Smartphone Apps to Support Falls Rehabilitation Exercise: App Development and Usability and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Falls have implications for older adults' health and well-being. Strength and balance interventions significantly reduce the risk of falls. However, patients do not always perform the unsupervised home exercise needed for fall reduction. OBJECTIVE: This study aims to develop motivational smartphone apps co-designed with health professionals and older adults to support patients to perform exercise proven to aid fall reduction and to explore the apps' usability and acceptability with both health professionals and patients. METHODS: There were 3 phases of app development that included analysis, design, and implementation. For analysis, we examined the literature to establish key app components and had a consultation with 12 older adults attending a strength and balance class, exercise instructors, and 3 fall services. For design, we created prototype apps and conducted 2 patient and public involvement workshops, one with 5 health professionals and the second with 8 older adults from an exercise group. The apps were revised based on the feedback. For implementation, we tested them with one fall service and their patients for 3 weeks. Participatory evaluation was used through testing, semistructured interviews, and focus groups to explore acceptability and usability. Focus groups were conducted with the service that tested the apps and two other services. Qualitative data were analyzed using the framework approach. RESULTS: On the basis of findings from the literature and consultations in the analysis phase, we selected Behavior Change Techniques, such as goal setting, action planning, and feedback on behavior, to be key parts of the app. We developed goals using familiar icons for patients to select and add while self-reporting exercise and decided to develop 2 apps, one for patients (My Activity Programme) and one for health professionals (Motivate Me). This enabled health professionals to guide patients through the goal-setting process, making it more accessible to nontechnology users. Storyboards were created during the design phase, leading to prototypes of "Motivate Me" and "My Activity Programme." Key changes from the workshops included being able to add more details about the patients' exercise program and a wider selection of goals within "Motivate Me." The overall app design was acceptable to health professionals and older adults. In total, 7 patients and 3 health professionals participated in testing in the implementation phase, with interviews conducted with 6 patients and focus groups, with 3 teams (11 health professionals). Barriers, facilitators, and further functionality were identified for both apps, with 2 cross-cutting themes around phone usability and confidence. CONCLUSIONS: The motivational apps were found to be acceptable for older adults taking part in the design stage and patients and health professionals testing the apps in a clinical setting. User-led design is important to ensure that the apps are usable and acceptable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".