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

Introduction

Unhealthy diets and physical inactivity are considered among the main modifiable risk factors for non-communicable diseases, the so-called Lifestyle Diseases. In fact, annually, 4.1 million deaths have been attributed to excess salt/sodium intake, and 1.6 million deaths attributed to physical inactivity (1). Many conventional approaches have been proposed and utilized to assess dietary intake and physical activity levels, mainly based on paper-based diaries and questionnaires (2, 3). However, inaccuracies and responder burden are common in observational dietary and physical activity assessments, alongside a significant degree of under-reporting that it is positively correlated with notable increased body mass index (BMI) (4). Fortunately, the advancement of technology has led to the development of innovative dietary and physical activity assessment methods, a key example is the use of nutrition/physical activity-related apps, which are readily available to access and use on smartphones or tablets (5, 6). Potential benefits that arise from these methods are the ease of use, convenience, and the wide availability of these apps via free downloading from app stores. Moreover, these mobile apps may also enable greater self-monitoring and disease management by individuals with chronic diseases such as obesity, cardiovascular disease, osteoporosis, and type 2 diabetes (7–10).

Mobile health apps, designed to support and reinforce health behaviors or to reduce risk behaviors, are among the most commonly downloaded apps, since they offer a novel and effective way for health professionals to engage users in the promotion and adoption of new behaviors (11, 12). Several mobile apps have been designed and developed specifically to increase physical activity levels (13, 14), encourage weight loss through personal coaching (15, 16), boost healthy food choices (17–20), reduce sedentary behavior (21, 22), assess and track dietary intake (23), assess psychological distress (e.g., anxiety, depression) (24), support smoke cessation (25), and reduce alcohol consumption (26). Moreover, mobile apps developed based on behavior change models and theories, including the integration of different behavior change strategies (e.g., cognitive behavioral therapy, goal setting, real time feedback), are considered the most effective in securing behavior change in their users (27, 28). Nevertheless, many apps designed for the purpose of changing behavior do not always involve input from health professionals and/or academics (29). From this perspective, it is clear that the adoption of the behavior change model alone is not sufficient and health professionals need also to consider how the mobile app is used to engage the user and, consequently, to facilitate behavior change. For instance, the Technology Acceptance Model (TAM), based on the Theory of Planned Behavior (TPB) (30) and originally proposed by Davis (31), has been widely used to evaluate user acceptance of general technologies. Overall, the TAM model investigates the drivers of technology acceptance, considering users' perceptions regarding innovations, and social/contextual aspects. Moreover, the validity of the TAM has been explored in different areas, such as wearable technologies (32) and telehealth (33), among others; however, limited explanatory power for particular system purposes has been identified (34).

Motivated by the aforementioned, this work aims to identify the main constructs and model their contributions to users' behavior change toward healthier living via personalized healthy diet and physical activity plans. To achieve this, the users' perspectives on the PROTEIN app (an artificial intelligence (AI)-based personalized nutrition mobile application for healthy living) are considered, and a modified Technology Acceptance Model (mTAM) is proposed. The main research problem that is examined here is whether specific interdependencies exist between the main constructs of a mTAM-based users' behavior model that could shed light upon the intention of the users to change their behavior and adopt a healthier living, undertaking personalized plans regarding their activity and nutrition, scaffolded by their interaction with the PROTEIN app.

Theoretical Background

Successful use of information technology (IT) applications and what influences such success have been thoroughly explored, and great attention has been paying by the information systems (IS) community for the past decades (35–37) as proved by the considerable number of research articles published in major IS-related outlets (38). In this vein, several theories that contribute to better explain the adoption and use of IT application have emerged, examples of which include the TAM (31, 39) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (40, 41). Both in TAM and UTAUT, the use of an IT application forms the dependent variable, i.e., the user's expected behavior when his/her working context (health status and lifestyle here) has been (or is about to be) affected by the introduction of an IT artifact (38). Subsequently, the independent variables refer to a wide range of factors, which have been known to influence the use behavior (42). Established theories from the field of social psychology, such as the TPB (30) and the Theory of Reasoned Action (TRA) (43), which explore the human behavior in general, were used to base both TAM and UTAUT. Acceptance, adoption, and use of IT applications are central behavioral constructs in TAM and UTAUT. Some related models have extended the perspective of the use of IT artifacts and focused on the success of IS, in terms of service, system, and information quality, with the DeLone and McLean's IS Success model (D&M-IS-SM) (44) being a milestone in such IS field. An epitomized description of the referenced models, i.e., TAM, UTAUT, and D&M-IS-SM, and their selection process follows.

Technology Acceptance Model (TAM)

The TAM model, developed by Davis et al. (31, 39), is used to measure the acceptance, adoption, and use of IT and has become very popular with many validation studies (32, 33). It relies on five different constructs, namely, perceived usefulness (PU), perceived ease of use (PEoU), usage attitude (UA), usage intention (UI), and actual usage (AU). TAM does not focus on the success of the IT application, but on studying and predicting the user's intention to use it. TAM can be used for actual system use in both subjective (self-reporting questionnaire) and objective measurement (system log) (45). Different extensions have been proposed ever since its arrival, in an effort to strengthen TAM's explanatory power. Indicative examples include the work from: a) Liang and Yeh (46), who replaced the PU with the Perceived Entertainment, in an effort to understand if the users have different feelings when interacting with smartphone-based games across different settings and locations; b) Kim et al. (47), who added the construct of Perceived Value, to explore the system/service quality on users' beliefs of hospitality industry information management systems; c) Morosan (48), who added the construct of Perceived Innovation, when using TAM for exploring biometric systems utilization by hotels; d) Lee and Wan (49), who augmented TAM with the constructs of Functional Confidence and Familiarity for acceptance of airline e-ticketing services acceptance by travelers. These works show potential for TAM evolution; one of its referenced versions is TAM 3, which contemplates a comprehensive nomological network of the determinants of IT adoption and use by individuals (50). TAM has been tested and validated numerous times, and as Benbasat and Barki pointed out (51), TAM is considered as one of the most influential theories in IS.

Unified Theory of Acceptance and Use of Technology (UTAUT)

A total of eight models already considered in, or resulting from, research on IT acceptance (40), are the combinatory basis for the formulation of the UTAUT, i.e., TRA (43); TAM (31, 39); motivational model (MM) (52, 53); TPB (30); a model combining TAM and the TPB (54); model of PC utilization (MPCU) (55); innovation diffusion theory (IDT) (56); and social cognitive theory (SCT) (57, 58). The behavioral intention is suggested by the UTAUT as the main factor that determines the use of IT application. A total of four key constructs have direct effect on the perceived likelihood of adopting the technology, i.e., Performance Expectancy (the degree to which a user believes that by using the IT application will gain attainment in his/her job performance), Effort Expectancy (the degree of ease associated with the use of the system), Social Influence (the degree to which a user is influenced by the opinions of important others, who believe that s/he should use the IT application), and Facilitating Conditions (the degree to which a user believes that an appropriate infrastructure, in terms of organization and technical perspectives, exists to support the use of the system) (40). Age, gender, experience, and voluntariness of use are considered as moderators of the effect of predictors (40, 59). The current version is UTAUT 2 (38, 41, 59), where the use of technology by individuals is underpinned by the effect of the three additional constructs, namely, Hedonic Motive (the fun or pleasure derived from using technology), Cost/Perceived Value (trade-off between perceived benefits and monetary cost of using the IT application), and Habit (the extent to which people tend to automatically perform behaviors) (41). The inclusion of hedonic motivation construct in the UTAUT 2 was found to be more important than performance expectancy (60), whereas the integration of price value provided a measure of the IS use cost within the consumer context (59).

DeLone and McLean's IS Success Model (D&M-IS-SM)

D&M-IS-SM was first proposed in (44) and it is a process/causal model. It is based on Shannon and Weaver's model of communication (61, 62) and the extension by Mason (63). Actually, the idea behind was based on the assumption that the process in IS resembles the one in the communication system, considering IS as a process of information production, conveying it and transmitting it to the recipients/users. Motivated by the metrics of Shannon and Weaver's communication model, D&M-IS-SM was built upon three parts of instruments for measuring IS success, i.e., technical (system quality), semantic (information quality), and effectiveness success (use, user satisfaction, individual impact, and organizational impact) (44). D&M-IS-SM was criticized for the inclusion of the Use construct and thus was later revised (64, 65), where the Service Quality and Use constructs were replaced by the Intention to Use/Use and the Individual Impact and Organizational Impact were substituted by Net Benefits. The D&M-IS-SM serves a dual purpose: it provides a classification of success measures, and it shows how the measures influence each other. The D&M-IS-SM has numerously been tested and validated since its inception, with some studies showing the strong validity of the construct, whereas some others reporting partial validity only (66, 67).

Technology Acceptance Framework Selection

As Madriana et al. (68) suggest, the selection of the suitable technology acceptance framework can be based on the availability of the variable(s) in the framework with evidence from previous studies that can be best predictors for the behavior intention, the validation of the framework (based on the popularity/citations), and the fecundity (“fertility” of the theory to generate new model and hypotheses). From a comparative analysis across the TAM, UTAUT, and D&M-IS-SM reported in (68), TAM exhibits the highest popularity and the most fecundity. With regard to the evidence from previous studies, TAM was preferred in evaluating users' adoption of mobile health apps for nutrition and active living, such as in (69–74). Furthermore, in the D&M-IS-SM, there is a very broad concept of Net Benefits; this poses some challenges in clearly and carefully defining the stakeholders and context in which such net benefits are to be measured (64, 65). Moreover, as D&M-IS-SM is focused on the quality of service, a consideration should be taken about the changes in the practice of service and in the conceptual view of service quality that the use of information technology introduced nowadays. The initial view of the service quality was on the services provided by the information system to its users. However, nowadays, the service is materialized via interactions between the user and the IT provider in a serving function; hence, the information and system quality are viewed as a resource owned by the IT provider, who can clearly influence the user's intentions to continue interacting with the IT provider (75).

