Dias Sofia Balula, Oikonomidis Yannis, Diniz José Alves, Baptista Fátima, Carnide Filomena, Bensenousi Alex, Botana José María, Tsatsou Dorothea, Stefanidis Kiriakos, Gymnopoulos Lazaros, Dimitropoulos Kosmas, Daras Petros, Argiriou Anagnostis, Rouskas Konstantinos, Wilson-Barnes Saskia, Hart Kathryn, Merry Neil, Russell Duncan, Konstantinova Jelizaveta, Lalama Elena, Pfeiffer Andreas, Kokkinopoulou Anna, Hassapidou Maria, Pagkalos Ioannis, Patra Elena, Buys Roselien, Cornelissen Véronique, Batista Ana, Cobello Stefano, Milli Elena, Vagnozzi Chiara, Bryant Sheree, Maas Simon, Bacelar Pedro, Gravina Saverio, Vlaskalin Jovana, Brkic Boris, Telo Gonçalo, Mantovani Eugenio, Gkotsopoulou Olga, Iakovakis Dimitrios, Hadjidimitriou Stelios, Charisis Vasileios, Hadjileontiadis Leontios J
CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Lisbon, Portugal.
Intrasoft International SA, Thessaloniki, Greece.
Front Nutr. 2022 Jul 1;9:898031. doi: 10.3389/fnut.2022.898031. eCollection 2022.
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 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 the acceptance/rejection of six related hypotheses (H1-H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related -value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1-H6 can be accepted ( < 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 ( ) 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.
智能手机拥有的普遍性、其广泛的应用和使用，以及其及时反馈的交互式传递，对于与健康相关的行为改变干预移动应用程序具有吸引力。然而，用户对这类应用程序的看法对于更好地弥合其设计意图与有效实际使用之间的差距至关重要。有鉴于此，本文提出了一种改进的技术接受模型（mTAM），以解释用户在使用基于人工智能的智能手机应用程序进行个性化营养和健康生活（即PROTEIN）时的看法与mTAM构建之间的关系，以及他们在营养和体育活动习惯方面的行为改变。具体而言，对2个月内来自85名PROTEIN应用程序用户的在线调查数据进行了验证性因子分析（CFA）和回归分析（RA），以揭示mTAM构建之间的关系，即感知有用性（PU）、感知易用性（PEoU）、感知新颖性（PN）、感知个性化（PP）、使用态度（UA）和使用意图（UI）与用户行为改变（BC）之间的关系，分别通过接受/拒绝六个相关假设（H1 - H6）来表示。所得的与CFA相关的参数，即因子载荷（FL）及其相关的p值、平均方差提取量（AVE）和组合信度（CR），以及RA结果表明，所有假设H1 - H6均可接受（p < 0.001）。特别是，发现在所有情况下，FL > 0.5，CR > 0.7，AVE > 0.5，这表明mTAM框架内的项目/构建具有良好的收敛效度。此外，调整后的决定系数（R²）在0.224 - 0.732范围内，证明了PU、PEoU、PN和PP对UA有积极影响，进而对UI有积极影响，从而导致BC。此外，使用层次回归分析，当将UI用作中介变量时，确定了从UA对BC预测的显著变化。所探索的mTAM框架为解释每个构建在PROTEIN应用程序功能中的作用提供了手段，该应用程序作为一种支持工具，帮助用户通过改变饮食和体育活动习惯来改善健康生活。本文的研究结果为制定新策略和政策提供了见解和参考，以改善应用程序设计师、开发者、行为科学家、营养学家、体育活动/运动生理学专家和营销专家之间的合作，以进行旨在实现行为改变的应用程序设计/开发。
