Exploring exercise in recovery from substance use disorder: A qualitative study
Bibliographic record
Abstract
Background: Many individuals in Canada struggle with substance use disorder (SUD) and over a quarter of these individuals are struggling with additional mental health disorders (i.e., polysubstance use disorder, or psychiatric comorbidity). There is a need for non-invasive, inexpensive and scalable interventions to assist in recovery. A possible adjunct intervention that warrants exploration is exercise. However, little is known about the acceptability of exercise in residential SUD treatment facilities. Methods: Semi-structured interviews were conducted with in-patients (n=15). The interviews were based on the Theoretical Domains Framework and included questions regarding physical activity knowledge, exercise preferences, and barriers/facilitators to exercise participation. A thematic analysis was conducted. Results: Four main themes were identified. First, there was a lack of knowledge regarding the recommended amounts of physical activity. Further, the participants lacked confidence in doing more difficult modalities of exercise (e.g., proper weight lifting form). Second, many environmental resources (e.g., cost, equipment and full schedule) were considered barriers to exercise participation both inside and outside the treatment facility. Third, technology (e.g., activity trackers/mobile phone applications) and social accountability were considered key to continued exercise participation. Finally, emotional and mood regulation seems to play an essential role as a facilitator for exercise (e.g., craving reduction, the alleviation of depression, the release of uncomfortable feelings). However, feeling anxious before exercise and self-conscious during exercise emerged as prominent barriers. Implications: The results from this work may inform future interventions in residential treatment facilities for SUD.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".