An Actor-Partner Interdependence Mediation Model for Assessing the Association Between Health Literacy and mHealth Use Intention in Dyads of Patients With Chronic Heart Failure and Their Caregivers: Cross-Sectional Study
Bibliographic record
Abstract
Background: Chronic heart failure (CHF) has become a serious threat to the health of the global population. Self-management is the key to treating CHF, and the emergence of mobile health (mHealth) has provided new ideas for the self-management of CHF. Despite the many potential benefits of mHealth, public utilization of mHealth apps is low, and poor health literacy (HL) is a key barrier to mHealth use. However, the mechanism of the influence is unclear. Objective: The aim of this study is to explore the dyadic associations between HL and mHealth usage intentions in dyads of patients with CHF and their caregivers, and the mediating role of mHealth perceived usefulness and perceived ease of use in these associations. Methods: This study had a cross-sectional research design, with a sample of 312 dyads of patients with CHF who had been hospitalized in the cardiology departments of 2 tertiary care hospitals in China from March to October 2023 and their caregivers. A general information questionnaire, the Chinese version of the Heart Failure-Specific Health Literacy Scale, and the mHealth Intention to Use Scale were used to conduct the survey; the data were analyzed using the actor-partner interdependence mediation model. Results: The results of the actor-partner interdependent mediation analysis of HL, perceived usefulness of mHealth, and mHealth use intention among patients with CHF and their caregivers showed that all of the model's actor effects were valid (β=.26-0.45; P<.001), the partner effects were partially valid (β=.08-0.20; P<.05), and the mediation effects were valid (β=.002-0.242, 95% CI 0.003-0.321; P<.05). Actor-partner interdependent mediation analyses of HL, perceived ease of use of mHealth, and mHealth use intention among patients with CHF and caregivers showed that the model's actor effect partially held (β=.17-0.71; P<.01), the partner effect partially held (β=.15; P<.01), and the mediation effect partially held (β=.355-0.584, 95% CI 0.234-0.764; P<.001). Conclusions: Our study proposes that the HL of patients with CHF and their caregivers positively contributes to their own intention to use mHealth, suggesting that the use of mHealth by patients with CHF can be promoted by improving the HL of patients and caregivers. Our findings also suggest that the perceived usefulness of patients with CHF and caregivers affects patients' mHealth use intention, and therefore patients with CHF and their caregivers should be involved throughout the mHealth development process to improve the usability of mHealth for both patients and caregivers. This study emphasizes the key role of patients' perception that mHealth is easy to use in facilitating their use of mHealth. Therefore, it is recommended that the development of mHealth should focus on simplifying operational procedures and providing relevant operational training according to the needs of the patients when necessary.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".