1. bookTom 27 (2022): Zeszyt 1 (June 2022)
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eISSN
2255-8691
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08 Nov 2012
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mHealth and User Interaction Improvement by Personality Traits-Based Personalization

Data publikacji: 23 Aug 2022
Tom & Zeszyt: Tom 27 (2022) - Zeszyt 1 (June 2022)
Zakres stron: 55 - 61
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski

[1] J. H. Park, J. H. Moon, H. J. Kim, M. H. Kong, and Y. H. Oh, “Sedentary lifestyle: Overview of updated evidence of potential health risks,” Korean Journal of Family Medicine, vol. 41, no. 6, pp. 365–373, 2020. https://doi.org/10.4082/kjfm.20.0165770083233242381 Search in Google Scholar

[2] World Health Organization. Global recommendations on physical activity for health. Genève: WHO, 2010. Search in Google Scholar

[3] P. J. Puccinelli, T. S. da Costa, A. Seffrin, C. A. de Lira, R. L. Vancini, P. T. Nikolaidis, B. Knechtle, T. Rosemann, L. Hill, and M. S. Andrade, “Reduced level of physical activity during COVID-19 pandemic is associated with depression and anxiety levels: An internet-based survey,” BMC Public Health, vol. 21, no. 1, 2021. https://doi.org/10.1186/s12889-021-10684-1800664633781231 Search in Google Scholar

[4] O. Bestsennyy, G. Gilbert, A. Harris, and J. Rost, “Telehealth: A quarter-trilliondollar post-COVID-19 reality?”, McKinsey & Company, 2021. [Online]. Available: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.mckinsey.com/~/media/McKinsey/Industries/Healthcare%20Systems%20and%20Services/Our%20Insights/Telehealth%20A%20quarter%20trillion%20dollar%20post%20COVID%2019%20reality/Telehealth-A-quarter-trilliondollar-post-COVID-19-reality.pdf Search in Google Scholar

[5] Office of the National Coordinator for Health Information Technology (ONC), “What is telehealth? how is telehealth different from telemedicine?”, 2019. [Online]. Available: http://www.healthit.gov/faq/what-telehealth-how-telehealth-different-telemedicine. Accessed on: Feb. 6, 2022. Search in Google Scholar

[6] V. Calcaterra, E. Verduci, M. Vandoni, V. Rossi, E. Di Profio, V. Carnevale Pellino, V. Tranfaglia, M. C. Pascuzzi, B. Borsani, A. Bosetti, and G. Zuccotti, “Telehealth: A useful tool for the management of nutrition and exercise programs in pediatric obesity in the COVID-19 ERA,” Nutrients, vol. 13, no. 11, 2021, Art no. 3689. https://doi.org/10.3390/nu13113689861818934835945 Search in Google Scholar

[7] A. Phaneuf, “The number of health and fitness app users increased 27% from last year,” Insider Intelligence, 20-Jul-2020. [Online]. Available: https://www.emarketer.com/content/number-of-health-fitness-app-users-increased-27-last-year. Accessed on: Feb. 6, 2022. Search in Google Scholar

[8] J. Murphy, T. Uttamlal, K. A. Schmidtke, I. Vlaev, D. Taylor, M. Ahmad, S. Alsters, P. Purkayastha, S. Scholtz, R. Ramezani, A. R. Ahmed, H. Chahal, A. Darzi, and A. I. Blakemore, “Tracking physical activity using smart phone apps: Assessing the ability of a current app and systematically collecting patient recommendations for future development,” BMC Medical Informatics and Decision Making, vol. 20, no. 1, 2020. https://doi.org/10.1186/s12911-020-1025-3699821432013996 Search in Google Scholar

[9] R. Hurling, M. Catt, M. De Boni, B. W. Fairley, T. Hurst, P. Murray, A. Richardson, and J. S. Sodhi, “Using internet and mobile phone technology to deliver an automated physical activity program: Randomized Controlled Trial,” Journal of Medical Internet Research, vol. 9, no. 2, 2007. https://doi.org/10.2196/jmir.9.2.e7187472217478409 Search in Google Scholar

