Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 02.10.20 in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at, first published Nov 05, 2019.

This paper is in the following e-collection/theme issue:

    Original Paper

    Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik

    1Department of Management Information Systems, Kadir Has University, Istanbul, Turkey

    2Centre for Research in Public Health and Community Care, University of Hertfordshire, Hatfield, United Kingdom

    *all authors contributed equally

    Corresponding Author:

    Gorkem Akdur, BSc, MSc, MA

    Department of Management Information Systems

    Kadir Has University

    Kadir Has St., Cibali / Fatih

    Istanbul, 34083


    Phone: 90 5360662866



    Background: Dietetics mobile health apps provide lifestyle tracking and support on demand. Mobile health has become a new trend for health service providers through which they have been shifting their services from clinical consultations to online apps. These apps usually offer basic features at no cost and charge a premium for advanced features. Although diet apps are now more common and have a larger user base, in general, there is a gap in literature addressing why users intend to use diet apps. We used Diyetkolik, Turkey’s most widely used online dietetics platform for 7 years, as a case study to understand the behavioral intentions of users.

    Objective: The aim of this study was to investigate the factors that influence the behavioral intentions of users to adopt and use mobile health apps. We used the Technology Acceptance Model and extended it by exploring other factors such as price-value, perceived risk, and trust factors in order to assess the technology acceptance of users.

    Methods: We conducted quantitative research on the Diyetkolik app users by using random sampling. Valid data samples gathered from 658 app users were analyzed statistically by applying structural equation modeling.

    Results: Statistical findings suggested that perceived usefulness (P<.001), perceived ease of use (P<.001), trust (P<.001), and price-value (P<.001) had significant relationships with behavioral intention to use. However, no relationship between perceived risk and behavioral intention was found (P=.99). Additionally, there was no statistical significance for age (P=.09), gender (P=.98), or previous app use experience (P=.14) on the intention to use the app.

    Conclusions: This research is an invaluable addition to Technology Acceptance Model literature. The results indicated that 2 external factors (trust and price-value) in addition to Technology Acceptance Model factors showed statistical relevance with behavioral intention to use and improved our understanding of user acceptance of a mobile health app. The third external factor (perceived risk) did not show any statistical relevance regarding behavioral intention to use. Most users of the Diyetkolik dietetics app were hesitant in purchasing dietitian services online. Users should be frequently reassured about the security of the platform and the authenticity of the platform’s dietitians to ensure that users’ interactions with the dietitians are based on trust for the platform and the brand.

    JMIR Mhealth Uhealth 2020;8(10):e16911





    The advancement of information and communication technologies has urged the health care industry to develop new solutions to bring better health functions to individuals by reducing costs and time as well as enhancing convenience for both service providers and users [1]. Over the last decade, considerable health services have been partially or wholly shifted to mobile phones. Since the concept of mobile health (mHealth) was first coined in 2005 [2,3], mHealth has incorporated innovative medical services, such as mobile dietetics services. Most mHealth services consist of innovative solutions such as web-based consultation systems with physicians, web-based health conferences, health-relevant data, medical inspection results via portable and wearable gadgets, and smartphone-based apps [3].

    Diet apps are a subset of mHealth and typically offer services including physical activity, calorie, water intake, and weight tracking; weight goal setting; meal recipes; and meal planning [4]. Due to the excessive number of diet apps that promote physical health and well-being, it is crucial to determine whether these apps are valid and what they provide for their users [5]. Generally, users pay attention to the number of downloads, average ratings, and reviews before downloading diet apps [6]. An important feature of Diyetkolik is its dietician consultation service. Diyetkolik allows dietitians to join the platform as experts and to offer their customized services to users.

    Diyetkolik was founded in 2011, and as of 2020, it has a base of around 1.5 million users [7]. It is Turkey's most widely used online dietetics platform [8]. The platform has a large database of food that is specific to Turkey. Diyetkolik has features such as calorie checker, calorie tracking, water tracking, exercise tracking, content, calculations, and reports. Users are offered 3 types of packages: users with a basic (free) membership can access Diyetkolik’s food database, track calorie intake, and read blog posts; users with a standard diet package are assigned noncustomized diet plans and can ask a limited number of questions to a dietitian; and users with a premium diet package can benefit from customized diet plans that are prepared by dietitians. Dietitians can create expert accounts on Diyetkolik, publish nutrition-related blog posts, and connect with clients who have subscribed to a premium membership. The business strategy of Diyetkolik is business to business to consumer, which is a novel approach in dietitian services in Turkey [9].

