Co-creation of mHealth intervention for older adults with hip fracture and family caregivers: a qualitative study
Bibliographic record
Abstract
PURPOSE: Hip fracture results in an older person's loss of independence. Limited healthcare resources make mobile Health (mHealth) an alternative. Engaging key stakeholders in health technology development is essential to overcome existing barriers. The aim of this study was to establish perspectives of older adults with hip fracture, family caregivers and health professionals (stakeholders) on the development of a mHealth system. MATERIALS AND METHODS: = 2)] with 45 participants (14 older adults, 21 caregivers and 10 health providers). Inclusion criteria were older adults ≥ 65 years who sustained a hip fracture in the previous 3 months; family caregiver of a person with hip fracture; and health providers with 2+ years of clinical experience working older adults with hip fracture. We followed standard methods for focus groups, including recording sessions, transcription and conducting an inductive content analysis. The same moderator, with clinical and research experience, conducted all focus groups. RESULTS: Three themes were generated to consider for a future mHealth intervention: (1) user-friendly design; (2) content to include recovery and prevention information; and (3) implementation factors. Our mHealth system was developed based on feedback from participants. CONCLUSIONS: Co-creating mHealth technology with stakeholders is essential for uptake and adherence. We provide an overview of the development of ActiveHip+, an mHealth system for the clinical care of older adults with hip fracture.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".