This research forms the qualitative phase of a sequential mixed methods design [19], carried out after the quantitative phase. The results of the quantitative analysis reported by Svenšek et al [20] indicated certain patterns that we wanted to explore in more detail. For this phase, we used a qualitative descriptive design [21], with data analyzed by inductive qualitative content analysis [22,23]. This approach was chosen because the aim of the study was descriptive and evaluative rather than theory-generating: we set out to characterize how patients who had completed the intervention arm, and the nurses working in the same preventive service who had been familiarized with it, experienced the Petal-X dashboard and CGM, including perceived usability, the perceived contribution to PCC and to motivation, and the barriers to implementation in routine practice. The study was not designed to develop a theory of a social or clinical process, and no theory-generating claims are made. We collected the data through individual semistructured interviews. We ensured the rigor of the qualitative study by adhering to Lincoln and Guba’s [24] criteria of credibility, dependability, confirmability, and transferability. Procedures are reported in accordance with the COREQ (Consolidated Criteria for Reporting Qualitative Research) 32-item checklist [25].

The study was conducted at a single community health center in central Slovenia that delivers preventive family medicine services. It was nested within a parallel randomized controlled trial (ClinicalTrials.gov NCT06226948) evaluating interactive digital visualization counseling for patients at risk of CVD [20]. Recruitment for the interviews was open to individuals across Slovenia, although the promotional campaign and study implementation were concentrated at this center. Patients were drawn from the trial intervention arm (n=30 at baseline), in which participants had a mean age of 51.9 (SD 7.2) years, 70% (21/30) were women, and the mean baseline 10-year SCORE2 risk was 4.87 (SD 2.61) and the highest of the 4 trial arms [20].

Patients were eligible if they were aged 40 years or older, had no diagnosed CVDs, and had at least 1 CVD risk factor (elevated systolic blood pressure, elevated fasting glucose, elevated blood lipids, or elevated BMI); nurses were eligible if they provided preventive consultations at the participating center, had been familiarized with the Petal-X dashboard and with CGM-based monitoring during the study period, and had at least 5 years of professional experience as a nurse, which was verified individually for each nurse before inclusion. Eligible patients who had completed the trial intervention arm were identified from the trial records by the study team, and eligible nurses were identified through the participating center; potential participants were then contacted individually by the first author (AS), in person or by telephone, informed about the aim and the voluntary nature of the study, and, if they agreed, gave written informed consent before an interview was scheduled. Recruitment was not purposive. We used a total population (census) approach within the closed, predefined cohort created by the parent trial: every patient who had completed the trial intervention arm and every nurse at the participating center who had been familiarized with the intervention was invited, and no further selection step was applied within this eligible pool, either on the basis of participant characteristics (eg, age, sex, and baseline risk category) or on the basis of the content of their experience. The size and composition of the sample were therefore determined by the boundaries of the parent trial and by willingness to participate, rather than by deliberate selection for particular characteristics, by maximum variation, or by an emergent analytic rationale. No more than 2 interviews were conducted per week so that each interview could be analyzed before the next. The final sample comprised 6 nurses and 6 patients.

Because eligibility for interview depended on having completed the intervention arm, we define here what completion involved in this study, independently of the report of the parent trial [20]. The intervention arm did not consist of a single encounter with the dashboard. Each patient attended a face-to-face preventive consultation, delivered by the first author (AS), an RN and PhD candidate, during which the patient’s individual 10-year SCORE2 risk was calculated and explored interactively using the Petal-X dashboard (duration of consultation: 30 minutes). Each patient was then fitted with a CGM sensor, which was worn during everyday life for 10 days, with real-time access to their own glucose readings through the associated smartphone app, and attended a further study visit 6 months later, at which the glucose record and the risk profile were reviewed together. The intervention therefore comprised both a nurse-led visualization consultation and a defined period of CGM-based self-monitoring. Patients were counted as completers of the intervention arm and were therefore eligible to be approached for an interview if they attended all 3 scheduled study visits (the baseline assessment, blood sampling at 3 months, and the final assessment at 6 months), wore the CGM sensor for at least 5 of the 10 intended days, and completed the final study assessment. Patients who withdrew, who discontinued sensor wear before this threshold, or who did not attend the final assessment were not counted as completers and were not invited to interview. Of the 30 patients randomized to intervention group 1 (Petal-X visualization combined with CGM), 27 met this definition of completion and were invited, and 6 agreed to be interviewed. Interviews were conducted a median of 5 weeks after the end of each patient’s participation, so that all patients described a completed rather than an ongoing experience of the intervention.

