Within-Person Dynamics between Lifestyle Factors and Cognitive Functioning using Accelerometer-Determined Physical Activity and Mobile Cognitive Assessments
Bibliographic record
Abstract
The Canadian population is fundamentally changing such that the proportion of seniors is expected to be one in four by 2030 (Government of Canada, 2014). This shift will undoubtedly be accompanied by a surge in the prevalence of age-related health issues, including cognitive decline and dementia. Compounded by increased life expectancy, this demographic change is expected to overwhelm the health care system (Wister & Speechley, 2015) and have grave economic impact (Wimo et al., 2013, 2017). As such, researchers have endeavoured to find innovative and efficient solutions that are preventative, rather than reactive. Lifestyle interventions, such as physical activity (PA) and stress reduction, have gained ample support for their role in protecting against cognitive decline. In tandem, digital cognitive assessment tools have also been developed to support the anticipated demand for efficient screening. Also known as mobile assessments, this state-of-the-art technology can simultaneously assess contextual, psychosocial, and lifestyle factors along with cognition. In this way, a nuanced understanding of the temporal association between cognition and lifestyles variables may be explored. To date, however, there is little research examining these relationships. Chapter 1 reviews psychometric evidence for mobile cognitive assessments and their efficacy in measuring cognitive functioning and daily variability, as well as provides results of a psychometric replication study. Both Chapters 2 (focusing on PA) and 3 (focusing on stress) look at between-person and within-person differences regarding how these lifestyle factors influence cognitive performance. More specifically, Chapter 2 presents results on the relationship between daily variation in PA and cognition, and Chapter 3 examines the relationship between momentary and daily stress and cognition. Chapter 4 provides a brief summary of potential clinical implications for advances in mobile and remote cognitive assessments and potential for lifestyle interventions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".