Based on the aforementioned evidence, the TAM model has been selected here as a starting point to build a modified version (mTAM) and to explore the role of the PROTEIN app as the technology artifact that could help its users to change their behavior and adopt a healthier living, in terms of improved nutrition and activity.

Materials and Methods

The PROTEIN Smartphone App

Several digital mobile health technologies for self-management of nutrition (76) and physical activity (77) have been proposed in the digital market place. Nevertheless, a lack of expert nutrition and physical activity advice provided by current mobile apps is identified, igniting further concerns regarding personalized advice tailored to specific user preferences (including dietary and allergies, PA for heart health, etc.). For instance, a content analysis of more than 50 weight loss mobile apps showed that more than 60% of the apps lack expert recommendations (78). This fact leads to the demand for high-quality and evidence-based mobile app design/development process, considered as a co-creative process between designers, developers, researchers, experts/clinicians, and end-users (79).

To address the aforementioned, a holistic approach is suggested with the introduction of the PROTEIN smartphone app. The latter is developed within the framework of an ongoing European H2020 research and innovation project (2019–2022), namely, “PROTEIN: PeRsOnalized nutriTion for hEalthy livINg” (https://protein-h2020.eu/). The latter involves a multidisciplinary team of 20 partners from a total of 11 European countries, including partners from industry, research, and technology organizations, aiming to promote a healthy lifestyle by combining the latest technologies, such as smartphones and sensors, to offer personalized nutrition and physical activity plans (80). Already available via Google Play Store¹ and currently supporting eight different languages (i.e., English, Italian, Dutch, German, Portuguese, Greek, French, and Spanish), the PROTEIN app incorporates information provided in user profiles. This includes anthropometric, biochemical, physiological, and physical activity data and personal preferences and objectives, including eating and/or physical activity habits, and proposes personalized healthy-eating and physical activity advice (through personalized nutrition and physical activity plans) (Figure 1). An additional feature enables specialized advice to be provided to individuals living with or at risk of chronic diseases (e.g., obesity, cardiovascular disease, and diabetes). The PROTEIN app leverages automated decision-making tools, including profiling and AI, to propose advice on personalized meal plans and physical activities. In particular, by including a deep learning component to generate/update recommendations for the user's dietary intake (taking into consideration their dietary preferences, daily intake, lifestyle and physiological variables), the PROTEIN app provides recommendations for intakes within the expert-established targets (81). Furthermore, PROTEIN app supports additional functionalities, such as nearby restaurant recommendations, offering a range of price points and food types; customization of user's shopping cart using ingredients of eating plans proposed by the system, and incorporation of new meals into the system by recognizing images. This capability provides automated food volume estimation from just a single food image (82), facilitating the data input to the PROTEIN app and contributing toward the development of an accurate dietary assistant application. PROTEIN app acknowledges the effectiveness of health-related games to engage users in dealing with their health (83, 84) and informing them about deterioration of symptoms in diseases (85), and thus, it also incorporates different dietary games. In this way, PROTEIN app fosters adherence to the suggested plans, making the app fun to use, and providing the means for encouragement and/or (re)education on nutritional value of the various food groups toward the adoption of a healthy and balanced diet.

FIGURE 1

Figure 1. Screen shots of the PROTEIN mobile application [Source: Google Play Srore].

The PROTEIN app also provides a “Help center” portal for questions and/or suggestions to improve the functionalities of the PROTEIN app. However, it is important to underline that the PROTEIN app is not a medical device, meaning that information and advice that PROTEIN app provides does not constitute medical advice. Finally, complying with the General Data Protection Regulation (EU GDPR) (https://gdpr.eu), the PROTEIN app allows the users to download and have access to their data, withdraw from the application at any time and delete all previously collected data.

Since the PROTEIN app is co-designed with the users and implemented for their benefit, the analysis of their perceptions when using the PROTEIN app is crucial, as it could shed light upon the role of specific structural characteristics and functionalities that further foster their intention to use the PROTEIN app for scaffolding their behavior change toward healthier living. Consequently, the PROTEIN app can have a positive footprint in people's nutritional and physical activity habits. In this vein, an experimental study was set up as a framework to acquire user's perspectives about the use of the PROTEIN app and model them, accordingly, as explained next.

Study Design

A total of ninety-three adult participants in total were asked to download and use the PROTEIN app within a 2-month period (15 April -−15 June 2020) and then to participate in an online PROTEIN survey (refer to Supplementary Material – Online Survey), to give feedback about their use of the PROTEIN app. The type of sample design followed was the non-probability sampling design, using as sampling technique the convenience sampling, i.e., each respondent is selected for inclusion in the sample based on the ease of the access. Since the subjects must be smartphone users, the sample frame adopted was social media platforms/channels (e.g., Twitter, Instagram, Facebook), emails, and web advertisements, supported with flyers. During the registration process of the PROTEIN app, the participants were asked to fill in the required field and access the “Terms of Use” and “Privacy Policy” pages to complete the registration process, being, thus, informed about the purposes of the PROTEIN project and the processing of their personal data. Once registered, users were sent an email to activate their account and allow them access to the PROTEIN app.

The online PROTEIN survey was designed and developed by a multidisciplinary team within the PROTEIN Consortium, based on a “co-creation” process (86), in which nutrition and physical activity experts, researchers, end-users, designers, and developers explore a problem and produce solutions together, considering their different approaches, needs, and perspectives (87, 88). In this way, an open, active, and creative process, where all relevant stakeholders are engaged, was followed.

For the design of the online PROTEIN survey, the following requirements were considered: i) least time consuming, i.e., no more than 20 min for completing it; ii) simple, objective, and quantitative structure, i.e., as little free text as possible; iii) inclusion of demographic information; and iv) feedback acquisition on what is more/less appealing to users. After review rounds within the PROTEIN Consortium, the final version of the online PROTEIN survey resulted in an efficient version. The online survey consisted of 28 questions, divided into sections, including rating scale, multiple-choice, dichotomous, and open-ended questions (refer to Supplementary Material – Online Survey). The online PROTEIN survey was provided to users utilizing the SurveyMonkey platform (SVMK Inc., CA, USA).

From the 93 participants in the study, 85 (refer to full demographic characteristics in the Results section) provided online consent and fully completed the online PROTEIN survey; hence, only their responses were kept for the modeling analysis, described next.

Proposed mTAM Framework and Related Hypotheses

Taking into consideration the TAM model (refer to theoretical background section), this study adopts the PU, PEoU, UA, and UI from TAM model and introduces two additional constructs, i.e., the “Perceived Novelty” (PN) and the “Perceived Personalization” (PP), toward the model output of “Behavior Change” (BC). This results in a modified TAM (mTAM) framework, as depicted in Figure 2.

FIGURE 2

Figure 2. The proposed mTAM framework and the corresponding hypothesis (H1–H6).

To explore the contribution of each mTAM construct to the BC, a series of hypotheses is introduced. Davis et al. (39) mention that both PU and PEoU directly impact usage intention (UI) via the influence of UA. Moreover, the study of Holden and Karsh (89) states that the PU and the PEoU positively impact UA, showing that when users are confident that the adoption of novel technologies helps to improve their work performance, they exhibit more positive UA for adopting these novel technologies. In addition, if users perceive that a novel technology is easier to learn and that they do not need to apply much effort on the technology, then they have a more positive UA toward adopting this technology. This is also supported by the findings of Chopra et al. (90) that stress the influence of PU and PEoU to the UA, when evaluating the acceptance of dietary service applications by smartphone users using TAM. In the same vein, Mohammadi and Isanejad (91) found an influence of PEoU to UA, when evaluating the acceptance of IT in activity organization. From these perspectives, Hypotheses 1 and 2 are proposed below:

Hypothesis 1 (H1): Users' perceived usefulness toward the AI-based smartphone PROTEIN app positively influences usage attitude.

Hypothesis 2 (H2): Users' perceived ease of use toward the AI-based smartphone PROTEIN app positively influences usage attitude.

A crucial characteristic of any innovation is its novelty. Wells et al. (92) tried to frame the PN of an IT innovation as a salient affective belief in the nomological network related to the technology adoption. They conclude that the PN is an affective belief that plays a significant role in the adoption of IT innovations. So, if users perceive that novelty, it influences the adoption process, representing new alternatives to the existing ones, then they have a more positive UA toward adopting this technology. This was supported by the work of Robinson et al. (93), who found that new trends in technology engage the users and transfer them the notion that they use new products and technology before others, i.e., they become the leaders in acceptance of new ideas and products. In fact, novelty indirectly affects user's intentions and behavior, especially regarding new technological innovation (94). In this line, Hypothesis 3 is proposed as follows:

Hypothesis 3 (H3): Users' perceived novelty toward the AI-based smartphone PROTEIN app positively influences usage attitude.

Personalized approaches seem to be needed to promote healthy behaviors. Personalization concepts associated with behavior change in mobile health can be categorized into four different dimensions, namely: users (related to user-related characteristics, e.g., personality, profile, need for cognition, and perception of social norms), system functionalities (related to the functionalities that can be found in apps, e.g., reminders, gamification features), information (related to the way information is transmitted, e.g., type of feedback provided), and mobile app properties (e.g., the aesthetics of the app) (95). In addition, a recent meta-analysis study that aimed to evaluate the impact of personalized mobile apps and fitness trackers on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption) revealed that studies must explore the integration of multiple data from different sources and include personalized features other than content (96). In fact, smartphone users seem highly receptive to personalized content and services; personalized recommendations, for instance, can generate better responses according to the user personal preferences and interests (97). Some studies have also suggested that users' attitudes and behaviors can be affected by personalization aspects (98, 99). In this vein, it is very probable that users' PP affects UA toward UI and then to BC. Thus, we hypothesize the following (H4):

Hypothesis 4 (H4): Users' perceived personalization toward the AI-based smartphone PROTEIN app positively influences usage attitude.

