Impact of patient engagement on the design of a mobile health technology for cardiac surgery
Bibliographic record
Abstract
ABSTRACT Objective The aims of this study were to describe the impact of patient engagement on the initial design and content of a mobile health (mHealth) technology that supports enhanced recovery protocols (ERPs) for cardiac surgery. Methods Engagement occurred at the level of consultation and took the form of an advisory panel. Patients that underwent cardiac surgery (2017-2018) at St. Boniface Hospital (Winnipeg, Manitoba) and consented to be contacted about future research, and their caregivers, were approached for participation. A qualitative exploration was undertaken to determine advisory panel members’ key messages about, and the impact of, patient engagement on mHealth technology design and content. Results Ten individuals participated in the advisory panel. Key design-specific messages centered around access, tracking, synchronization, and reminders. Key content-specific messages centered around roles of cardiac surgery team members and medical terms, educational videos, information regarding cardiac surgery procedures, travel before/after surgery, nutrition (i.e., what to eat), medications (i.e., drug interactions), resources (i.e., medical devices), and physical activity (i.e., addressing fears and providing information, recommendations, and instructions). These key messages were a rich source of information for mHealth technology developers and were incorporated as supported by the existing capabilities of the underlying technology platform. Conclusions Patient engagement facilitated the development of a mHealth technology whose design and content were driven by the lived experiences of cardiac surgery patients and caregivers. The result was a detail-oriented and patient-centered mHealth technology that helps to empower and inform patients and their caregivers about the patient journey across the perioperative period of cardiac surgery. KEY QUESTIONS What is already known about this subject? Enhanced recovery protocols (ERPs) have been proposed as a clinical strategy to effectively address complex and multi-system vulnerabilities, like those commonly present in older adults undergoing cardiac surgery. Mobile health (mHealth) technologies have the potential to improve delivery and patient experience with ERPs, but their development in the academic research setting is often limited by a lack of end-user (e.g., i.e., patient and caregiver) involvement. What does this study add? To our knowledge, this is one of the first studies to engage patients and caregivers in the development of a mHealth technology that supports ERPs for cardiac surgery. This study describes a process for engaging patients and caregivers as “co-producers” of a mHealth technology to support delivery of ERPs during the perioperative period of cardiac surgery. It also demonstrates that engaging patients and caregivers in research, through the formation of an advisory panel, yields a rich source of information to guide the design and content of mHealth technologies in cardiac research. How might this impact on clinical practice? In an era in which mHealth technologies are being increasingly looked to for the optimization of healthcare delivery, this study underscores the utility of using patient and caregiver voices to drive the development of patient-centered mHealth technologies to support clinical practice.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".