Existing studies on the requirements and perceptions of CTR technologies have been conducted in heterogeneous populations and in patients with low exercise capacity [38,39] and a sedentary lifestyle [40] or specifically in older adults [41]. Nonetheless, the needs of highly active patients (ie, athletes) regarding CTR have not yet been addressed. In a wider perspective, apart from a few clinical case reports, there is a clear lack of documented information regarding athletes in CR [23,42,43]. Existing knowledge of this population points to the many benefits that CTR interventions could have for their rehabilitation. First, athletes need tailored evaluation and sport-specific guidance before resuming intense activities, preferably in their sports setting and not in traditional hospital-based CR [24]. Furthermore, there is evidence that athletes are eager to return to sports quickly [23,42-44]. CTR helps achieve this by (1) starting right after hospitalization, shortening the outpatient gap between hospitalization and the CR program; and (2) providing remote supervision to alleviate sport-related anxiety in the posthospitalization phase [23,45]. In addition, in the realm of HCI literature concerning recreational athletes, there is a notable emphasis on their heightened data literacy and the practice of monitoring health and performance via wearable and mobile technologies [46]. Athletes use fitness trackers and mobile apps to measure palpable goals such as speed, duration, and intensity and recalibrate their subjective, lived sense of performance [47]. Digital and data literacy and self-monitoring proficiency are positive indicators for adherence to CTR and more effective self-management [45,48].

Given that our research sits at the crossroads of recreational athletes and patients with cardiac conditions, we delved deeper into the established traits of these groups. Regarding recreational athletes, Tholander and Nylander [47] highlight the significance of soft, less measurable goals such as lifestyle and well-being maintenance and identity building as a foundation for sports performance and exercise. Dionigi et al [49] reinforce the intrinsic motivation and self-reliance that athletes exhibit, with less dependence on social support or external incentives. On the other hand, the values of patients with cardiac conditions regarding health care have been identified in recent work by Bente et al [50]. These values include the need for security; support; not wanting to feel anxious; tailoring of treatment; and personalized, accessible care. Nonetheless, the convergence of values and traits between these 2 groups remains unexplored. Transitioning from a highly active lifestyle to a sudden halt caused by a CAD diagnosis can alter one’s relationship with sports, health, and technology. We aimed to investigate this aspect in our research.

During CR, patients are offered hospital-based support that consists of therapies addressing healthy and sustainable behaviors (eg, group exercise training 2-3 times per week, education, psychoeducational therapy, and smoking cessation therapy) with a focus on increasing physical activity and fitness [51,52]. CTR interventions facilitate the delivery of these therapies in the home environment [34]. CTR interventions can be remote (with asynchronous communication between clinicians and patients during exercise sessions), virtual (with real-time audiovisual communication between clinicians and patients during exercise sessions), or hybrid (a combination of in-person CR and remote or virtual services) [33]. While our study emphasized technology-driven rehabilitation design, we did not exclude elements of hybrid interventions (eg, face-to-face consultations) from our card-sorting FG. Previous research indicates that certain patients prefer a blend of remote and in-person services for the social benefits [11]. Thus, we aimed to provide all options to our participants without exclusion.

According to recent reviews of CTR design features and the definition of CTR, there are four clusters of design features: (1) education and assistance; (2) consultations, coaching, and guidance; (3) monitoring behaviors for supervision and self-management; and (4) peer support [4,53,54]. Nonetheless, in recent work, Andersen et al [45] point out that existing cardiac technologies mostly focus only on individual information processing. Within our work, we aimed to address the relational aspects of rehabilitation in the home by renaming the fourth category as coexperience (social aspect), focusing on features related to data in a social context and connecting with peers and family.

