Mobile technology and health apps: Patient and provider experiences in cardiac rehabilitation
Bibliographic record
Abstract
This study focused on the use of mobile health and wellness applications (apps) in chronic disease management. There are over a hundred thousand health apps available for download on public app stores. These include apps in key areas for chronic disease management such as exercise and diet. However, there is little evidence on patient use of health apps to support self-management of chronic conditions. Therefore, the study objective was to describe cardiac rehabilitation patient and provider experiences with health apps and perceived impact on self-management and the patient-provider relationship. An exploratory mixed methods design was used to gain an understanding of patient and provider perspectives and experiences. The study was conducted in a cardiac rehabilitation program in Ontario, Canada. A quantitative survey (n=242) focused on patient demographics and technology use profiles. Patient interviews (n=30) and a provider focus group (n=8) were conducted to explore perspectives on mobile technology and health app use as a part of self-management and the patient-provider relationship. Results from this study describe an aging patient population with a range of cardiac diagnoses and co-morbidities. Ninety-two percent of patients in this study used mobile technology and 50% of those with mobile technology were using health apps. Most patients and providers felt that health apps can support chronic disease management, particularly with respect to tracking progress against exercise and diet goals. Patients and providers also felt that they needed more support in using health apps and integrating them into care processes. This included the need for education on how to use apps as well as access to information on app accuracy and how to choose or recommend health apps given the large number available. Participants also emphasized the desire for health apps to connect patients and providers during and after the rehabilitation program. Health apps were mostly used by patients in the study in the absence of provider recommendations and without connectivity between patients and providers. Findings highlighted the need for health care practices to leverage and support health apps as a part of care during rehabilitation and post-discharge for patients self-managing in the community.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".