Mobile Health App for Elderly with Functional Impairments Based on Fuzzy KANO Entropy Weighted TOPSIS: Evaluation Method (Preprint)

Abstract

BACKGROUND Mobile health app applications alleviate the shortage of elderly care services. However, due to the characteristics of the homebound disabled elderly, a more precise classification of their needs is required. Research concerning the evaluation methods for mobile health applications among the homebound disabled elderly remains insufficient. OBJECTIVE To address this challenge, this paper proposes an evaluation method based on the fuzzy KANO combined with entropy weight TOPSIS. The proposed fuzzy KANO model enhances the classical KANO model by employing scoring within a specific range, accommodating respondent uncertainty and fuzziness, and accurately discerning user needs. In combination with the entropy weight TOPSIS model, the proposed method effectively assists homebound disabled elderly individuals in addressing their needs. METHODS Firstly review of research literature is conducted to assess the needs of homebound disabled elderly individuals. The fuzzy KANO model was applied to classify these needs. Secondly, experts were invited to score satisfaction indicators using the Likert scale. By combining this information with the entropy weight method, objective design element weights were obtained. TOPSIS was then used to determine the importance ranking of each design element. RESULTS Finally, it was concluded that Plan C is the optimal design solution. Health and safety, emotional communication, ease of use, convenient services, and information privacy have become important factors for homebound disabled elderly individuals when choosing mobile health app applications. CONCLUSIONS This approach is anticipated to offer more reasoned decision support in various other domains as well.