Digital Health and Virtual Care Innovations: Applications for Long-Term Care and Self-Management Support

Background and objectives: Numerous studies and meta-analyses have established the efficacy of telemonitoring for blood pressure and other components of metabolic syndrome in improving disease management. Nevertheless, the adoption of telemonitoring technologies is often hindered by personal, technological, and systemic barriers. In Slovakia, where patient–physician contact rates are high, there is limited research on patients’ perspectives regarding telemedicine adoption for cardiovascular risk management. The objective of this study was to examine patients’ perspectives on and perceived obstacles to the use of telemonitoring for arterial hypertension and dyslipidemia in Slovakia. Methods: This cross-sectional, questionnaire-based survey targeted a cohort of 18,053 patients. The survey instrument was designed to gather data on several key areas: patient demographic characteristics, blood pressure measurement habits, the utilization of smart technologies, perceived benefits and barriers to telemonitoring, and patients’ knowledge of their lipid profiles and cardiovascular risk factors. Statistical analysis included chi-square tests, ANOVA, and effect size calculations with 95% confidence intervals (CI). Results: A total of 1787 patient responses (9.9%) were collected. Among the respondents, 67.4% (n = 1204) had arterial hypertension, while 7.9% (n = 95) were on non-pharmacological therapy. Only 21.2% (n = 255) of hypertensive patients measured their blood pressure daily, with a significantly higher proportion of men than women (28.6% vs. 12.7%, p = 0.011, Cohen’s d = 0.42). The most frequent users of blood pressure monitoring were in the 31–45 age group (p = 0.001, η² = 0.08). A total of 19.4% (n = 347) of respondents used wearable devices, and 6.3% (n = 113) used blood pressure monitors connected to an application. Smart technology use was significantly more common in the 31–45 age group (p = 0.01, Cramer’s V = 0.15). Moderate interest in telemedicine was expressed by 69.8% (n = 1247) of respondents, though only 27.4% (n = 490) showed strong interest. The majority of patients (73.8%, n = 1319) did not know their LDL-C levels, and 45.7% (n = 817) of those who did had elevated levels. Conclusions: The findings suggest that while interest in telemedicine methods for the management of arterial hypertension and dyslipidemia exists among Slovak patients, it is more moderate than initially assumed. Importantly, expressed willingness to participate in a study should not be directly equated with readiness to adopt new technologies in daily practice. Successful integration of telemonitoring into the Slovak healthcare system will therefore require not only patient engagement but also active support from healthcare providers to overcome practical and motivational barriers. These findings highlight the need for targeted implementation strategies that address the specific barriers identified in the Central and Eastern European healthcare context. Full article

Background: In an era of unprecedented technology adoption in healthcare, it is imperative to understand and predict factors influencing users’ perspective. This study employs a risk-integrated technology acceptance model aiming to identify the determinants of the intention to use mobile health applications among patients with chronic diseases in Romania. Methods: A face-to-face survey method was used to collect research data from 207 subjects, and the partial least squares structural equation modeling approach was employed for data analysis. Results: The behavioral intention to use mobile health applications (INT) was influenced positively by the perceived ease of use (PEOU, f² = 0.358, β = 0.500, p < 0.001) and perceived usefulness (PU, f² = 0.271, β = 0.678, p < 0.001). Another core predictor, with a negative effect on the intention to use, was the user’s perceived risk of using the technology (RISK, f² = 0.239, β = −0.321, p < 0.001), in turn influenced by the perceived degree of cyber-insecurity (CYBER, f² = 0.492, β = 0.639, p < 0.001). Digital self-efficacy (DSE) was identified as an external determinant with strong positive influence on PEOU (f² = 0.486, β = 0.610, p < 0.001). The model shows strong performance, reflected in a high Tenenhaus goodness-of-fit index (0.770) and solid explanatory power for the outcome variable (adjusted R² = 0.718). Conclusions: This study validates an extended risk-integrated technology acceptance model, offering robust insights into the determinants of mobile health application adoption among chronic patients in Romania. The findings provide actionable guidance for designing targeted interventions and healthcare policies to enhance technology adoption in this population. Full article