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Record W2171248718 · doi:10.1177/0145721710378538

Physical Activity Preferences and Type 2 Diabetes

2010· article· en· W2171248718 on OpenAlexaff
Cynthia C. Forbes, Ronald C. Plotnikoff, Kerry S. Courneya, Normand G. Boulé

Bibliographic record

VenueThe Diabetes Educator · 2010
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsType 2 diabetesPsychologyDiabetes mellitusMedicineEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to examine physical activity counseling and program preferences in a sample of adults with type 2 diabetes. Specifically, the objectives were to determine physical activity preferences (objective 1), and whether there were any significant differences between age and/or sex groups for these preferences (objective 1a). A subsidiary objective was to explore potential associations of key social-cognitive constructs (ie, self-efficacy and social support) with physical activity preferences (objective 2). METHODS: This exploratory study consisted of a quantitative, secondary analysis of survey data from a national sample of adults with type 2 diabetes (N = 244). A qualitative follow-up employing telephone interviews was conducted with 14 individuals. RESULTS: Consistent with hypotheses, walking was the most preferred physical activity behavior and there was a preference for engaging in physical activity with others. There were significant (P values < .05) differences in counseling and program preferences between demographic (age and sex), and physical activity cognitive scores. For example, a significantly (P < .05) higher physical activity intensity preference was found in men and younger participants. CONCLUSIONS: Tailoring interventions and physical activity programs to the specific preferences of individuals is an important component for health professionals and researchers in facilitating this 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.028
GPT teacher head0.322
Teacher spread0.294 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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