Role of Technology Innovation in Telemedicine: Focus on Sport Nutrition

Abstract

Due to the COVID-19 pandemic, there has been a significant transformation in the field of telehealth and telemedicine, as systems have been improved to meet the increased demand for remote healthcare services. Many ordinary technologies have been equipped to facilitate the normal relationship between patients and specialists. These technologies were put into action in a short period of time, creating a gap between the limits of common technologies and the special needs of telemedicine patients. Furthermore, focusing the lens on the special needs of sports in terms of nutrition, we see that research demonstrates the possibility of improving athletic performance by introducing technological diet support. This review aims to provide an overview of the technologies successfully implemented in telemedicine systems, a look at new modeling approaches, and a study on the roles of new enabling technologies in the process. It aims to highlight the results of the employment of telemedicine in sports dietary support and present open research challenges and recommendations for future research on a new application of technologies in telemedicine, for both the industrial and academic sectors. Literature was identified through intensive research work, reviewing articles related to the topics of new technologies in telemedicine and sports dietary support systems. The review concludes that it is possible to envisage the design of future models in the eHealth sector related to nutrition and sports, confirming the important role of telemedicine in a healthy lifestyle.

1. Introduction

During the COVID-19 pandemic, there has been a reassessment of the importance of certain aspects that contribute to “well-being”. One of the observations made evident by this situation is the lack of infrastructure in health and care services, particularly regarding telemedicine [1]. Investments to improve the infrastructure in the medical field have allowed for the implementation and expansion of telemedicine, which has proven beneficial during the pandemic. Telemedicine has enabled remote consultations, reducing the risk of infection for patients and healthcare workers. Additionally, it has allowed for the efficient allocation of medical resources and the continuation of medical services in hard-to-reach areas. However, the adoption of telemedicine is not without limitations [2]. These include issues related to internet connectivity, the need for proper training of healthcare providers, and the challenges of diagnosing and treating patients remotely. Nonetheless, the pandemic has spurred investments and innovation on the eHealth front, which will likely continue to benefit the healthcare system [3]. Although telemedicine was previously seen as a detached form of the doctor–patient relationship and as a complication in carrying out activities, it has now gained a prominent position among the tools useful for empowering the welfare network. Examples of the correct and functional use of “enabling technologies” in the healthcare sector [4] include the applications of artificial intelligence and machine learning in “ICUs” [5] for the control of patients in intensive care and of machine learning in the “ER” [6] for the classification of the severity of a patient’s situation upon arrival. Regarding remote control [7] and the possibility of “remote visits”, telemedicine has successfully incorporated the already widespread “mobile” technologies, creating a new trend for eHealth, namely mHealth (or mobile Health). Among the applications in this sense, there are correct applications for treating patients in parenteral nutrition, professional sportsmen undergoing continuous visits, and patients with chronic kidney disorders [8]. Among the indicators of the success of these applications, there are parallels with the user-centered model. According to this model, during the conception of telemedicine systems and the creation of applications connected to it, it is necessary to translate the final goal from the “customer” to the “user”, prioritizing the patient’s needs over market needs.

Based on the observations made during the COVID-19 pandemic, the lack of infrastructure for telemedicine services, particularly in healthcare and care services, is a pressing issue. However, investments in improving healthcare infrastructure have led to the implementation and expansion of telemedicine, which has proven to be beneficial during the pandemic. Telemedicine has enabled remote consultations, which has reduced the risk of infection for both patients and healthcare workers. Additionally, it has allowed for the efficient allocation of medical resources and the continuation of medical services in remote areas that are difficult to access. Despite the benefits of telemedicine, its adoption is not without limitations, including issues related to internet connectivity, the need for proper training of healthcare providers, and the challenges of diagnosing and treating patients remotely. Nonetheless, the pandemic has led to investments and innovation in the eHealth sector, which will likely continue to benefit the healthcare system.

The use of enabling technologies, such as artificial intelligence and machine learning, in the healthcare sector has been successful, particularly in the cases of application in ICUs and ERs. Remote control and the possibility of remote visits have been successfully incorporated into telemedicine, creating a new trend known as mHealth. The use of mHealth has been successful in treating patients with parenteral nutrition, professional sportsmen undergoing continuous visits, and patients with chronic kidney disorders.

