Cohort Profile: Baseline characteristics and design of the McMaster Monitoring My Mobility (MacM3) Study, a prospective digital mobility cohort of community-dwelling older Canadians from Southern Ontario
Bibliographic record
Abstract
ABSTRACT Purpose The McMaster Monitoring My Mobility (MacM3) study aims to understand trajectories of mobility decline in later life using multi-sensor wearable technology. To our knowledge, MacM3 is the first major cohort to combine accelerometry and GPS to track real-world mobility in community-dwelling older adults. Participants Between May 2022 and May 2024, MacM3 recruited 1,555 community-dwelling older adults (mean age 73.9 years, SD = 5.5) from Hamilton and Toronto, Ontario. Of the cohort, 68.4% were female, 62.4% married/partnered, 75.3% had post-secondary education, and 63.0% had ≥3 comorbidities. Most were Canadian born (69.4%) and White/Caucasian (88.0%), with greater ethnocultural diversity observed at the Toronto site. Findings to date At baseline, 56.7% of participants reported no mobility limitations, 15.9% had preclinical limitations, and 27.4% had minor mobility limitations. Mean gait speed for the total sample was 1.23 m/s, with a mean Timed Up and Go time of 9.4 seconds and a 5x sit-to-stand time of 13.0 seconds. A total of 1,301 participants had valid wrist-worn device data, and 1,008 participants who agreed to wear the thigh-worn device had valid data (≥7 days with ≥10 hours of wear per day). Step count data (n = 1008) revealed a mean of 8,437 steps per day (SD = 2,943), with 5,073 steps in the lowest quartile and 12,303 steps in the highest. Future plans Ongoing work aims to develop predictive models of mobility decline by integrating wearable, clinical, and environmental data. Pipeline enhancements will enable GPS/inertial measurement unit fusion to explore mobility-environment interactions and support aging-in-place tools.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 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 | 0.000 |
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".