CTR features encompass technology and care service features. Technology features facilitate users’ achievement of a certain goal (eg, data interpretation and irregularity alerts), whereas care service features involve patient actions supported by technology (eg, consultation requests) and services for CTR technology provision (eg, wearable technology support) [4]. These 2 categories are strongly intertwined as the accuracy and engagement of the technology can influence the quality of the care service (eg, asynchronous SMS text messaging or video calling facilitating consultations [55,56], wearable sensors enabling remote supervision [30], or gamified user experiences providing exercise instructions [57]), whereas the delivered care service can dictate the perceived value and engagement with technology (eg, the type of information provided through digital technologies [58] or allowing family members to participate in remote exercise sessions [59]). HCI practitioners design or optimize interactions between care technologies and patients based on the nature of the intervention (remote, virtual, or hybrid) and its focus (eg, educational content technologies). For instance, in a recent study, Tadas et al [11] looked into patient-data interaction mechanisms that facilitate the transition from program to self-management. Similarly, Kjærup et al [60] investigated user preferences for a data collection application that turns patients into active diagnostic agents. On the other hand, by tackling ways in which care services are provided through technologies, Sankaran et al [27] designed more intelligible CTR exercise prescriptions and progress reports to stimulate patient behavior change motivation in mixed sedentary and more active patients. However, generally, the aim of current CTR interventions revolves around supporting patients in learning about and maintaining healthy behaviors by using various techniques, theories, or methods related to cognition and behavior in their design [37,54,61]. A multitude of CTR interventions are designed using behavior change techniques [55,58,62] and motivational techniques [59,63,64] to stimulate patients to implement healthier behaviors and sustain them in self-management [8,37]. For instance, Dithmer et al [65] redesigned remote training sessions into a collaborative game by using collaboration, goals, and rewards in an attempt to increase adherence to the physical activity component of CTR. On the other hand, Ding et al [66] used individualized goal-setting strategies based on exercise progress to engage patients with myocardial infarction in home-based exercise, whereas Hallberg et al [67] used motivational messages and periodic clinical feedback to support patients in hypertension self-management. Nonetheless, these interventions might not align well with existing indications of operational preferences of the athletic population regarding lifestyle choices, potentially leading to low end-user engagement, satisfaction, and self-management [23,32]. In this study, we aimed to advance the design of technology-centric, exercise-based CTR interventions by investigating the larger spectrum of CTR design features through the lens of the values of athletes with cardiac conditions.

Although CTR has been proven a safe and effective alternative to center-based CR for patients with CAD [39,54], engagement with and adherence to the program are highly impacted by the usability, reliability, and engagement of the designed intervention [6,68]. Ensuring ease of use and user satisfaction can be achieved through human-centered design (HCD) approaches (eg, user research or participatory design)—increasingly used in eHealth design [69]. Recent HCI research on eHealth for chronic conditions stresses the need for human-centric, in-depth investigations into target group challenges. For instance, Min et al [70] investigated epilepsy care challenges and proposed design guidelines for information management and care coordination technology. Cha et al [71] studied parental risk management for the glucose levels of children with diabetes and suggested support technology guidelines. Similarly, Sepehri et al [72] consulted parents of children with health complexity to gather needs for digital management systems. Finally, Bhat et al [73] gathered qualitative data on informal caregivers’ roles in chronic disease management to inform technologically assisted care options. In the context of CTR interventions, several studies use human-centered methods to elicit needs or desired functionalities. For instance, the Teledialog CTR program was designed through qualitative user need elicitation, cultural probes, and user testing [74]. Similarly, Beleigoli et al [31] conceptualized a technology-centric CTR program through multi-stakeholder engagement in barrier elicitation and co-design. By innovatively using data-enabled design mixed with human-centric methods, Khanshan et al [75] iterated on a CTR platform. Using a more traditionally qualitative approach, Dinesen et al [76] investigated the needs of homogeneous populations regarding CR and, accordingly, designed a system of multimodal CTR technologies. Nonetheless, a recent systematic scoping review points out that less than one-fifth of CTR interventions reported including end users in the design and development of these systems [54]. HCI practitioners and technology designers are increasingly encouraged to actively involve patients in the design and development of CTR technologies [6,8].

In our pursuit to define design guidelines for CTR interventions that address the needs of the athlete subpopulation, we conducted an empirical study inspired by the VSD tripartite methodology [77,78]. Holistic approaches to eHealth design, such as the Centre for eHealth Research Roadmap, stress the importance of identifying the diverse and potentially conflicting values of various stakeholders (eg, “what do patients value in health and life that they ultimately expect technology to cater to?” [79]). There are recent HCI inquiries that demonstrate the application of VSD methods in the design and development of eHealth interventions (eg, an artificial intelligence application targeting individuals with dementia [80] or a mobile app aimed at weight loss maintenance [81]). An in-depth inquiry into the design of cardiac technologies by Cruz-Martínez et al [32] underlines the importance of addressing patient values in the design of cardiac technologies. Similarly, Ramachandran et al [54] emphasize in their review that considering the needs and preferences of patients during the initial stages of program design can help alleviate issues with poor implementation and adoption of CTR. Although the values of patients with cardiac conditions as a foundation for eHealth design have been identified in recent work by Bente et al [50], these values are generic (eg, “To have or maintain a healthy lifestyle”); do not explore specific behaviors tackled by CR, such as exercise behavior or coping mechanisms; and do not address the athlete subpopulations, whose objectives might differ from those of the general population. In the following sections, we present our investigation into the value-centric design of CTR technologies for athletes.