[10] J. Hamari and J. Koivisto, “Working out for likes”: An empirical study on social influence in exercise gamification”, Computers in Human Behavior, vol. 50, pp. 333–347, 2015. https://doi.org/10.1016/j.chb.2015.04.018 Search in Google Scholar

[11] H. Bitar and S. Alismail, “The role of eHealth, telehealth, and telemedicine for chronic disease patients during COVID-19 pandemic: A rapid systematic review,” Digital Health, vol. 7, pp. 1–19, 2021. https://doi.org/10.1177/20552076211009396806077333959378 Search in Google Scholar

[12] A. Hassoon, Y. Baig, D. Q. Naiman, D. D. Celentano, D. Lansey, V. Stearns, J. Coresh, J. Schrack, S. S. Martin, H.-C. Yeh, H. Zeilberger, and L. J. Appel, “Randomized trial of two artificial intelligence coaching interventions to increase physical activity in cancer survivors,” npj Digital Med., vol. 4, no. 1, 2021. https://doi.org/10.1038/s41746-021-00539-9866078534887491 Search in Google Scholar

[13] L. Laranjo et al., “Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression”, British Journal of Sports Medicine, vol. 55, no. 8, 2022. https://doi.org/10.1136/bjsports-2020-10289233355160 Search in Google Scholar

[14] G. S. Aljuraiban, “Use of weight-management mobile phone apps in Saudi Arabia: A Web-based survey”, JMIR MHealth UHealth, vol. 7, no. 2, Feb 2019, Art no. e12692. https://doi.org/10.2196/12692640623030794205 Search in Google Scholar

[15] X. Guo, X. Zhang, and Y. Sun, “The privacy- personalization paradox in mHealth services acceptance of different age groups”, Electronic Commerce Research and Applications, vol. 16, pp. 55–65, Mar. 2016. https://doi.org/10.1016/j.elerap.2015.11.001 Search in Google Scholar

[16] S. Hamine, E. Gerth-Guyette, D. Faulx, B. B. Green, and A. S. Ginsburg, “Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review”, J. Med. Internet Res., vol. 17, no. 2, Feb. 2015, Art no. e52. https://doi.org/10.2196/jmir.3951437620825803266 Search in Google Scholar

[17] F. H. McKay, C. Cheng, A. Wright, J. Shill, H. Stephens, and M. Uccellini, “Evaluating mobile phone applications for health behaviour change: A systematic review”, J. Telemed. Telecare, vol. 24, no. 1, pp. 22–30, Jan. 2018. https://doi.org/10.1177/1357633X1667353827760883 Search in Google Scholar

[18] N. Mohammadzadeh and R. Safdari, “Patient monitoring in mobile health: opportunities and challenges”, Med. Arch., vol. 68, no. 1, pp. 57–60, 2014. https://doi.org/10.5455/medarh.2014.68.57-60427247024783916 Search in Google Scholar

[19] M. Tomlinson, M. J. Rotheram-Borus, L. Swartz, and A. C. Tsai, “Scaling up mHealth: Where is the evidence?”, PLOS Medicine, vol. 10, no. 2, pp. 1–5, Feb. 2013. https://doi.org/10.1371/journal.pmed.1001382357054023424286 Search in Google Scholar

[20] G. Castelnuovo, G. Pietrabissa, G. M. Manzoni, S. Corti, M. Ceccarini, M. Borrello, E. M. Giusti, M. Novelli, R. Cattivelli, N. A. Middleton, S. G. Simpson, and E. Molinari, “Chronic care management of Globesity: Promoting healthier lifestyles in traditional and mHealth based settings,” Frontiers in Psychology, vol. 6, Oct. 2015. https://doi.org/10.3389/fpsyg.2015.01557460604426528215 Search in Google Scholar

[21] A. Beaudry, I. Vaghefi, F. Bagayogo, and L. Lapointe, “Impact of IT user behavior: Observations through a new lens”, Communications of the Association for Information Systems, vol. 46, pp. 331–364, Jan. 2020. https://doi.org/10.17705/1CAIS.04615 Search in Google Scholar

