Does daily measured physical activity predict weekly self-reported physical activity? An application of the peak-end rule and serial position effect
Bibliographic record
Abstract
In spite of well-documented benefits of physical activity (PA), most breast cancer survivors engage in low levels of PA when measured objectively, yet self-report higher PA levels. Understanding the accelerometer-assessed PA patterns that inform self-report assessments may have theoretical and practical implications. For example, is it PA on different days or an average of PA generally over a week that best predicts women's self-report PA? The purpose of this study was to explore the associations between objectively assessed and self-report PA using the tenants of memory (serial position effect) and perceived experience (modified peak-end rule). Breast cancer survivors (N=196) wore an accelerometer for seven days and self-reported their PA at the end of the week. Hierarchical linear regressions were used to explore the independent influence of objective PA at each intensity during days one (beginning), four (middle), and seven (end) and the average across the seven days on self-report PA at different intensities, while controlling for age, stage of breast cancer, and body mass index. In line with the peak-end rule and the serial position effect, objective moderate PA on all days significantly explained 23% of the variance in self-reported moderate PA with the end day being the strongest predictor. Mild (R2=.03) and vigorous (R2=.04) intensities did not demonstrate significant effects for the objective PA days. Average levels of moderate PA across the week also predicted slightly lower variance in self-report scores (R2=.22). Future research is needed in order to understand how PA levels, intensity, and sequence may predict self-report PA.Acknowledgments: The original research study is supported by the Canadian Institutes of Health Research
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".