Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach

Sofia Balula Dias, Yannis Oikonomidis, José Alves Diniz, Fátima Baptista, Filomena Carnide, Alex Bensenousi, José María Botana, Dorothea Tsatsou, Kiriakos Stefanidis, Lazaros Gymnopoulos, Kosmas Dimitropoulos, Petros Daras, Anagnostis Argiriou, Konstantinos Rouskas, Saskia Wilson-Barnes, Kathryn Hart, Neil Merry, Duncan Russell, Jelizaveta Konstantinova, Elena LalamaAndreas Pfeiffer, Anna Kokkinopoulou, Maria Hassapidou, Ioannis Pagkalos, Elena Patra, Roselien Buys, Véronique Cornelissen, Ana Batista, Stefano Cobello, Elena Milli, Chiara Vagnozzi, Sheree Bryant, Simon Maas, Pedro Bacelar, Saverio Gravina, Jovana Vlaskalin, Boris Brkic, Gonçalo Telo, Eugenio Mantovani, Olga Gkotsopoulou, Dimitrios Iakovakis, Stelios Hadjidimitriou, Vasileios Charisis, Leontios J. Hadjileontiadis

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1–H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1–H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R2) was found within the range of 0.224–0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

Original languageBritish English
Article number898031
JournalFrontiers in Nutrition
Volume9
DOIs
StatePublished - 1 Jul 2022

Keywords

  • AI-based personalized nutrition
  • behavior change
  • healthy living
  • mobile application
  • modified Technology Acceptance Model (mTAM)
  • PROTEIN app
  • smartphone app-based nutrition support

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