Davis et al. (39) and Robbins (100) state that UA directly influences UI, meaning that users' real usage behaviors are determined by UI, and that their UI are determined by their individual UA. In addition, Heberlein and Black (101) suggest that if UA and UI are more specific, then the relationship between the two is more distinct. From these perspectives, it becomes clear that if the users have a more positive UA toward a system, then they will have a higher UI toward its use. This is also supported by the work of Chen et al. (102), who explored the individual's attitude toward and intention to download and use dietary and fitness apps, and the findings of Okumus et al. (103), who explored the factors that influence customers' acceptance of smartphone diet apps when ordering food at restaurants. Hence, the Hypothesis 5 is proposed:

Hypothesis 5 (H5): Usage attitude of the users toward the AI-based smartphone PROTEIN app positively influences usage intention.

Usage intention defines the degree to which an individual is willing to engage in a particular behavior (43). When individuals' UI is strong, the probability that they will engage in that behavior is higher, and consequently, UI and real behaviors present a strong correlation. Furthermore, UI has been considered the best variable to predict an individual's behavior (43). This view is also adopted in the work of West et al. (104), who analyzed how diet and nutrition-related mobile apps lead to behavior change, and in the work of Requero et al. (105), where the enhancement of the correspondence between attitudes and behavioral intentions was explored as promoter of healthy eating. In this vein, Hypothesis 6 is proposed, as follows:

Hypothesis 6 (H6): Usage intention of the users toward the AI-based smartphone PROTEIN app positively influences behavior change.

Statistical Analysis

Stata version 14 (StataCorp) was used for the statistical analysis. Descriptive statistics were calculated for demographics and the related questions from the online survey (refer to Supplementary Material – Online Survey). To explore the magnitude of the influence between the mTAM constructs, a linear regression analysis was adopted and the corresponding parameters, i.e., coefficient of determination (R²), adjusted R² (adj R²= 1 - $\left[\frac{(1-R^2)(N-1)}{N-k-1}\right]\le R^2$, where N is the number of points in the data sample and k is the number of independent regressors (variables), excluding the constant), regression coefficient with standard error (SE), t ratio (the estimate divided by the SE), p - value, and 95% confidence interval (CI), were estimated. Values of p < 0.05 and |t| > 1.96 were set as thresholds for adopting statistically significantly difference from 0 of the coefficient's estimate at the 95% CI, hence for adopting the corresponding hypothesis. Moreover, the F-statistics, i.e., the ratio of the mean sum of squares of the model to that of the residual, were also estimated for measuring how the ratio of the explainable mean variance to the unexplainable mean variance is statistically >1. The corresponding degrees of freedom for the model and residual are given in parentheses.

The regression analysis has been selected instead of the structural equation modeling (SEM) (106–110), as the latter represents and relies upon the causal assumptions of the researcher, and its credibility depends on the credibility of the causal assumptions in each application (111). Moreover, there is a semantic difference between the coefficients originating with a regression where no causal assumptions are made vs. from a SEM that makes strong and weak causal assumption (111). In this way, with the adopted regression analysis, any bias from the classification to strong and weak causal assumptions in the construction of the mTAM framework is avoided.

The reliability and validity of the items used in the proposed mTAM framework have been tested, accordingly. In particular, for the reliability analysis to examine the internal consistency and stability of grouped variables, denoting how closely related a set of items are as a group, the Cronbach's α was estimated as a $=\frac{M\overline{C}}{\overline{var}+(M-1)\overline{C}},$ where M is the number of grouped items, $\overline{C}and\overline{var}$ are the average covariance between item - pairs and average variance, respectively. Values of a ≥ 0.70 denote acceptable internal consistency (112). With regard to the validity analysis, the confirmatory factor analysis (CFA) (113) was adopted, and the factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR) parameters were estimated. According to Nunnally (114), if FL > 0.5, AVE > 0.5, and CR > 0.7, then the research variables exhibit good convergent validity.

Results

Demographic Characteristics

The PROTEIN app was evaluated by eighty-five (n = 85) volunteers coming from eight European countries (i.e., Greece, Portugal, Germany, UK, Belgium, Italy, the Netherlands, and Serbia) with average age of 49.3 years (±std = 16.3 years; age range = [25−44] years), who responded anonymously to the online survey (average missing values <4). The highest percentage of the respondents, 32%, were in the 35–44 age groups followed by 30% of 25–34 years old, then 16% of 18–24 years old, 12.5% of 44–54, 7% of 55–64, and the least, 2.5% was for 65 and over years old. Moreover, the distribution of male/female was 56% male and 41.5% female. In terms of body weight and body height, the means were 72.5 kg (±std = 16.5 kg) and 164 cm (±std = 8.4 cm). The majority of users (74%) were classified as general public and therefore did not belong to any clinically defined group (such as users under medical/nutrition supervision).

General Users' Perspectives

The survey responses of the PROTEIN app users provided some insights into their general opinions of the app. In particular:

1. Primary usage goal: the most important primary health/well-being goal pointed out by the respondents were “Be more active,” “Eat more healthily,” and “Lose weight,” revealing that good nutrition, physical activity, and a healthy body weight are the essential components of a person's overall health and well-being. On the opposite side, the following statements: “Manage an existing health condition,” and “Manage specific dietary requirements” were considered by the respondents as the least important primary health and well-being goal.

2. Usefulness: this was perceived by the users as the ability of PROTEIN app to improve health, encourage more exercise, and provide suitable recommendations for activities and healthier meal plans.

3. Ease of use: this was perceived by the users via the easiness of interacting with and organizing the supermarket shopping list, fostering the easy customization of personalized plans with wide variety and alternative options within the meal plan and activities (number and types of suggested meals and activities), or fast and easy-to-use functionalities of adding a meal and/or an activity.

4. Personalization: adaptation to the personalized characteristics of each user was considered important in the user profile of the PROTEIN app, covering many important attributes related to potential allergies, insufficiencies, intolerances, or personal diet. In addition, the personalized feedback, in terms of personalized notifications and achievements, contributed to boosting the user perception of PROTEIN app personalization feature.

5. Novelty: the users expressed their perception of novelty via the uniqueness and variety of the features provided by the PROTEIN app, compared to the other apps that relate to nutrition and physical activity, especially mentioning the feature of recording the meal/activity.

The aforementioned perspectives are propagated through the usage attitude and intention as means for activity and eating behavior change, as reflected in the adopted mTAM and the related hypothesis testing results as presented below.

Hypotheses Testing

Figure 3 depicts the path relationships between mTAM constructs (Figure 2) and the related items via the values of adj R² (^* p < 0.05;^** p < 0.01; ^*** p < 0.001), as they were derived from the performed regression analysis. Moreover, Table 1 tabulates all the corresponding results regarding different regression paths that relate items with separate constructs (nos. 19), and the regression paths between constructs that support the hypothesis testing outcome (nos. 1015), along with the corresponding Cronbach's α (where applicable). The effect of sex, age range, and BMI was tested for all the regression paths tabulated in Table 1. From this analysis, statistically significant differences related to sex were found for the regression paths of nos. 14 (p = 0.028, 0.013, 0.019, and 0.017, respectively). Moreover, statistically significant differences related to age range and BMI were only found for the regression paths of no 12 (p = 0.041) and no 13 (p = 0.002), respectively.

FIGURE 3

Figure 3. Path relationships between mTAM main constructs (Figure 2) and related items via the values of adj R² and the estimated regression coefficients (in italics). *p < 0.05; **p < 0.01; ***p < 0.001.

TABLE 1

Table 1. Reliability, validity and least squares regression analysis results for the adopted mTAM model (Figures 2, 3).

From a general perspective of the results presented in Table 1, it can be shown that the Cronbach's α values range from 0.7674 (group of items nos. 5, 6) up to 0.8456 (group of constructs nos. 10–13), revealing that the grouped items and constructs all have good internal consistency and reliability. In addition, the corresponding validity results, also tabulated in Table 1, show that all items have FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. All estimated values of F-statistics were significantly >1 supporting the acceptance of the related hypothesis. Moreover, in almost all cases, the estimated p - values are less than 0.001, with the exception of the regression paths no. 1 (p <0.01) and no. 5 (p <0.05), indicating extreme statistical significance. Additionally, the t ratio ranges from 2.54 (no. 5) up to 12.53 (no. 14), justifying the statistical validity of the estimated regression coefficients. Furthermore, R² varies from 0.110 (no. 5) up to 0.740 (no. 14) and the corresponding adj R² ranges from 0.100 (no. 5) up to 0.732 (no. 14), showing that, in most cases, there is a good fit of the regression models used to express the relation between the items/constructs of the proposed mTAM framework.

Following the paths of Figure 3, combined with the results of Table 1, it can be seen that the “Helps me to improve my health”, “Helps me to exercise more,” “Recommends me suitable activities,” and “Helps me plan healthier meals” items collectively contribute to the PU construct (Cronbach's α = 0.8367) of the mTAM. The corresponding adj R² values are 0.129, 0.193, 0.309, and 0.328, respectively, showing that the plan of healthier meals explains the highest part of the PU variance, compared to the “Recommends me suitable activities,” “Helps me to exercise more,” and “Helps me to improve my health” items. Moreover, the “Easy to organize supermarket shopping list” and “Easy in interaction” items both contribute to the PEoU construct (Cronbach's α= 0.7757) of the mTAM. The corresponding adj R² values are 0.327 and 0.387, respectively, revealing almost an equal contribution in the explanation of the PEoU variance. In its turn, the “Provides novel meal/activity recording” and “Differs from other related app” items collectively contribute for the PN construct of the mTAM α (Cronbach's α = 0.7674). Clearly, the latter item exhibits much higher adj R² value (0.719) when compared to the one from the former (0.100). This is anticipated, as the concept of novelty is first conveyed to the users via the comparison of the PROTEIN app with the state - of - the - art and then via the feature of the meal/activity recording. For the PP construct of the mTAM, the “Provides personalized notifications,” “Provides personalized achievements,” and “Provides complete personalized profile” items collectively contribute (Cronbach's α = 0.8071). From these items, the “Provides personalized notifications” item exhibits the highest adj R² of 0.411, followed by the “Provides complete personalized profile” (adj R²= 0.341) and the “Provides personalized achievements” (adj R² = 0.313) items. This shows that the sense of personalization is perceived by the users textitvia personalized feedback and the ability to customize the user's profile, taking into consideration the specificity of each user's preferences and sensitivities (e.g., allergies, insufficiencies).