    Previous reviews [10] have found that the use of nutrition and diet mHealth apps can change nutrition health behavior and improve diet. According to Carter et al [11], app users show greater adherence, retention, and weight loss compared to those shown by other nutrition and diet site users. Yet, what is unclear is why people choose to use mHealth apps and how they use them [12]. Generally, users choose any specific mobile app based on such factors as reliability, ease of use, quality, usefulness, aesthetics, trust, and recommendations by others [13]. Hence, engineers, managers, and app developers must acknowledge what end users demand. Many previous studies [14-19] have focused on mHealth apps, but there are only a few studies [20-24] that identify diet mHealth apps from the user’s perspective. The objective of this research was to analyze the influencing factors for user acceptance of a diet mHealth app and investigate and test the importance of external constructs affecting users’ behavioral intention to use. Only a few studies [3,25-27] have analyzed end-user perspectives on diet app acceptance in non-European Union countries; this study offers additional insights on mHealth user acceptance in Turkey.

    Theoretical Framework and Research Hypotheses

    One of the major influential models that has been used to assess the acceptance of technologies is the Technology Acceptance Model (TAM) [28], originally developed by Davis [29] in 1989. Davis [30] has stated that regulating circumstances to obtain and adopt any technology are based on some presumptions, and the major key factors are perceived ease of use and perceived usefulness [30]. According to McFarland and Hamilton [31], extended models can increase the explanation and forecasting of acceptance. Considering innovative technologies and their effects, Thompson et al [32] discussed that perceived ease of use and perceived usefulness are not the only suitable factors to determine technology adoption. For particular settings, using different variables from other information technology acceptance models could enhance the specificity and explanatory power of the extended models [30]. For this reason, instead of using the original TAM, or its well-known extensions such as the Technology Acceptance Model 2 (TAM2) [33,34] and the Unified Theory of Acceptance and Use of Technology (UTAUT), we adopted the most relevant and complementary external factors to extend TAM and explain user adoption of diet apps [35].

    Perceived usefulness is one of the main factors determining, and main indicator of, behaviors of any kind of technology usage [29,30]. Davis defined perceived usefulness as “the degree to which a person believes that using a particular system would enhance his or her job performance” [29]; in this study, we integrated and adapted this definition of perceived usefulness—the degree to which a person believes that using a particular system would enhance their health and well-being to make it more user-centric. Our second construct was perceived ease of use, which is described by Davis as ”the degree to which a person believes that using a particular system would be free from effort [29],“ and we wholly integrated this definition into our model.

    The major goal of the TAM framework is to understand the behavioral intention of users (acceptance) toward and their actual use of a technology [36,37]. Davis suggested that future technology acceptance studies should focus on other principles that influence usefulness, ease of use, and user acceptance [37]. Thus, price-value, trust, and perceived risk factors were used to extend the TAM framework in this study. These factors are crucial to exploring the perspective of users in the mHealth domain [3,38,39].

    The price-value factor is related to the subscription fees of the service offered by the mobile health services. It has a direct effect on user acceptance; therefore, we adopted price-value as an external factor. We investigated the direct relationship of user satisfaction on the price that the users pay and whether the service matched their needs [38,40,41]. Moreover, trust is a powerful external factor since trust plays a major role in attracting new users and retaining existing users. If a person believes in the service administered by an mHealth app, they are likely to adopt the service. According to the TAM framework, a person’s beliefs can encourage their assertiveness and intention to adopt the technology [3,42,43]. Hence, we integrated trust as another external factor in this study.

    The external factor perceived risk can be defined as a “person’s perception of uncertainty in the use of mHealth services and its severity in terms of consequences [39].” There are some risks posed by mHealth apps, such as privacy invasion, legal, and performance risks [44]. Researchers have shown that privacy and perceived risk can predict user adoption [45,46], hence we used perceived risk in our extended model. Finally, some control variables were added to enhance the explanatory potential of the study. Gender, age, and previous mHealth app experience were selected as covariates. Considering these factors, hypotheses and the research framework were established; they are illustrated in Figure 1 and are summarized in Table 1.