To ensure transparency and minimize researcher bias, the data analysis process was iterative and collaborative. Both the interview and FG recordings were initially auto-transcribed using Amberscript (Amberscript Global B.V.). The transcriptions were then reviewed alongside the recordings to correct errors from the automatic transcription process and subsequently translated. Thematic analysis was conducted following the 6-step framework by Braun and Clarke [93] to identify and analyze patterns in the data. First, the researchers familiarized themselves with the data by reading the transcripts and notes taken during the interviews and FGs multiple times.

IBS analyzed the interview transcriptions (n=25) to identify quotations about what athletes with CAD find important in life and care. Inductive coding was used to identify codes related to athletes’ individual needs regarding exercising, CR, and lifestyle. These codes were grouped under subthemes illustrating more generic needs of the group (eg, “exercise support for one’s fitness level”). LF checked the identified needs for analysis validation. To define values, we followed their VSD definition of being more generic ideals or drivers of athletes’ needs [50,79]. Consequently, according to the VSD methods, we developed a value-oriented coding manual by assigning a small description to each initial code related to a need (by rereading the data; Multimedia Appendix 3). Afterward, the needs were grouped based on underlying themes (ie, desires) that could drive the specific needs—the values. LF verified the links between needs and values, and the coding manual together with representative codes were discussed among the group of authors. Codes that did not directly refer to athletes’ needs (eg, “miscommunication between healthcare professionals”) were grouped under non–value-related themes and, because of the scope of this work, were not emphasized in this paper.

The results of the card-sorting activity were organized based on the preferences of each FG—each feature received a score (2—must have, 1—nice to have, and 0—not needed) and was ordered from highest scoring to lowest scoring. If participants could not agree on a category, the scores were averaged. By ordering them by score in Microsoft Excel, we identified preferred CTR features from the most desired (highest scoring) to the least desired (lowest scoring). Participants’ observations during the FGs (either recorded or written on cards) were noted next to each corresponding feature to be used in the requirement definition phase.

The features (cards) that were a “must have” for at least 2 FGs together with participant observations were clustered under themes representing requirements. Occasionally, participants within a group held differing opinions on specific features—some felt that they did not need a feature, whereas others felt that it must be included. We opted to incorporate disputed features as requirements if they provided substantial value to certain participants. For example, one athlete whose family resided at a distance and lacked a strong local social support system considered sharing data with family crucial. In contrast, other participants who lived with family did not consider this feature desirable. Given the importance of this feature for the peace of mind and safety of some athletes, we included data sharing with family as a requirement.

Through small iterations during the analysis process, the emerging requirements were linked to one or more identified values. This enabled an iterative reflective process of requirement adaptation according to the expressed needs and values of athletes. These phases were conducted by IBȘ, with LF consistently checking after each iteration. The resulting list of requirements connected with values was checked and discussed among the rest of the research team. This led to defining high-level requirements and guidelines for a technology-mediated CTR intervention that could support athlete values. Preferences and requirements about improvements to colocated CR services also surfaced in our FGs. As these insights are out of the scope of our study, they will be mentioned but not discussed in this paper.

The results from the interviews conducted with athletes and other stakeholders (n=25) revealed the presence of 12 key themes encapsulating athletes’ values within the context of CR and the broader context of personal health management. Each of these key values (ie, high-level desire) was generally supported by multiple identified needs (ie, demands described by participants concerning CR participation and health management). In some instances, a single need could be associated with multiple values. For instance, the need to “observe progress in one’s physical performance during the recovery process” could be interconnected with values such as “a goal and performance-oriented approach” and “health and performance quantification.” Moreover, the identified values represented the overarching desires of athletes, whereas some needs might only pertain to some individuals. The subsequent sections will provide insights into findings related to each of the 12 identified key values clustered in 3 groups: body centric, care centric, and data and technology centric. An overview of the values and needs and example quotes can be found in Multimedia Appendix 4.

The card-sorting activity allowed participants to present an overview of desirable or undesirable CTR features for athletes. The characterization among must have, nice to have, and not needed differed from group to group based on their personal needs, group dynamic, composition (patients vs HCPs), perceived independence in one’s health management journey, and experience with technology. We present a summary of the card-sorting results in the following sections and in full in Multimedia Appendix 5.

