Factors associated with intention to use an educative mHealth application among high-risk target groups in health prevention

Introduction

Background

The most recent Organisation for Economic Co-operation and Development (OECD) report highlights the critical health behavior across its 38 member states, where only 40% of the population still meet the physical activity guidelines (1). In an industrialized country like Germany for example, only half of the population meets the recommended guidelines for physical activity (2), approximately one-fifth sits for eight or more hours daily (3) and over half experiences obesity (4). Furthermore, only 40% rate their mental health as at least ‘good’ (5). Such trends highlight an urgent need for interventions to address unhealthy lifestyles, which are significant contributors to chronic diseases (6). Adding to these challenges, the healthcare system in Germany faces increasing strain due to a growing shortage of skilled professionals and the effects of demographic change (7,8). Without a shift towards (digital) preventive measures, the combined pressures of an aging population and insufficient workforce could lead to a systemic crisis, particularly in nursing and elder care (9). To address these impending challenges facing the healthcare system, improving health literacy is considered one key factor (10).

Preventive health measures have the potential to reduce the incidence of chronic diseases, enhance quality of life, and decrease healthcare costs. Digital health solutions [mobile health (mHealth) and electronic health (eHealth)] have shown positive effects on health behavior, such as increased physical activity, improved nutrition, and better sleep patterns (11). In response to this need, the authors, in collaboration with a German health insurance company, developed the Digital Health Companion (DHC), a chatbot-based mHealth application for Android and iOS, designed to support users in adopting a holistic health-oriented lifestyle. The chatbot uses an NLU-engine (Natural Language Understanding) and only responds to users with answers that were previously tested and approved by human experts. Additionally, this app provides personalized content, including videos and podcasts, aimed at enhancing users’ knowledge and skills in physical activity, nutrition, and stress management as published in a previous study (12). Additionally, users collaborate with the chatbot to set individual health goals, incorporating various practical exercises.

Rationale and knowledge gap

Despite the potential benefits of mHealth applications, factors influencing use among individuals with preventive health needs remain uncertain. Factors such as technological literacy, perceived ease of use, and sociodemographic variables, including age and gender, can influence BI user acceptance (13). Understanding these determinants is critical for designing user-centered mHealth applications that effectively support preventive health behaviors. A key challenge to sustained use is the Law of Attrition (14) which describes the rapid decline in user engagement over time. High attrition rates, often driven by usability challenges and diminished motivation, can undermine the long-term impact of mHealth interventions. Addressing these barriers is vital to maximize the effectiveness of digital health prevention initiatives.

Objective

This study aims to explore BI to use the DHC among individuals with high health prevention needs and to identify practical strategies to enhance long-term user engagement. By examining the factors that affect BI, this research seeks to provide insights that will inform the design and development of mHealth applications. The findings will contribute to ensuring that these tools effectively meet the needs of users and support sustained health behavior changes in populations with urgent preventive care requirements.

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model used in this study to analyze BI to use the mHealth application is based on the development of Venkatesh and colleagues (15). Based on similar studies, the selection of the model’s variables was limited to these five (13) and are defined as follows (15):

• Performance expectancy (PE): the degree to which an individual believes that using a technology will help them to gain a profit in performance, in our case a healthier lifestyle.
• Effort expectancy (EE): the degree of simplicity and ease of use of a system.
• Social influence (SI): the degree to which an individual perceives that others (such as peers, authority figures, and family members) believe he or she should use a technology.
• Facilitating conditions (FC): consumers’ perceptions of the resources and support available to perform a behavior.
• Hedonic motivation (HM): the user’s pleasure of using a technology.

The model examines how these variables affect the user’s BI to use the app in the future which is described as the degree to which an individual has formulated conscious plans to perform a specified future behavior (15). Within the framework of the UTAUT2 model, user’s age, gender, and experience with mHealth apps serve as moderators, influencing the effects of the different variables on the BI (13,16). Based on these studies, the recent study also collects additional data and checks whether they affect the various variables of the UTAUT2 model and may act as additional moderators. These moderators to be checked are age, gender, experience with mHealth applications, highest level of education and health competence in the areas of physical activity, nutrition and stress management.

