Exercise Barriers Among Females With Type 2 Diabetes: Exploring Age-Related Differences
Bibliographic record
Abstract
PurposeThe purpose of this study was to explore age-specific differences in exercise barriers among females with type 2 diabetes (T2D).MethodsAn online survey explored psychosocial and logistical barriers to exercise among Australian females with T2D (≥18 years). Participants were categorized into younger (18-54 years) and older (≥ 55 years) age groups to explore age-related differences. The survey also collected sociodemographic, general health and T2D management data, and current physical activity (PA) levels and behaviors.ResultsA total of 119 females participated (younger: n = 36, age: 42.7 ± 9.6 years; older: n = 83, age: 66.8 ± 6.1 years). Although 57.1% reported being physically active, 77.1% of these individuals were not meeting the PA guidelines. Younger females more frequently cited barriers related to time constraints and lack of transport, and older females more frequently reported insufficient social support and not knowing how to initiate an exercise routine. Exercise knowledge and self-efficacy were low across all participants. Only 42.9% believed they could realistically meet the PA guidelines.ConclusionsAustralian females with T2D face distinct age-related barriers to exercise, highlighting the need for tailored strategies. Interventions should focus on improving exercise knowledge and self-efficacy while mitigating key barriers to enhance exercise self-management in females with T2D.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".