Participants considered periodic checks (either through electronic consultations or surveys), remote supervision of sport-specific training, and personalized content comprising sport-specific actionable guidelines as must haves. The detection of alarming symptoms in the collected data was deemed very important, as was receiving actionable feedback on how to react to these symptoms or prevent them, especially during intense exercise. Frictions arose among athletes regarding remotely supervised group training—most participants considered it essential at the beginning of the program, whereas 15% (2/13) of the athletes believed that independent training was best as they could “go at their own pace.” In-person consultations were a must have except in FG 1, whose participants deemed them unnecessary as long as remote consultations were provided. A system that facilitated communication about exercise data between the athlete and HCPs and athlete-HCP feedback on training recommendations was considered a must have or nice to have. Athletes had varying opinions on monitoring emotions and receiving emotional support from clinicians. Some viewed it as essential, especially when struggling to accept their condition, whereas others believed that emotional support was better provided by family or friends. Finally, recommendations from virtual assistants (eg, automated recommendation systems) were generally ill-favored because of a lack of trust in their reliability.

Participants felt confident about receiving notifications when irregularities in their data were detected. Most participants preferred wearable sensor monitoring of physical activity to subjective self-reporting mostly because of the trust in and objectivity of sensor data, as well as the familiarity with and pragmatic benefits of automatic data collection. Nonetheless, some participants recognized the added value of subjective data collection for clinical decision-making and depersonalization of data and for expressing anxieties and insecurities related to data inaccuracies and safe sports. Athletes underlined the importance of support for collaboratively adapting individually set performance goals with HCPs. Receiving personalized goals from clinicians was disagreed upon—some athletes felt confident in setting their own goals, whereas others felt that clinical guidance was essential at the beginning of rehabilitation until confidence in one’s athletic abilities was regained. Graphical representations of performance and health data, as well as parallels between actual performance and clinical recommendations, were labeled as must have or nice to have. In line with case studies describing experiences of athletes with CR [2,75,91], our participants expressed little to no interest in motivational queues, rewards, and reminders because they already felt motivated enough to rehabilitate (exercising but also eating healthily and taking medication).

Technical assistance and instructions on using CTR technologies, as well as having the possibility of integrating their own wearable sensors into the monitoring system for familiarity, convenience, and trust, were deemed a must have by all participants—HCPs underlined that, at times, athletes used sensors that were superior to the equipment provided by the hospital. Access to digital information about being an athlete with CAD was important to most participants as similar reliable resources were not available online. Receiving information from virtual assistants (eg, chatbots) was not needed because of a lack of trust in the clinical validity of the information—some clinicians regarded it as a nice addition that could alleviate the workload of HCPs. Similarly, some participants considered information about why monitoring is beneficial for health as common knowledge and, therefore, not necessary.

Peer-based support was appreciated only within the context of the program and, preferably, in person. Online communication with peers was not viewed as important as athletes felt confident in themselves and their existing support systems and expressed that they would not make use of online communities. Sharing data with family also obtained a low score, with the exception of 15% (2/13) of the participants, who already shared their location with loved ones for safety, alleviating anxiety, and shared decision-making. Virtual environments that facilitate training with peers were not highly valued as athletes already had social groups to train with or preferred training individually.

Recent developments in the design of cardiac eHealth have encouraged HCI practitioners and designers to account for the patients’ latent and hidden needs, desires, and life values [32,48]. A recent scoping review on acceptance and adherence factors of CTR interventions emphasizes that early involvement of users in the design of CTR technologies is essential for designing meaningful and efficient interventions [54]. Nonetheless, there has been a lack of HCI studies on acknowledging the needs and values of patient subpopulations in CTR [33,34,54]. In this study, we outlined design requirements for technology-centric CTR interventions that cater to the values of a distinctive cardiac population—athletes with established CAD. We used methods from VSD and uncovered 12 key values of this specific population supported by underlying needs. These values included “independence and confidence in one’s body” and “concise, actionable guidelines,” alongside technology features grouped into design requirements addressing these values. In the following sections, we discuss the implications for the value-oriented design of CTR technologies in the context of the athletic population. We first situate our findings in the context of existing CTR research for general populations and then explore how alignments and conflicts among values can inform the design process. We discuss design guidelines based on the connections inferred between athlete values and the identified design requirements.