[22] M. Alshawmar, H. Mombini, B. Tulu, and I. Vaghefi, “Investigating the affordances of wellness mHealth apps,” in Proceedings of the Annual Hawaii International Conference on System Sciences, 2021. https://doi.org/10.24251/HICSS.2021.462 Search in Google Scholar

[23] L. Delrieu, O. Pérol, B. Fervers, C. Friedenreich, J. Vallance, O. Febvey-Combes, D. Pérol, B. Canada, E. Roitmann, A. Dufresne, T. Bachelot, P.-E. Heudel, O. Trédan, M. Touillaud, and V. Pialoux, “A personalized physical activity program with activity trackers and a mobile phone app for patients with metastatic breast cancer: Protocol for a single-arm feasibility trial,” JMIR Research Protocols, vol. 7, no. 8, Aug. 2018. https://doi.org/10.2196/10487613728330166274 Search in Google Scholar

[24] I. Vaghefi and B. Tulu, “The continued use of mobile health apps: Insights from a longitudinal study,” JMIR mHealth and uHealth, vol. 7, no. 8, Aug. 2019. https://doi.org/10.2196/12983674016631469081 Search in Google Scholar

[25] Deloitte, “Health plans: What matters most to the health care consumer Deloitte’s 2016 Consumer Priorities in Health Care Survey”, 2016, [Online]. Available: www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/healthcare-consumer-experience-survey.html. Accessed on: Feb. 6, 2022. Search in Google Scholar

[26] O. Ogbanufe and N. Gerhart, “Exploring smart wearables through the lens of reactance theory: Linking values, social influence, and Status Quo,” Computers in Human Behavior, vol. 127, Feb. 2022, Art no. 107044. https://doi.org/10.1016/j.chb.2021.107044 Search in Google Scholar

[27] G. B. Svendsen, J.-A. K. Johnsen, L. Almås-Sørensen, and J. Vittersø, “Personality and technology acceptance: the influence of personality factors on the core constructs of the technology acceptance model”, Behav. Inf. Technol., vol. 32, no. 4, pp. 323–334, Apr. 2013. https://doi.org/10.1080/0144929X.2011.553740 Search in Google Scholar

[28] A. Nunes, T. Limpo, and S. L. Castro, “Effects of age, gender, and personality on individuals’ behavioral intention to use health applications,” in Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health, Madeira, Portugal, 2018, pp. 103–110. https://doi.org/10.5220/0006674101030110 Search in Google Scholar

[29] K. Subaramaniam and O. F. Baker, “Human personality types and software interface design: HCI from a different perception”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 1, no. 3, pp. 253–256, Jan. 2011. https://doi.org/10.18517/ijaseit.1.3.53 Search in Google Scholar

[30] T. L. James, L. Wallace, and J. K. Deane, “Using organismic integration theory to explore the associations between users’ exercise motivations and fitness technology feature set use,” MIS Quarterly, vol. 43, no. 1, pp. 287–312, 2019. https://doi.org/10.25300/MISQ/2019/14128 Search in Google Scholar

[31] D. C. Dryer, “Getting personal with computers: How to design personalities for agents”, Appl. Artif. Intell., vol. 13, no. 3, pp 273–295, Apr. 1999. https://doi.org/10.1080/088395199117423 Search in Google Scholar

[32] A. A. Peck, “The future is digital healthcare,” The National Law Review. [Online]. Available: https://www.natlawreview.com/article/future-digital-healthcare. Accessed on: Feb. 6, 2022. Search in Google Scholar

[33] Ā. Karpova, Personība. Teorijas un to radītāji. Zvaigzne ABC, 1998. Search in Google Scholar

[34] A. Upmane, “Psiholoģijas skolotāju profesionālo kompetenču pilnveide,” Vārdnīca. Personība. [Online]. Available: https://profizgl.lu.lv/mod/glossary/view.php?id=19814&mode=cat. Accessed on: Feb. 6, 2022. Search in Google Scholar

[35] A. Vinciarelli and G. Mohammadi, “A survey of personality computing,” IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 273–291, Jun. 2014. https://doi.org/10.1109/TAFFC.2014.2330816 Search in Google Scholar

[36] H. J. Eysenck, “Four ways five factors are not basic,” Personality and Individual Differences, vol. 13, no. 6, pp. 667–673, Jun. 1992. https://doi.org/10.1016/0191-8869(92)90237-J Search in Google Scholar

