A computational model for identifying behavioral patterns in people with neuropsychiatric disorders
Keywords:Context-Aware Computing, Context History, Similarity Analysis, Ubiquitous Computing
Neuropsychiatric disorders represent a great economic and social burden, whose estimates range from 14% to 32% of the global burden of diseases. One in four people suffer from mental disorders during their lifetime and the estimates of people suffering from depression and some type of neuropsychiatric disorder are about 322 million and 420 million, respectively. Therefore, this paper presents Eigenroutines, a model based on the hypothesis that the ubiquitous availability of mobile devices and the massive usage of these devices have created an opportunity of identifying behavior, supporting the management of neuropsychiatric disorders. The model is also capable of building psychiatric epidemiological profiles, using context histories. The model evaluation occurred in three steps, where the first confirmed that the model has the ability to identify and distinguish neuropsychiatric disorders, analyzing context histories from a group of 55 individuals, composed of 23 people with unipolar or bipolar depression and 32 healthy people. The model presented an accuracy of 78% for classifying the routine of an individual in depressed or healthy. The second step evaluated the extraction of epidemiological profiles based on context histories from 1066 individuals stored by the platform. Finally, the last step evaluated the limitations of the model regarding temporal complexity and scalability.
D. Vigo, G. Thornicroft, and R. Atun, “Estimating the true global burden of mental illness,” The Lancet Psych., vol. 3, no. 2, pp. 171–178, 2016. https://doi.org/10.1016/S2215-0366(15)00505-2.
World Health Organization, “Depression and Other Common Mental Disorders: Global Health Estimates,” tech. rep., 2017.
J. H. Da Rosa, J. L. Barbosa, and G. D. Ribeiro, “ORACON: An adaptive model for context prediction,” Expert Systems with App., vol. 45, pp. 56–70, 2016. https://doi.org/10.1016/j.eswa.2015.09.016.
J. Hong, E.-H. Suh, J. Kim, and S. Kim, “Context-aware system for proactive personalized service based on context history,” Expert Systems with App., vol. 36, no. 4, pp. 7448–7457, 2009. https://doi.org/10.1016/j.eswa.2008.09.002.
A. S. Filippetto, R. Lima, and J. L. V. Barbosa, “A risk prediction model for software project management based on similarity analysis of context histories,” Inf. and Software Technology, vol. 131, 2021. https://doi.org/10.1016/j.infsof.2020.106497.
J. A. S. Aranda, R. S. Bavaresco, J. V. de Carvalho, A. C. Yamin, M. C. Tavares, and J. L. V. Barbosa, “A computational model for adaptive recording of vital signs through context histories,” J. of Ambient Intell. and Hum. Comp., pp. 1–15, 2021. https://doi.org/10.1007/s12652-021-03126-8.
B. G. Martini, G. A. Helfer, J. L. V. Barbosa, R. C. Espinosa Modolo, M. R. da Silva, R. M. de Figueiredo, A. S. Mendes, L. A. Silva, and V. R. Q. Leithardt, “Indoorplant: A model for intelligent services in indoor agriculture based on context histories,” Sensors, vol. 21, no. 5, 2021. https://doi.org/10.3390/s21051631.
O. B. Sezer, E. Dogdu, and A. M. Ozbayoglu, “Context-aware computing, learning, and big data in internet of things: A survey,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 1–27, 2018. https://doi.org/10.1109/JIOT.2017.2773600.
P. C. Buttenbender, J. L. V. Barbosa, and M. G. Martins, “Ubiquitous computing applied to mental health: Trends and research focus,” in Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, WebMedia ’18, (New York, NY, USA), p. 73–76, Association for Computing Machinery, 2018. https://doi.org/10.1145/3243082.3267456.
N. Palmius, M. Osipov, A. C. Bilderbeck, G. M. Goodwin, K. Saunders, A. Tsanas, and G. D. Clifford, “A multi-sensor monitoring system for objective mental health management in resource constrained environments,” in Appropriate Healthcare Technologies for Low Resource Settings (AHT 2014), pp. 1–4, 2014. https://doi.org/10.1049/cp.2014.0764.
M. Dang, C. Mielke, A. Diehl, and R. Haux, “Accompanying depression with fine - a smartphone-based approach,” Studies in health tech. and informatics, vol. 228, pp. 195–9, 2016.