Involving users in the early stages of designing healthcare systems has many benefits, including identifying any unmet needs of the system, developing better usability and desirability of the system, facilitating an understanding of the users’ goals, and generating trust. However, current methods and inclusion models involve user participation only during the prototype testing stages. This lack of involvement can be traced to reasons such as power dynamics, poor value perception, lack of resources, distrust, and inflexibility [9]. In light of this evidence, this review aims to analyze new modeling approaches in telemedicine for sports nutrition management through literature revision. This will present open research challenges and recommendations for future research on new applications of technologies in telemedicine for both the industrial and academic sectors. This analysis is valuable and necessary, as it focuses on the relationship between sports and nutrition and their link with telemedicine.

2. Current Technology

As technology rapidly advances, it is not surprising that it has impacted nearly every aspect of our lives. In recent years, there has been a significant increase in the use of technology in healthcare and sports, leading to the emergence of new and exciting fields such as eHealth and sports technology. eHealth refers to the use of information and communication technologies in healthcare, which can potentially transform how healthcare is delivered. With the help of enabling technologies, such as electronic health records, telemedicine, and health information exchange, healthcare providers can offer personalized and efficient care. Similarly, in sports, technology has opened new frontiers, enabling athletes to push their limits and achieve their goals. Technology is revolutionizing how athletes train and compete, from wearable technology that tracks performance metrics to virtual reality training programs. Moreover, sports have become an area of interest for nations and states, with governments investing heavily in sports infrastructure and initiatives to boost participation in sports. This trend is fueled by the recognition of the numerous benefits of sports, including improved physical and mental health, social integration, and economic development. However, for the widespread adoption of technology in healthcare and sports to be successful, it is essential to consider user perception and acceptance of these systems. In the case of telemedicine, for example, patients’ attitudes towards remote consultations and virtual care can impact the success of these programs. Modelling approaches, such as the technology acceptance model (TAM), can help identify the factors influencing users’ acceptance of these systems and guide the development of effective technology solutions. In this article, we explore the enabling technologies related to eHealth, new technological frontiers in sports, and the nations’ interest in sports. Additionally, we delve into the user perception of telemedicine systems, system acceptance, and modelling approaches that can guide the development of effective technology solutions in these areas. Described as a revolutionary innovation in healthcare, telemedicine has been transforming the way patients access medical care and has the potential to improve healthcare outcomes globally [10]. Telemedicine allows patients to receive medical consultations remotely using technology, such as video conferencing, mobile apps, and remote monitoring devices. This eliminates the need for in-person visits, reducing the burden on healthcare systems and improving access to care, especially for those living in rural or remote areas [11]. Numerous studies have shown the benefits of telemedicine in improving healthcare outcomes, reducing healthcare costs, and increasing patient satisfaction [12].

2.1. Application Framework

During our research, several gaps were found in the various telemedicine application models [13]. Despite a decreasing trend, a majority segment of users still face difficulty in using these systems, as shown by the publications associated with these models. As stated in the first paragraph, modifying the design core of telemedicine systems to prioritize the end-user’s needs requires an immediate adjustment to the project framework. The user-centered design model and other models analyzed [14] differ in that the former prioritizes usability over factors such as sales logic, production logic, process planning, and disposal/recycling. The project steps required for producing any application system based on the user-centered logic are shown in Figure 1 [15], with the necessary features for each step listed in the second column. The planning stage (PSS planning) requires improved reliability, user involvement, and data security for product features. The implementation step (PSS project) must simplify interactions, improve reliability, optimize resources, enable short development cycles, provide flexible interfaces, ensure interoperability among various systems and technologies, and offer autonomy for product characteristics. Finally, the design step Figure 2 is crucial for developing or modifying the PSS planning step and necessitates meeting requirements for resource optimization, short development cycles, innovation, cross-sectoral knowledge creation, real-time data exchange, and closed-loop operations for the macro category of data communication.

