A control system model of capability-opportunity-motivation and behaviour (COM-B) framework for sedentary and physical activity behaviours
Bibliographic record
Abstract
Objective: Theoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults. Methods: The COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration. Result: A fluid analogy of the COM-B system is presented. Conclusions: The proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".