The following hypotheses arise from the study’s structure and the results of the aforementioned literature (13,15,16):

• H1: PE will affect BI positively.
• H2: EE will affect BI positively.
• H3: SI will affect BI positively.
• H4: FC will affect BI positively.
• H5: HM will affect BI positively.
• H6: age will affect BI negatively.
• H7: experience using mHealth applications will affect BI positively.

In addition to these hypotheses based on the literature mentioned, the possible effect of two other variables is examined:

• H8: the user’s highest education level will affect BI positively.
• H9: health competence, more precisely physical activity-related health competence, food literacy, or stress literacy will affect BI positively.

Methods

Recruitment and participants

A total of 105 participants took part in this study. They had to be at least 18 years old and could only participate if they spoke German, as the app is only available in German. Thirty-five participants in each area—physical activity, nutrition, and stress management—used the app and completed corresponding questionnaires. The app was used in its final version and was installed on a test smartphone. They were recruited via digital ads on social media which were displayed to people who were within a radius of five kilometers of the laboratory in the center of a major German city. Before participants were able to anonymously register in a calendar to take part in the study, they had to complete a short digital questionnaire to identify those who do not meet recommendations in physical activity, nutrition, or stress management. The participants were only able to register if this questionnaire identified a need for action in at least one of the three areas examined. Then, to be able to review the entire app, three times 35 people took part in this study to test the part of the app they failed recommendations for, i.e., the area of physical activity, nutrition or stress management. If a participant failed with more than one recommendation, the area for this user was selected randomly. After complete participation, participants received 10 euros in compensation.

Materials

All questionnaires were presented to the participants in German. As stated, only participants who did not meet general health recommendations in one of the three areas were able to participate in the study. These recommendations are:

• Exercise for at least 30 minutes or more on 5 or more days per week (17).
• Eat at least 5 portions of fruit and/or vegetables per day (18).
• Rate your mental health with 4 or better on a 5-point Likert scale (2).

Three single-item self-rated questions were used to check for health recommendations (19,20).

The questionnaire for BI is presented in Appendix 1. Most of the questions were used in similar studies for BI and acceptance before (13,15,21-26). All questions were asked in randomized order. The second part of the questionnaire includes questions on age, gender, educational status and experience with mHealth apps (13). For competence in the area of physical activity, the “Physical Activity-related Health Competence as an Integrative Objective in Exercise Therapy and Health Sports - a short Questionnaire” was used (27), the short food literacy questionnaire (SFLQ) for nutrition (28) and the Stress and Coping Inventory (SCI) in the area of stress management (29).

In order to compare the usability of the application with the results of the first small study, the System Usability Scale (SUS) was also answered (30).

Design

Participants did the pre-screening with a digital questionnaire a few days before the study at home on their own mobile device or computer. After the screening, participants received an individual and anonymized participant code and anonymously reserved a slot in a digital calendar to take part. The study itself was conducted in person by health scientists in a laboratory. All participants signed a declaration of consent in which they were assured that their data would only be processed in anonymized form and that they could withdraw their consent to participate in the study at any time without any disadvantages. For the study, all participants used the same mobile device (iPhone SE 2022). The selected area (physical activity, nutrition, stress management) to test was decided by the pre-screening. Based on this, participants got one out of three task lists (Appendix 2). They were asked to exactly follow this list in order to learn the app. The lists contain 15 tasks and briefly cover all the app’s functions. Once the task list has been completed, participants were able to use the app independently. Finally, all questionnaires were answered by the participants in digital form on a computer. To ensure that no personal data could be traced back to the participants or accessed by the commissioning health insurance provider, all participants used an anonymized subject code. Additionally, a version of the app that was independent of the health insurance company, but had the same content, was used for the study, which was not hosted within the technical infrastructure of the health insurance provider but instead operated on a server specifically set up for this study. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethical committee of the German Sport University Cologne (No. 098/2022). All participants signed a declaration of consent in which they were assured that their data would only be processed in anonymized form.

Data collection

All participants signed privacy policy, which assured them that their data would be handled exclusively anonymously and tested the app in the laboratory on the same device. Questionnaires were answered digitally using unipark and exported and sorted in SPSS (version 29).

Statistical analysis

Data was analyzed using the software R (version 4.4.0) with the “tidyverse” and “dplyr” packages. The software SmartPLS (version 4.0.9) was used for structural equation modeling (SEM) and for analyzing moderating effects. The significance level was set to P<0.05. Verbal feedback from the participants beyond the questions in the questionnaire was noted by the investigators.

