH por sus siglas en inglés) a través de la subvención R01CA244777.es_ES
dc.description.upvformatpfin308es_ES
dc.description.upvformatpinicio297es_ES
dc.description.volume19es_ES
dc.identifier.doi10.4995/riai.2022.16798
dc.identifier.eissn1697-7920
dc.identifier.issn1697-7912
dc.identifier.urihttps://riunet.upv.es/handle/10251/186933
dc.languageEspañoles_ES
dc.publisherUniversitat Politècnica de Valènciaes_ES
dc.relation.ispartofRevista Iberoamericana de Automática e Informática industriales_ES
dc.relation.pasarelaOJS\16798es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/NSF//IIS-449751es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/NIH//R01CA244777es_ES
dc.relation.publisherversionhttps://doi.org/10.4995/riai.2022.16798es_ES
dc.relation.references10.1371/journal.pone.0082901es_ES
dc.relation.references10.1017/CBO9781139174794es_ES
dc.relation.references10.1109/CDC.2011.6160722es_ES
dc.relation.references10.1007/s11912-012-0265-5es_ES
dc.relation.references10.1016/j.conengprac.2014.09.011es_ES
dc.relation.references10.1109/ACC.2014.6859462es_ES
dc.relation.references10.23919/ACC53348.2022.9867350es_ES
dc.relation.references10.23919/ACC.2017.7962940es_ES
dc.relation.references10.1109/19.119778es_ES
dc.relation.references10.2196/jmir.8622es_ES
dc.relation.references10.23919/ACC53348.2022.9867669es_ES
dc.relation.references10.1371/journal.pone.0062613es_ES
dc.relation.references10.1007/s10865-017-9878-3es_ES
dc.relation.references10.1049/cp:19940169es_ES
dc.relation.references10.1016/j.ifacol.2015.12.219es_ES
dc.relation.references10.1109/ACC.2015.7172261es_ES
dc.relation.references10.1016/j.ifacol.2015.12.315es_ES
dc.relation.references10.1109/CDC.2015.7402627es_ES
dc.relation.references10.1109/ACC.2016.7525468es_ES
dc.relation.references10.1109/TCST.2018.2873538es_ES
dc.relation.references10.1377/hlthaff.21.2.78es_ES
dc.relation.references10.1109/TCST.2011.2177525es_ES
dc.relation.references10.1080/13873954.2010.520409es_ES
dc.relation.references10.2196/mhealth.3335es_ES
dc.relation.references10.1016/j.jbi.2018.01.010es_ES
dc.relation.references10.1016/j.automatica.2014.01.001es_ES
dc.relation.references10.1016/j.jprocont.2008.08.006es_ES
dc.relation.references10.1146/annurev.clinpsy.3.022806.091415es_ES
dc.relation.references10.1080/00207179.2013.874080es_ES
dc.relation.references10.1109/ACC.2014.6859466es_ES
dc.rightsReconocimiento - No comercial - Compartir igual (by-nc-sa)es_ES
dc.rights.accessRightsAbiertoes_ES
dc.subjectModel predictive control of hybrid systemses_ES
dc.subjectControl of physiological and clinical variableses_ES
dc.subjectSystem identificationes_ES
dc.subjectControl predictivo híbridoes_ES
dc.subjectControl automático de variables fisiológicas y clínicases_ES
dc.subjectIdentificación de sistemas y estimación de parámetroses_ES
dc.titleUn esquema de decisiones para intervenciones adaptativas comportamentales de actividad física basado en control predictivo por modelo híbrido: ilustración con Just Walkes_ES
dc.title.alternativeA decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control: illustration with Just Walkes_ES
dc.typeArtículoes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dspace.entity.typePublication
upv.uuidd5f6e13f-233d-4cac-8367-25a534e576dfes_ES

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