    Figure 1. Theoretical framework.
    View this figure
    Table 1. Hypotheses list.
    View this table


    Study Design

    Multiple-itemed scales were used to quantify each construct. All questions were carefully selected and adapted from the questionnaires used in previous studies [1,3,29,43,47-50]. We adjusted most items to make them more suitable for an mHealth context (please see Multimedia Appendix 1 for references for each item). Perceived usefulness was measured with 4 items (PU1-PU4), perceived ease of use was measured with 4 items (PEOU1-PEOU4), behavioral intention to use was measured with 5 items (BI1-BI5), price-value was measured with 3 items (PV1-PV3), perceived risk was measured with 6 items (PR1-PR6), and trust was measured with 3 items (T1-T3).

    All of these items were rated by users in a 32-question online survey using a 5-point Likert scale (1, strongly disagree; 3, indecisive; 5, strongly agree). Questions were shuffled and one attention check (trap) question was added to eliminate bias responses. The aim of the attention check question was to ensure that the users were focused. One of the perceived usefulness items was stated as the exact opposite of another perceived usefulness item. In this way, we attempted to minimize the risk of collecting less reliable and inaccurate data from users who were not being attentive [51]. Demographic questions were asked at the beginning of the survey: gender, age group, education level, usage frequency of the app (which directly answers the actual use factor), previous mobile-health app experience, main aim for the use of the app (users were allowed to choose more than one answer from the pool), and membership type (free 1-month standard membership, 3-month standard membership, premium-dietitian service). The survey was prepared in the Turkish language, which is the native language of the app and its users. The survey was sent to Diyetkolik managers to be placed on their survey platform.

    Data Collection

    Data were collected with an online survey method on a Typeform web-based platform [52]. This platform is used by Diyetkolik to gather feedback from its users. The survey was distributed randomly to active users who subscribed to Diyetkolik’s mailing lists. Out of 100,000 active users, 50,000 users who checked their weekly emails from Diyetkolik, randomly received the survey link via email. A brief explanation of the project was given in the email and indicated that participation in the survey was voluntary and completely anonymous (please see Multimedia Appendix 1 for the English version and Multimedia Appendix 2 for the Turkish version of the survey).

    The data collection phase took 4 weeks between June 11, 2019 and July 10, 2019. Responses were collected from 840 Diyetkolik users. In order to increase the reliability and accuracy, surveys that had missing entries and inattentive responses were eliminated. Hence, the data count was reduced to 658 from the original 840.

    Statistical Procedure

    Data were analyzed using SmartPLS 3 (SmartPLS GmbH) and SPSS (version 24.0; IBM Corp) software. Data were gathered in an Excel (Microsoft Inc) file and the shuffled items were ordered categorically. Partial least square, a component-based structural equation modeling technique, was used to examine the research model and test the first 8 hypotheses. Partial least square methods are particularly convenient for complex and large research models to test both reflective and formative constructs. Hence, partial least square-based structural equation modeling was better suited to this research than covariance-based structural equation modeling techniques [39]. For the last 3 hypotheses and for demographics, analysis of variance (ANOVA) was used (at the significance levels P<.01 and P<.05).

    Analysis of the Measurement Model in Structural Equation Modeling

    To assess construct validity and internal accuracy, we used discriminant and convergent validity measures that complemented each other [1]. Cronbach α and composite reliability measures must be around 0.70 for each factor to have a valid internal consistency. Additionally, the average variance extracted and component loadings should be more than 0.50 to have acceptable convergent and construct validity [53].

    Calculating the standardized outer loadings of main variables can explain individual variance level, convergent validity, and individual manifest reliability. Thus, items that have more than 0.70 outer loadings count as highly satisfactory. Items which have a value of 0.50 or below should be eliminated to increase the composite reliability. Items valued between 0.40 to 0.70 should be reviewed before dropping out [54].

    Composite reliability and Cronbach α values play an important role in analyzing internal consistency. A common rule of thumb indicates that values 0.6 or higher are adequate for exploratory purposes [55].

    Discriminant Validity Measurement

    Discriminant validity was checked using the Fornell-Larcker criterion (correlation analysis). According to this criterion, an average variance extracted value above 0.50 is acceptable, and an average variance extracted value above 0.70 shows significant validity.

    Another method to check validity is by analyzing the R2 values. It is a goodness-of-fit measure for linear regression models. It explains the estimated variance of the construct’s relationship with independent variables and higher R2 values show significantly better model fits [56]. The value of R2 of underlying variables should be greater than 0.26 [57].

