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18.10.2019 | Long Paper

Toward a ubiquitous model to assist the treatment of people with depression

verfasst von: Milene Martini Petry, Jorge Luis Victória Barbosa, Sandro José Rigo, Lucas Pfeiffer Salomão Dias, Paulo César Büttenbender

Erschienen in: Universal Access in the Information Society | Ausgabe 4/2020

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Abstract

The World Health Organization predicts that by 2020, depression will be the second-most common cause of debility. Also, ubiquitous computing advances are offering people and applications the necessary information for the most diversified necessities. In this sense, researchers are using ubiquitous health in applications focused on mental health and well-being. This article proposes Hígia, a model to assist in the treatment of people suffering from depression. Hígia detects the need to contact the caregivers of a depressed person, based on the users’ historical context, allowing a faster response action. Hígia constantly evaluates patients’ characteristics on social networks, emails, and interactions with smartphones, computers, or other devices. Hígia also proposes the monitoring of users’ displacements. A prototype was developed and applied in an evaluation involving users and psychologists. The results showed 85.7% of acceptance regarding perceived ease of use by users and that 100% agree, partially or totally, about the system utility. These results were encouraging and show the potential for implementing Hígia in real-life situations.

Vorheriger Artikel Self-efficacy and anxiety as determinants of older adults’ use of Internet Banking Services

Nächster Artikel The value of a Patient Access Portal in primary care: a cross-sectional survey of 62,486 registered users in the UK

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Agoulmine, N., Deen, M.J., Lee, J.S., Meyyappan, M.: U-health smart home. IEEE Nanotechnol. Mag. 5(3), 6–11 (2011). https://doi.org/10.1109/MNANO.2011.941951CrossRef

Aguirre, A., Rodriguez, M.D., Andrade, A.G.: A pervasive system for enabling older adults to cope with depression by motivating them to socialize. In: 2009 Sixth International Conference on Information Technology: New Generations, pp. 1653–1654 (2009). https://doi.org/10.1109/ITNG.2009.310

Arnrich, B., Osmani, V., Bardram, J.: Mental health and the impact of ubiquitous technologies. Pers. Ubiquitous Comput. 17(2), 211–213 (2013). https://doi.org/10.1007/s00779-011-0464-3CrossRef

Association, A.P.: Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th edn. American Psychiatric Pub, Philadelphia (2013)CrossRef

Bardram, J.E., Christensen, H.B.: Pervasive computing support for hospitals: an overview of the activity-based computing project. IEEE Pervasive Comput. 6(1), 44–51 (2007). https://doi.org/10.1109/MPRV.2007.19CrossRef

Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)CrossRef

Buttussi, F., Chittaro, L.: Smarter phones for healthier lifestyles: an adaptive fitness game. IEEE Pervasive Comput. 9(4), 51–57 (2010). https://doi.org/10.1109/MPRV.2010.52CrossRef

Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008CrossRef

Dey, A.K., Estrin, D.: Perspectives on pervasive health from some of the field’s leading researchers. IEEE Pervasive Comput. 10(2), 4–7 (2011). https://doi.org/10.1109/MPRV.2011.29CrossRef

10.

Dickerson, R.F., Gorlin, E.I., Stankovic, J.A.: Empath: a continuous remote emotional health monitoring system for depressive illness. In: Proceedings of the 2nd Conference on Wireless Health, ACM, New York, WH ’11, pp. 5:1–5:10 (2011). https://doi.org/10.1145/2077546.2077552

11.

Gartenberg, D., Thornton, R., Masood, M., Pfannenstiel, D., Taylor, D., Parasuraman, R.: Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback. Pers. Ubiquitous Comput. 17(3), 561–570 (2013). https://doi.org/10.1007/s00779-012-0508-3CrossRef

12.

Hu, B., Hu, B., Wan, J., Dennis, M., Chen, H.H., Li, L., Zhou, Q.: Ontology-based ubiquitous monitoring and treatment against depression. Wirel. Commun. Mob. Comput. 10(10), 1303–1319 (2010). https://doi.org/10.1002/wcm.716CrossRef

13.

Huhns, M.N., Stephens, L.M.: Multiagent systems. In: Weiss G (ed) Multiagent Systems and Societies of Agents, pp. 79–120, MIT Press, Cambridge (1999). http://dl.acm.org/citation.cfm?id=305606.305608

14.

Jennings, N.R.: An agent-based approach for building complex software systems. Commun. ACM 44(4), 35–41 (2001)CrossRef

15.

Kota, R., Gibbins, N., Jennings, N.R.: Decentralized approaches for self-adaptation in agent organizations. ACM Trans. Auton. Adapt. Syst. (TAAS) 7(1), 1–28 (2012)CrossRef

16.

Leon, E., Montejo, M., Dorronsoro, I.: Prospect of smart home-based detection of subclinical depressive disorders. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 452–457 (2011). https://doi.org/10.4108/icst.pervasivehealth.2011.246106

17.

Likert, R.: A Technique for the Measurement of Attitudes, vol. 22. New York University, New York (1932)

18.

Lim, J.E., Choi, O.H., Na, H.S., Baik, D.K.: A context-aware fitness guide system for exercise optimization in u-health. IEEE Trans. Inf. Technol. Biomed. 13(3), 370–379 (2009). https://doi.org/10.1109/TITB.2009.2013941CrossRef

19.