Results

A total of 105 participants took part in this study to meet the minimum sample size-recommendations for SEM (31). The range of participants was 18 to 77 years, with an average of 38.4 years. Health recommendations were assessed through the mentioned pre-screening. Participants clearly fell short of the World Health Organization (WHO) guidelines of five days of at least 30 minutes of physical activity per week and five servings of fruits and vegetables per day. Additionally, the self-rated mental health status, measured on a scale from 1 to 5, was rated below average, with a mean score of 2.71. Table 1 shows detailed information about the participants. Score for usability on the SUS was 74.52 (±14.33, median: 77.50).

Table 2 shows the frequency distribution of answers for each question regarding the UTAUT2 model with the number of participants per score value. The exact wording of the individual questions can be found in Appendix 1. Overall, it is evident that the average scores for SI are the lowest. The influence of the various variables on the intention to use the health application (BI) is illustrated by an SEM model in the next section.

Figure 1 shows the hypothesized conceptual SEM model of this study with path coefficients. Confirmed hypotheses are shown in black, unconfirmed in grey. The results show a significant influence of PE and HM (both P<0.001) on BI. The remaining variables of the UTAUT2 model, EE (P=0.16), SI (P=0.27) and FC (P=0.14), did not show a significant influence on BI. With a P value of 0.27, SI had the statistically weakest effect on BI. As already mentioned in Table 2, the social functions of the mHealth application were rated the lowest overall by the participants.

Figure 1 Structural equation model with path coefficients (and P values) and conceptual framework of the study. Paths with significant P values are marked in black.

Since, contrary to the assumption in literature, no significant influence on BI by factors like age (P=0.81) and mHealth experience (P=0.19) was found, it was additionally tested for a moderating effect of these and other variables on the effect of the variables of the original UTAUT2 model. The analysis revealed no statistically significant moderating effects for any of the tested variables on the relationships between the original UTAUT2 constructs and BI. A selection of results with the significant factors PE and HM (both P<0.001) is shown in Table 3.

Discussion

PE as a predictor for BI

Consistent with findings from similar studies analyzing the BI to use mHealth applications, PE emerged as the strongest predictor (13,32) with a path coefficient of 0.48 and P<0.001. A novel contribution of this study is its focus on individuals with a high need for action in health prevention. In previous research involving individuals with direct health concerns, such as diabetes patients, the influence of PE on BI was predictable due to the immediate health implications (32,33). However, this study provides new insights into preventive healthcare, where users may not immediately recognize their personal need for action. The fact that this population values the app’s benefits for their health underscores the importance of raising awareness about preventive measures.

HM as a key driver for engagement

HM also significantly influenced BI with a path coefficient of 0.37 and P<0.001, aligning with results from similar studies (34). The design of the app intentionally incorporates engaging features, such as personalized conversations with a chatbot, the ability to track progress toward health goals, and an expanding library of videos and podcasts. These elements aim to make the app enjoyable, fostering long-term engagement. This finding is particularly relevant to health prevention, a domain often perceived as unmotivating due to the delayed visibility of benefits, the need for behavioral change, and the intangible nature of success (14). By integrating gamification and low-threshold features, the app attempts to overcome these hurdles. For instance, quick and simple interactions with the chatbot allow users to build a personalized media library, providing immediate gratification and reinforcing continued use. These elements are crucial for reducing high dropout rates that are common in mHealth interventions and for promoting sustained user engagement in preventive healthcare.

Role of EE, FC and SI

EE (path coefficient: −0.09; P=0.16) and FC (path coefficient: 0.11; P=0.14) were not found to significantly affect BI in this study. SI received the lowest average rating and showed no significant effect on BI as well (path coefficient: 0.08; P=0.27). However, the potential of SI to enhance BI should not be overlooked. Previous research highlights SI as a critical factor in increasing acceptance and usage of mHealth applications, particularly among younger and older populations (34,35). To leverage SI in future iterations of the app, strategies could include integrating features inspired by social media to foster community engagement or enabling users to share progress and experiences within the app. Additionally, support from users’ personal environments, such as family, friends, or healthcare professionals, could further strengthen SI. These approaches align with the app’s broader goal of supporting preventive health behaviors and ultimately reducing the risk of dependency on healthcare services in later life.

