Promoting Physical Activity and Preventing Falls Among Older Adults in a Nursing Home Setting: Protocol for Development and Implementation of the BeSt Age Mobile App
Bibliographic record
Abstract
Background: Most nursing home residents do not meet physical activity guidelines. Many interventions to promote physical activity and prevent falls in nursing home residents have low adherence rates, lack a theoretical foundation, or require much time from staff for preparation or delivery. Objective: This study aims to describe the rationale, development, and implementation approach of the BeSt Age app through a cluster randomized controlled trial. We also provide baseline characteristics of the study sample and discuss possible implications for further app developments. Methods: We iteratively developed a novel, tablet-based mobile app (BeSt Age) that enables nursing home staff to deliver individualized physical exercise training to residents with or without motor or cognitive impairments. The app was designed and developed based on an intervention-mapping approach. A needs assessment was performed, followed by defining objectives, theory-based methods, program development, implementation, and evaluation. We took several steps to ensure that the app was based on a sound theoretical background and considered limitations identified in prior research. For implementation and evaluation purposes, we conducted a study among 229 older adults from 19 nursing homes (171 females, 58 males; mean age 85, SD 7 years). Results will be used to examine the effectiveness of the app with regard to different outcomes. Primary outcomes among participating nursing home residents are quality of life, fall risk evaluated through 2 performance-oriented balance tests, and fall incidence. Secondary outcomes include motor performance, cognition, activities of daily living, physical activity behavior, and fall efficacy. In this paper, we examined differences between intervention group (IG) and control group (CG) participants at baseline using the chi-square test, the Mann-Whitney U test, or the t test. Results: The IG (n=137 from 11 nursing homes) received a 12-week intervention with the BeSt Age app in small, homogenous groups of 5-7 nursing home residents, with 2 exercise sessions per week, each lasting 25-30 minutes. The CG (n=92 from 8 nursing homes) received usual care. At baseline, the IG had a statistically significantly larger number of females, participants had a higher BMI, and more participants rated attending physical activity programs as important. There were no further statistically significant differences between the groups. Results with regard to the effectiveness of the BeSt Age app are expected to be published in spring 2026. Conclusions: If proven effective, the BeSt Age app may be a viable solution for physical activity promotion and fall prevention among older adults residing in nursing homes, thereby contributing to maintaining quality of life and overall well-being in this vulnerable population. The app can support nursing home staff in delivering exercise training to residents with minimal additional workload and without requiring specific resources.
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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 | 0.042 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".