    Hypothesis Testing

    To identify the relationships between the variables in the structural model, path coefficient (β) and t values were tested by bootstrapping with 1000 subsamples. The relationships among factors are statistically proven with P values lower than .01 and with t values larger than 1.96.


    Demographic Characteristics

    The demographic characteristics of respondents (n=658) are presented in Table 2. The majority of the respondents were female (438/658, 66.6%) and between the ages of 18-41 years (496/658, 75.4%). Most of the respondents had attained a bachelor’s degree or higher (362/658, 55.0%). Two-thirds of the respondents did not disclose their membership type, and almost one-third of the respondents disclosed that their membership type was basic (211/658, 32.1%).

    Analysis of the Measurement Model in Structural Equation Modeling

    In the first round, the items BI5 (0.403), PV3 (0.594), PR1 (–0.772), PR2 (0.330), PR4 (0.556), and PR6 (0.450) were removed from our partial least square structural equation model. Only the items that had outer loadings greater than 0.6 remained, and the final data analysis table was constructed.

    Table 3 shows the final measurement model with the mean values. According to the results, internal consistency is satisfied; perceived risk, price-value, and trust (the 3 external variables) had mean values of approximately 3 indicating that the average respondent was indecisive about perceived risk, trust, and price-value.

    Discriminant Validity Measurement

    The model passed the validity test with satisfactory results. All variables in Table 4 show discriminant validity with cross loadings above 0.70 (actual use: 1; behavioral intention: 0.732; perceived ease of use: 0.739; price-value: 0.796; perceived risk: 0.873; perceived usefulness: 0.789; trust: 0.873).

    For this study, 2 out of 3 R2 values satisfied the criteria (behavioral intention: R2=0.635; perceived usefulness: R2=0.434; perceived ease of use: R2<0.26).

    Table 2. Demographics of sample data.
    View this table
    Table 3. The measurement model with mean values.
    View this table
    Table 4. Correlation matrix of the square root of the average variance extracted for discriminant validity.
    View this table

    Hypothesis Testing

    The results of partial least square model for the first 8 hypotheses of the study are illustrated in Table 5. Perceived risk’s direct effect on behavioral intention (β=0, t=0.01, P=.99) was not accepted. The direct effects of perceived ease of use on perceived usefulness (β=0.506, t=12.98, P<.001), perceived ease of use on behavioral intention (β=0.293, t=6.85, P<.001), behavioral intention on actual use (β=0.322, t=9.10, P<.001), and price-value on behavioral intention (β=0.303, t=7.67, P<.001) were significant.

    Table 5. Results of partial least square for H1 to H8.
    View this table

    Gender had no statistically significant role on the behavioral intention to use the app (mean 3.21, SD 0.873; F1,599=0 P=.98. In addition, age groups showed no association between age and behavioral intention (mean 3.19, SD 0.872; F5,652=1.931, P=.09). Finally, previous app use was not significant (mean 3.19, SD 0.872; F1,656=2.137, P=.14). Hence, H9, H10, and H11 were all rejected (Multimedia Appendix 3).



    Considering the vast influence of information and communication technologies in our daily lives, this research aimed to extend TAM framework and to understand the main factors that influenced Diyetkolik users’ acceptance of mobile dietitian services. Identified factors were tested for the variables' hypothesized positive associations with user acceptance of the diet app. The findings showed that perceived ease of use, perceived usefulness, price-value, and trust had a positive impact on the app acceptance at the behavioral level. We only demonstrated that high behavioral intention of users translated into higher usage frequency of the app (actual use). Previous studies [21,29,33,56,58-62] on TAM had also shown similar results. Our study's results were consistent with Ajzen and Fishbein’s theory [62,63] and also in congruence with findings [29] that suggested that behavioral intention was a good predictor of actual technology use. However, perceived risk did not show any significant relationship with behavioral intention to use (P=.99). Users used mHealth apps and benefit from the services if they perceived the app to be useful, easy to control, and trustworthy. Moreover, the analysis of age groups and previous app use data showed that there were no statistically meaningful differences (age: P=.09; use: P=.14) in behavioral intention to use. Our results also revealed there was no major role of gender in the behavioral intention to use (P=.98). This conflicts with findings from another study [64] that showed that men perceived mobile diet apps to be more beneficial in managing their lifestyle and eating habits.

