Evaluating the Use of Mobile Health Technology in Older Adults With Heart Failure: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is associated with high rates of hospitalizations, morbidity, mortality, and costs. Remote patient monitoring (mobile health, mHealth) shows promise in improving self-care and HF management, thus increasing quality of care while reducing hospitalizations and costs; however, limited information exists regarding perceptions of older adults with HF about mHealth use. OBJECTIVE: This study aimed to compare perspectives of older adults with HF who were randomized to either (1) mHealth equipment connected to a 24-hour call center, (2) digital home equipment, or (3) standard care, with regard to ease and satisfaction with equipment, provider communication and engagement, and ability to self-monitor and manage their disease. METHODS: We performed a pilot study using a mixed-methods descriptive design with pre- and postsurveys, following participants for 12 weeks. We augmented these data with semistructured qualitative interviews to learn more about feasibility, satisfaction, communication, and self-management. RESULTS: We enrolled 28 patients with HF aged 55 years and above, with 57% (16/28) male, 79% (22/28) non-Hispanic white, and with multiple comorbid conditions. At baseline, 50% (14/28) rated their health fair or poor and 36% (10/28) and 25% (7/28) were very often/always frustrated and discouraged by their health. At baseline, 46% (13/28) did not monitor their weight, 29% (8/28) did not monitor their blood pressure, and 68% (19/28) did not monitor for symptoms. Post intervention, 100% of the equipment groups home monitored daily. For technology anxiety, 36% (10/28) indicated technology made them nervous, and 32% (9/28) reported fear of technology, without significant changes post intervention. Technology usability post intervention scored high (91/100), reflecting ease of use. A majority indicated that a health care provider should be managing their health, and 71% reported that one should trust and not question the provider. Moreover, 57% (16/28) believed it was better to seek professional help than caring for oneself. Post intervention, mHealth users relied more on themselves, which was not mirrored in the home equipment or standard care groups. Participants were satisfied with communication and engagement with providers, yet many described access problems. Distressing symptoms were unpredictable and prevailed over the 12 weeks with 79 provider visits and 7 visits to emergency departments. The nurse call center received 872 readings, and we completed 289 telephone calls with participants. Narrative data revealed the following main themes: (1) traditional communication and engagement with providers prevailed, delaying access to care; (2) home monitoring with technology was described as useful, and mHealth users felt secure knowing that someone was observing them; (3) equipment groups felt more confident in self-monitoring and managing; and finally, (4) uncertainty and frustration with persistent health problems. CONCLUSIONS: mHealth equipment is feasible with potential to improve patient-centered outcomes and increase self-management in older adults with HF.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".