An Interactive-Technology Health Behavior Promotion Program for Heart Failure Patients: A Pilot Study of Experiences and Needs of Patients and Nurses in the Hospital Setting
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a chronic condition, prevalent especially among older people, characterized by acute episodes leading to hospitalization. To promote HF patients' engagement in physical activity (PA) and adherence to medication, we developed Motivate4Change: a new interactive, information and communication technology (ICT)-based health promotion program for delivery in the hospital. The development of this program was guided by the Intervention Mapping protocol for the planning of health promotion programs. The users of Motivate4Change were defined as hospitalized HF patients and hospital nurses involved in HF patient education. OBJECTIVE: Two aims were addressed. First, to explore the use of interactive technology in the hospital setting and second, to evaluate user needs in order to incorporate them in Motivate4Change. METHODS: Participant observations at a hospital in the United Kingdom and semistructured interviews were conducted with hospitalized HF patients and HF nurses following their completion of Motivate4Change. Interviews were recorded, transcribed, and analyzed according to a thematic coding approach. RESULTS: Seven patients and 3 nurses completed Motivate4Change and were interviewed. Results demonstrated that patient needs included empathic and contextual content, interactive learning, and support from others, including nurses and family members. The nurse needs included integration in current educational practices and finding opportunities for provision of the program. CONCLUSIONS: The current work provides insight into user needs regarding an interactive-technology health promotion program for implementation in the hospital setting, such as Motivate4Change.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".