Eduardo Nelson Chavez
ABSTRACT
There are various factors that result in the lack of involuntary adherence to medication, among these factors forgetfulness is one of the most relevant. The technologies associated with the improvement of adherence facilitate the management of the taking of medications in the elderly, however these technologies often do not consider the context of people's daily life, such as eating habits, rest, and entertainment. One strategy older adults use for adherence to medication is to link their medication regimens to daily activities through relatively preserved and relatively automatic associative recovery processes. These processes facilitate the recall of a planned action. For this reason, we propose, through technology-based intervention, the use of a graphical interface that associates contextual signals, which supports the formation of habits and improves adherence to medication in older adults.
- M. D. Rodríguez , “A Qualitative Assessment of an Ambient Display to Support In-Home Medication of Older Adults,” Proceedings, vol. 2, no. 19, p. 1248, 2018.Google Scholar
Cross Ref
- D. E. Patton, “Improving Medication Adherence in Older Adults Prescribed Polypharmacy,” Queen's University Belfast, 2017.Google Scholar
- S. A. Khowaja, A. G. Prabono, F. Setiawan, B. N. Yahya, and S. L. Lee, “Contextual activity based Healthcare Internet of Things, Services, and People (HIoTSP): An architectural framework for healthcare monitoring using wearable sensors,” Comput. Networks, vol. 145, pp. 190–206, 2018.Google ScholarCross Ref
- D. Nilanjan, Amira, and Ashou, “Ambient Intelligence in Healthcare: A State-of-the-Art,” Glob. J. Comput. Sci. Technol. H Inf. Technol., vol. 17, no. 3, 2017.Google Scholar
- M. D. Rodríguez, J. Beltrán, M. Valenzuela-Beltrán, D. Cruz-Sandoval, and J. Favela, “Assisting older adults with medication reminders through an audio-based activity recognition system,” Pers. Ubiquitous Comput., 2020.Google ScholarDigital Library
- K. Stawarz, A. L. Cox, and A. Blandford, “Don't forget your pill! Designing effective medication reminder apps that support users’ daily routines,” Conf. Hum. Factors Comput. Syst. - Proc., pp. 2269–2278, 2014.Google Scholar
- K. Stawarz, B. Gardner, A. Cox, and A. Blandford, “What influences the selection of contextual cues when starting a new routine behaviour? An exploratory study,” BMC Psychol., vol. 8, no. 1, pp. 1–11, 2020.Google ScholarCross Ref
- A. G. Khan and S. B. Hofer, “Contextual signals in visual cortex,” Curr. Opin. Neurobiol., vol. 52, pp. 131–138, 2018.Google ScholarCross Ref
- E. Zárate-Bravo , “Supporting the medication adherence of older Mexican adults through external cues provided with ambient displays: Feasibility randomized controlled trial,” JMIR mHealth uHealth, vol. 8, no. 3, 2020.Google Scholar
- Q. Kong, T. Siauw, and A. M. Bayen, Python Programming and Numerical Methods. A Guide for Engineers and Scientists, Elsevier. Academic Press, 2021.Google Scholar
- L. Vinet and A. Zhedanov, “A ‘missing’ family of classical orthogonal polynomials,” Antimicrob. Agents Chemother., vol. 58, no. 12, pp. 7250–7257, Nov. 2010.Google Scholar
- K. Jaskolka, J. Seiler, F. Beyer, and A. Kaup, “A Python-based laboratory course for image and video signal processing on embedded systems,” Heliyon, vol. 5, no. 10, 2019.Google Scholar
- S. Nagar, Introduction to python for engineers and scientists: Open source solutions for numerical computation. 2017.Google ScholarCross Ref
- R. A. Vazeux Blanco, “Desarrollo de un sistema operativo para Raspberry Pi con sus drivers básicos,” Universidad Politécnica de Madrid, 2017.Google Scholar
- S. Venu, Asp . Net Core and Azure with Raspberry Pi 4. APress, 2020.Google ScholarCross Ref
- A. Kurniawan, Raspbian OS Programming with the Raspberry Pi. 2019.Google ScholarCross Ref
- C. Meng and H. Baier, “bring2lite: A Structural Concept and Tool for Forensic Data Analysis and Recovery of Deleted SQLite Records,” Digit. Investig., vol. 29, pp. S31–S41, 2019.Google ScholarDigital Library
- S. Nemetz, S. Schmitt, and F. Freiling, “A standardized corpus for SQLite database forensics,” DFRWS 2018 EU - Proc. 5th Annu. DFRWS Eur., vol. 24, pp. S121–S130, 2018.Google ScholarCross Ref
- J. Feiler, Introducing SQLite for Mobile Developers. 2015.Google ScholarDigital Library
- W. Watanabe, R. Maruyama, H. Arimoto, and Y. Tamada, “Low-cost multi-modal microscope using Raspberry Pi,” Optik (Stuttg)., vol. 212, no. March, p. 164713, Jun. 2020.Google ScholarCross Ref
- M. Vinodhini and N. Ameena Bibi, “Haze image restoration based on physical optics model using raspberry pi B+V1.2,” Mater. Today Proc., no. xxxx, pp. 2–6, Jun. 2020.Google ScholarCross Ref
- V. Bharathi, M. Karpagam, S. Jeeva, and L. K. Kiran, “Smart parking guidance system using RASPBERRY-PI,” Mater. Today Proc., no. xxxx, 2020.Google ScholarCross Ref
- P. Kanani and M. Padole, “Improving Pattern Matching performance in Genome sequences using Run Length Encoding in Distributed Raspberry Pi Clustering Environment,” Procedia Comput. Sci., vol. 171, pp. 1670–1679, 2020.Google ScholarCross Ref
- Pi Raspberry, “Raspberry Pi 4 Computer,” no. June, p. 6, 2019.Google Scholar
- “Buy a Raspberry Pi Touch Display – Raspberry Pi.” [Online]. Available: https://www.raspberrypi.org/products/raspberry-pi-touch-display/. [Accessed: 07-Feb-2021].Google Scholar
- “Raspberry Pi Touch Display - Raspberry Pi Documentation.” [Online]. Available: https://www.raspberrypi.org/documentation/hardware/display/. [Accessed: 07-Feb-2021].Google Scholar
- A. Karmel, A. Sharma, M. Pandya, and D. Garg, “IoT based Assistive Device for Deaf, Dumb and Blind People,” Procedia Comput. Sci., vol. 165, pp. 259–269, 2019.Google ScholarDigital Library
- Y. K. Paunski and G. T. Angelov, “Performance and power consumption analysis of low-cost single board computers in educational robotics,” IFAC-PapersOnLine, vol. 52, no. 25, pp. 424–428, 2019.Google ScholarCross Ref
- U. B. Gohatre, M. V. D. Chaudhari, and D. K. P. Rane, “Rasp-Pi based Remote Controlled Smart Advertising of Still and Moving Images,” Int. J. Eng. Comput. Sci., no. May 2017, Oct. 2015.Google Scholar
- W. Harrington, Learning Raspbian. BIRMINGHAM - MUMBAI: Packt publishing, 2015.Google Scholar
- L. Lutz and R. Ray, CODING PYTHON & RASPBERRY PI, vol. 53, no. 9. 2018.Google Scholar
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