Habit formation following routine‐based versus time‐based cue planning: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Habit formation has been identified as one of the key determinants of behaviour change. To initiate habit formation, self-regulation interventions can support individuals to form a cue-behaviour plan and to repeatedly enact the plan in the same context. This randomized controlled trial aimed to model habit formation of an everyday nutrition behaviour and examined whether habit formation and plan enactment differ when individuals plan to enact their behaviour in response to a routine-based versus time-based cue. DESIGN: Following a baseline assessment, N = 192 adults (aged 18-77 years) were randomly assigned to a routine-based cue or a time-based cue planning intervention, in which they selected an everyday nutrition behaviour and linked it to a daily routine or a time cue. METHODS: Participants responded to daily questionnaires over 84 days assessing plan enactment and the behaviour's automaticity (as an indicator of habit formation). Multilevel models with days nested in participants were fitted. RESULTS: As indicated by asymptotic curves, it took a median of 59 days for participants who successfully formed habits to reach peak automaticity. Group-level analyses revealed that both routine-based and time-based cue planning led to increases in automaticity and plan enactment, but no between-condition differences were found. Repeated plan enactment was a key predictor for automaticity. CONCLUSIONS: Linking one's nutrition behaviour to a daily routine or a specific time was similarly effective for habit formation. Interventions should encourage persons to repeatedly carry out their planned behaviour in response to the planned cue to facilitate habit formation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".