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Record W2163151244 · doi:10.1177/014572170002600312

Exercise Behavior in a Community Sample With Diabetes: Understanding the Determinants of Exercise Behavioral Change

2000· article· en· W2163151244 on OpenAlexaff
Ronald C. Plotnikoff, Sharon Brez, Stephen B. Hotz

Bibliographic record

VenueThe Diabetes Educator · 2000
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of OttawaOttawa HospitalUniversity of Alberta
Fundersnot available
KeywordsPsychosocialType 2 diabetesBehavior changePopulationTranstheoretical modelPsychologySelf-efficacyMedicineClinical psychologyGerontologyDiabetes mellitusSocial psychologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to investigate the factors associated with exercise behavior among adults with diabetes. METHODS: Exercise behavior (stage of exercise readiness and energy expenditure) and potential determinants were measured on a subsample (n = 46) of adults with type 1 or type 2 diabetes from a randomized population-based telephone survey. Participants were assessed at baseline and at a 6-month follow-up. RESULTS: Sociodemographic and biomedical characteristics did not significantly differ between the stages of exercise behavior. Scores on the psychosocial constructs of self-efficacy, behavioral processes, self-concept, and social support were significantly higher for those in the action stage than those in the preaction stage of exercise readiness. Self-efficacy and behavioral process of change were significantly associated with energy expenditure; self-efficacy was the strongest predictor in the longitudinal analysis. CONCLUSIONS: These findings may generate direction for theory development and guide health and medical practitioners when intervening on the specific constructs. Population- and community-based surveys have utility for assessing diabetes health-related behavior (e.g., exercise behavior).

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)0.000
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 score0.032
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.122
GPT teacher head0.352
Teacher spread0.230 · 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

Citations109
Published2000
Admission routes1
Has abstractyes

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