Physiological and behavioral characteristics of SuperAgers from everyday activities: An NIH U19 SuperAging Research Initiative study protocol
Bibliographic record
Abstract
Abstract Background Studying factors contributing to exceptional memory performances beyond the 8th decade, can be a valuable model for understanding dementia risk, prevention, and underlying mechanisms. Age‐associated changes in biological and physiological complexity, or system responsivity, form the foundation of the loss of complexity hypothesis in aging (Lipsitz & Goldberger, JAMA, 1992). Project 1 under the multisite NIH U19 SuperAging Research Initiative uses state‐of‐the‐art wearable technology, and data from real‐world contexts, to test the supposition that SuperAgers have preserved physiologic and behavioral complexity relative to their Controls in the domains of physical activity, autonomic responsiveness, sleep, and social engagement. Method SuperAgers are enrolling from five U.S. and Canadian sites of the SuperAging Research Initiative. SuperAgers refer to individuals aged 80 and older who have memory capacity considered average for those 2‐3 decades younger. In a fully remote data collection protocol, participants wear a multi‐sensor array comprised of 1 skin‐mounted trunk sensor (with ECG) and 2 wearable limb sensors, continuously over a 14‐day period with scheduled rest breaks every 3‐4 days. Participants complete a baseline orientation visit, and three brief check‐in visits with the study team over video conference. Using custom algorithms, developed by our research team, we will examine time‐varying changes in indices of physiologic and behavioral complexity, across multiple body systems, using both volume‐based and established multi‐scale entropy approaches to quantify differences between SuperAgers and their typically aging Controls. We plan to analyze sensor derived complexity metrics in relation to self‐reports of life‐space mobility. Result We will overview our remote data collection protocol and semi‐automated analytics pipeline. Barriers and facilitators to remote data collection will be discussed Preliminary data (N = ∼ 40) will be presented, with select case study examples. We will provide preliminary sensor adherence and user‐feedback from the cohort, comprised of a diverse sample of SuperAgers and Controls. Conclusion This is the first study to test the loss of complexity hypothesis across multiple physiological and behavioral domains simultaneously and in exceptional cognitive aging. Using sensitive tools to capture dynamic and complex behaviors, we will characterize SuperAgers in a way not afforded by point‐in‐time assessments that dominate the current literature.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".