For nurses, participation did not involve delivering the intervention. All trial consultations, including the presentation of the Petal-X visualization and the application and explanation of the CGM sensor, were carried out by the first author (AS), and the participating nurses had no formal delivery role in the trial. They were RNs providing routine preventive consultations at the same center who had been familiarized with the intervention during the study period: each nurse took part in a single structured familiarization session of approximately 30 minutes, in which the Petal-X dashboard was presented and explored with example risk profiles, the rationale of the SCORE2-based visualization was explained, and the principles and practical use of CGM-based monitoring were demonstrated. Within this session, the nurses also used the dashboard themselves on example profiles, so that their appraisals were based on direct, if brief, hands-on contact with the tool rather than on observation alone. They also worked alongside the trial in the same preventive service and were therefore familiar with how it was organized and with the patients taking part. At the time of the interview, each nurse had approximately 7 months of familiarity with the tool and the intervention, which was delivered between May and December 2024, and the dashboard was displayed again at the start of the interview so that the discussion could refer to specific features of the visualization.

In this study, therefore, “completion” has a different meaning for the 2 groups. For patients, it denotes completion of the intervention arm as defined in the section Setting, Participants, and Recruitment; for nurses, it denotes completion of this familiarization and continuous presence in the setting for the whole intervention period, rather than a threshold of sessions delivered. Their accounts should accordingly be read as the informed appraisal of experienced preventive care nurses who knew the intervention and the tool well, and who work daily with the paper-based SCORE2 charts that the dashboard is intended to replace, rather than as the reflections of practitioners who had themselves delivered it in routine care; this distinction is revisited in the Limitations section.

Interviews were conducted by the first author (AS), a female RN holding a master’s degree (MSc), who was trained in qualitative research methods and was a doctoral researcher at the University of Maribor and teaching assistant at the time of the study. The interviewer had no prior clinical or supervisory relationship with the participants before the study commenced. Participants were informed of the researcher’s professional and academic background and that the interviews formed part of a doctoral research project on digital tools for cardiovascular risk communication and prevention. The researcher’s background in nursing and digital health shaped an interest in PCC and technology-supported prevention; to limit the influence of these assumptions, reflexive memos were kept, and the developing interpretation was discussed in regular peer-debriefing meetings within the research team.

We conducted interviews with RNs who had been familiarized with the Petal-X dashboard for CVD risk visualization and with the potential for real-time blood glucose monitoring during the study period; at the start of each interview, the dashboard was displayed again so that the discussion could refer to specific features of the visualization. The patients we interviewed had all taken part in the intervention arm that combined the Petal-X dashboard with real-time monitoring of blood glucose levels. We were interested in the participants’ perceptions of the intervention and the use of new technologies. We used semistructured interviews to preserve certain concepts [24]. With the help of semistructured interviews, we obtained all the necessary information for the research, allowing participants as much freedom as possible in their responses. We opted for semistructured interviews because we did not want to stray from our topic and wanted to obtain an accurate description and perception of the participants’ involvement in interventions and visualizations related to CVD risk. With semistructured interviews and semi–open-ended questions, we also aimed to gather information about the experiences, feelings, and advice of RNs and research participants regarding the visualization of the risk of CVD using Petal-X and the potential for real-time monitoring of blood glucose levels. The interview guides for nurses and for patients are provided in Multimedia Appendices 1 and 2.

All interviews were audio-recorded, transcribed verbatim, and anonymized prior to analysis. Interviews were conducted and transcribed in Slovenian, and all coding and analysis were carried out in Slovenian to stay close to participants’ original wording; the illustrative quotations presented in the Results section were subsequently translated into English by the bilingual research team and checked against the original transcripts for accuracy and meaning. Data were analyzed by inductive qualitative content analysis [22,23]. Analysis proceeded alongside data collection, so that preliminary observations could inform the emphasis given to particular questions in later interviews.

