Training older adults to inhibit the automatic attraction to sedentary stimuli: A cognitive-bias-modification protocol
Bibliographic record
Abstract
Background: To counteract the pandemic of physical inactivity, current interventions rely mainly on reflective processes that focus on increasing the motivation to be physically active. Yet, while these interventions successfully increase the intention to be active, their effect on actual behavior is weak. Recent findings in line with the theory of effort minimization in physical activity (TEMPA) suggest that this inability to turn the intention into action is explained by positive automatic reactions to stimuli associated with sedentary behaviors. These automatic reactions could be particularly strong in older adults, who are more likely to associate physical activity with fear, pain, or discomfort. Objective: The aim of this program is to test the effect of an intervention aiming to counteract their automatic attraction toward sedentary stimuli and to respond positively to physical-activity stimuli in older adults. Training older adults to inhibit the automatic attraction toward sedentary stimuli is hypothesized to increase physical activity, thereby contributing to improved physical functioning and quality of life. Methods: To test these hypotheses, older adults (≥ 60 years) will be enrolled in a controlled double-blinded study with 1-, 3-, 6-, and 12-month follow-up. Participants will be randomized (1:1 ratio) to receive a 12-session cognitive-bias modification training for 3-week based on a go / no-go task either in an experimental or a control condition (placebo). The primary outcome is the number of steps per week. Secondary outcomes include automatic approach-avoidance tendencies toward sedentary and physical activity stimuli, explicit affective attitudes toward physical activity, physical function, and quality of life. Discussion: The study is expected to inform public-health policies and improve interventions aiming to counteract the pandemic of physical inactivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".