The Value of Technology to Support Dyadic Caregiving for Individuals Living With Heart Failure: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: The demand for health services to meet the chronic health needs of the aging population is significant and remains unmet because of the limited supply of clinical resources. Specifically, in managing heart failure (HF), digital health sought to address this gap during the COVID-19 pandemic but highlighted an access issue for those who could not use technology-mediated health care services without the support of their informal caregivers (ICs). The complexity of managing HF symptoms and recurrent exacerbations requires many patients to comanage their illness with their ICs in a care dyad, working together to optimize patient outcomes and health-related quality of life. However, most HF programs have missed the opportunity to consider the dyadic perspective despite interdependencies on HF outcomes. OBJECTIVE: This study aims to characterize the value of technology in supporting caregiving for individuals living with HF. METHODS: Motivated by an observed unique pattern of engagement in patients enrolled in our Medly HF management program at the Peter Munk Cardiac Centre in Toronto, Canada, we conducted 20 semistructured interviews with a convenience sample of ICs. All interviews were analyzed using the iterative refinement of a codeveloped codebook. The team maintained reflexivity journals to reflect the impact of their positionality on their coding. Themes were first derived deductively using HF typologies (patient-oriented dyads, caregiver-oriented dyads, and collaboratively oriented dyads) and then inductively refined and recategorized based on concepts from the van Houtven et al framework. RESULTS: We believe that there is a need to formally and intentionally expand HF technologies to include dyadic needs and goals. We suggest defining 3 opportunities in which value can be added to technological design. First, identify how technology may be leveraged to increase psychological bandwidth by reducing uncertainty and providing peace of mind. We found that actionable feedback was highly desired by both partners. Second, develop technology that can serve as a member of the dyad's support system. In our experience, automated prompts for patients to take measurements can mimic the support typically provided by ICs and ease their workload. Third, consider how technology can mitigate the dyad's clinical knowledge requirements and learning curve. Our approach includes real-time actionable feedback paired with a human-in-the-loop, nurse-led model of care. CONCLUSIONS: Our findings identified a need to focus on improving the dyadic experience as a whole by building IC functionality into digital health self-management interventions. Through a shared model of care that supports the role of the patient in their own HF management, includes ICs to expand and enhance the patient's capacity to care, and acknowledges the need of ICs to care for themselves, we anticipate improved outcomes for both partners.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".