N. Watanabe, M. Horikoshi, M. Yamada, S. Shimodera, T. Akechi, K. Miki, M. Inagaki, N. Yonemoto, H. Imai, A. Tajika, Y. Ogawa, N. Takeshima, Y. Hayasaka, and T. A. Furukawa, “Adding smartphonebased cognitive-behavior therapy to pharmacotherapy for major depression (FLATT project): study protocol for a randomized controlled trial,” Trials, vol. 16, no. 293, pp. 1–13, 2015. https://doi.org/10.1186/s13063-015-0805-z.
D. C. Mohr, E. Montague, C. Stiles-Shields, S. M. Kaiser, C. Brenner, E. Carty-Fickes, H. Palac, and J. Duffecy, “Medlink: A mobile intervention to address failure points in the treatment of depression in general medicine,” in 2015 9th Int. Conf. on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 100–107, 2015. https://doi.org/10.4108/icst.pervasivehealth.2015.259042.
M. Faurholt-Jepsen, M. Vinberg, M. Frost, E. M. Christensen, J. E. Bardram, and L. V. Kessing, “Smartphone data as an electronic biomarker of illness activity in bipolar disorder,” Bipolar Disorders, vol. 17, no. 7, pp. 715–728, 2015. https://doi.org/10.1111/bdi.12332.
O. Mayora, B. Arnrich, J. Bardram, C. Drager, A. Finke, M. Frost, S. Giordano, F. Gravenhorst, A. Grunerbl, C. Haring, R. Haux, P. Lukowicz, A. Muaremi, S. Mudda, S. Ohler, A. Puiatti, N. Reichwaldt, C. Scharnweber, G. Troester, L. V. Kessing, and G. Wurzer, “Personal health systems for bipolar disorder anecdotes, challenges and lessons learnt from monarca project,” in 2013 7th Int. Conf. on Pervasive Computing Technologies for Healthcare and Workshops, pp. 424–429, 2013. https://doi.org/10.4108/icst.pervasivehealth.2013.252123.
G. Valenza, C. Gentili, A. Lanat´ı, and E. P. Scilingo, “Mood recognition in bipolar patients through the psyche platform: Preliminary evaluations and perspectives,” Artif. Intell. Med., vol. 57, no. 1, pp. 49–58, 2013. https://doi.org/10.1016/j.artmed.2012.12.001.
L. G. Jaimes, M. Llofriu, and A. Raij, “Preventer, a selection mechanism for just-in-time preventive interventions,” IEEE Transact. on Affect. Comp., vol. 7, no. 3, pp. 243–257, 2016. https://doi.org/10.1109/TAFFC.2015.2490062.
M. R. Kamdar and M. J. Wu, “Prism: A data-driven platform for monitoring mental health.,” Pac. Symp. on Biocomp., vol. 21, pp. 333–344, 2016.
M. Kerz, A. Folarin, N. Meyer, M. Begale, J. MacCabe, and R. J. Dobson, “Sleepsight: A wearables-based relapse prevention system for schizophrenia,” in Proceedings of the 2016 ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing: Adjunct, UbiComp ’16, (New York, NY, USA), pp. 113–116, Association for Computing Machinery, 2016. https://doi.org/10.1145/2968219.2971419.
S. C. L. Telles, Erika Antunes Corr ´ ea, B. L. Caversan, J. de Moraes Mattos, and R. S. C. Alves, “Significado cl´ınico da actigrafia,” Rev. Neurociencias ˆ , vol. 19, no. 1, pp. 153–161, 2011. https://doi.org/10.34024/rnc.2011.v19.8413.
N. Eagle and A. S. Pentland, “Eigenbehaviors: identifying structure in routine,” Behavioral Ecology and Sociobiology, vol. 63, no. 7, pp. 1057–1066, 2009. https://doi.org/10.1007/s00265-009-0739-0.
E. Garcia-Ceja, M. Riegler, P. Jakobsen, J. Tørresen, T. Nordgreen, K. J. Oedegaard, and O. B. Fasmer, “Depresjon: A motor activity database of depression episodes in unipolar and bipolar patients,” in Proceedings of the 9th ACM Multimedia Systems Conference, MMSys ’18, (New York, NY, USA), p. 472–477, Association for Computing Machinery, 2018. https://doi.org/10.1145/3204949.3208125.
S. A. Montgomery and M. Asberg, “A new depression scale designed to be sensitive to change,” British J. of Psych., vol. 134, no. 4, pp. 382–389, 1979. https://doi.org/10.1192/bjp.134.4.382.
N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression,” The American Statistician, vol. 46, no. 3, pp. 175–185, 1992. https://doi.org/10.2307/2685209.