    We predict that most respondents who did not respond to the membership question had basic membership, because the marketing and sales managers of Diyetkolik have stated that the majority of users own free accounts. Disclosing the subscription type will be made compulsory in any future research, because it could positively affect our analysis to identify the respondent type clearly. By doing this, we can differentiate our questions for different segmentations instead of focusing on all respondents as a homogenous population.

    The findings from the survey’s price-value questions suggested that users were ambivalent about the benefits of premium membership (the subscription type that provides dietitian services to the users). Having dietitian services straight from a mobile device, instead of a physical consultation, seems eminently practical for some respondents, but other respondents were indecisive about consulting a dietitian online through the platform. Most users have vigorously opposed or were hesitant about purchasing health services online. Some users were also indecisive about the reliability of the information that dietitians provide on the Diyetkolik platform. These might stem from issues in trust concerning the accreditation and the authenticity of the platform’s dietitians.

    Two external factors (trust and price-value) that were used in this study in addition to the TAM factors (perceived ease of use and perceived usefulness) improved our understanding of user acceptance of an mHealth app and showed relevance with behavioral intention to use. The third external factor (perceived risk) did not provide any explanation regarding intention to use, but it can be divided into further categories (legal concerns, privacy risks, etc), and its statistical relationship with behavioral intention can be retested in future research. Therefore, based on our findings, we propose an extended TAM framework.


    This study had some limitations. It was conducted in one country (Turkey), and the findings might not be generalizable to related apps in European Union countries or in North America. Demographic variables such as sociocultural differences with regard to diet would create differences between different countries. Future research could study different country settings and compare cross-country data. Although the investigation had a good representative sample, it was conducted online which can cause some problems regarding the attention of respondents. We tried to minimize this effect to a degree by including a trap question.

    Other data collection methods might be considered for future studies, for example, inviting previous or occasionally active Diyetkolik users to take part in the study. The insights from former users could be important to understanding the factors behind technology abandonment, resistance, or rejection; there may be close links with behavioral intention to use.

    Due to the number of users preferring not to disclose their type of membership, we were unable to identify additional insights from comparing user perceptions about different subscriptions. Future research can leverage digital trace data of user actions on the mobile app and can use data analytics to examine further hypotheses.


    Measuring the user acceptance of mHealth by using an extended TAM model was the primary aim of this study. Our research showed that the perceived ease of use and the perceived usefulness of an mHealth app were associated with the behavioral intention of users to adopt and continue using the app. A mHealth-specific extended TAM model that was developed showed that price-value and trust factors directly affected the behavioral intention to use the app. However, perceived risk and behavioral intention were not associated, and gender, age groups, and previous app use experience had no direct or moderating roles on the intention to use the app.

    Despite the aforementioned limitations, the results and findings of the study contribute to mHealth app design and the development of dietary and nutrition mHealth apps. The success of apps is highly dependent on the user’s willingness to adopt them. On a practical level, the insights from this research can benefit nutrition and diet app developers and managers to increase the adoption and actual use of apps. Furthermore, the study proposes a theoretical extension to the literature. TAM is a generic framework which is often used by researchers to study the user acceptance of technologies, however, there are only a few studies [22,44,65] that have explored diet-based mHealth apps. Hence, our expanded TAM framework lays out additional constructs and contributes to technology acceptance literature. We believe that further research will use this model and possibly identify new factors that could be useful in studying user acceptance of diet apps.

    As a recommendation, managers might find it useful to focus on promoting the perceived usefulness of the service in order to increase the uptake of premium services by the users. The average respondent in our Diyetkolik case study was hesitant about purchasing health services online, based on the app’s price-value. If the benefits of the premium packages are communicated to the users in innovative ways, and if the users are frequently reassured about the security of the app and the authenticity of the platform’s experts (eg, Diyetkolik’s dietitians), then they might feel more inclined to purchase premium services. This recommendation can be generalizable to other mHealth apps where the user concerns are similar.


    The authors would like to thank the Diyetkolik's head of marketing and sales and Diyetkolik’s management team for their support. There is no funding to declare.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    English version of survey questions.

    PDF File (Adobe PDF File), 185 KB

    Multimedia Appendix 2

    Turkish version of survey questions.

