Telephone coaching supports exercise in people with prediabetes and diabetes: A mixed-methods study
Bibliographic record
Abstract
Objective: To evaluate whether a telephone coaching intervention can help people with prediabetes and diabetes to exercise as recommended by diabetes guidelines after a lifestyle intervention. Methods: The telefone coaching intervention included problem-solving strategies to address barriers to successfully implementing an action plan to achieve exercise goals. Data were collected from January to December 2020 in Juiz de Fora, Minas Gerais, Brazil. Confidence, motivation, barriers to exercise, and weekly exercise duration were evaluated using both quantitative (questions with response options on a 0-10 Likert scale) and qualitative (open-ended questions) data, which were analyzed complementarily using mixed methods. Results: Thirty-one individuals (20 to 74 years old, 55% male, 71% type 2 diabetes) answered, on average, 4.0 ± 1.4 phone calls. The averages of confidence and motivation to exercise scores were between 8.0 to 8.7 and 7.0 to 8.9, respectively. The most frequently reported barriers to exercise were weather, pain or physical injuries, and lack of adequate space. The average self-reported time exercising was higher than 150 minutes/week in all phone calls. The main reasons for feeling confident and motivated to exercise were knowledge about exercise, joy in exercising, and benefits in physical health. Conclusion: The telephone coaching intervention can support people with prediabetes and diabetes in adhering to exercise recommendations outlined in diabetes guidelines, especially after participating in programs focused on promoting lifestyle changes.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".