Moving on to the hypotheses testing, the results from Table 1 justify that all hypotheses, i.e., H1-H6 (refer to Figure 2) can be accepted. In particular, there is a collective contribution of the PU, PEOU, PN, and PP constructs to the UA construct (Cronbach's α = 0.8456). In addition, the corresponding adj R² values are 0.306, 0.436, 0.224, and 0.424, respectively, showing that the PEoU explains a high part of the variation in the UA, followed by the PP, PU, and PN. There is a strong connection between the UA and UI constructs, as the corresponding adj R² value is 0.732, revealing that the establishment of a solid usage attitude will effectively lead to an established usage intention. Consequently, this solidification could feed the BC construct (adj R² = 0.445), explaining a significant part of its variance.

The aforementioned results showcase the validity of the proposed mTAM framework and related hypotheses to explain the interaction between the items and constructs that are activated during the process of PROTEIN app evaluation and its potential users' acceptance.

Discussion

Here, we present the use of the mTAM framework as a means to model the users' evaluation of the PROTEIN app, a smartphone-based nutrition and activity app developed by a multidisciplinary team to support self-management of dietary intake and physical activity and scaffold any necessary behavior change that would lead to healthier living. In view of this approach, the six hypotheses, i.e., H1–H6, of the proposed mTAM framework provide a fine-grained breakdown of the elements that are gradually built during the user's interaction with the PROTEIN app, explaining the dependencies and power of these constructs to provide a meaningful explanation of the users' perspectives on usefulness, easiness, novelty, personalization, usage attitude, and usage intention toward behavior change.

Overall, nutrition and physical activity-related mobile apps show promise as tools to successfully facilitate positive health behavior change. Furthermore, mobile apps that focus on improving motivation, feedback, self-efficacy, attitudes, knowledge, and goal setting may be particularly useful (104), as in the case of the PROTEIN app. Meal planning including meal variety, in particular, is considered one of the nutrition counseling strategies that facilitate food behavior changes (115). Moreover, meal planning can be viewed as one technique to deliver nutrition knowledge in a more practical way. When expert-verified knowledge is embedded within the related knowledge-based systems, personalization could be enabled in providing personalized feedback about healthy lifestyle, complying, at the same time, with established and ethical guidelines of different fields of nutrition research.

Based on the Methontology, i.e., a methodology for building ontologies (116), the PROTEIN app has included the nutrition and activity (NAct) ontology. The latter was created based on experts' knowledge from the fields of nutrition, activity, and health fields. In NAct ontology, each subject's implicit/explicit goals related to nutrition and well-being are connected with his/her situational condition and standardized European nutritional and well-being directives (117). Overall, the NAct ontology models: i) in a slim and holistic manner food-specific nutritional information and activity-specific well-being information; ii) nutritional and well-being user goals and relates them with nutritional and well-being information; iii) medical conditions, allergies, intolerances, deficiencies, and lifestyle dietary choices and relates them with nutritional and well-being information; and iv) properties that define specificities of the aforementioned relationships that aid in the selection of appropriate meals and physical activities for a given person. Adopting a holistic approach toward behavioral change perspectives, the NAct ontology has been integrated with the PROTEIN app AI advisor, which employs the LiFR fuzzy reasoning (118) scheme. In this way, the PROTEIN app infers the optimal recommendations for meals, restaurant menu items, and physical activities to each user, taking into consideration his/her dietary and medical profile.

The aforementioned design of the PROTEIN app, which combines NAct ontology with LiFR, contributes to the constructs of the adopted mTAM framework. Clearly, the users' evaluation results showcase the various capabilities of the PROTEIN app to facilitate meal and physical activity planning, to reflect about and create appropriate shopping lists, to be aware of the impact of eating healthy food and be more active, to easily interact with and find it as a useful tool, to have valuable personalized feedback and profile, taking into consideration specific nutrition (e.g., allergies, intolerances) or physical activity (e.g., herniated disk) issues. All these contribute to the users' UA of the PROTEIN app (H1–H4) and support UI to adopt it as a means (H5) that could potentially lead to their behavior change (H6), in terms of clarifying/improving nutritional and physical activity beliefs/habits. To further examine the mediating role of UA and UI in the BC, we performed hierarchical linear regression, examining the prediction of BC from UA and the prediction of BC from UA considering as mediator the UI. The results of this analysis are tabulated in Table 2. As it is seen from the latter, there is a significant change in the prediction of BC from UA when the UI is used as a mediating variable, since the regression coefficient in Path No. 1 (refer to Table 2), i.e., 0.640 (p < 0.001), is reduced to−0.071 (p = 0.742) when moving to Path No. 2. This is further justified by the difference in the R² and adj R² between the Path No. 1 and Path No. 2 (Table 2), which are statistically significant, since the corresponding estimated p-value equals to 0.001. On the contrary, the prediction of BC from UI when considering the UA is not severely affected, since the estimated regression coefficient (0.295; p < 0.001) does not significantly deviate from the estimated value in the regression Path No. 15 (Table 1), i.e., 0.273 (p < 0.001).

TABLE 2

Table 2. Hierarchical regression results for examining the mediation role of UA, UI in BC prediction in the adopted mTAM model (Figures 2, 3).

The role of the mobile apps as moderators for changing diet or physical activity behaviors/habits has been explored in various clinical settings (13, 119–122). In particular, the work of Spring et al. (119), through a 12-month randomized controlled trial (RCT) involving 70 adults (BMI∈[35−40] kg/m²), revealed that users with a personal digital assistant (PDA) to input a description of their daily meals and self-regulate their energy intake via a “goal feedback thermometer” updated with their current caloric intake, achieved greater weight loss than their control group counterparts. In another 24-month RCT, namely, Self-Monitoring and Recording Using Technology (SMART) (120), involving 210 adults with overweight/obesity, it has shown that the use of PDA with appropriate feedback contributed to the adults' dietary behavior change, resulting in significant weight loss when compared to those using paper diaries only for weight loss and maintenance monitoring. A 3-month RCT, namely, DialBetics (121), highlighted the benefit of using a smartphone-based app for self-managing type 2 diabetes with better control of the nutritional related parameters, i.e., the HbA1c and fasting glucose, when compared to the control group users. In a 10-week RCT described by Zhou et al. (122), the use of a smartphone app (incorporating machine learning) for delivering personalized information for step goals and physical activity monitoring was more effective in keeping the users more active than the allocation of a preset step goal per day. This shows the effect of the app personalization feature in achieving and maintaining active living. In a 9-month mPED RCT reported in (13), including 210 community-dwelling physically inactive women, the use of a smartphone app that fed back to the intervention group users their steps and physical activity resulted in increased physical activity when compared to the control group ones in the first 3 months and allowed them to maintain their physical activity for the next 6 months.

The aforementioned works highlight the efficiency of the related apps to be incorporated in clinical settings and act as the mediators for scaffolding the users' efforts toward healthier living. Actually, incorporation of mobile apps in the design of health promotion programs is growing, following the profound mobile app market exponential growth (123). Nevertheless, careful evaluation of the underlying “science” of such mobile apps is needed, to ensure that their characteristics and functionalities really reflect evidence-/theory-based principles of behavior change, maximizing, and their effectiveness (12, 124, 125). Moreover, the actual use (especially sustained use) of mobile apps to track health and related behaviors is questionable (126), especially when the user is overwhelmed and saturated by the manual tasks that s/he has to undertake, to successfully interact with the mobile app. A recent systematic review (13) explored the role of the mobile apps in the improvement in health behaviors and outcomes, such as physical activity, nutrition, drug, alcohol use, and mental health, showcasing their effectiveness as health behavior change moderators. The findings highlight the lack of appropriate framework(s) that accurately evaluate the role of the mobile health app in effectively promoting behavior change, hence affecting the evidence in supporting their key role in this behavior change (13). This finding, however, enhances the contribution of the proposed mTAM framework as a means for explaining the key constructs that could most contribute to shaping and supporting such health behavior change. Taking into consideration the validation of the hypotheses H1–H6 in the mTAM framework, the focus on various constructs could shed more light on the necessary features that the related nutrition and physical activity app should have, maximizing their contribution to the achievement of such constructs. Apparently, the PROTEIN app includes a variety of such features that makes it appealing to the users, with high perceived novelty (adj R² = 0.718), showing adequate acceptance and intention to be used in self-managing and supporting healthy eating and physical activity.

As expressed via the identified interdependencies between the mTAM items/constructs (Figure 3, Tables 1, 2), the users appreciate various perspectives of interaction with the PROTEIN app, i.e., assistive, informative, adaptive, personalized, novel, and rewarding, to build their attitude and intention of usage toward behavior change. From the users' perspective, these results reveal also some insights into understanding more about how they link themselves with the PROTEIN app. In particular, it seems that the establishment of the sense of instant assistance and/or real-time reliable information acquisition about their nutrition and activity in a personalized way positively affects their receptiveness to different PROTEIN app features and/or interaction strategies. PROTEIN app adaptivity and personalization using its AI-based engine can absorb any predisposition of the user to benefit from its use or put them at risk for negative consequences by influencing what type of information/features one attends to, how one processes that information, and subsequently uses the information/features to self-regulate own behavior (127, 128). There are limited data on the associations between mHealth app use and psychological characteristics of its users (126), and the demographic characteristics of the participants of this study, along with the online survey focus, do not support exploration of such dimension here. However, some works have linked different aspects of personality to different features of persuasive technologies (129, 130) and social cognitions (e.g., barriers, attitudes, and behavioral control) being predictive of technology uptake outcomes [e.g., intention to (131) or intensity of (132) mHealth app use], pointing to some potential dimensions for further incorporation within the mTAM framework.

