Establishing global standards on wearable technology for measuring mobility in ageing populations: an international consensus exercise
Bibliographic record
Abstract
BACKGROUND: Mobility, defined as movement in all its forms, is a hallmark of healthy ageing. As wearable technologies become increasingly integrated into population health surveillance and ageing research, the absence of standardised terminology, measurement protocols and reporting practices presents a major barrier to progress. This consensus exercise aimed to establish minimum standards for measuring mobility with wearable technology in ageing populations and set priorities for future research in the field. METHODS: A two-day, in-person consensus meeting was convened with 24 international experts in ageing, mobility and digital health. Using a modified nominal group technique facilitated by a trained moderator, participants engaged in structured small-group brainstorming, followed by iterative large-group discussions. Consensus was achieved through anonymised digital voting on proposed measures, principles and priorities. FINDINGS: Consensus (≥80% agreement) was reached on 20 core device-derived mobility measures and 30 guiding principles for the optimal use of wearable technology in older populations. Experts also identified and ranked 16 priority areas for future research, with the top five including: (i) longitudinal studies and data collection, (ii) digital biomarkers and health outcomes, (iii) contextual data capture, (iv) algorithm development and validation and (v) integration with healthcare systems. INTERPRETATIONS: These consensus-based standards provide a foundational framework for the consistent and transparent use of wearable devices in ageing research and practice. They can inform the development of regulations and guidelines, support harmonisation across studies and chart a path for future research to enhance the utility and impact of wearable technologies in ageing populations.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".