Each transcript was first read repeatedly to obtain a sense of the whole. Meaning units relevant to the aim of the study were then identified and condensed, and each condensed meaning unit was labeled with a code that stayed close to participants’ own wording. The first transcripts were coded independently by 2 researchers (AS and LG), who compared their code lists and agreed on a shared coding frame; this frame was applied to the remaining transcripts and revised whenever new codes were added. Codes were compared systematically within and across the 2 participant groups and sorted, on the basis of similarity in content, into subcategories and then into categories. Finally, an overarching theme expressing the thread of meaning that ran across the categories was formulated. The developing category structure was reviewed and refined together with a senior researcher (GS) in peer-debriefing meetings until consensus was reached. Categories were refined iteratively by writing analytic memos, revisiting transcripts, and actively searching for discrepant accounts (eg, accounts of CGM worry or of limited benefit for some users). An audit trail (coding memos and versioned code lists) was maintained. To strengthen credibility, verbatim transcripts were returned to participants for review and possible correction (member checking).

Because the sample was bounded by the closed intervention arm of the parent trial, the size of the sample was fixed in advance rather than determined by the progress of the analysis. We therefore considered code saturation and the adequacy of the data for the descriptive aim of the study [26]. Toward the end of data collection, later interviews were adding few, if any, new codes, and the research team judged the material sufficient to describe the categories reported. We present this as an assessment of data adequacy rather than as a formal claim of saturation, since the number of available participants was fixed by the parent trial. In the Results section, quotes are attributed using participant identifiers to preserve anonymity: P denotes patients and N denotes RNs (eg, P1, N2). Each quotation is additionally accompanied by brief, nonidentifying contextual information about the speaker, namely, sex, age, and baseline 10-year SCORE2 risk category for patients, and years of professional experience for nurses, so that readers can interpret each quotation in relation to the individual speaking rather than only in relation to the participant group.

Qualitative rigor was addressed using established criteria for qualitative rigor: credibility, dependability, confirmability, and transferability. Credibility was supported by verbatim transcription, systematic comparison of codes within and across the 2 participant groups, member checking (returning transcripts to participants for review and possible correction), regular peer-debriefing meetings within the research team, and the deliberate search for and analysis of negative cases. Dependability and confirmability were supported by maintaining an audit trail of coding memos and versioned code lists, so that the development of the codes, subcategories, and categories could be traced back to the underlying data. Transferability was supported by providing a thick description of the setting, participants, and context, enabling readers to judge the applicability of the findings to other primary care settings.

The study was approved by the Ethics Committee for Nursing Research at the Faculty of Health Sciences, University of Maribor (approval number 02/7K-2023), and the National Medical Ethics Committee of the Republic of Slovenia (approval number 0120-217/2023/4). We have also registered the research protocol of the clinical trial (ClinicalTrials.gov ID: NCT06226948) [27]. All participants were informed that their data would be used exclusively for research purposes. All participants received written and oral information about the study and gave written informed consent before their interview, including explicit consent to audio-recording and to the use of anonymized verbatim quotations in publications. Recordings were transcribed verbatim and deidentified; direct identifiers were removed and participants are referred to only by coded identifiers (P for patients and N for nurses). Data were stored securely, were accessible only to the research team, and were used exclusively for research purposes. Participants did not receive any financial compensation for taking part in the study.

The sample comprised 6 RNs and 6 patients at increased CVD risk, all from a single community health center in central Slovenia. All patients had taken part in the intervention arm of the parallel randomized controlled trial [20] that combined the Petal-X dashboard with CGM; they were aged 40 years or older, had no diagnosed CVD, and had at least 1 CVD risk factor. Among the 6 patients interviewed, 4 were women and 2 were men, aged 46‐67 (mean 57.8, SD 7.8) years; at baseline, 1 was at low, 2 at moderate, and 3 at high 10-year SCORE2 risk. The interviewed patients were therefore somewhat older than the intervention arm as a whole, in which the mean age was 51.9 (SD 7.2) years [20]. All 6 participating nurses were women, aged 26‐50 (mean 33.3, SD 10.2) years, and each had at least 5 years of professional experience as a nurse and worked in preventive family medicine consultations at the participating center throughout the trial period. To avoid duplication, participant characteristics are reported in the text only; brief individual descriptors accompany each quotation so that quotations can be read in relation to the individual speaker.