[37] P. T. Costa and R. R. McCrae, “Age differences in personality structure: a cluster analytic approach”, J. Gerontol., vol. 31, no. 5, pp. 564–570, Sep. 1976. https://doi.org/10.1093/geronj/31.5.564950450 Search in Google Scholar

[38] E. C. Tupes and R. E. Christal, “Recurrent personality factors based on trait ratings,” Journal of Personality, vol. 60, no. 2, pp. 225–251, Jun. 1992. https://doi.org/10.1111/j.1467-6494.1992.tb00973.x1635043 Search in Google Scholar

[39] W. T. Norman, “Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings,” The Journal of Abnormal and Social Psychology, vol. 66, no. 6, pp. 574–583, 1963. https://doi.org/10.1037/h004029113938947 Search in Google Scholar

[40] L. R. Goldberg, “Language and individual differences: The search for universals in personality lexicons”, in Review of Personality and Social Psychology, vol. 2, L. I. L. Wheeler, Ed., Red Sage Publication, 1981, pp. 141–165. Search in Google Scholar

[41] P. T. Costa and R. R. Mccrae, “Neo PI-R professional manual”, Psychological Assessment Resource, Jan. 1992. https://www.researchgate.net/publication/240133762_Neo_PIR_professional_manual Search in Google Scholar

[42] M. Piletić and M. Čabarkapa, “Differences in personality traits and motivation for recreational practice of yoga & fitness in women”, Yoga Federation of Serbia: Yoga – the Light of Microuniverse, Belgrade, 2010. Search in Google Scholar

[43] J. Y.-C. Lin, E. S.-T. Wang, and J. M.-S. Cheng, “The relationship between extroversion and leisure motivation: Evidence from fitness center participation,” Social Behavior and Personality: an international journal, vol. 35, no. 10, pp. 1317–1322, 2007. https://doi.org/10.2224/sbp.2007.35.10.1317 Search in Google Scholar

[44] A. Rieder, C. Lehrer, and R. Jung, “Affordances and behavioral outcomes of wearable activity trackers”, in European Conference on Information Systems (ECIS 2020), Jun. 2020. https://www.researchgate.net/publication/341490853_Affordances_and_Behavioral_Outcomes_of_Wearable_Activity_Trackers Search in Google Scholar

[45] A. Kazemeini, S. Fatehi, Y. Mehta, S. Eetemadi, and E. Cambria, “Personality trait detection using Bagged SVM over BERT word embedding ensembles”, arXiv preprint, Oct. 2020. https://doi.org/10.6084/m9.figshare.13012421 Search in Google Scholar

[46] J. Tao and T. Tan, “Affective computing: A Review,” in Affective Computing and Intelligent Interaction, Tao, J., Tan, T., Picard, R.W., Eds. Lecture Notes in Computer Science, vol. 3784, Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_125 Search in Google Scholar

[47] F. Ren, “Affective information processing and recognizing human emotion,” Electronic Notes in Theoretical Computer Science, vol. 225, pp. 39–50, Jan. 2009. https://doi.org/10.1016/j.entcs.2008.12.065 Search in Google Scholar

[48] I. Bisio, A. Delfino, F. Lavagetto, and M. Marchese, “Opportunistic detection methods for emotion-aware smartphone applications”, in Creating Personal, Social, and Urban Awareness through Pervasive Computing, Nov. 2013, pp 53–85. https://www.igi-global.com/gateway/chapter/8879710.4018/978-1-4666-4695-7.ch003 Search in Google Scholar

[49] M. X. Zhou, G. Mark, J. Li, and H. Yang, “Trusting virtual agents,” ACM Transactions on Interactive Intelligent Systems, vol. 9, no. 2–3, pp. 1–36, Sep. 2019. https://doi.org/10.1145/3232077 Search in Google Scholar

[50] A. Kachur, E. Osin, D. Davydov, K. Shutilov, and A. Novokshonov, “Assessing the big five personality traits using real-life static facial images,” Scientific Reports, vol. 10, no. 1, 2020, Art no. 8487. https://doi.org/10.1038/s41598-020-65358-6724458732444847 Search in Google Scholar

