MétaCan
Menu
Retour à la cohorte
Enregistrement W7129414273 · doi:10.1093/eurjcn/zvaf236

Harnessing mobile health to support recovery after cardiac surgery

2025· article· en· W7129414273 sur OpenAlexaff
Maria Hayes

Notice bibliographique

RevueEuropean Journal of Cardiovascular Nursing · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueEnhanced Recovery After Surgery
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésCardiac surgeryMEDLINETelemedicinemHealth

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,958
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,003
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,273
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEuropean Journal of Cardiovascular NursingMême sujetEnhanced Recovery After SurgeryTravaux en français237 207