The analysis produced 11 subcategories, which were grouped into 4 categories (Table 1). Running across all 4 categories was an overarching theme, formulated as “Visualization and digital tools as support for PCC to enhance motivation and lifestyle change in individuals at risk of CVD.”

The 4 categories describe complementary aspects of participants’ experience rather than stages of a process. PCC describes the character of the consultation as participants experienced it: goal-oriented, individualized, and involving the patient in decisions. Knowledge and monitoring of risk factors describes what participants reported learning and noticing through Petal-X and CGM, including the recognition of deviations and the connection between everyday behavior and measurable indicators. Visualization and interpretation of results and the impact of technology use on lifestyle changes describes how the display itself was used and appraised, together with the dietary and physical activity changes that patients associated with it. Identification of problems and challenges and recommendations for improvement gathers the limitations that both groups reported, fear or mistrust of technology, the burden of continuous monitoring, variable digital literacy, information overload, and the cost of CGM, alongside their suggestions for addressing them.

Across all 4 categories, patients and nurses converged on the perceived usefulness of the visual display but differed consistently in emphasis. Patients spoke primarily about personal meaning, reassurance, and self-regulation; nurses spoke primarily about communication, the efficiency of the consultation, and the fit of the tool with existing workflows. Where the 2 groups diverged, this is made explicit in the sections that follow.

This paper reports the qualitative phase of a sequential mixed methods study; the quantitative randomized controlled trial is reported separately. We did not statistically integrate the 2 datasets in this paper. Instead, consistent with sequential mixed methods designs, the qualitative findings are connected to the trial at the level of interpretation: they help explain the mechanisms that may underlie the trial results, namely, greater understanding, involvement, self-perceived control, and motivation supported by visualization and real-time feedback. The literature review informed the selection of technologies and risk factors incorporated into the intervention.

In this qualitative study, both nurses and patients experienced interactive CVD risk visualization (Petal-X), particularly when combined with CGM, as a meaningful support for PCC. Across 4 categories and 11 subcategories, participants described a clearer understanding of risk factors, more individualized and shared consultations, a greater sense of involvement and control, and stronger motivation for dietary and physical activity change, which some patients reported maintaining after the study. At the same time, participants and nurses highlighted important barriers, including variable digital literacy among older users, fear or mistrust of the technology, the burden of continuous self-monitoring, potential information overload, workflow and training needs, and the affordability and accessibility of CGM. Qualitative analysis of interviews with nurses and participants reveals key aspects of PCC that were identified during the introduction of new technologies in the family medicine practice. The participants expressed satisfaction with the individualized approach, which includes adapting care to the needs and wishes of individuals and actively involving patients in the treatment process.

The perceptions of nurses and participants regarding the use of cardiovascular risk visualization (Petal-X) and CGM are crucial for understanding how effectively these technologies improve management and motivation for lifestyle changes. The categories that enable us to answer the research question are the following: goal-oriented care and an individual approach, involvement in treatment, monitoring of vital functions and technology, motivation and lifestyle changes, understanding of impacts and risk factors, and opportunities for improvement. The category that most strongly organized participants’ accounts was PCC.

PCC, supported by technologies that enable personalization and visualization of health data, emerged in our study as an effective strategy for encouraging lifestyle changes among patients in the family medicine setting. Such an approach not only enhances the quality of care but also fosters collaboration between patients and health care professionals, ultimately contributing to improved long-term health outcomes. These findings are consistent with previous interventions that have focused on patient education, structured follow-up and enhanced support for self-management, such as programs for disease self-management education [28,29], structured discharge plans with tailored follow-up [30], and lifestyle counseling, home visits, and telephone support led by health care professionals [31].

Our results suggest that interventions that combine education, personalized care plans, and lifestyle adaptation to the individual’s cardiovascular risk could be further strengthened by incorporating visual risk displays [9,32-35], mobile apps [36-39], and additional biometric measurements. CVDs remain a leading cause of morbidity and mortality worldwide, and early identification and engagement of at-risk individuals are crucial for effective prevention. In this context, digital tools that translate complex prognostic information into intuitive, patient-friendly formats align strongly with the principles of PCC and offer a promising route to more effective preventive strategies.

