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

被引：13

作者：

Dias, Sofia Balula ^{[1
]}

Oikonomidis, Yannis ^{[2
]}

Diniz, Jose Alves ^{[1
]}

Baptista, Fatima ^{[1
]}

Carnide, Filomena ^{[1
]}

Bensenousi, Alex ^{[2
]}

Botana, Jose Maria ^{[3
]}

Tsatsou, Dorothea ^{[4
]}

Stefanidis, Kiriakos ^{[4
]}

Gymnopoulos, Lazaros ^{[4
]}

Dimitropoulos, Kosmas ^{[4
]}

Daras, Petros ^{[4
]}

Argiriou, Anagnostis ^{[5
]}

Rouskas, Konstantinos ^{[5
]}

Wilson-Barnes, Saskia ^{[6
]}

Hart, Kathryn ^{[6
]}

Merry, Neil ^{[7
]}

Russell, Duncan ^{[7
]}

Konstantinova, Jelizaveta ^{[7
]}

Lalama, Elena ^{[8
]}

Pfeiffer, Andreas ^{[8
]}

Kokkinopoulou, Anna ^{[10
]}

Hassapidou, Maria ^{[10
]}

Pagkalos, Ioannis ^{[10
]}

Patra, Elena ^{[9
,10
]}

Buys, Roselien ^{[9
,11
,12
]}

Cornelissen, Veronique ^{[11
,12
]}

Batista, Ana ^{[13
]}

Cobello, Stefano ^{[14
]}

Milli, Elena ^{[14
]}

Vagnozzi, Chiara ^{[15
]}

Bryant, Sheree ^{[16
]}

Maas, Simon ^{[17
]}

Bacelar, Pedro ^{[18
]}

Gravina, Saverio ^{[19
]}

Vlaskalin, Jovana ^{[20
]}

Brkic, Boris ^{[20
]}

Telo, Goncalo ^{[21
]}

Mantovani, Eugenio ^{[22
]}

Gkotsopoulou, Olga ^{[22
]}

Iakovakis, Dimitrios

Hadjidimitriou, Stelios

Charisis, Vasileios

Hadjileontiadis, Leontios J.

机构：

[1] Univ Lisbon, Fac Motricidade Humana, CIPER, Estr Costa, Lisbon, Portugal

[2] Intra soft Int SA, Thessaloniki, Greece

[3] Grp CMC, Madrid, Spain

[4] Ctr Res & Technol Hellas, Thessaloniki, Greece

[5] Ctr Res & Technol Hellas, Inst Appl Biosci, Thessaloniki, Greece

[6] Univ Surrey, Fac Hlth & Med Sci, Sch Biosci & Med, Guildford, England

[7] OCADO Technol, London, England

[8] Charite Univ med Berlin, Dept Endocrinol Diabet & Nutr, Berlin, Germany

[9] Charite Univ med Berlin, German Inst Human Nutr, Berlin, Germany

[10] Int Hellen Univ, Dept Nutr Sci & Dietet, Thessaloniki, Greece

[11] Katholieke Univ Leuven, Dept Rehabil Sci, Dept Biomed Engn, Leuven, Belgium

[12] Katholieke Univ Leuven, Dept Cardiovasc Sci, Leuven, Belgium

[13] Sport Lisboa Benf Futebol, Lisbon, Portugal

[14] Polo Europeo Conoscenza, Verona, Italy

[15] Fluviale SRL, Rome, Italy

[16] European Assoc forthe Study Obes EASO, Middlesex, England

[17] AgriFood Capital BV, sHertogenbosch, Netherlands

[18] Healthium Nutrium Software, Porto, Portugal

[19] Datawizard SRL, Rome, Italy

[20] BioSense Inst, Res & Dev Inst Informat Technol Biosyst, Novi Sad, Serbia

[21] PLUX, Wireless Biosignals, Lisbon, Portugal

[22] Vrije Univ Brussel, Res Grp Law Sci Technol & Soc, Fac Law & Criminol, Ixelles, Belgium

来源：

FRONTIERS IN NUTRITION | 2022年 / 9卷

基金：

欧盟地平线“2020”;

关键词：

smartphone app-based nutrition support; AI-based personalized nutrition; healthy living; PROTEIN app; mobile application; modified Technology Acceptance Model (mTAM); behavior change; INFORMATION-SYSTEMS SUCCESS; BEHAVIOR-CHANGE TECHNIQUES; MOBILE HEALTH; WEIGHT-LOSS; ADOPTION; FOOD; PERCEPTIONS; MOTIVATION; ATTITUDES; TRUST;

D O I：

10.3389/fnut.2022.898031

中图分类号：

R15 [营养卫生、食品卫生]; TS201 [基础科学];

学科分类号：

100403 ;

摘要：

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 (R-2) 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.

引用

页数：18