From an implications' perspective, our findings provide actionable insights. In particular, the involved constructs in the mTAM framework highlight the need for: a) clear understanding of the users' health needs and introduction of apps with high degree of novelty, compared to the ones currently in the market, personalization, adaptation capabilities, and scalability within the growing market to meet different users' needs; the use of AI (as in the PROTEIN app) can contribute toward such need; b) easy-to-use apps, since the individual's decision to actually perform a health behavior has to be acted on in efficient and quick way (133). Consequently, the simplification in the registration and in the means of data acquisition provides a clear reduction in the user's effort when interacting with the app, and c) apps with clear goal setting, self-monitoring functionalities, personalized feedback, and instructions, as a means of establishing and retaining users to establish and extent users' retention, sustaining a balanced interaction flow between challenges and skills, helping the users in setting challenging but achievable tasks as goals to pursue.

Despite the promising results, some limitations of this work should be highlighted. Clearly, the number of participants (n = 85) may not be considered adequate for generalization of the findings to larger populations. However, as the average age of the participants is around 50 years, coming from eight European countries, we could argue that the sample is representative enough to ground the proposed mTAM model, assuming that, as adults with living experience, their understanding of the PROTEIN app use and the related survey may be adequate. Extending the sampling to larger and more diverse populations could further justify the validity of the current findings. The PROTEIN consortium has already scheduled the testing of the proposed mTAM framework in a second round of pilot studies that will involve a higher number of participants, an updated version of the PROTEIN app to include also iOS smartphone users, and populations, apart from the healthy ones, with specific characteristics, e.g., patients with diabetes, patients with cardiovascular disease, patients with obesity/overweight, and vulnerable population. The users' time period of the PROTEIN app use (2 months) sets also another limitation. In general, meeting the expectation for the healthier lifestyle goal requires long-term effort, and immediate rewards usually do not appear as substantial positive outcomes. However, the proposed mTAM framework provides the opportunity for a dynamic perception of its constructs, using it in a time-scale manner. In fact, mTAM outcomes could be evaluated upon meeting intermediate (short/mid-term) goals, providing output for the constructs that strongly participate in the sustainability of the users' positive perspective of the app, hence keeping them using the app. In this context, time-scaled versions of the mTAM could be combined to provide an explanation of the time-based dynamics of users' continuance intention (CI) for health apps (134–136). This would shed light upon the factors that can explain why many users discontinue to use the health app after its initial adoption. Thus, the mTAM model at specific time scale(s) could be enriched with additional constructs that may focus at explaining continued use or abandonment. For instance, some social, e.g., subjective norms (136) and psychological factors, e.g., health consciousness and flow (136–138) have been proposed to explain some technology usage patterns. A clear example of such factors is the disruptive effect of the COVID-19 pandemic on many people's health routines and status. The restriction on outdoors activities shifted the work out space from the gym to home. This has resulted in a download spike of health and fitness apps, combined with home work out equipment sales (139). Moreover, a spike in food delivery and online shopping due to COVID-19 altered the way nutrition is perceived (140). Considering that the PROTEIN app was evaluated during the core COVID-19 pandemic period, its functionality to assist the users in organizing their supermarket list (adj R² = 0.397) showed its capabilities to support healthy online food ecosystem.

In conclusion, this study provides evidence for the efficiency of the mTAM framework to express the users' perspectives about the PROTEIN app, an AI-based smartphone app that provides personalized recommendations for self-managing food intake and physical activity. The six hypotheses examined showcase the importance of different mTAM constructs to promote the behavior change in the users toward healthier living, by improving their eating and physical activity habits. In addition, the mediating role of the usage intention to the behavior change potential was explored, recommending focus upon the way the intention of using the PROTEIN app is scaffolded by its actual structural characteristics. The proposed mTAM framework can be useful in constructing recommendations for the relevant apps' design, revealing which aspects, in terms of structure, user interface, functionality, way of interaction, personalization, are considered important by the users to lead them in behavior change toward healthier living. Further evaluation studies are warranted to validate the power and generalization of the proposed mTAM framework, employing its time-scaled versions for also exploring the users' continuance intention.

Data Availability Statement

All data generated and analyzed during the current study are available from the corresponding author on a reasonable request.

Ethics Statement

The studies involving human participants were reviewed and approved by Local Ethics Committees and the corresponding IRB approvals were obtained for the research (Lisbon, Portugal CEIFMH 14/2021; Thessaloniki, Greece 2/20.09.2021; Berlin, Germany EA4-110-20). Online informed consent was obtained from every participant prior to enrolment in the online PROTEIN survey. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SD: conceptualization, study design, organization, and writing the manuscript. YO: online PROTEIN survey realization in the SurveyMonkey platform. LH: conceptualization, study design, organization, statistical analysis, manuscript review, and update. All authors contributed to the conceptual design and development of the PROTEIN mobile application and online PROTEIN survey, discussed the results and reviewed the manuscript, have read, and agreed to the published version of this manuscript.

Funding

The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 817732 (PROTEIN: PeRsOnalized nutriTion for hEalthy living).

Conflict of Interest

CV was employed by Fluviale SRL. YO and AB were employed by the company Intrasoft International SA. SG was employed by the company Datawizard. GT was employed by the company PLUX. JB was employed by the company Grupo CMC. DR and JK were employed by the company OCADO. SM was employed by the company AgriFood Capital BV. PB was employed by the company Healthium/Nutrium.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to thank all participants who volunteered to participate in this study for their time, commitment, and contribution. In addition, the authors would also like to thank and acknowledge all members of the PROTEIN consortium for their contributions.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2022.898031/full#supplementary-material

Footnotes

1. ^https://buff.ly/2Qz3M7i

References

1. World Health Organization. Noncommunicable diseases. (2021) Available online at: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed January 5, 2022).

Google Scholar

2. Johnson RK. Dietary intake-how do we measure what people are really eating?. Obesity. (2002) 10:63S−8S. doi: 10.1038/oby.2002.192

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Jacobs DR Jr, Ainsworth BE, Hartman TJ, Leon AS. A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med Sci Sports Exerc. (1993) 25(1):81-91. doi: 10.1249/00005768-199301000-00012

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Nielsen BM, Nielsen MM, Toubro S, Pedersen O, Astrup A, Sørensen TI, et al. Past and current body size affect validity of reported energy intake among middle-aged Danish men. J. Nutr. (2009) 139(12):2337-2343. doi: 10.3945/jn.109.112599

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Tison GH, Marcus GM. Will the smartphone become a useful tool to promote physical activity? The Lancet Digi Health. (2019) 1:e322-e323. doi: 10.1016/S2589-7500(19)30154-2

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Hamideh D, Arellano B, Topol EJ, Steinhubl SR. Your digital nutritionist. The Lancet. (2019) 393:19. doi: 10.1016/S0140-6736(18)33170-2

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Berry R, Kassavou A, Sutton S. Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis. Obes Rev. (2011) 22:e13306. doi: 10.1111/obr.13306

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Villarreal V, Berbey-Alvarez A. Evaluation of mHealth applications related to cardiovascular diseases: a systematic review. Acta Inform Med. (2020) 28:130–7. doi: 10.5455/aim.2020.28.130-137

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Alhussein G, Hadjileontiadis L Digital health technologies for long term self-management of osteoporosis: a systematic review and meta-analysis. JMIR mHealth uHealth. (2022) 3:7963. doi: 10.2139/ssrn.3897963

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Wahbeh A, El-Gayar O, Sarnikar S. A close look at socio-technical design features of mobile applications for diabetes self-management. Health Technol. (2021) 11:227–38. doi: 10.1007/s12553-020-00497-4

CrossRef Full Text | Google Scholar

11. Curtis K, Lahiri S, Brown K. Targeting parents for childhood weight management: Development of a theory-driven and user-centered healthy eating app. JMIR mHealth uHealth. (2015) 3:e69. doi: 10.2196/mhealth.3857

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Yang C-H, Maher JP, Conroy DE. Implementation of behavior change techniques in mobile applications for physical activity. Am. J. Prev. Med. (2015) 48:452–5. doi: 10.1016/j.amepre.2014.10.010

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Fukuoka Y, Haskell W, Lin F, Vittinghoff E. Short- and long-term effects of a mobile phone app in conjunction with brief in-person counseling on physical activity among physically inactive women: The mPED randomized clinical trial. JAMA Netw Open. (2019) 2:e194281. doi: 10.1001/jamanetworkopen.2019.4281

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Degroote L, De Paepe A, De Bourdeaudhuij I, Van Dyck D, Crombez G. Effectiveness of the mHealth intervention ‘MyDayPlan'to increase physical activity: An aggregated single case approach. Int. J. Behav. Nutr. Phys. Act. (2021) 18:1–12. doi: 10.1186/s12966-021-01163-2

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Lin P, Grambow S, Intille S, Gallis JA, Lazenka T, Bosworth H, et al. The association between engagement and weight loss through personal coaching and cell phone interventions in young adults: randomized controlled trial. JMIR mHealth uHealth. (2018) 6:e10471. doi: 10.2196/10471

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Lim SL, Ong KW, Johal J., Han CY, Yap QV, Chan YH, et al. a smartphone app-based lifestyle change program for prediabetes (d'lite study) in a multiethnic asian population: a randomized controlled trial. Front Nutr. (2022) 8:780567. doi: 10.3389/fnut.2021.780567

PubMed Abstract | CrossRef Full Text | Google Scholar

17. López D, Torres M, Vélez J, Grullon J, Negrón E, Pérez CM, et al. Development and evaluation of a nutritional smartphone application for making smart and healthy choices in grocery shopping. Health Inform Res. (2017) 23:16–24. doi: 10.4258/hir.2017.23.1.16

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Kakoschke N, Hawker C, Castine B, de Courten B, Verdejo-Garcia A. Smartphone-based cognitive bias modification training improves healthy food choice in obesity: a pilot study. Eur Eat Disord Rev. (2018) 26:526–32. doi: 10.1002/erv.2622

