Development and Implementation of Strong Foundations, a Digitally Delivered Fall Prevention Program: Usability and Feasibility Pilot Exercise Cohort Study
Bibliographic record
Abstract
Background: Falls remain a major public health problem and a significant cause of preventable injury. Maintaining strength and balance by staying active can prevent falls in older adults, and public health advocates support referral to community exercise programs. Given the growth in use and acceptance of technological interfaces, there remains an interest in understanding the role of a synchronous exercise program designed to improve strength, postural alignment, and balance specifically designed to be delivered in a digital environment with respect to usability and feasibility. Objective: This study aims to design and implement a synchronously delivered digital fall prevention program to adults aged 60 years and older, to understand the usability, feasibility, and attendance. Methods: The "Strong Foundations" program, a 12-week, live, digitally delivered fall-prevention exercise program was informed from different existing in-person exercises and piloted to older adults who were considered a low fall risk by scores of 4 or less from the Centers for Disease Control and Prevention's (CDC's) Stopping Elderly Accidents and Deaths Initiative (STEADI) Staying Independent questionnaire. The System Usability Scale (SUS) measured usability and feasibility at the completion of this program, and digital measures of age-related function (timed up and go [TUG] and 30-second chair stand [30 CS]) were collected pre- and postintervention. Data were collected in 2021. Results: A total of 39 older adults were recruited and 38 completed the 12-week program with an average age of 72 years. The average SUS was 80.6, with an 85% attendance rate and an 8.5 (out of 10) self-reported satisfaction score. Digitally collected TUG and 30 CS statistically improved pre- and postintervention by 9% and 24%, respectively; by week 12, 64% (23/36) of participants improved in the timed up and go and 91% (32/35) improved the chair stands. Conclusions: There was excellent usability and acceptability for Strong Foundations, a novel fall-prevention program designed to be delivered digitally and promising improvement of objective measures of fall risk.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".