A central finding of our qualitative analysis is that goal-oriented care and an individualized, data-driven approach are key to tailoring care for patients at risk of CVD. Participants consistently reported that they experienced the consultation as more holistic, with more time for conversation and shared decision-making about their health. This tailored approach took into account specific needs, values, and goals, and included adjustments to diet, physical activity, and daily routines, often with the support of family members. New forms of visualization, such as interactive graphs and personalized analyses of risk factors, were perceived as particularly helpful. They improved communication, enhanced understanding of patients’ health status, and encouraged them to participate actively in planning their care.

The Petal-X visualization dashboard, developed and used in our study, represents a concrete step toward augmenting SCORE2 with an explanation layer and operationalizing PCC in routine practice. By providing a clear, structured overview of individual CVD risk factors, Petal-X made the risk profile more transparent and easier to interpret for both patients and nurses. Visualizations increased the sense of overview and control among participants, allowing them to see how different risk factors contribute to their overall risk and what might change if they modify certain behaviors [9,27,32,34,35]. Nurses emphasized that this approach improved the quality of consultations, simplified workflows, and made it easier to involve patients in setting realistic, personalized goals, findings that echo positive outcomes reported in other technology-supported interventions [40-43].

In addition to visualization, the use of CGM, typically reserved for people with diabetes, introduced a dynamic and highly engaging element to lifestyle counseling. Participants carried the CGM device with them and were able to observe real-time changes in their glucose levels in response to food intake, physical activity, stress, and other daily situations. This immediate feedback created a direct link between behavior and physiological response, which several participants identified as a strong motivator for changing and maintaining healthier habits. Similar benefits of real-time monitoring and digital feedback loops have been documented in studies using mobile apps and wearable technologies to support self-management and lifestyle modification [28,29,36-39,41].

Overall, our qualitative findings indicate improvements in patients’ understanding of their health status, awareness of CVD risk, satisfaction with the integration of technologies, and willingness to participate in planning their lifestyle changes. Participants reported an increased motivation to continue monitoring and adjusting their lifestyle even after the study ended. From the perspective of nurses, the combined use of Petal-X and CGM supported more structured, person-centered consultations and facilitated the integration of preventive interventions into everyday practice. These experiences are consistent with evidence from other technology-enhanced interventions, which have demonstrated positive outcomes when new tools are embedded in supportive, PCC models [42-44].

The Slovenian context adds an important system-level dimension to these findings. Currently, there is no standardized digital tool that integrates multiple solutions for both nurses and patients, nor is there a unified national electronic health record system that would seamlessly support such approaches. Our implementation of Petal-X and CGM within this fragmented digital environment underscores the potential of these tools to enhance care even in the absence of a comprehensive national infrastructure. At the same time, it underscores the necessity for national guidelines on integrating new technologies into health care, particularly in the areas of prevention and chronic disease management.

Finally, lifestyle modification remains one of the most important modifiable risk factors for reducing the prevalence of CVD risk, making research and innovation in this area especially valuable in Slovenia. Our results suggest that lifestyle change is closely linked to patients’ perception of PCC, especially the experience of an individualized approach, personalized visualization of risk, and a holistic, respectful consultation process. The inclusion of new technologies in lifestyle interventions can increase patient motivation, enable real-time monitoring of health status, and support higher-quality PCC. Further research should explore how such tools can be scaled up, integrated into national digital ecosystems, and combined with established interventions, such as education, structured follow-up, and community-based support [28,30,31], to strengthen prevention and long-term management of CVD risk.

Several directions for future research follow from these findings. First, larger studies across multiple primary care settings are needed to test whether the perceived benefits of risk visualization and CGM extend to more diverse populations and to evaluate how the tools can be scaled and embedded in routine workflows and in national digital health infrastructure, including electronic health record systems. Second, because CGM carries nontrivial costs, future work should include an affordability and risk-benefit analysis from a public health perspective, so that decisions about wider adoption weigh the added value of continuous monitoring against its cost and its equity implications for patients who cannot afford such devices. Third, given the focus on secondary prevention and PCC, future studies should more explicitly incorporate patients’ own preferences, needs, and values, for example, through a structured “What Matters to You?” approach, so that digitally supported risk communication is aligned with what patients themselves consider important. Finally, longer follow-up is needed to determine whether the motivation and behavioral changes that participants described translate into the long-term sustainability of lifestyle change and into measurable cardiovascular risk reduction over time.

