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Record W7129414273 · doi:10.1093/eurjcn/zvaf236

Harnessing mobile health to support recovery after cardiac surgery

2025· article· en· W7129414273 on OpenAlexaff
Maria Hayes

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCardiac surgeryMEDLINETelemedicinemHealth

Abstract

fetched live from OpenAlex

This invited commentary refers to ‘A pilot randomized controlled trial to test the feasibility of a mobile health (mHealth) self-help intervention for adults after cardiac surgery’, by R. Wynne et al., https://doi.org/10.1093/eurjcn/zvaf190. As healthcare systems worldwide increasingly turn to digital solutions to enhance patient engagement and self-management, this study offers valuable insight into both the potential advantages as well as the challenges of deploying such technology within a complex, post-surgical care pathway.1 The strength of this trial lies in its design: a randomized, controlled feasibility study embedded within a real-world tertiary hospital setting. By focusing on patients recovering from elective cardiac surgery; a group at high risk of post-operative complications and readmission, the researchers targeted a population for whom self-management and adherence to recovery protocols are crucial. The use of the GoShare mHealth bundles, incorporating patient narrative videos and tailored educational resources, reflects a thoughtful application of person-centred care principles. Patient stories, as the study rightly emphasizes, can humanize information delivery, normalize recovery experiences, and motivate behaviour change in ways that purely clinical instructions often cannot. The study also achieved a commendably high recruitment rate, with 87% of eligible patients consenting to participate. This suggests that digital self-help interventions are both appealing and acceptable to many cardiac surgery patients. An encouraging finding in a demographic that often includes older adults who may be less digitally literate. This high engagement rate also reinforces the acceptability of this digital intervention. In terms of context and limitations, the authors are appropriately cautious in interpreting their results, acknowledging the limitations inherent in pilot work. The modest sample size and short follow-up period (90 days) mean that definitive conclusions about clinical effectiveness cannot be drawn. This finding aligns with prior work suggesting that mHealth interventions in cardiac populations often improve knowledge and engagement but require larger samples to demonstrate clinical benefit.2 Furthermore, selection bias, driven by technology access and English-language proficiency, likely shaped the participant pool, favouring younger, more digitally capable patients. This is an important reminder that the digital divide remains a barrier to equitable care. Future research should explore strategies for inclusion, such as multilingual resources or hybrid delivery models combining digital tools with personalized nurse follow-up. The COVID-19 pandemic also looms large in the study context. The significant number of non-eligible or transferred cases due to pandemic-related service disruptions highlights the fragility of surgical pathways during this period. Yet it also reinforces the importance of remote, flexible support systems like mHealth interventions that can bridge gaps in continuity of care when in-person services are constrained. The detailed analysis of engagement metrics provides an illuminating picture of how patients interacted with the mHealth bundles. The most frequently accessed resources were those related to diagnosis and immediate post-operative recovery, while rehabilitation materials attracted less attention. This pattern mirrors a common behavioural trend: patients tend to seek information most actively when facing immediate concerns or uncertainty, with interest tapering off during the longer rehabilitation phase. Future iterations of such interventions could build on these insights by introducing adaptive content delivery, such as timed reminders, goal tracking, or interactive features, to sustain engagement beyond hospital discharge. Equally noteworthy is the focus on patient activation as a secondary outcome. The concept of patient activation, defined as a patient’s knowledge, skills, and confidence in managing their health, has emerged as a key predictor of outcomes across chronic disease management.3 Although this small trial did not demonstrate statistically significant differences between groups, the observed trend toward greater self-management behaviours among intervention participants is clinically meaningful. Similar findings have been observed in cardiovascular populations, where personalized mHealth interventions, such as tailored messaging and digital coaching have been shown to enhance activation and adherence.4 These parallels underscore the potential of digital tools not only to inform but to empower patients, an outcome that may translate into reduced readmissions and improved quality of life when tested at larger scale. This trial adds to a growing evidence base suggesting that mHealth interventions can be a feasible and acceptable adjunct to traditional post-surgical care. It aligns with broader healthcare trends emphasizing digital transformation, patient empowerment, and value-based outcomes. Importantly, it also raises critical design considerations: engagement cannot be assumed merely from availability. Sustained use depends on personalization, ease of access, and perceived relevance. The authors’ suggestion to integrate behavioural change theory, structured follow-up and nurse-led reinforcement into future versions of the GoShare platform is particularly well-founded. Such hybrid interventions, where human support complements digital content, have shown promise in improving adherence and outcomes in other chronic disease contexts.5 From a systems perspective, even small improvements in self-management or reductions in unplanned readmissions can yield substantial economic and operational benefits for health services. Thus, the next logical step is a larger, adequately powered trial capable of evaluating not just feasibility but also cost-effectiveness and long-term clinical outcomes. This study is an encouraging step toward integrating mHealth into the continuum of cardiac surgical care. By foregrounding patient voices through narrative media and enabling access to tailored educational resources, it represents a shift from provider-driven to patient-centered recovery models. While the quantitative outcomes are preliminary, the qualitative message is clear: patients are willing to engage with digital tools when these tools feel relevant, supportive, and human. In a healthcare environment increasingly defined by digital connectivity and resource constraints, such findings are both timely and hopeful. With thoughtful refinement and scaling, interventions like GoShare could play a pivotal role in empowering patients to take charge of their recovery, transforming not only outcomes, but the very experience of post-surgical care. Maria Veronica Hayes (MSc (Writing – original draft [lead])), and Suzanne Fredericks (PhD (Writing—review & editing [supporting])) Nothing to declare. This commentary does not contain new data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.273
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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