No influence by age, gender, experience or education and moderating factors

The findings of this study indicate that age (P=0.81) and gender (P=0.35) did not affect BI significantly, either directly or as moderating factors. While previous research has shown that age can affect BI, with older individuals often demonstrating lower acceptance of mHealth applications (13), this study found no such effect. Similarly, gender, which has generally been reported as having limited effect on mHealth acceptance (36), showed no significant impact. Although some studies suggest women are more likely to use digital health applications, including mHealth apps (37), this was not observed here. These results suggest that the app’s design may effectively neutralize barriers associated with demographic factors, enabling broader accessibility across diverse user groups.

Prior experience with mHealth applications, often a positive predictor of BI (13), also showed no significant effect (P=0.19). This could be attributed to the app’s high usability, which likely reduces dependency on prior experience. High usability ensures that even individuals without prior exposure to mHealth can adopt the app easily, emphasizing the importance of user-centered design. Incorporating user feedback during development can further enhance usability and accessibility (38).

Similarly, educational status, which has been noted as an influencing factor in some studies (39), showed no significant effect on BI (P=0.98). This is a positive finding, as it suggests the app is accessible to users regardless of their education level. This inclusivity is critical, as lower education levels are often associated with barriers to using mHealth apps (40).

Implications and actions needed

The app is designed to promote a holistic and sustainable health-oriented lifestyle by increasing health literacy through interactive media. Features such as a chatbot and personalized health goals aim to maintain user motivation and engagement over the long term. These elements address a critical challenge in mHealth: the high dropout rates often attributed to low motivation and the delayed visibility of preventive health outcomes (14).

The study highlights the critical role of PE in predicting BI for mHealth apps. Even users with limited awareness of their need for preventive health measures must perceive the app as beneficial to their health. Ensuring that users recognize the personal positive impact of the app should remain a top priority during development. This focus can help overcome common barriers in preventive healthcare, such as a lack of immediate results and perceived low relevance.

Long-term motivation is another key factor in sustaining user engagement. As demonstrated in this and similar studies, HM affects BI (34). Gamification elements, such as the use of a chatbot to provide personalized advice and interactive health goals, enhance user enjoyment and motivation. With advances in artificial intelligence (AI), chatbots are becoming increasingly relevant in digital health prevention. However, concerns about data privacy, empathy, and security must be addressed to build trust in AI-driven healthcare solutions (41,42). Combining analog and digital content, such as integrating in-person health courses recommended by the app, can further enhance trust and acceptance.

To further situate the findings within the evolving digital health landscape, it is important to recognize the growing role of AI in health literacy and preventive healthcare in general. Recent studies emphasize the integration of AI-driven chatbots to enhance e-health literacy and user engagement (43). It demonstrates how personalized AI interactions can affect users’ motivation and understanding of health-related behaviors. Incorporating AI-based personalization and predictive analytics into mHealth applications like the DHC could offer tailored feedback, adaptive goal-setting, and real-time health coaching, thus reinforcing PE and HM.

Moreover adolescents’ engagement with emerging digital environments like the metaverse also affects their e-health literacy and health behaviors (44). These insights underline the potential for future mHealth applications to integrate more immersive and interactive AI-driven features, appealing to diverse user groups across different age ranges. Embedding AI functionalities that adapt dynamically to users’ changing behaviors and preferences may address attrition challenges and support sustained lifestyle modifications.

Given the rapid developments in AI, ethical considerations regarding data privacy, algorithmic transparency, and digital inclusivity must be thoroughly addressed to maintain user trust. This includes ensuring compliance with stringent data protection regulations, particularly when working with sensitive health data in cooperation with healthcare providers or insurers.

While SI did not significantly affect BI in this study, its potential role in increasing acceptance cannot be ignored. Traditional social media functions may not be feasible in health prevention due to strict data protection regulations, particularly for apps developed in collaboration with German health insurance providers. However, limited social features, such as sharing progress with family or close networks, could be explored to foster SI in a privacy-compliant manner.

However, integrating AI-driven peer support functionalities, such as anonymized group challenges or AI-mediated health communities, could enhance the feeling of social connectedness without violating privacy standards (43). Furthermore, embedding motivational messaging systems, facilitated through AI chatbots, could simulate the effects of social encouragement, thereby subtly strengthening SI effects.