Several limitations should be considered when interpreting these findings. First, this was a qualitative descriptive study based on semistructured interviews with a small sample of nurses (n=6) and intervention participants (n=6) recruited by total population sampling from a closed, predefined trial cohort, which supports depth of description but limits transferability to broader primary care populations and settings. Second, although recruitment was open across Slovenia, the promotional campaign and implementation were concentrated in a single Community Health Centre in central Slovenia, so local organizational routines and context may have shaped participants’ experiences and perceptions. In addition, recruitment from a single center, combined with the small sample size, resulted in a relatively homogeneous sample, which may further limit the transferability of the findings. Third, the study relied on self-reported experiences, which may be influenced by recall bias and social desirability. Finally, perceptions of Petal-X and CGM may reflect novelty effects and practical implementation constraints; importantly, participants and nurses also reported barriers such as variable digital literacy (particularly among older users), fear or mistrust of technology, potential information overload, workflow integration needs, and affordability or access limitations of CGM, which may affect scalability and equity of implementation.

A further limitation, and one that follows directly from the sampling strategy described, concerns who was eligible to be interviewed. Both samples were restricted to individuals who had completed their part of the intervention: patients who had completed the intervention arm, as defined in the Methods section, and nurses who had completed the familiarization with the intervention and remained in the preventive service throughout the trial period. Patients who withdrew, who discontinued sensor wear, or who did not attend the final assessment were by definition unavailable for interview, and their reasons for discontinuing, which may include precisely the burdens, anxieties, and usability problems that an intervention of this kind is most likely to generate, are therefore not represented in our data. Completer-only sampling plausibly selects for higher engagement with digital tools, greater tolerance of continuous self-monitoring and of the practical and attentional demands of wearing a sensor, and a degree of comfort with self-quantification that may not be typical of the wider primary prevention population. The barriers reported here, including the initial apprehension about what the results would show, the compulsive checking described by one participant, and the burden of keeping a smartphone within range of the sensor, should therefore be read as difficulties reported by people who nevertheless persisted to the end of the intervention, and they are likely to understate both the frequency and the intensity of the problems that would arise in an unselected population.

A related but distinct consideration applies to the nurses. As described in the Methods section, all trial consultations were delivered by the first author (AS), and the nurses interviewed had been familiarized with the intervention and the Petal-X dashboard rather than having used them with their own caseload in routine practice. Their appraisals of usability, workflow fit, and feasibility are therefore the anticipatory judgments of informed prospective users rather than reports of sustained delivery, and they may not capture the frictions that would emerge once such a tool is embedded in everyday consultations, nor the adaptations that practitioners make over time. Equally, the nurses who agreed to be interviewed were those who had engaged with the familiarization offered at the center, which may again select for interest in digital tools. Research in which nurses themselves deliver visualization-supported consultations over a sustained period is needed before conclusions about routine implementation can be drawn. It should also be noted that the interviewed patients were somewhat older than the intervention arm as a whole (mean 57.8 vs 51.9 years), so that the older group whose digital literacy the nurses identified as a barrier was, if anything, overrepresented among the interviewees, although these were older adults who had nonetheless completed the intervention successfully. Studies that deliberately sample noncompleters, partial users, and individuals who decline such interventions are needed to characterize the full range of responses to visualization-supported and CGM-supported cardiovascular prevention.

This qualitative study demonstrates that interactive CVD visualization (Petal-X), particularly when combined with real-time glucose feedback via CGM, can enhance key elements of PCC in primary prevention. Both patients and nurses described that those personalized visual displays improved comprehension of risk factors, supported shared decision-making, and increased patients’ sense of involvement and control, mechanisms that were perceived as essential drivers of motivation and lifestyle change.

At the same time, implementation challenges were identified, including variable digital literacy (particularly among older users), concerns about information overload, the need for training of health care professionals, and affordability and accessibility of devices such as CGM. Future work should evaluate how these tools can be integrated into routine workflows and national digital infrastructure, and how they can be scaled in a way that preserves usability, equity, and sustained lifestyle benefits.