Health‐Related Behaviours in Octogenarians and Nonagenarians with Robust Cognitive Longevity: Progress and Initial Data from thee SuperAging Research Initiative
Bibliographic record
Abstract
Abstract Background SuperAgers—individuals age 80+ with episodic memory performance at least as good as those 20‐30 years younger—provide a unique perspective on cognitive resilience and resistance in aging. The SuperAging Research Initiative (SRI), spearheaded by The University of Chicago and involving multiple academic partners, investigates factors underpinning robust cognitive aging. One key SRI project, leverages a fully remote data collection paradigm to: 1) discern activity patterns that characterize SuperAgers and 2) explore the 'complexity hypothesis in aging'—whether dynamic physiological responsiveness is a hallmark of exceptional cognitive aging. Here we report on feasibility and initial outcomes from this project. Method Participants don wearable sensors, including an ECG sensor (chest) and two inertial measurement units (wrist, ankle), for a 10–12‐day period of continuous data collection whilst performing their usual daily activities. The protocol includes a virtual orientation and periodic check‐ins to ensure wear‐compliance and provide technical support. Structured sensor‐wear breaks facilitate protocol compliance and acceptance. Result To date, recruitment efforts have led to enrollment of 91 individuals (Mean age = 84.1 years; 62 women), which approximates half of the total SRI sample. Reasons for ineligibility or non‐enrollment include dermatological contraindications, ‘busy’ lifestyles, and perceived participation burden. Initial analyses of 44/58 participants who have completed data collection show exceptional adherence (>95% sensor wear compliance) (Table 1). Limited data loss due to sensor non‐wear and/or sensor failure demonstrates high data quality (Table 1). Notably, participants demonstrate a high level of independence, with study partner assistance required in < 5% of cases. Withdrawals have been minimal (3%) and primarily attributed to skin irritation due to undisclosed dermatological contraindications. Initial data are provided in Figure 1 and Table 2 including daily activity summary data and group comparisons between SuperAgers and Controls. Updated data will be presented at the conference. Conclusion Wearable technologies are feasible for remotely assessing daily activities of octogenarians and nonagenarians with high compliance. Objective quantitative data hold promise for expanding our understanding of lifestyle factors relevant to superior cognitive aging. Insights from this study may influence preventive intervention strategies against age‐associated neurological diseases and in support of enhancing cognitive longevity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".