By addressing these considerations, future developments can further refine mHealth solutions to effectively support preventive healthcare. These findings underscore the transformative potential of inclusive, user-friendly apps to reduce the burden on nursing and healthcare systems, particularly as they face growing challenges due to demographic changes and workforce shortages.

To ensure future mHealth interventions are resilient to user disengagement, designers should increasingly look toward AI-enhanced personalization strategies, digital literacy promotion, and privacy-preserving social features as foundational elements of app development. A continued focus on engagement, retention, and accessibility will ensure that mHealth applications contribute meaningfully to the sustainability of healthcare services.

Limitations and further research

While analyzing acceptance provides valuable insights into potential mHealth usage, actual user engagement and sustained usage may differ once the app is in real-world use. Usability issues, technical problems, or discrepancies between initial impressions and actual benefits could affect long-term adoption (45). To make valid conclusions about the app’s real-world impact, post-launch evaluations of user engagement, satisfaction, and the effectiveness of features designed to enhance performance PE and HM are essential.

Additionally, the app aims to enhance user competence in physical activity, nutrition, and stress management, contributing to a holistic, health-oriented lifestyle. However, achieving competence does not automatically translate into sustained behavior change, as external factors such as work routines or social environments can limit users’ ability to adopt healthier lifestyles (46). The app attempts to account for these constraints through personalized goal-setting, but the effectiveness of this approach must be assessed after release. Future studies should evaluate whether the app meaningfully impacts not only knowledge but also measurable health outcomes and lifestyle changes.

The study’s sample was recruited via social media advertisements targeted at individuals within a 5-kilometer radius of the research laboratory. Participants were screened via a questionnaire to confirm they fit the desired health profile but were unaware that deficits in physical activity, nutrition, or stress management were prerequisites for participation. This unawareness may differentiate the findings from studies involving participants who are fully conscious of their health problems.

Moreover, participation in the study required a level of engagement, including responding to the advertisement, completing a questionnaire, scheduling a visit, and attending the laboratory. While participants were compensated with 10 euros, their willingness to engage reflects a degree of intrinsic motivation. Consequently, the findings may not generalize to individuals with low motivation, a critical yet difficult-to-reach target group in health prevention. Future research should explore strategies to engage this population, as their participation is essential to maximizing the public health impact of mHealth applications.

Evaluating the app’s long-term effectiveness in real-world conditions, particularly among less motivated users, will be a crucial step in understanding its potential to contribute to health promotion and reduce systemic pressures on healthcare and nursing services.

Conclusions

Preventive mHealth applications often struggle with declining user engagement. This study shows that PE and HM significantly influence (both P<0.001) BI among individuals with high prevention needs, while SI offers potential for improvement. However, strict data protection regulations limit the integration of familiar social features, reducing appeal compared to entertainment apps.

To ensure future mHealth applications can effectively address these challenges, several practical strategies should be considered:

• Optimize user experience through intuitive, low-threshold designs that minimize usability barriers across diverse demographic groups.
• Enhance engagement via AI-driven personalization, offering adaptive goal-setting, predictive feedback, and real-time coaching tailored to users’ individual progress and needs.
• Strengthen social reinforcement mechanisms by integrating privacy-compliant, AI-facilitated peer support features (e.g., anonymized group challenges, motivational messaging systems).
• Foster trust and long-term adherence by combining digital tools with optional analog elements, such as recommended in-person health courses, thus creating hybrid models of preventive healthcare support.

By strategically implementing these approaches, future mHealth interventions can increase BI and acceptance, sustain engagement over time, and meaningfully contribute to the prevention of chronic diseases and the reduction of systemic burdens on healthcare systems.

Moreover, as the landscape of digital health rapidly evolves, particularly through advances in AI, it is crucial to continuously evaluate ethical implications, data protection measures, and the accessibility of mHealth technologies for vulnerable populations.

Embedding these considerations into the development process will ensure that mHealth applications not only achieve higher acceptance rates but also realize their full potential in promoting long-term health behavior change and delaying the need for nursing care.

Acknowledgments

The authors thank Isabelle Allstadt, Leonie Börner and Leon Uschwa for their support in data collection.

