Physical activity may be an adjuvant treatment option for substance use disorders during pregnancy: A scoping review
Bibliographic record
Abstract
BACKGROUND: Substance abuse in pregnancy increases the chance of physical and neurobehavioral disabilities as well as many other undesirable fetal outcomes. In nonpregnant populations, physical exercise has shown to be an effective adjunctive therapy option for substance use disorders. Given the known positive maternal and fetal physiological and mental health benefits associated with prenatal exercise, perhaps exercise during pregnancy may also be a viable adjuvant therapy option for women with substance use disorders. The purpose of this scoping review was to summarize the available literature that has assessed the relationship between prenatal exercise and substance use disorders. METHODS: A search strategy was developed combining the terms pregnancy, exercise/physical activity, and substance use. A systematic search was completed in the following databases: Medline/PubMed, SPORTDiscus, and ProQuest. Substances eligible for inclusion included illicit drugs, alcohol, and cannabis. Retrieved data were categorized as animal or human model studies, and were summarized narratively. RESULTS: Eight studies were included in this review (five human studies, three animal model studies). Studies in humans suggest that pregnant women with substance use disorders are interested in engaging in physical activity interventions; however, known acute metabolic and physiological responses to prenatal exercise may be impaired in this population. Rodent models show preliminary evidence for improved mental health outcomes following prenatal exercise for substance use disorders. CONCLUSION: The findings from this review may inform the development of future clinical trials to test the effect of structured exercise programs as an adjunctive treatment option for pregnant women with substance use disorders.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".