[51] K. Ilmini and T. G. I. Fernando, “Persons’ personality traits recognition using machine learning algorithms and image processing techniques”, Advances in Computer Science: An International Journal, vol. 5, no. 1, pp. 40–44, Jan. 2016. https://www.researchgate.net/publication/323356879_Persons’_Personality_Traits_Recognition_using_Machine_Learning_Algorithms_and_Image_Processing_Techniques Search in Google Scholar

[52] C. Segalin, D. S. Cheng, and M. Cristani, “Social profiling through image understanding: Personality inference using Convolutional Neural Networks,” Computer Vision and Image Understanding, vol. 156, pp. 34–50, Mar. 2017. https://doi.org/10.1016/j.cviu.2016.10.013 Search in Google Scholar

[53] Y. Mehta, N. Majumder, A. Gelbukh, and E. Cambria, “Recent trends in deep learning based personality detection,” Artificial Intelligence Review, vol. 53, no. 4, pp. 2313–2339, Oct. 2019. https://doi.org/10.1007/s10462-019-09770-z Search in Google Scholar

[54] A. Phaneuf, “How mHealth apps are providing solutions to the healthcare market’s problems”, Insider, 04-Dec-2019. [Online]. Available: https://www.businessinsider.com/mhealth-apps-definition-examples. Accessed on: Jan. 29, 2022. Search in Google Scholar

[55] Statista, “Most popular health and fitness apps in the United States as of May 2018, by monthly active users”, 2018. [Online]. Available: https://www.statista.com/statistics/650748/health-fitness-app-usage-usa/. Accessed on: Jan. 31, 2022. Search in Google Scholar

[56] 1188, “5 lieliskas Endomondo alternatīvas dažādām gaumēm”, 2021. [Online]. Available: https://www.1188.lv/padomi/5-lieliskasendomondo-alternativas-dazadam-gaumem/5083. Accessed on: Jan. 29, 2022. Search in Google Scholar

[57] Fitbit, “Information we collect”. [Online]. Available: https://www.fitbit.com/global/uk/legal/privacy-summary?utm_source=androidapp&utm_medium=fitbitapp. Accessed on: Jan. 30, 2022. Search in Google Scholar

[58] GooglePlay, “Fitbit”, 2022. [Online]. Available: https://play.google.com/store/apps/details?id=com.fitbit.FitbitMobile. Accessed on: Jan. 30, 2022. Search in Google Scholar

[59] GooglePlay, “Strava: track running, cycling & swimming”, 2022. [Online]. Available: https://play.google.com/store/apps/details?id=com.strava. Accessed on: Jan. 30, 2022. Search in Google Scholar

[60] Strava, “Privacy policy”. [Online]. Available: https://www.strava.com/legal/privacy#full_policy. Accessed on: Jan. 30, 2022. Search in Google Scholar

[61] GooglePlay, “Running app – GPS run tracker”, 2022. [Online]. Available: play.google.com/store/apps/details?id=running.tracker.gps.map. Accessed on: Jan. 29, 2022. Search in Google Scholar

[62] GooglePlay, “Leap fitness group”, 2022. [Online]. Available: https://play.google.com/store/apps/developer?id=Leap+Fitness+Group&hl=en. Accessed: Jan. 29, 2022. Search in Google Scholar

[63] Map Runner, “Privacy policy”. [Online in mobile application]. Accessed on: Jan. 30, 2022. Search in Google Scholar

[64] S. D. Costa, M. P. Barcellos, R. de Falbo, T. Conte, and K. M. de Oliveira, “A core ontology on the human–computer interaction phenomenon,” Data & Knowledge Engineering, vol. 138, Mar. 2022, Art no. 101977. https://doi.org/10.1016/j.datak.2021.101977 Search in Google Scholar

[65] EU General Data Protection Regulation (GDPR): Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ 2016 L 119/1. Search in Google Scholar

[66] C. Fischer, “The legal protection against inferences drawn by AI under the GDPR”. Tilburg University. Jul. 2020. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arno.uvt.nl/show.cgi?fid=151926 Search in Google Scholar

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