Enhancing patient activation in cardiovascular health through personalized mobile health interventions and social support

This invited commentary refers to ‘Patient activation improves with a multi-component personalized mHealth intervention in older patients at risk of cardiovascular disease: a pilot randomized controlled trial’, by D. Candelaria et al., https://doi.org/10.1093/eurjcn/zvae159. Despite the widespread utilization of mobile health (mHealth) for cardiovascular care in the face of rising cardiovascular disease (CVD) prevalence and ageing populations, major challenges exist in activating and empowering patients in self-management.1–3 Partnering with patients and fostering their participatory role is strongly advocated in mHealth; however, non-usage attrition and limited digital literacy among older adults are widely reported barriers. This study under discussion investigates the effectiveness of a personalized, text-message–enhanced mHealth intervention (Get FIT+) on patient activation in older adults at risk for CVD.4 This randomized controlled trial reveals promising evidence that personalized messaging, when coupled with social support, significantly enhances patient activation, namely self-perceived knowledge, skills, and in managing CVD risk factors. This commentary evaluates the study’s findings in light of current literature on mHealth interventions and patient engagement strategies, focusing on its implications for future mHealth initiatives in cardiovascular care. Patient activation has emerged as a popular concept in mHealth because the real-world usage of mHealth and applying the knowledge and skills acquired through mHealth into daily life are largely presided by patients themselves. The improvement of patient activation is considered conducive to patients’ engagement with mHealth and actual practice of self-management, both of which are pivotal in slowing or reversing the progression of CVDs.5 The Get FIT+ intervention successfully improved patient activation scores at 3- and 6-month intervals, whereas the control group showed no statistically significant improvement. This finding supports previous research highlighting the role of mHealth technologies in enhancing patient engagement through tailored support. For instance, studies indicate that mHealth interventions using motivational text messages can effectively lower LDL cholesterol and improve physical activity levels in patients with coronary heart disease.6–8 The inclusion of personalized text messaging in the Get FIT+ group likely fostered a sense of individual care, empowering participants to take active roles in managing their health. This study reinforces the significance of social support as an underlying factor in the success of mHealth interventions for older adults. After controlling for demographic variables, the authors found that social support was a primary predictor of patient activation. Social networks provide both emotional security and practical encouragement, which are essential for older adults in adhering to health interventions. Previous studies showed that incorporating professional and peer support in mHealth settings such as through moderated chatrooms or multimedia communication enhances motivation and adherence to behavioural goals.9–11 The Get FIT+ programme’s integration of personalized text messages with social support highlights an effective, dual-component approach that could serve as a model for future interventions aimed at older adults managing CVD risks.12 Despite the positive outcomes, several limitations of the Get FIT study warrant consideration. The use of a single-item question to measure social support restricts the granularity of insights into the types of support that may influence patient activation. Future research could benefit from a comprehensive assessment of social support, examining emotional, instrumental, and informational types, to better understand their respective impacts on patient engagement. Additionally, this study was conducted with a small sample size and limited to a single demographic region, which may limit its generalizability. Scaling this intervention across diverse populations with larger sample sizes would help validate its efficacy and provide more robust data for policymakers and healthcare providers. The findings from Get FIT+ align well with the Behavior Change Wheel framework, which outlines effective intervention functions for behavioural change. Future mHealth programmes might incorporate shared decision-making processes, which have been shown to empower patients and align treatment plans with individual health goals.13 Digital health tools that allow for patient–provider interaction could deepen patient engagement, enhancing both adherence to treatment and overall satisfaction with healthcare services. Furthermore, user-centred design principles should guide the development of mHealth tools. Engaging patients in the initial phases of programme design can improve the relevance and usability of these tools, as seen in other studies where iterative feedback loops have been used successfully to address barriers to mobile app engagement. For instance, simplified app interfaces and culturally tailored messages could ensure broader adoption and retention among older adults who may have limited digital literacy.14 The integration of mHealth interventions with personalized communication and social support represents a forward-looking approach to preventive cardiovascular care. As was highlighted, compassionate and individualized digital interactions can effectively address patient needs and encourage engagement.15 Clinicians should consider incorporating similar digital strategies to extend care beyond traditional settings, particularly in managing CVD among older adults. The Get FIT+ model demonstrates that with appropriate personalization and support mechanisms, mHealth interventions can effectively increase patient activation, thereby fostering a proactive approach to chronic disease management. This study also underscores the need for training healthcare professionals to support digital tools, as they can provide the essential human touch to technology-mediated interventions. Clinical staff could be trained to deliver mHealth messages in ways that reinforce the therapeutic alliance and make patients feel cared for, which, in turn, might further enhance activation scores.16 In summary, the Get FIT+ study adds valuable insights to the growing body of literature supporting mHealth interventions for patient activation. By demonstrating that personalized messaging combined with social support can meaningfully impact patient engagement in older adults, this study paves the way for future work on scaling and refining such interventions. Expanding digital health tools that promote patient activation and foster self-management could be a crucial step towards addressing the global burden of CVD. Further research should focus on integrating more interactive elements and broader measures of social support to maximize the impact of these promising mHealth interventions. Ladislav Batalik (Writing—original draft [lead]), Jing Jing Su (Conceptualization [equal]; Writing—review & editing [lead]), Chen Yang (Writing—review & editing [equal]), and Garyfallia Pepera (Supervision [lead]; Writing—review & editing [supporting]) This research was funded by the Ministry of Health, Czech Republic, conceptual development of research organization (FNBr, 65269705). No new data were generated or analysed in support of this research.