UNIFIED FRAMEWORK FOR MEASURING MOBILITY IN OLDER PEOPLE: EMERGING DATA FROM A CANADIAN AGING COHORT
Bibliographic record
Abstract
Abstract In the 2015 World Health Organization (WHO) World Report on Aging and Health, mobility is described as movement in all its forms either powered by the body or by a vehicle. Mobility thus encompasses basic movements such as standing up from a chair to more complex activities such as walking or using transportation. Many outcome measures have been developed to assess mobility, however, the variability in constructs being assessed and lack of standardisation in terminology present challenges to advance research and practice in this area. We propose a unified framework for mobility measurement in older populations consistent with the latest language and terminology endorsed by the WHO. The framework outlines three distinct constructs: perceived mobility (“what can you do?”), locomotor capacity for mobility (“what could you do?”), and actual mobility (“what do you do in daily life?”). The latter construct, actual mobility, has been less well studied until recently. Wearable devices offer the unique advantage of comprehensively monitoring the real-world actual mobility of older people in their homes and communities. In this talk, we will present emerging data from the McMaster Monitoring My Mobility (MacM3) study, a digital mobility cohort of over 1200 community-dwelling older adults with detailed mobility measurements spanning all three aspects of mobility and examine their relationships with clinically important health outcomes. In this way, we will be able to test the suitability of our framework and highlight the potential of digitally derived measures of actual mobility for informing aging research and practice.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".