2.2. Enabling Technologies and eHealth

Thanks to advancements in cross-sectoral technology, remote prevention, diagnosis, therapy services [16], consultancy [17], follow-up services [18], remote monitoring, and rehabilitation connected to telemedicine have become more efficient since 1906 [15]. Additionally, the increasingly “open” network, while being controlled for data security, has made it possible to integrate patient-owned technologies with eHealth models used globally. This information can be found in telemedicine application trends such as “mobile health” or mHealth [19], which encompasses mobile devices, such as smartphones, dedicated apps, and smart wearables in the healthcare model, or “social health” [20], which involves the use of tablets as a dispensing technology for “serious games” [21] intended for patients suffering from neurodegenerative diseases [22]. The new frontiers of advanced technological industry (i.e., Industry 5.0) offer numerous opportunities for improving telemedicine systems. Key enabling technologies [23], sets of tools/resources capable of communicating between systems and the network, are among the most important elements to consider while planning the future of telemedicine systems. These include edge computing [24], which involves data computation on local devices rather than on servers; artificial intelligence or AI, a series of algorithms with human-like capabilities, such as learning; cobots, collaborative robots that fall within the sphere of additive manufacturing; 6G, the sixth generation of mobile communication technologies; digital twins, a digital copy of a real “asset” that reports any changes, following the lifecycle of the original or vice versa; blockchain, a type of programming based on nodes that favors transparency and data security; the Internet of Everything (IoE), a natural evolution of the philosophy linked to the IoT, which aims to interconnect what is not yet digitizable or integrated today into a digital system; and big data analysis, which can analyze vast amounts of data in a short time [25].

2.3. New Frontiers in Sports Technology

Based on the advice presented in the examined articles, the best approach for future application studies is to start the project planning process by identifying the needs of the patient in relation to the progress of technologies, rather than starting with the technologies to be implemented in the medical field [26]. Following this recommendation, the focus of this review is on the segment of patients identified as “professional sportsmen” and their needs in relation to telemedicine and nutrition. One of the first elements analyzed by systems applied to sports is the athlete’s energy consumption related to training sessions. Among the hypothesized models, the most suitable is the one based on IoT technology, often integrated with cloud computing [27]. This model involves mobile/wearable devices [28] and provides a new vision of the athlete’s performance, compatible with the user-centered design philosophy. Personalized and balanced nutrition is also essential for multiple functions related to training and recovery [29], based on the patient and their daily activities. Combining the evidence published in the sports nutrition sector, personalized and controlled nutrition is necessary to optimize the recovery of connective tissues [30], improve performance [31,32], and balance carbohydrate and protein intake for endurance-type sports [33]. However, the efforts of professionals alone may not be enough to achieve such a level of personalization in sports nutrition [34,35]. In sports such as swimming and skiing, other needs for analysis and improvement of the athlete’s skills have been identified. For example, at high levels of sporting competition, the difference between the winner and the loser is very subtle, and motion capture technology and analysis through artificial intelligence are required to predict sports efficiency in swimming [36]. Skiing also benefits from audio–visual support during the training phase, allowing the athlete to have a tangible reference for their movements and reduce the incidence of injuries [37].

2.4. National Sports and Interests

The information discussed so far applies to various levels of sporting performance, from amateur to professional. When considering stakeholders, how do states interpret “performance”, and what are their objectives? Overseas, there are evident differences in sports performance evaluation models, particularly at high levels. The term “sporting performance” no longer refers to an individual athlete’s results but rather to the ambition of obtaining international awards [38]. Programs such as Own the Podium (OTP) [39] demonstrate the state’s interest in developing a method that involves both private and public schools and sports infrastructure to increase the number of medals won, especially in the Olympics and Paralympics. The model suggested by OTP has led to the creation of the “Top Secret” program, which concentrates efforts from a “dream team of researchers” to develop new training techniques, equipment, and technologies.

These joint interventions have enabled incredible levels of technological implementation. The evolution of technology aimed at improving athlete performance has resulted in new instruments, such as wearables and tools, as well as innovations implemented in the stadiums and circuits [40] where the competition takes place. For instance, reducing the friction of skates in speed skating and reducing water resistance in swimsuits [41] has allowed athletes to break existing records remarkably. Reducing the weight of swimsuits, bicycles, and all wearable equipment worn by athletes has also enabled unprecedented performance.

Although studies and research aim to enhance athletes’ performance and competition, there are two distinct paths of technological development concerning supporting professionals: the first focuses on research aimed at promoting healthy training, energy balance, and expression of the athlete’s complete physical potential, while the second aims to outperform athletes from other countries to ensure the prestige and reputation of the athlete’s reference institution [42]. Technologies used to select the athlete with the highest probability of achieving a high performance, such as artificial intelligence applied to swimmer analysis, do not benefit the expression of the athlete’s performance, but rather support economic or strategic decisions.

