Capturing Objective Functional Measures Using Smartphone Inertial Sensors: Feasibility and Usability Study With Older Adults
Bibliographic record
Abstract
Background: Digital platforms and smartphone apps have the potential to help patients with musculoskeletal conditions receive targeted interventions and physiotherapy support at home. As musculoskeletal conditions are much more prevalent in older adults, it is important to determine whether these technologies are accessible and acceptable to this demographic, who may possess lower levels of digital literacy compared to younger adults. Objective: The study aims to evaluate the feasibility and usability of completing functional assessments while recording the activity using smartphone inertial sensors in adults 60 years or older. Methods: Participants (N=21) were recruited from a range of community settings to complete a 4-week home-based trial, recording unsupervised sit-to-stand and single-leg balance activities at least once per week using their smartphone. We analyzed the data quality and adherence by number of assessments per week from the uploaded datasets. Feedback on usability was assessed using interviews and the System Usability Score. Results: Inductive content analysis was used to identify 5 top-level categories: app, device, task, time, and personal perception. The mean System Usability Scale score was 81.2 (SD 17.5). The proportion of valid data uploads was 63.8% (81/127) for single-leg balance and 93.5% (58/62) for sit-to-stand measures. Adherence was high, with no significant deviations in the mean number of sessions completed or duration between sessions. Conclusions: Smartphone-based monitoring of functional activities can facilitate unsupervised, remote assessments, thus reducing burden on physiotherapy services and increasing the ability to monitor progress objectively. Activities should be considered for complexity and, where necessary, increase in difficulty over time. App-based feedback is essential to inform users of the progress and adherence to the activities.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".