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Palacios C, Torres M, López D, Trak-Fellermeier MA, Coccia C, Pérez CM. Effectiveness of the nutritional app ‘MyNutriCart' on food choices related to purchase and dietary behavior: a pilot randomized controlled trial. Nutrients. (2018) 10:1967. doi: 10.3390/nu10121967

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Kliemann N, Croker H, Johnson F, Beeken RJ. Development of the top tips habit-based weight loss app and preliminary indications of its usage, effectiveness, and acceptability: Mixed-methods pilot study. JMIR mHealth uHealth. (2019) 7:e12326. doi: 10.2196/12326

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Arrogi A, Bogaerts A, Seghers J, Devloo K, Abeele VV, Geurts L, et al. Evaluation of stAPP: A smartphone-based intervention to reduce prolonged sitting among Belgian adults. Health Promot Int. (2019) 34:16–27. doi: 10.1093/heapro/dax046

PubMed Abstract | CrossRef Full Text | Google Scholar

22. King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, et al. Effects of three motivationally targeted mobile device applications on initial physical activity and sedentary behavior change in midlife and older adults: a randomized trial. PLoS ONE. (2016) 11:e0156370. doi: 10.1371/journal.pone.0156370

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Ahn JS, Kim DW, Kim J, Park H, Lee JE. Development of a smartphone application for dietary self-monitoring. Front. Nutr. (2019) 6:149. doi: 10.3389/fnut.2019.00149

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Donker T, Petrie K, Proudfoot J, Clarke J, Birch M-R, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J. Med. Internet Res. (2013) 15:e247. doi: 10.2196/jmir.2791

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, mpereliz trial. The Lancet. (2011) 378:49-55. doi: 10.1016/S0140-6736(11)60701-0

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Crane D, Garnett C, Michie S, West R, Brown J. A smartphone app to reduce excessive alcohol consumption: Identifying the effectiveness of intervention components in a factorial mpereliz control trial. Sci Rep. (2018) 8:4384. doi: 10.1038/s41598-018-22420-8

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Direito A, Dale L, Shields E, Dobson R, Whittaker R, Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health. (2014) 14:646. doi: 10.1186/1471-2458-14-646

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Klein M, Mogles N, Van Wissen A. Intelligent mobile support for therapy adherence and behavior change. J. Biomed. Inform. (2014) 51:137–51. doi: 10.1016/j.jbi.2014.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Sagar R, Pattanayak R. Use of smartphone apps for mental health: Can they translate to a smart and effective mental health care? J. Ment. Health Hum. Behav. (2015) 20:1–3. doi: 10.4103/0971-8990.164791

CrossRef Full Text | Google Scholar

30. Ajzen I. From intentions to actions: A Theory of Planned Behavior. In: Kuhl J, Beckmann J, editors. Action Control. Berlin, Heidelberg: Springer (1985). P. 11-39. doi: 10.1007/978-3-642-69746-3_2

CrossRef Full Text | Google Scholar

31. Davis FD. Technology Acceptance Model for Empirically Testing New End-user Information Systems Theory and Results. [Unpublished Doctoral Dissertation] Cambridge, MA: Massachusetts Institute of Technolog. (1986).

Google Scholar

32. Huarng K-H, Yu THK, fang Lee C. Adoption model of healthcare wearable devices. Technol. Forecast. Soc. Change. (2022) 174:121286. doi: 10.1016/j.techfore.2021.121286

CrossRef Full Text | Google Scholar

33. Nguyen M, Fujioka J, Wentlandt K, Onabajo N, Wong I, Bhatia RS, et al. Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC Palliat Care. (2020) 19:138. doi: 10.1186/s12904-020-00644-8

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Jansen-Kosterink S, Dekker-van Weering M, van Velsen L. Patient acceptance of a telemedicine service for rehabilitation care: A focus group study. Int J Med Inform. (2019) 125:22-29. doi: 10.1016/j.ijmedinf.2019.01.011

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Pereira J, Varajão J, Takagi N. Evaluation of information systems project Success-Insights from practitioners. Inf Syst Manag. (2021) 3:1–18. doi: 10.1080/10580530.2021.1887982

CrossRef Full Text | Google Scholar

36. Bezdrob M, Brkic S, Gram M. The pivotal factors of it projects' success-Insights for the case of organizations from the federation of Bosnia and Herzegovina. Int J Inf Syst Proj. Manag. (2020) 8:23-41. doi: 10.12821/ijispm080102

CrossRef Full Text | Google Scholar

37. Hefley WE, Bottion M. Skills of junior project management professionals and project success achieved by them. Int J Inf Syst Proj Manag. (2021) 9:56–75. doi: 10.12821/ijispm090103

CrossRef Full Text | Google Scholar

38. Varajão J, Carvalho JÁ, Silva T, Pereira J. Lack of awareness of IT adoption and use theories by IT/IS project managers: poor relevance, unfocused research or deficient education? Information. (2022) 13:48. doi: 10.3390/info13020048

CrossRef Full Text | Google Scholar

39. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. (1989) 35:9821003. doi: 10.1287/mnsc.35.8.982

CrossRef Full Text | Google Scholar

40. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. (2003) 27:425–78. doi: 10.2307/30036540

CrossRef Full Text | Google Scholar

41. Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. (2012) 36:157–78. doi: 10.2307/41410412

CrossRef Full Text | Google Scholar

42. Varajão J, Silva T, Trigo A. ISRI-Information systems research constructs and indicators: A web tool for information systems researchers. J Inf Sci Theory Pract. (2021) 9:54–67. doi: 10.1633/JISTaP.2021.9.1.4

CrossRef Full Text | Google Scholar

43. Ajzen I, Fishbein M. Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice-Hall. (1980).

Google Scholar

44. DeLone WH, McLean ER. Information systems success: The quest for the dependent variable. Inf Syst Res. (1992) 3:60–95. doi: 10.1287/isre.3.1.60

CrossRef Full Text | Google Scholar

45. Turner M, Kitchenham B, Brereton P, Charters S, Budgen D. Does the technology acceptance model predict actual use? A systematic literature review. Inf Softw Technol. (2010) 52:463–79. doi: 10.1016/j.infsof.2009.11.005

CrossRef Full Text | Google Scholar

46. Liang TP, Yeh YHE. Effect of use contexts on the continuous use of mobile services: the case of mobile games. Pers Ubiquit Comput. (2010) 15:87–196. doi: 10.1007/s00779-010-0300-1

CrossRef Full Text | Google Scholar

47. Kim TG, Lee JH, Law R. An empirical examination of the acceptance behavior of hotel front office systems: an extended technology acceptance model. Tour. Manag. (2008) 29:500–13. doi: 10.1016/j.tourman.2007.05.016

CrossRef Full Text | Google Scholar

48. Morosan C. Theoretical and empirical considerations of guests' perceptions of biometric systems in hotels: Extending the technology acceptance model. J Hosp Tour Res. (2010) 36:52–84. doi: 10.1177/1096348010380601

CrossRef Full Text | Google Scholar

49. Lee CBP, Wan G. Including subjective norm and technology trust in the technology acceptance model: a case of E-ticketing in China. Adv Inform Syst. (2010) 41:40–51. doi: 10.1145/1899639.1899642

CrossRef Full Text | Google Scholar

50. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. (2008) 39:273–315. doi: 10.1111/j.1540-5915.2008.00192.x

CrossRef Full Text | Google Scholar

51. Benbasat I, Barki H. Quo vadis, TAM? J Assoc Inf Syst. (2007) 8:211–8. doi: 10.17705/1jais.00126

CrossRef Full Text | Google Scholar

52. Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Soc Psychol. (1992) 22:1111–32. doi: 10.1111/j.1559-1816.1992.tb00945.x

CrossRef Full Text | Google Scholar

53. Vallerand RJ. Toward a hierarchical model of intrinsic and extrinsic motivation. Adv Exp Soc Psychol. (1997) 29:71–360. doi: 10.1016/S0065-2601(08)60019-2

CrossRef Full Text | Google Scholar

54. Taylor S, Todd P. Assessing IT usage: the role of prior experience. MIS Q. (1995) 19:561–8. doi: 10.2307/249633

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Thompson RL, Higgins CA, Howell JM. Personal computing: Toward a conceptual model of utilization. MIS Q. (1991) 15:125–43. doi: 10.2307/249443

CrossRef Full Text | Google Scholar

56. Moore GC, Benbasat I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res. (1991) 2:192–222. doi: 10.1287/isre.2.3.192

CrossRef Full Text | Google Scholar

57. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall (1986).

Google Scholar

58. Compeau D, Higgins C. Computer self-efficacy: development of a measure and initial test. MIS Q. (1995) 19:189–210. doi: 10.2307/249688

CrossRef Full Text | Google Scholar

59. Marikyan M, Papagiannidis P. Unified theory of acceptance and use of technology. In: Papagiannidis S, editor TheoryHub Book (2021). Available online at: http://opennclacuk

Google Scholar

60. Alalwan AA, Dwivedi YK, Rana NP. Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. Int J Inf Manag. (2017) 37:99–110. doi: 10.1016/j.ijinfomgt.2017.01.002

CrossRef Full Text | Google Scholar

61. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. (1948) 27:379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Shannon CE, Weaver W. The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press (1949).

Google Scholar

63. Mason RO. Measuring information output: a communication systems approach. Inf Manag. (1978) 1:219–34. doi: 10.1016/0378-7206(78)90028-9

CrossRef Full Text | Google Scholar

64. DeLone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst. (2003) 19:9–30. doi: 10.1080/07421222.2003.11045748

CrossRef Full Text | Google Scholar

65. DeLone WH, McLean ER. Information systems success measurement. Found. Trends^®Inf Syst. (2016) 2:1–116. doi: 10.1561/290000000

CrossRef Full Text | Google Scholar

66. Petter S, DeLone W, McLean E. Measuring information systems success: models, dimensions, measures, and interrelationships. Eur J Inf Syst. (2008) 17:236–63. doi: 10.1057/ejis.2008.15

CrossRef Full Text | Google Scholar

67. Petter S, McLean ER. A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Inf Manag. (2009) 46:159–66. doi: 10.1016/j.im.2008.12.006

CrossRef Full Text | Google Scholar

68. Mardiana S, Tjakraatmadja JH, Aprianingsih A. DeLone–McLean information system success model revisited: The separation of intention to use-use and the integration of technology acceptance models. Int. J. Econ. Financial Issues. (2015) 5:172–82.

