MétaCan
Menu
Back to cohort
Record W4385619872 · doi:10.1080/08870446.2023.2241777

The impact of action plans on habit and intention strength for physical activity in a web-based intervention: is it the thought that counts?

2023· article· en· W4385619872 on OpenAlexaff
Amanda L. Rebar, Rebecca Williams, Camile E. Short, Ronald C. Plotnikoff, Mitch J. Duncan, Kerry Mummery, Stephanie Alley, Stephanie Schöeppe, Quyen G. To, Corneel Vandelanotte

Bibliographic record

VenuePsychology and Health · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Alberta
FundersNational Health and Medical Research CouncilNational Heart Foundation of Australia
KeywordsHabitPsychologyConsistency (knowledge bases)Action (physics)Psychological interventionIntervention (counseling)Physical activityAction planSocial psychologyApplied psychologyDevelopmental psychologyPhysical therapyMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Action planning is a common approach used in physical activity interventions. The aim of this study was to assess the association of frequency, consistency and content of action planning with physical activity behaviour, intention strength and habit strength. METHODS AND MEASURES: age =43.9; range = 22-73 years) could create 6 rounds of action plans for 4 activities each (24 total). RESULTS: Consistency of action planning during the intervention was associated with change in physical activity at 9-months, and intention and habit strength at 3-months and 9-months. Frequency of action planning was negatively associated with intention at 3-months and 9-months. The effect of action planning consistency on physical activity behaviour was no longer significant when accounting for change in intention and habit strength. CONCLUSION: Consistency of how, where, when and with whom people plan their physical activity may translate into stronger physical activity habits. Interventions should avoid encouraging making many distinct action plans, but rather encourage stable contexts through consistent action planning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.246
GPT teacher head0.551
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuePsychology and HealthSame topicBehavioral Health and InterventionsFrench-language works237,207