    PDF File (Adobe PDF File), 354 KB

    Multimedia Appendix 3

    Hypotheses 9, 10, and 11 one-way ANOVA results.

    PDF File (Adobe PDF File), 99 KB


    1. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 2017 May;101:75-84. [CrossRef] [Medline]
    2. Schuster L, Tossan V, Drennan J. Consumer acceptance of mhealth services: a comparison of behavioral intention models. Services Marketing Quarterly 2017 Apr 21;38(2):115-128. [CrossRef]
    3. Deng Z, Hong Z, Ren C, Zhang W, Xiang F. What predicts patients' adoption intention toward mhealth services in china: empirical study. JMIR Mhealth Uhealth 2018 Aug 29;6(8):e172 [FREE Full text] [CrossRef] [Medline]
    4. Whisner C, Porter A, Pecor N, Maciejewski R. A survey of personal visualization in mhealth nutrition apps. Arizona State University Faculty Profile. 2015.   URL: [accessed 2020-09-24]
    5. Franco RZ, Fallaize R, Lovegrove JA, Hwang F. Popular nutrition-related mobile apps: a feature assessment. JMIR Mhealth Uhealth 2016 Aug 01;4(3):e85 [FREE Full text] [CrossRef] [Medline]
    6. Schumer H, Amadi C, Joshi A. Evaluating the dietary and nutritional apps in the Google Play Store. Healthc Inform Res 2018 Jan;24(1):38-45 [FREE Full text] [CrossRef] [Medline]
    7. Sikca Sorulan Sorular [in Turkish]. Diyetkolik.   URL: [accessed 2020-09-24]
    8. Diyetkolik home page [in Turkish]. Diyetkolik.   URL: [accessed 2020-09-24]
    9. What is Diyetkolik? How it works? Diyetkolik.   URL: [accessed 2019-07-19]
    10. Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc 2013 May 01;20(3):513-518 [FREE Full text] [CrossRef] [Medline]
    11. Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: pilot randomized controlled trial. J Med Internet Res 2013 Apr 15;15(4):e32 [FREE Full text] [CrossRef] [Medline]
    12. Ernsting C, Dombrowski SU, Oedekoven M, O Sullivan JL, Kanzler M, Kuhlmey A, et al. Using smartphones and health apps to change and manage health behaviors: a population-based survey. J Med Internet Res 2017 Apr 05;19(4):e101 [FREE Full text] [CrossRef] [Medline]
    13. Handel MJ. mHealth (mobile health)-using apps for health and wellness. Explore (NY) 2011;7(4):256-261. [CrossRef] [Medline]
    14. Ewais S, Alluhaidan A. Classification of stress management mHealth apps based on Octalysis framework. 2015 Presented at: 21st Americas Conference on Information Systems; August 13-15; Puerto Rico.
    15. Grundy QH, Wang Z, Bero LA. Challenges in assessing mobile health app quality: a systematic review of prevalent and innovative methods. Am J Prev Med 2016 Dec;51(6):1051-1059. [CrossRef] [Medline]
    16. Higgins JP. Smartphone applications for patients' health and fitness. Am J Med 2016 Jan;129(1):11-19. [CrossRef] [Medline]
    17. Aungst TD, Clauson KA, Misra S, Lewis TL, Husain I. How to identify, assess and utilise mobile medical applications in clinical practice. Int J Clin Pract 2014 Feb;68(2):155-162. [CrossRef] [Medline]
    18. Larson RS. A path to better-quality mhealth apps. JMIR Mhealth Uhealth 2018 Jul 30;6(7):e10414 [FREE Full text] [CrossRef] [Medline]
    19. Akter S, Ray P. mhealth - an ultimate platform to serve the unserved. Yearb Med Inform 2018 Mar 07;19(01):94-100. [CrossRef]
    20. Chen J, Lieffers J, Bauman A, Hanning R, Allman-Farinelli M. The use of smartphone health apps and other mobile health (mHealth) technologies in dietetic practice: a three country study. J Hum Nutr Diet 2017 Aug;30(4):439-452. [CrossRef] [Medline]
    21. Okumus B, Bilgihan A, Ozturk AB. Factors affecting the acceptance of smartphone diet applications. 2015 Nov 03;25(6):726-747. [CrossRef]
    22. Lee HE, Cho J. What motivates users to continue using diet and fitness apps? application of the uses and gratifications approach. Health Commun 2017 Dec;32(12):1445-1453. [CrossRef] [Medline]
    23. Okumus B, Ali F, Bilgihan A, Ozturk AB. Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management 2018 Jun;72:67-77. [CrossRef]