Google Scholar

69. Akdur G, Aydin MN, Akdur G. Adoption of mobile health apps in dietetic practice: case study of diyetkolik. JMIR mHealth uHealth. (2020) 8:e16911. doi: 10.2196/16911

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Russell E, Lloyd-Houldey A, Memon A, Yarker J. Factors influencing uptake and use of a new health information app for young people. J Technol Hum Serv. (2018) 36:222–40. doi: 10.1080/15228835.2018.1536911

CrossRef Full Text | Google Scholar

71. Angosto S, García-Fernández J, Valantine I, Grimaldi-Puyana M. The intention to use fitness and physical activity apps: A systematic review. Sustainability. (2020) 12:6641. doi: 10.3390/su12166641

CrossRef Full Text | Google Scholar

72. Bronte J, Bagala M, Creighton A, Leavey T, Nicholls S, Wood C, et al. Mobile phone applications and their use in the self-management of type 2 diabetes mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr. (2019) 11:1–17. doi: 10.1186/s13098-019-0480-4

PubMed Abstract | CrossRef Full Text | Google Scholar

73. LoBuono DL, Shea KS, Tovar A, Leedahl SN, Mahler L, Xu F, et al. Acceptance and perception of digital health for managing nutrition in people with Parkinson's disease and their caregivers and their digital competence in the United States: a mixed-methods study. Health Sci Rep. (2021) 4:e412. doi: 10.1002/hsr2.412

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Samadbeik M, Garavand A, Kordi M, Abtin A, Asadi H. Factors affecting the acceptance of mobile health by medical sciences students: A cross-sectional study. Iran. J. Nurs. Res. (2020) 25:476–81. doi: 10.4103/ijnmr.IJNMR_49_19

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Nugroho Y, Prasetyo A. Assessing information systems success: a respecification of the DeLone and McLean model to integrating the perceived quality. Probl. Perspect. Manag. (2018) 16:348–60. doi: 10.21511/ppm.16(1).2018.34

CrossRef Full Text | Google Scholar

76. König LM, Attig C, Franke T, Renner B. Barriers to and facilitators for using nutrition apps: Systematic review and conceptual framework. JMIR mHealth uHealth. (2021) 9(6):e20037. doi: 10.2196/20037

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Paganini S, Terhorst Y, Sander LB, Catic S, Balci S, Küchler AM et al. Quality of physical activity apps: Systematic search in app stores and content analysis. JMIR mHealth uHealth. (2021) 9:e22587. doi: 10.2196/22587

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Schoffman DE, Turner-McGrievy G, Jones SJ, Wilcox S. Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: Just fun and games? Transl Behav Med. (2013) 3(3):320–5. doi: 10.1007/s13142-013-0206-3

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Rivera J, McPherson A, Hamilton J, Birken C, Coons M, Iyer S, Agarwal A, Lalloo C, Stinson J. Mobile apps for weight management: a scoping review. JMIR mHealth uHealth. (2016) 4:e87. doi: 10.2196/mhealth.5115

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Wilson-Barnes S, Gymnopoulos LP, Dimitropoulos K, Solachidis V, Rouskas K, Russell D, et al. PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project. Nutr Bull. (2021) 46:77–87. doi: 10.1111/nbu.12482

CrossRef Full Text | Google Scholar

81. Hart KH, Wilson-Barnes S, Stefanidis K, Tsatsou D, Gynopoulos L, Dimitropoulos K, et al. The suitability of dietary recommendations suggested by artificial intelligence technology via a novel personalised nutrition mobile application. Proceedings of the Nutrition Society. (2022) 81(OCE1). doi: 10.1017/S0029665122000374

CrossRef Full Text | Google Scholar

82. Graikos A, Charisis V, Iakovakis D, Hadjidimitriou S, Hadjileontiadis L. Single image-based food volume estimation using monocular depth-prediction networks. In: Proceedings of the 14^th International Conference on Human-Computer Interaction. Copenhagen, Denmark: Springer, Cham (2020). P. 532-543. doi: 10.1007/978-3-030-49108-6_38

CrossRef Full Text | Google Scholar

83. Dias SB, Grammatikopoulou A, Diniz JA, Dimitropoulos K, Grammalidis N, Zilidou V, et al. Innovative Parkinson's disease patients' motor skills assessment: the i-PROGNOSIS paradigm. Front Comput Sci. (2020) 2:20. doi: 10.3389/fcomp.2020.00020

CrossRef Full Text | Google Scholar

84. Dias SB, Diniz JA, Konstantinidis E, Savvidis T, Zilidou V, Bamidis PD, et al. Assistive HCI-serious games co-design insights: the case study of i-PROGNOSIS Personalized Game Suite for Parkinson's disease. Front Psychol. (2021) 11:612835. doi: 10.3389/fpsyg.2020.612835

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Mahboobeh DJ, Dias SB, Khandoker AH, Hadjileontiadis LJ. Machine leaning-based analysis of digital movement assessment and ExerGame scores for Parkinson's disease severity estimation. Front Psychol. (2022) 3:249. doi: 10.3389/fpsyg.2022.857249

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Ind N, Coates N. The meanings of co-creation. Eur Bus Rev. (2013) 25:86–95. doi: 10.1108/09555341311287754

CrossRef Full Text | Google Scholar

87. van Dijk-de Vries A, Stevens A, van der Weijden T, Beurskens AJHM. How to support a co-creative research approach in order to foster impact. The development of a Co-creation impact compass for healthcare researchers. PloS ONE. (2020) 15(10):e0240543. doi: 10.1371/journal.pone.0240543

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Tardivo G, Thrassou A, Viassone M, Serravalle F. Value co-creation in the beverage and food industry. Br Food J. (2017) 119:2359–372. doi: 10.1108/BFJ-02-2017-0119

CrossRef Full Text | Google Scholar

89. Holden R, Karsh BT. The Technology Acceptance Model: Its past and its future in health care. J. Biomed. Inform. (2010) 43:159-172. doi: 10.1016/j.jbi.2009.07.002

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Chopra K, Pabalkar V, Hebbal A. Acceptance of dietary service applications by smartphone users using tam model. Indian J Ecol. (2020) 47:135–7.

Google Scholar

91. Mohammadi S, Isanejad O. Presentation of the extended technology acceptance model in sports organizations. Ann Appl Sport Sci. (2018) 6:75–86. doi: 10.29252/aassjournal.6.1.75

CrossRef Full Text | Google Scholar

92. Wells JD, Campbell DE, Valacich JS, Featherman M. The effect of perceived novelty on the adoption of information technology innovations: a risk/reward perspective. Decis. (2010) 41:813–43. doi: 10.1111/j.1540-5915.2010.00292.x

CrossRef Full Text | Google Scholar

93. Robinson L, Marshall GW, Stamps MB. Sales force use of technology: Antecedents to technology acceptance. J Bus Res. (2005) 58:1623–31. doi: 10.1016/j.jbusres.2004.07.010

CrossRef Full Text | Google Scholar

94. Ajzen I. Attitudes, Personality and Behavior (2^nd Edition). McGraw-Hill, Milton-Keynes: Open University Press (2005).

Google Scholar

95. Gosetto L, Ehrler F, Falquet G. Personalization dimensions for mHealth to improve behavior change: a scoping review. Stud Health Technol Inform. (2020) 275:77–81. doi: 10.3233/SHTI200698

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Tong HL, Quiroz JC, Kocaballi AB, Fat SCM, Dao KP, Gehringer H, et al. Personalized mobile technologies for lifestyle behavior change: a systematic review, meta-analysis, and meta-regression. Prev. Med. (2021) 148:106532. doi: 10.1016/j.ypmed.2021.106532

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Tu Z, Li Y, Hui P, Su L, Jin D. Personalized mobile App recommendation by learning user's interest from social media. IEEE Trans. Mob Comput. (2019) 19:2670–83. doi: 10.1109/TMC.2019.2929388

CrossRef Full Text | Google Scholar

98. Ho SY, Bodoff D. The effects of Web personalization on user attitude and behavior: An integration of the elaboration likelihood model and consumer research theory. MIS Q. (2014) 38:497–520. doi: 10.25300/MISQ/2014/38.2.08

CrossRef Full Text | Google Scholar

99. Kim J, Gambino A. Do we trust the crowd or information system? Effects of personalization and bandwagon cues on users' attitudes and behavioral intentions toward a restaurant recommendation website. Comput Hum Behav. (2016) 65:369–79. doi: 10.1016/j.chb.2016.08.038

CrossRef Full Text | Google Scholar

100. Robbins SP. Organizational Behavior. Englewood Cliffs, NJ, USA: Prentice-Hal. (1993).

Google Scholar

101. Heberlein TA, Black JS. Attitudinal specificity and the prediction of behavior in a field setting. J Pers So. Psychol. (1976) 33:474–9. doi: 10.1037/0022-3514.33.4.474

CrossRef Full Text | Google Scholar

102. Chen MF, Lin NP. Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Res. (2018) 48:351–73. doi: 10.1108/IntR-03-2017-0099

CrossRef Full Text | Google Scholar

103. Okumus B, Ali F, Bilgihan A, Ozturk AB. Psychological factors influencing customers' acceptance of smartphone diet apps when ordering food at restaurants. Int. J. Hosp. Manag. (2018) 72:67–77. doi: 10.1016/j.ijhm.2018.01.001

CrossRef Full Text | Google Scholar

104. West JH, Belvedere LM, Andreasen R, Frandsen C, Hall PC, Crookston BT. Controlling your “app” etite: How diet and nutrition-related mobile apps lead to behavior change. JMIR mHealth uHealth. (2017) 5: e7410. doi: 10.2196/mhealth.7410

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Requero B, Briñol P, Moreno L, Paredes B, Gandarillas B. Promoting healthy eating by enhancing the correspondence between attitudes and behavioral intentions. Psicothema. (2020) 32:60–6. doi: 10.7334/psicothema2019.154

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Blalock HM Jr. Correlation analysis and causal inferences. Am Anthropol. (1960) 62:624–31. doi: 10.1525/aa.1960.62.4.02a00060

CrossRef Full Text | Google Scholar

107. Blalock HM Jr. Multiple indicators and the casual approach to measurement error. Am J Sociol. (1969) 75:264–72. doi: 10.1086/224771

CrossRef Full Text | Google Scholar

108. Duncan OD. Introduction to structural equation models. New York, NY: Academic (1975).

Google Scholar

109. Jöreskog KG. A general model for estimating a linear structural equation system. In: Goldberger AS, Duncan OD, editors. Structural equation models in the social sciences. New York, NY, USA: Seminar Press. (1973).

