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Record W2324364580 · doi:10.1017/jsc.2014.13

Imagine that: Examining the Influence of Exercise Imagery on Cigarette Cravings and Withdrawal Symptoms

2014· article· en· W2324364580 on OpenAlexaff
Lisa M. Cooke, L. Fitzgeorge, Cristal Hall, Harry Prapavessis

Bibliographic record

VenueThe Journal of Smoking Cessation · 2014
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsFanshawe CollegeWestern University
Fundersnot available
KeywordsAbstinenceMoodPsychologyAnalysis of varianceNegative moodCravingMental imageRepeated measures designRandomized controlled trialPhysical therapyClinical psychologyMedicinePsychiatryCognitionAddictionInternal medicine

Abstract

fetched live from OpenAlex

Evidence highlights that an acute bout of exercise can contribute to reductions in cravings and withdrawal symptoms. However, it is unknown how low in intensity or movement one can go before these effects no longer exist. The current study examined if exercise imagery could contribute to reductions in smoking cravings and withdrawal symptoms after a short period of abstinence (CO≤6ppm). Regular smokers (N = 29) were randomized into one of three treatment groups: exercise imagery, moderate intensity exercise, or control. Each completed questionnaires including: the Strength of Desire to Smoke item (primary outcome) and the Mood and Physical Symptoms Scale (Secondary outcomes) pre- and post-treatment. A 3 (Condition) by 2 (Time) repeated measures ANOVA showed a medium non-significant interaction effect (partial η 2 = .06) favouring the exercise group for reductions in desire to smoke. A large significant interaction effect (partial η 2 = .24) was found for tension. Medium-to-large non-significant interaction effects favouring the exercise group were found for various withdrawal symptoms. These data, taken together, suggest that exercise imagery is not as powerful as moderate intensity exercise in reducing cravings and withdrawal symptoms following temporary abstinence. Implications and future directions are discussed.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.029
GPT teacher head0.322
Teacher spread0.293 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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