Madan, A., Cebrian, M., Moturu, S., Farrahi, K., Pentland, A.: Sensing the health state of a community. IEEE Pervasive Comput. 11(4), 36–45 (2012)CrossRef

20.

Musen, M.A., Team, T.P.: The protégé project: a look back and a look forward. AI Matters 1(4), 4–12 (2015). https://doi.org/10.1145/2757001.2757003CrossRef

21.

Musen, M.A., Noy, N.F., Shah, N.H., Whetzel, P.L., Chute, C.G., Story, M.A., Smith, B.: The national center for biomedical ontology. J. Am. Med. Inform. Assoc. 19(2), 190–195 (2012)CrossRef

22.

Ng, J.K.Y., Wang, J., Lam, K.Y., Kam, C.H.C., Han, S.: Capturing and analyzing pervasive data for smarthealth. In: 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pp. 985–992 (2014). https://doi.org/10.1109/AINA.2014.119

23.

Orwat, C., Rashid, A., Holtmann, C., Wolk, M., Scheermesser, M., Kosow, H., Graefe, A.: Adopting pervasive computing for routine use in healthcare. IEEE Pervasive Comput. 9(2), 64–71 (2010). https://doi.org/10.1109/MPRV.2010.25CrossRef

24.

Padgham, L., Winikoff, M.: Developing Intelligent Agent Systems: A Practical Guide. Wiley, New York (2004)CrossRef

25.

Palmius, N., Tsanas, A., Saunders, K.E.A., Bilderbeck, A.C., Geddes, J.R., Goodwin, G.M., Vos, M.D.: Detecting bipolar depression from geographic location data. IEEE Trans. Biomed. Eng. 64(8), 1761–1771 (2017)CrossRef

26.

Pampouchidou, A., Simos, P., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., Tsiknakis, M.: Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans. Affect. Comput. 1(99), 1 (2017)

27.

Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Elsevier, Amterdam (2013)MATH

28.

Satyanarayanan, M.: Pervasive computing: vision and challenges. IEEE Pers. Commun. 8(4), 10–17 (2001)CrossRef

29.

Silva, M.T., Galvao, T.F., Martins, S.S., Pereira, M.G.: Prevalence of depression morbility among brazilian adults: a systematic review and meta-analysis. Braz. J. Psychiatry 36, 262–270 (2014). https://doi.org/10.1590/1516-4446-2013-1294CrossRef

30.

Sokolova, M.V., Fernández Caballero, A.: A Review on Frameworks for Decision Support Systems, pp. 19–45. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-25544-1_2CrossRef

31.

Suhara, Y., Xu, Y., Pentland, A.: Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, WWW ’17, pp. 715–724 (2017)

32.

Thangarajah, J., Padgham, L., Winikoff, M.: Prometheus design tool. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, ACM, New York, AAMAS ’05, pp. 127–128 (2005). https://doi.org/10.1145/1082473.1082817

33.

Thiyagaraja, S.R., Dantu, R., Shrestha, P.L., Thompson, M.A., Smith, C.: Optimized and secured transmission and retrieval of vital signs from remote devices. In: 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 25–30 (2017)

34.

Vianna, H.D., Barbosa, J.L.V.: A model for ubiquitous care of noncommunicable diseases. IEEE J. Biomed. Health Inf. 18(5), 1597–1606 (2014). https://doi.org/10.1109/JBHI.2013.2292860CrossRef

35.

Wagner, S.: Towards future reliable pervasive healthcare with adherence strategy engineering. In: Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, PervasiveHealth ’16, pp. 289–294 (2016)

36.

Wan, J., Hu, B., Moore, P., Ashford, R.: Intelligent mobile computing to assist in the treatment of depression. In: 2008 Third International Conference on Pervasive Computing and Applications, vol. 2, pp. 650–655 (2008). https://doi.org/10.1109/ICPCA.2008.4783691

37.

Weiser, M.: The computer for the 21st century. Sci. Am. 265(3), 94–105 (1991)CrossRef

38.

Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRef

39.

World Health Organization, G.: The world health report: 2001: mental health: new understanding, new hope (2001). http://www.who.int/whr/2001/en. Accessed 20 Nov 2018

40.

World Health Organization, G.: The world health report: 2017: depression and other common mental disorders global health estimates (2017). http://apps.who.int/iris/bitstream/10665/254610/1/WHO-MSD-MER-2017.2-eng.pdf. Accessed 20 Nov 2018

41.

Yoon, C., Kim, S.: Convenience and tam in a ubiquitous computing environment: the case of wireless LAN. Electron. Commer. Res. Appl. 6(1), 102–112 (2007). https://doi.org/10.1016/j.elerap.2006.06.009CrossRef

42.

Zayas, A.D., Gomez, P.M., Tocado, F.R.: Mobile application profiling for connected mobile devices. IEEE Pervasive Comput. 9(1), 54–61 (2010)CrossRef

Titel: Toward a ubiquitous model to assist the treatment of people with depression
verfasst von: Milene Martini Petry
Jorge Luis Victória Barbosa
Sandro José Rigo
Lucas Pfeiffer Salomão Dias
Paulo César Büttenbender
Publikationsdatum: 18.10.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Universal Access in the Information Society / Ausgabe 4/2020
Print ISSN: 1615-5289
Elektronische ISSN: 1615-5297
DOI: https://doi.org/10.1007/s10209-019-00697-4

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