    24. Wang Q, Egelandsdal B, Amdam GV, Almli VL, Oostindjer M. Diet and physical activity apps: perceived effectiveness by app users. JMIR Mhealth Uhealth 2016 Apr 07;4(2):e33 [FREE Full text] [CrossRef] [Medline]
    25. Huang G, Ren Y. Linking technological functions of fitness mobile apps with continuance usage among Chinese users: moderating role of exercise self-efficacy. Comput Human Behav 2020 Feb;103:151-160. [CrossRef]
    26. Jospe MR, Fairbairn KA, Green P, Perry TL. Diet app use by sports dietitians: a survey in five countries. JMIR Mhealth Uhealth 2015 Jan 22;3(1):e7 [FREE Full text] [CrossRef] [Medline]
    27. Cho J, Quinlan MM, Park D, Noh GY. Determinants of adoption of smartphone health apps among college students. Am J Health Behav 2014 Nov;38(6):860-870. [CrossRef] [Medline]
    28. Hu PJ, Chau PY, Sheng ORL, Tam KY. Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems 2015 Dec 02;16(2):91-112. [CrossRef]
    29. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 1989 Sep;13(3):319. [CrossRef]
    30. Davis FD. A technology acceptance model for empirically testing new end-user information systems: theory and results. Massachusetts Institute of Technology. 1986.   URL: [accessed 2020-09-24]
    31. McFarland DJ, Hamilton D. Adding contextual specificity to the technology acceptance model. Comput Human Behav 2006 May;22(3):427-447. [CrossRef]
    32. Thomson R, Compeau D, Higgins C. Intentions to use information technologies: an integrative model. Journal of Organizational and End User Computing 2006;18(3):25-46. [CrossRef]
    33. Venkatesh V, Morris G, Davis F. User acceptance of information technology: toward a unified view. MIS Quarterly 2003;27(3):425-478. [CrossRef]
    34. Venkatesh V, Davis F. A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science 2000 Feb;46(2):186-204. [CrossRef]
    35. Agarwal R, Prasad J. A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research 1998 Jun;9(2):204-215. [CrossRef]
    36. Blagoeva KT, Mijoska M. Applying TAM to study online shopping adoption among youth in the Republic of Macedonia. 2017 Presented at: Management International Conference; May 24-27, 2017; Venice, Italy p. 543   URL:
    37. Momani A, Mamoun JM. The evolution of technology acceptance theories. International Journal of Contemporary Computer Research 2017 Mar;1(1):51. [CrossRef]
    38. 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 Jul 10;5(7):e95 [FREE Full text] [CrossRef] [Medline]
    39. Luo X, Li H, Zhang J, Shim J. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems 2010 May;49(2):222-234. [CrossRef]
    40. Hoj TH, Covey EL, Jones AC, Haines AC, Hall PC, Crookston BT, et al. How do apps work? an analysis of physical activity app users' perceptions of behavior change mechanisms. JMIR Mhealth Uhealth 2017 Aug 03;5(8):e114 [FREE Full text] [CrossRef] [Medline]
    41. Yuan S, Ma W, Kanthawala S, Peng W. Keep using my health apps: discover users' perception of health and fitness apps with the UTAUT2 model. Telemed J E Health 2015 Sep;21(9):735-741. [CrossRef] [Medline]
    42. Hoque MR, Bao Y, Sorwar G. Investigating factors influencing the adoption of e-Health in developing countries: a patient's perspective. Inform Health Soc Care 2017 Jan;42(1):1-17. [CrossRef] [Medline]
    43. Chong AY. Understanding mobile commerce continuance intentions: an empirical analysis of chinese consumers. Journal of Computer Information Systems 2015 Dec 10;53(4):22-30. [CrossRef]
    44. Macdonald AJ. Acceptance and continuance factors associated with mobile medical app use: a qualitative case study of diabetes apps. Northcentral University.: ProQuest Dissertations Publishing; 2017.   URL: [accessed 2020-09-24]
    45. Kim DJ, Ferrin DL, Rao HR. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems 2008 Jan;44(2):544-564. [CrossRef]
    46. Alaiad A, Zhou L. The determinants of home healthcare robots adoption: an empirical investigation. Int J Med Inform 2014 Nov;83(11):825-840. [CrossRef] [Medline]