Footnote

Data Sharing Statement: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-29/dss

Peer Review File: Available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-29/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-25-29/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethical committee of the German Sport University Cologne (No. 098/2022). All participants signed a declaration of consent in which they were assured that their data would only be processed in anonymized form.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. OECD. Health at a Glance 2023. OECD; 2023. Available online: https://www.oecd.org/en/publications/health-at-a-glance-2023_7a7afb35-en.html
2. Loss J. Health behaviour in Germany - ongoing cause for concern! J Health Monit 2022;7:3-5. [Crossref] [PubMed]
3. Manz K, Domanska OM, Kuhnert R, et al. How much do adults sit? Results from the German Health Update (GEDA 2019/2020-EHIS). J Health Monit 2022;7:29-37. [Crossref] [PubMed]
4. Schienkiewitz A, Kuhnert R, Blume M, et al. Overweight and obesity among adults in Germany - Results from GEDA 2019/2020-EHIS. J Health Monit 2022;7:21-8. [Crossref] [PubMed]
5. Walther L, Junker S, Thom J, et al. High-Frequency Surveillance of Mental Health Indicators in the Adult Population of Germany: Trends From 2022 to 2023. Dtsch Arztebl Int 2023;120:736-7. [Crossref] [PubMed]
6. Santos AC, Willumsen J, Meheus F, et al. The cost of inaction on physical inactivity to public health-care systems: a population-attributable fraction analysis. Lancet Glob Health 2023;11:e32-9. [Crossref] [PubMed]
7. Abbasi S. Desperation of Demographic Change in the Health Sector in Germany: Necessary Strategy Orientation for Medical Practices in the Countryside. UCPA 2019;16:7-15.
8. Kuhlmann E, Hoeper K, Witte T, et al. Health workforce needs of small medical specialties: findings from rheumatology in Germany. European Journal of Public Health 2022;32:Suppl 3.
9. Heidel A, Hagist C, Spinler S, et al. Removing Dust From the German Health Care System by Introducing Health Apps Into Standard Care: Semistructured Interview Study. JMIR Hum Factors 2023;10:e42186. [Crossref] [PubMed]
10. Kobel S, Wartha O. We Need More Health Literacy. Dtsch Z Sportmed 2024;75:3-4.
11. Singh B, Ahmed M, Staiano AE, et al. A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. NPJ Digit Med 2024;7:179. [Crossref] [PubMed]
12. Bickmann P, Froböse I, Grieben C. An mHealth Application in German Health Care System: Importance of User Participation in the Development Process. J Med Syst 2024;48:20. [Crossref] [PubMed]
13. Uncovska M, Freitag B, Meister S, et al. Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study. J Med Syst 2023;47:14. [Crossref] [PubMed]
14. Eysenbach G. The law of attrition. J Med Internet Res 2005;7:e11. [Crossref] [PubMed]
15. Venkatesh V, Thong JYL, Xu X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly 2012;36:157-78.
16. Chang CM, Liu LW, Huang HC, et al. Factors Influencing Online Hotel Booking: Extending UTAUT2 with Age, Gender, and Experience as Moderators. Information 2019;10:281.
17. World Health Organization. Global Action Plan on Physical Activity 2018-2030: More Active People for a Healthier World. Geneva: World Health Organization; 2019.
18. DGE. 14. DGE-Ernährungsbericht. 1. Auflage. Bonn: Deutsche Ges. f. Ernährung; 2020. Available online: https://www.dge.de/fileadmin/dok/dge/jahresberichte/DGE-Jahresbericht-2020.pdf
19. Maguire PA, Reay RE, Nolan CJ, et al. The single-item Self-Rated Mental Health Question in women with gestational diabetes mellitus. Australas Psychiatry 2022;30:472-5. [Crossref] [PubMed]
20. Wanner M, Probst-Hensch N, Kriemler S, et al. What physical activity surveillance needs: validity of a single-item questionnaire. Br J Sports Med 2014;48:1570-6. [Crossref] [PubMed]
21. Cimperman M, Makovec Brenčič M, Trkman P. Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model. Int J Med Inform 2016;90:22-31. [Crossref] [PubMed]
22. Deng Z. Understanding public users' adoption of mobile health service. IJMC 2013;11:351-73.
23. Koivumäki T, Pekkarinen S, Lappi M, et al. Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study. J Med Internet Res 2017;19:e429. [Crossref] [PubMed]