2.5. System User Perception

After discussing how enabling technologies interact with nutrition and sports, we now turn to one of the critical issues of these systems: their acceptability [43]. Several indicators point to the perceived ease of use and usefulness of a system as leading factors in the introduction of a technology into an existing system. Equally important is the user’s attitude toward the technology. If the user perceives a well-defined purpose for the technology within the system, their expectation and desire to implement it will increase, as long as they believe the purpose is effective and aligned with their real goals [44]. To investigate these factors, the technology acceptance model (TAM) has been hypothesized and tested in several fields. Its goal is to understand the gap between system producers and the acceptance of the information technologies involved, specifically studying user perception and perceived ease of use. From 1989 to 2018, the study of the technology acceptance model allowed for the identification of additional factors [45], such as technology anxiety, which is the feeling resulting from the absence or intrusiveness of the technology embedded in the system; technology self-efficacy, which is the feeling of being effective in achieving goals due to a tool embedded in the system; perceived enjoyment; and user satisfaction. Another essential correlation is found between attitude and user satisfaction. When the user perceives strong efficacy of the technology to achieve an upgrade in fulfilling a task of their job before implementation, there is a reduction in user satisfaction at the time of use, reinforcing the distance between the producer and the user. Therefore, finding a solution to reduce the gap between the company and the market and involving the user in system creation phases is essential. Three moments in the relationship between the system and the end-user can be defined based on the factors found so far. The first is the moment before system implementation, in which attitude and intention of use act as facilitators or barriers to the user’s approach. The second is the period of actual use of the system, in which technological self-efficacy, technological anxiety, and perceived enjoyment play a role in the user’s experience. Finally, the third moment is after use, in which user satisfaction is evaluated and related to the previously mentioned factors.

2.6. Technological Self-Efficacy

Moving on to the factors that affect the second moment, which is the central period of system usage, there are two variables to consider: the achievement of preset goals and the fulfillment of planned tasks through the system. The achievement of goals will affect the feeling of self-efficacy [46], which refers to the sense of competence and confidence in performing a task. Specifically, technological self-efficacy indicates feeling capable of learning the use of the system and being able to use it to accomplish a specific task. Another variable to consider is self-directed learning, which refers to the end-user’s drive to autonomously learn and understand the different parts of the system, either during the implementation or the usage phases. Various studies have shown that self-directed learning is a crucial variable in technological acceptability.

2.7. Technological Anxiety

The other factor of the “moment of use” by the end-user is technology anxiety [47] (formerly referred to as technostress). One of the found paths for studying this factor is to examine infrastructures where technology embedding has been highly impactful. In the researched studies, great results have been found in the school system, due to the COVID-19 pandemic, which has seen the inclusion of numerous information and communication technologies and the shift in lesson structure itself from offline to online. It was possible to find both creator and inhibitory components of technological anxiety through analysis. Among the most obvious stressors, the following were found: the possibility of working faster and longer (overload), the possibility of being reached at any time due to technological invasion (invasion), the feeling of continuous evolution of technology and systems (technological insecurity), and the perceived complexity of systems and technologies (complexity). In compensation, two factors were also evinced among the inhibitors, including technical support, both as far as those implementing the system and end-users, and the inclusion and the participation of users from the planning stages through the implementation of system.

2.8. User Satisfaction and System Acceptance

The culminating phase of integrating the system into a given infrastructure is the end-user’s overall evaluation of the technology. User satisfaction, technological self-efficacy, perceived enjoyment, and technological anxiety are all influenced by various factors, which can differ depending on the specific technological system in question. User satisfaction, which is closely related to a user’s attitude towards the system, can be affected by factors such as ease of use, design, and overall functionality. Technological self-efficacy, or a user’s belief in their ability to use and master new technologies, may be influenced by their prior experience with technology and level of comfort. Perceived enjoyment, or the level of pleasure a user derives from using the technology, can be influenced by factors such as the level of support provided and the overall usability of the system. Finally, technological anxiety, which refers to feelings of fear or apprehension towards new technologies, may be influenced by various factors such as the user’s level of confidence and familiarity with the technology. By understanding these factors and how they vary depending on the specific system being used, designers and researchers can create more effective and user-friendly technologies that better meet the needs and expectations of users. Different examples can be found in the e-commerce field [48], where various attributes can be detected through sentiment analysis, attribute analysis, speech analysis, etc. In the nutritional/sports field, examples can be inferred from the medical field [49], where results obtained in the implementation of telemonitoring systems are reported, considering the percentage of system users, periods of activity on the platforms, and the type of communication with the contact center.

