Designing Personalized Persuasive Game Elements for Older Adults in Health Apps

Abstract

The use of gamification elements in health apps has been shown to promote healthy behaviors. However, one-size-fits-all gamification strategy does not have the best persuasive effect. Therefore, the aim of this paper was to determine how to select personalized game elements for older adults and use them in health education app design. Firstly, based on the DMC model, the Kano model was used to identify the demand attributes of game mechanisms, and three gamification mechanisms (winning, feedback and reward) preferred by the elderly were selected. Secondly, the corresponding gamification elements were selected by the focus group method, and nine virtual products were generated. Thirdly, the virtual products were rated and conjointly analyzed to obtain the relative importance of gamification mechanisms and the utility values of gamification elements, and a comparative analysis was conducted on four characteristics of older adults: age, gender, personal income, and education. The results obtained the best combination of gamification elements chosen by the elderly under different classifications. Finally, design guidelines on persuasive gamification elements were developed based on the characteristics of older adults, and a modified gamification model was proposed. The results of this study provide suggestions and guidelines for the design of persuasive gamification in health education apps, which will help improve the satisfaction of older adults with health apps.

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“…These attributes have underlying levels, marketable to any type of consumer. This statistical technique's major advantage is the reliability of results despite the limited number of samples [18]. The study gathered fifty-five (55) participants and this number was relatively small.…”

“…The study gathered fifty-five (55) participants and this number was relatively small. Nevertheless, Guo et al [18] noted that experiments with minimal samples could still be performed. In addition, the researchers…”

“…Conjoint analysis is a well-known statistical approach that identifies consumer preferences by evaluating business attributes [18,19]. Attributes refer to the value of product or service characteristics [18].…”

“…Conjoint analysis is a well-known statistical approach that identifies consumer preferences by evaluating business attributes [18,19]. Attributes refer to the value of product or service characteristics [18]. These attributes comprised different sets of levels to scrutinize the fundamental characteristics [18].…”

“…Attributes refer to the value of product or service characteristics [18]. These attributes comprised different sets of levels to scrutinize the fundamental characteristics [18]. Moreover, studies utilized conjoint analysis to leverage business products [20] and services [21] to encourage potential consumers and maintain existing ones.…”