    47. Ming-Syan C, Jiawei H, Yu P. Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 1996;8(6):866-883. [CrossRef]
    48. Kim TG, Lee JH, Law R. An empirical examination of the acceptance behaviour of hotel front office systems: an extended technology acceptance model. Tourism Management 2008 Jun;29(3):500-513. [CrossRef]
    49. Venkatesh, Thong, Xu. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly 2012;36(1):157-178. [CrossRef]
    50. Cilliers L, Viljoen KL, Chinyamurindi WT. A study on students' acceptance of mobile phone use to seek health information in South Africa. Health Inf Manag 2018 May 07;47(2):59-69. [CrossRef] [Medline]
    51. Liu M, Wronski L. Trap questions in online surveys: results from three web survey experiments. International Journal of Market Research 2018 Feb 05;60(1):32-49. [CrossRef]
    52. What is Typeform? Typeform. 2020.   URL: [accessed 2019-10-01]
    53. Kedah Z, Ismail Y, Ahasanul AKM, Ahmed S. Key success factors of online food ordering services: an empirical study. Malaysian Management Review 2015;50(2):19-36 [FREE Full text]
    54. Memon AH, Rahman IA. SEM-PLS analysis of inhibiting factors of cost performance for large construction projects in Malaysia: perspective of clients and consultants. Scientific World Journal 2014;2014:165158-165159 [FREE Full text] [CrossRef] [Medline]
    55. Garson GD. Partial Least Squares: Regression and Structural Equation Models. Asheboro, NC: Statistical Associates Publishing; 2016.
    56. Peker C. An analysis of the main critical factors that affect the acceptance of technology in hospital management systems. The Middle East Technical University. 2010.   URL: [accessed 2020-09-24]
    57. Wassertheil S, Cohen J. Statistical power analysis for the behavioral sciences. Biometrics 1970 Sep;26(3):588. [CrossRef]
    58. Okumus B, Bilgihan A. Proposing a model to test smartphone users' intention to use smart applications when ordering food in restaurants. JHTT 2014 Mar 11;5(1):31-49. [CrossRef]
    59. Surendran P. Technology acceptance model: a survey of literature. International Journal of Business and Social Research 2012;2(4):175-178.
    60. Zhao Y, Ni Q, Zhou R. What factors influence the mobile health service adoption? a meta-analysis and the moderating role of age. International Journal of Information Management 2018 Dec;43:342-350. [CrossRef]
    61. Tao D. Intention to use and actual use of electronic information resources: further exploring Technology Acceptance Model (TAM). AMIA Annu Symp Proc 2009 Nov 14;2009:629-633 [FREE Full text] [Medline]
    62. Vallerand RJ, Deshaies P, Cuerrier J, Pelletier LG, Mongeau C. Ajzen and Fishbein's theory of reasoned action as applied to moral behavior: a confirmatory analysis. Journal of Personality and Social Psychology 1992 Jan;62(1):98-109. [CrossRef]
    63. Ajzen I, Fishbein M. Understanding Attitudes And Predicting Social Behavior. New Jersey: Prentice-Hall; 1980.
    64. Zhang X, Guo X, Lai K, Guo F, Li C. Understanding gender differences in m-health adoption: a modified theory of reasoned action model. Telemed J E Health 2014 Jan;20(1):39-46. [CrossRef] [Medline]
    65. Scheibe M, Reichelt J, Bellmann M, Kirch W. Acceptance factors of mobile apps for diabetes by patients aged 50 or older: a qualitative study. Med 2 0 2015 Mar 02;4(1):e1 [FREE Full text] [CrossRef] [Medline]


    ANOVA: analysis of variance
    mHealth: mobile health
    TAM: Technology Acceptance Model
    UTAUT: Unified Theory of Acceptance and Use of Technology

    Edited by E Borycki; submitted 05.11.19; peer-reviewed by E Stringer, J Moon; comments to author 31.03.20; revised version received 25.05.20; accepted 23.08.20; published 02.10.20

    ©Gorkem Akdur, Mehmet Nafiz Aydin, Gizdem Akdur. Originally published in JMIR mHealth and uHealth (, 02.10.2020.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.