24. Lin JSC, Chang HC. The role of technology readiness in self-service technology acceptance. Managing Service Quality: An International Journal 2011;21:424-44.
25. Moore GC, Benbasat I. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 1991;2:192-222.
26. van der Vaart R, Atema V, Evers AW. Guided online self-management interventions in primary care: a survey on use, facilitators, and barriers. BMC Fam Pract 2016;17:27. [Crossref] [PubMed]
27. Sudeck G, Pfeifer K. Physical activity-related health competence as an integrative objective in exercise therapy and health sports – conception and validation of a short questionnaire. Sportwiss 2016;46:74-87.
28. Gréa Krause C, Beer-Borst S, Sommerhalder K, et al. A short food literacy questionnaire (SFLQ) for adults: Findings from a Swiss validation study. Appetite 2018;120:275-80. [Crossref] [PubMed]
29. Satow L. Stress- und Coping-Inventar (SCI): Test- und Skalendokumentation (2., überarbeitete und neunormierte Version); 2024. Available online: https://www.drsatow.de/tests/stress-und-coping-inventar/
30. Jordan PW. Usability Evaluation in Industry. Boca Raton: Chapman and Hall/CRC; 2014.
31. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice 2011;19:139-52.
32. Schretzlmaier P, Hecker A, Ammenwerth E. Extension of the Unified Theory of Acceptance and Use of Technology 2 model for predicting mHealth acceptance using diabetes as an example: a cross-sectional validation study. BMJ Health Care Inform 2022;29:e100640. [Crossref] [PubMed]
33. Zhang Y, Liu C, Luo S, et al. Factors Influencing Patients' Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey. J Med Internet Res 2019;21:e15023. [Crossref] [PubMed]
34. Schomakers EM, Lidynia C, Vervier LS, et al. Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study. JMIR Mhealth Uhealth 2022;10:e27095. [Crossref] [PubMed]
35. Alam MZ, Hu W, Kaium MA, et al. Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society 2020;61:101255.
36. Spankulova L, Asanova Z. The Impact of Information and Communication Technologies on Gender Aspects in the Health System: A Systematic Review. Eurasian Journal of Gender Studies 2024;1:5-14.
37. Grobe TG, Weller L, Braun A, et al. BARMER Arztreport 2024: Digitale Gesundheitsanwendungen - DiGA; 2024. Available online: https://www.barmer.de/resource/blob/1255894/d58c1bd7c1a99c78a54d410c490920c8/dl-arztreport-2024-data.pdf
38. Mohammed AH, Abdul Wahab N, Ibrahim N. Enhancing mHealth Applications through User-Centred Design: A Conceptual Framework for Improved Healthcare Accessibility and User Experience. JCRINN 2025;10:15-24.
39. Jahnel T, Schüz B. Participatory development of digital public health: tension between user perspectives and evidence. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2020;63:153-9. [Crossref] [PubMed]
40. Tona DM, Causio FA, Beccia F, et al. Public Perceptions and Engagement in mHealth Across Eight European Countries. Eur J Public Health 2024;34:ckae144.1176.
41. Abd-Alrazaq AA, Alajlani M, Ali N, et al. Perceptions and Opinions of Patients About Mental Health Chatbots: Scoping Review. J Med Internet Res 2021;23:e17828. [Crossref] [PubMed]
42. Moldt JA, Festl-Wietek T, Madany Mamlouk A, et al. Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots. Med Educ Online 2023;28:2182659. [Crossref] [PubMed]
43. Coşkun AB, Elmaoğlu E, Buran C, et al. Integration of Chatgpt and E-Health Literacy: Opportunities, Challenges, and a Look Towards the Future. J Health Rep Technol 2024;10:e139748.
44. Coşkun AB, Elmaoğlu E. Examining the Impact of Adolescents' Metaverse Perception on E-health Literacy and Perceptions of Healthy Living. Archives of Physiotherapy and Global Researches 2023;26:11-6.
45. Regan C, Rosen PV, Andermo S, et al. The acceptability, usability, engagement and optimisation of a mHealth service promoting healthy lifestyle behaviours: A mixed method feasibility study. Digit Health 2024;10:20552076241247935. [Crossref] [PubMed]
46. Carl J, Sudeck G, Pfeifer K. Competencies for a Healthy Physically Active Lifestyle-Reflections on the Model of Physical Activity-Related Health Competence. J Phys Act Health 2020;17:688-97. [Crossref] [PubMed]