678
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Elderly-Oriented Improvement of Mobile Applications Based on Self-Determination Theory

, , , , &
Pages 1071-1086 | Received 01 Mar 2022, Accepted 29 Sep 2022, Published online: 16 Nov 2022
 

Abstract

At present, the research on elderly-oriented experience for mobile applications is relatively scattered, and improvements have not been explored to meet the major psychological needs of the elderly. In this study, 15 principles for the design of mobile applications for the elderly in three aspects, i.e., autonomy, competence and relatedness, have been proposed on the basis of self-determination theory, literature review and results obtained from semi-structured interviews. In order to improve an existing mobile application with respect to these three aspects, data were collected from 45 elderly participants. The results revealed that the improved mobile application effectively catered to the basic psychological needs of older users, as compared to the earlier version. Hence, the design principles identified in the study can effectively guide designers in developing elderly-oriented mobile applications to improve the user experience and the wellbeing of older adults.

Ethics statement

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

Conceptualization, L.Z. (Lekai Zhang); methodology, L.Z.(Lingyan Zhang), C.J. and L.Z.(Li Zhang); writing original draft, L.Z.(Li Zhang) and L.Z. (Lekai Zhang); supervision, Z.T. and J.W.

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

This research was supported by the Philosophy and Social Science Planning Fund Project of Zhejiang Province [23NDJC101YB, 22NDJC007Z], the National Social Science Fund of China [22CTQ016], and Innovation Center of Yangtze River Delta, Zhejiang University.

Notes on contributors

Lekai Zhang

Lekai Zhang is an assistant professor at Zhejiang University of Technology, Hangzhou, China. He is fully indulged in affective computing and digital wellbeing, quite into directing a collaborative, user-centered design process, carrying out integrated interactive works.

Li Zhang

Li Zhang is a postgraduate student at Zhejiang University of Technology, Hangzhou, China. Her research lies in the field of user research and design thinking.

Chuchu Jin

Chuchu Jin is a postgraduate student at Zhejiang University of Technology, Hangzhou, China. Her research lies in the field of user research and design thinking.

Zhichuan Tang

Zhichuan Tang is an associate professor at Zhejiang University of Technology, Hangzhou, China. His research interests include human-computer interface, intelligent design and industrial design.

Jianfeng Wu

Jianfeng Wu is an associate professor of industrial design at Zhejiang University of Technology, Hangzhou, China. His research focused on Information and interaction design, technological innovation and design management.

Lingyan Zhang

Lingyan Zhang is currently a master at Zhejiang University of Technology, Hangzhou, China. Her major research interests include user research and service experience, and she is focused on using design as a way to promote cultural development.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 306.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.