2.9. New Modeling Approach

The impact of modeling approaches on telehealth systems is significant as they enable healthcare professionals to evaluate telehealth services’ performance, optimize resource allocation, and improve patient outcomes. While several modeling techniques are available, generalized nets [50] (GNs) provide a unique approach that can capture the dynamic and uncertain nature of the telehealth environment. By using GNs to model telehealth services, healthcare professionals can optimize resource utilization, improve care quality, and reduce healthcare costs. Moreover, GNs can lead to the development of more accurate and efficient telehealth services, resulting in improved patient outcomes and increased satisfaction. Therefore, incorporating GNs as a modeling approach in telehealth can bring substantial benefits to healthcare systems, making it a crucial perspective to consider when designing and improving telehealth technology.

3. Discussion

By connecting all the arguments presented in the different paragraphs, it is possible to identify a common thread that connects the user’s point of view, economic strategic needs, and research needs. In the previous paragraph, we defined the easily applicable and currently available technologies (IoT and cloud computing) while placing them in the user-centered design model. With this in mind, the next applicable interventions in telemedicine and eHealth refer to improved “real-time data exchange” and the implementation of “closed-loop operations” [51], as far as the “data communication” Figure 1 side of the presented model is concerned. Additionally, there are shorter production cycles for the “organizational strategy” side. This progress makes the gap between the obtained data and the actual data smaller, which is necessary for the further attainment of the health of patients and sportsmen. As a logically deducible consequence of improving the application model of technologies, a new amount of data is expected to be generated for each degree of technological advancement, such as to fall under big data. This is useful for improving organizational strategy aspects such as “knowledge reuse” and “innovation”. The integration of the presented user-centered design model is found in the planning and design process, in later stages than those of technology choice, as suggested by the analyzed studies. Among the positive factors that refer to better involvement of users (patients in the eHealth field), we find the emergence of new “patient expert professionals” either directly on the development team or through platforms that allow for continuous interaction with the patients involved. However, such a shift in perspective aimed at increasingly integrating the patient requires effort from all stakeholders involved in the development of the model, in terms of education and awareness (of the patient, medical staff, technical staff, etc.) [52]. Tracing the common thread of the discourse, it is currently possible to develop new ways of implementing IoT technologies, cloud computing, AI, and other KETs in the professional sports sector, especially regarding one of the key factors in athletic training and performance optimization: nutrition [53]. Among the peculiarities of KETs is the possibility of interconnecting systems, allowing for an applicative analysis of telemedicine models in the field of food control and models related to the athletes’ energy consumption during their day. To make the “merge” process possible [54], it is necessary to plan suitable technologies, common programming languages, and “open” application models (predisposed for interfacing with other “tangent” systems from the user’s point of view) in the various project stages (identified as projecting, planning, and usage). It is essential to design future models using the user-centered design philosophy, disadvantaging the market-oriented philosophy, to obtain a better result in the end-user’s acceptance [55,56] of the system. Including the patient in model development processes denotes positive opportunities for patients themselves, researchers, and the healthcare system to achieve better outcomes. However, current integration efforts are often limited by necessary and preliminary activities that pose barriers in certain processes (e.g., limited time frame for production, subject knowledge, and understanding of guidelines and validated methods) [57]. The findings of Bouabida and colleagues [58] align with the suggestion for increased patient involvement in telehealth models. The study highlights the need for greater patient intervention in analyzing models and verifying their perceived value. The study also suggests that patient involvement is necessary in comparing how to communicate objectives and in the training of medical and technical staff, which could improve the design of future models in the eHealth sector related to nutrition and beyond. Thus, achieving model efficiency in terms of acceptability and usability of the systems and improving data communication as far as the technological aspect is concerned is desirable, seeking to further implement and test the human-centered [59] and user-centered design models.

4. Conclusions and Future Developments

It is evident that involving patients in decision-making processes regarding a system’s structure is crucial to enhance the system’s compatibility with patients, especially in the eHealth sector with a focus on nutrition. To avoid potential friction and ensure acceptability and understanding of the system, as well as to address needs that may not have been highlighted by preliminary analyses (as can be the case with a classical top-down approach) and to fully convey the system’s goal, co-participation of patients/users (in the form of associations, representatives, or stakeholders) in decision-making processes regarding the hypothesized system is essential. Limiting participation in consultation may also be necessary.