Google Scholar

110. Goldberger AS, Duncan OD. Structural Equation Models in the Social Sciences. New York, NY, USA: Seminar Press (1973).

Google Scholar

111. Bollen KA, Pearl J. Eight myths about causality and structural equation modeling. In: Morgan SL, editor. Handbook of causal analysis for social research. Dordrecht. The Netherlands: Springer (2013). P. 301-328.

Google Scholar

112. Tavakol M, Dennick R. Making sense of cronbach's alpha. Int J Med Educ. (2011) 2:53–55. doi: 10.5116/ijme.4dfb.8dfd

PubMed Abstract | CrossRef Full Text | Google Scholar

113. Brown TA. Confirmatory Factor Analysis for Applied Research (2^nd ed.). New York, NY: Guilford Publications (2015).

Google Scholar

114. Nunnally JC. Psychometric Theory. New York, NY: McGraw-Hill. (1978).

Google Scholar

115. Mustafa N, Hadi A, Samsiah N, Izham M, Zawawi A, Ahmad A. iDietScore: Meal recommender system for athletes and active individuals. Int. J. Adv. Comput. Sci. Appl. (2020) 11:269–76. doi: 10.14569/ijacsa.2020.0111234

CrossRef Full Text | Google Scholar

116. Fernández-López M, Gómez-Pérez A, Juristo N. Methontology: From ontological art towards ontological engineering. In: Proceedings of the Symposium on Ontological Engineering of AAAI, Stanford University, CA: AAAI Press (1997). p. 33–40.

Google Scholar

117. Tsatsou D, Lalama E, Wilson-Barnes SL, Hart K, Cornelissen V, Buys R et al. Nact: The nutrition & activity ontology for healthy living. In: Neuhaus F, Brodaric B, editors. Frontiers in artificial intelligence and applications, formal ontology in information systems, Proceedings of the 12^th International Conference (FOIS 2021), Bozen-Bolzan, Italy: IOS Press (2022). P. 129-143. doi: 10.3233/FAIA210377

CrossRef Full Text | Google Scholar

118. Tsatsou D, Dasiopoulou S, Kompatsiaris I, Mezaris V. LiFR: A lightweight fuzzy DL reasoner. In: Presutti V, Blomqvist E, Troncy R, Sack H, Papadakis I, Tordai A, editors. The Semantic Web: ESWC 2014 Satellite Events. Proceedings of the ESWC 2014 Satellite Events. Lecture Notes in Computer Science, Springer, Cham (2014). doi: 10.1007/978-3-319-11955-7_32

CrossRef Full Text | Google Scholar

119. Spring B, Duncan JM, Janke EA, Kozak AT, McFadden HG, DeMott A, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern. Med. (2013) 173:105-11. doi: 10.1001/jamainternmed.2013.1221

PubMed Abstract | CrossRef Full Text | Google Scholar

120. Burke LE, Styn MA, Sereika SM, Conroy MB, Ye L, Glanz K, et al. Using mHealth technology to enhance self-monitoring for weight loss: a randomized trial. Am J Prev Med. (2012) 43:20-26. doi: 10.1016/j.amepre.2012.03.016

PubMed Abstract | CrossRef Full Text | Google Scholar

121. Waki K, Fujita H, Uchimura Y, Omae K, Aramaki E, Kato S, et al. DialBetics: a novel smartphone-based self-management support system for type 2 Diabetes patients. J. Diabetes Sci. Technol. (2014) 8:209–15. doi: 10.1177/1932296814526495

PubMed Abstract | CrossRef Full Text | Google Scholar

122. Zhou M, Fukuoka Y, Mintz Y, Goldberg K, Kaminsky P, Flowers E, et al. Evaluating machine learning-based automated personalized daily step goals delivered through a mobile phone app: Randomized controlled trial. JMIR mHealth uHealth. (2018) 6:e28. doi: 10.2196/mhealth.9117

PubMed Abstract | CrossRef Full Text | Google Scholar

123. Statista. Worldwide Mobile App Revenues 2020|Statistic. (2020) Available online at: https://www.statista.com/statistics/269025/worldwide-mobile-app-revenue-forecast/ (accessed May 11, 2022).

Google Scholar

124. Conroy DE, Yang C-H, Maher JP. Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med. (2014) 46:649–52. doi: 10.1016/j.amepre.2014.01.010

PubMed Abstract | CrossRef Full Text | Google Scholar

125. Middelweerd A, Mollee JS, van der Wal CN, Brug J, Te Velde SJ. Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nutr Phys Act. (2014) 11:97. doi: 10.1186/s12966-014-0097-9

PubMed Abstract | CrossRef Full Text | Google Scholar

126. Elavsky S, Smahel D, Machackova H. Who are mobile app users from healthy lifestyle websites? Analysis of patterns of app use and user characteristics. Transl Behav Med. (2017) 7:891–901. doi: 10.1007/s13142-017-0525-x

PubMed Abstract | CrossRef Full Text | Google Scholar

127. Viswanath K, Finnegan JR Jr, Gollust S. Communication and health behavior in a changing media environment. In: Glanz K, Rimer BK, Viswanath K, editors. Health Behavior: Theory, Research, and Practice. San Francisco, CA: Jossey-Bassy A Wiley Brand (2015). P. 327-348.

Google Scholar

128. Baumgartner SE, Hartmann T. The role of health anxiety in online health information search. Cyberpsychol Behav Soc Netw. (2011) 14:613–8. doi: 10.1089/cyber.2010.0425

PubMed Abstract | CrossRef Full Text | Google Scholar

129. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. (2012) 14:e152. doi: 10.2196/jmir.2104

PubMed Abstract | CrossRef Full Text | Google Scholar

130. Ginters N. A review: How user characteristics affect the effectiveness of persuasive strategies in the health promotion domain of online interventions. [Master's Thesis] The Netherlands: University of Twente, Positive Psychology and Technology (2016).

Google Scholar

131. Verkasalo H, López-Nicolás C, Molina-Castillo FJ, Bouwman H. Analysis of users and non-users of smartphone applications. Telematics Inform. (2010) 27:242–55. doi: 10.1016/j.tele.2009.11.001

CrossRef Full Text | Google Scholar

132. Kwon M-W, Mun K, Lee JK, Mcleod DM, D'angelo J. Is mobile health all peer pressure? The influence of mass media exposure on the motivation to use mobile health apps. Convergence: Int J Res New Media. (2016) 23:565–86. doi: 10.1177/1354856516641065

CrossRef Full Text | Google Scholar

133. Milne-Ives M, Lam C, De Cock C, Van Velthoven MH, Meinert E. Mobile apps for health behavior change in physical activity, diet, drug and alcohol use, and mental health: systematic review. JMIR mHealth uHealth. (2020) 8:e17046. doi: 10.2196/17046

PubMed Abstract | CrossRef Full Text | Google Scholar

134. Fishbach A, Shah JY. Self-control in action: implicit dispositions toward goals and away from temptations. J. Pers. Soc. Psychol. (2006) 90:820–32. doi: 10.1037/0022-3514.90.5.820

PubMed Abstract | CrossRef Full Text | Google Scholar

135. Beldad AD, Hegner SM. Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users' willingness to continue using a fitness app: a structural equation modeling approach. Int J Hum Comput Int. (2018) 34:882–93. doi: 10.1080/10447318.2017.1403220

CrossRef Full Text | Google Scholar

136. Cho J. The impact of post-adoption beliefs on the continued use of health apps. Int J Med Inform. (2016) 87:75–83. doi: 10.1016/j.ijmedinf.2015.12.016

PubMed Abstract | CrossRef Full Text | Google Scholar

137. Yan M, Filieri R, Raguseo E, Gorton M. Mobile apps for healthy living: Factors influencing continuance intention for health apps. Technol Forecast Soc Change. (2021) 166:120644. doi: 10.1016/j.techfore.2021.120644

CrossRef Full Text | Google Scholar

138. Bhattacherjee A, Perols J, Sanford C. Information technology continuance: A theoretic extension and empirical test. J. Comput. Inf. Syst. (2008) 49(1):17-26. doi: 10.1080/08874417.2008.11645302

CrossRef Full Text | Google Scholar

139. Yahoo Finanace Coronavirus: Exercise equipment Sales Spike 5 800% As Brits question Returning to the Gym. (2020). Available online at: https://uk.finance.yahoo.com/news/coronavirus-exercise-equipment-sales-spike-5800-as-brits-cancel-gym-memberships-145959385.html [Accessed Jan 2, 2022].

Google Scholar

140. Liu C-F, Lin C-H. Online food shopping: A conceptual analysis for research propositions. Front. Psychol. (2020) 11:583768. doi: 10.3389/fpsyg.2020.583768

PubMed Abstract | CrossRef Full Text | Google Scholar