On the basis of our findings and previous, related work, the core experience of being a patient with a cardiac condition is fundamentally the same for both athletes and general populations. However, the key difference is that athletes have distinct attitudes toward physical activity, and their identity and discipline around it shape their expectations of care, particularly regarding physical activity training, the main component of CR. As a result, while there are similarities, CTR technologies should be designed differently for athletes compared to general populations. For example, general populations typically track any physical activity, such as steps (eg, MI-Pace [66]) or standardized exercises (eg, recommended aerobic exercises for patients with CVD [62]), whereas athletes prefer sport-specific tracking and feedback (eg, for cycling, running, or weight lifting). This difference can influence monitoring system design as our participants preferred relying on their own high-performance wearables, which they were trained to use and were of better quality than standard hospital-issued devices, whereas CTR systems for general populations are designed to offer patients standard monitoring devices (eg, Fitbit [62,76], Mio Alpha and ActiGraph [64], smartphone apps [30], and Philips health watch [66]). This can also affect personal insight platforms and clinician decision support systems, shifting from generic goals such as 10,000 steps per day (eg, shared care platform [94] and HeartPortal [74]) or 150 activity minutes per week [62] to sport-specific milestones based on frequency, distance, HR zones, and perceived effort.

Some needs remain the same, such as clear technical instructions and technical support for CTR platforms (eg, the MedFit app [62]) and seamless, continuous data transmission (eg, SmartCare-CAD [64]). However, graphical data representations alongside exercise limitations are particularly relevant for athletes as they need to moderate activity rather than increase it. Their familiarity with data tracking can make them more critical of inaccuracies and more confident in interpreting data, influencing their preference for precise alarm systems. In contrast, general populations may feel overwhelmed by excessive or highly technical data [4].

While data-focused discussions with clinicians are valuable for all patients [60,66], athletes require highly personalized exercise recommendations based on personal metrics, sport-specific testing, and clinical cardiology expertise. In addition, information resources designed for general populations (eg, ActiveHeart [76]) need to be enriched with athlete-specific content to prevent misinformation. The need for periodic consultations and clinician validation remains constant with athletes and general populations [64,66,76,94], but athletes may have more specialized questions, which could benefit from new communication methods such as prestructured forms, data annotations, or digital messaging for tailored interactions. We discuss the specific implications for athletic CTR technologies in the following subsections.

Aligning with VSD theory, we observed value interrelations and tensions at various levels of human experience—among athletes, as well as between athletes and general populations of patients with cardiac conditions. The VSD framework acknowledges that human values do not exist in isolation [36]. Framing a design process to engage constructively in the interconnectedness of human values is a matter of design thinking and can be mediated through design trade-offs, value conflicts, or value tensions [78]. Such tensions, conflicts, and trade-offs have also been discussed in similar studies applying VSD methods to eHealth design [74,81]. The term value tension encourages the design of solutions that balance opposing values such that “the adjudication of the tension holds each value intact” [36]. In our case, most athletes valued data from wearable sensors to become better at sports or recalibrate internal feelings of exercise intensity, whereas other athletes (eg, P13 or P14) trusted the physical “feeling” enough and did not consider fitness trackers necessary. Therefore, one design outcome can be a wearable sensor system that offers insights into one’s performance and the meaning of data in the context of CAD after hospital dismissal, when athletes lack confidence in their bodies the most. Subsequently, tracking one’s training can become optional and periodical. On the other hand, a “design trade-off” conveys an approach in which designing for one value will diminish another value. For instance, designing a CTR technology that formalizes the role of a close family member and allows athletes to share personal data such as location and HR while exercising with them will come at the expense of the independence of other athletes who want to keep their experiences private. Offering this functionality for the safety and comfort of some athletes takes the form of a trade-off.

Our findings also align with previous research on the values of patients with cardiac conditions. The 11 values of patients with cardiac conditions as a foundation for eHealth development identified by Bente et al [50] overlap with the 12 identified values in our study (eg, “To have an overview of personal health data” overlaps with athletes’ value of “Health and performance quantification”). Nonetheless, there are also value conflicts. For instance, one key value of the population in the study by Bente et al [50] was “to be extrinsically motivated to accomplish goals or activities (related to health/lifestyle).” On the other hand, athletes valued a “goal and performance-oriented approach” to rehabilitation and were very independent in their health journey. The term value conflict proposes that, when values come into conflict, design resolutions can offer solutions responsive to both values. In the context of CTR individualization, one solution can be offering a standardized CTR platform that encompasses 2 different modules: one for patients who need behavior change (including information about the importance of exercise, motivational queues, and reminders) and one for athletes (including a more performance- and data-oriented interface comprising sport-specific goal setting, data overviews, and means to address sport-related questions).

While our results mostly show generalizations of common themes and requirements, designers should be aware that values and needs are expressed through the lens of individual experiences [79]. Two patients might have completely different rehabilitation experiences, whereas clinicians might benefit from solutions that patients do not necessarily need (eg, virtual assistants that can ease clinicians’ workload but are not perceived as trustworthy by athletes).