The psychosocial impact of exercise as an intervention for persons living with obesity and female infertility: A rapid scoping review and pilot study
Bibliographic record
Abstract
Persons living with obesity and experiencing infertility are counselled on healthy behaviours, such as increasing physical activity levels, to improve fertility-related outcomes. However, due to the known psychological burden of receiving an infertility diagnosis, there is an important need to consider the psychosocial impact of administering exercise programming for this population. This study aims to assess the psychosocial impact of exercise-based interventions on individuals with obesity and infertility through a rapid scoping review and a pilot study. The rapid scoping review was conducted in MedLine, Embase, and CINAHL. Studies involving exercise-based lifestyle interventions for people with obesity and infertility were included only if they measured a psychosocial outcome as a primary or secondary measure. Expanding upon evidence from our rapid review, a pre-experimental feasibility study (pre-test, post-test with one group) was conducted. The pilot study implemented a virtual exercise intervention for people with obesity and experiencing infertility. Questionnaires measuring anxiety and depression, quality of life (QoL), social support, fertility-related stress, and hopelessness were administered. Based on the nine articles from our rapid review, there is evidence that lifestyle interventions combining exercise, diet, and psychobehavioral components improve psychosocial outcomes like anxiety, depression, QoL, and self-esteem. In our pilot study, 11 participants (Age: 34 ± 3.7, BMI: 40.3 ± 4.54) engaged in the virtual exercise intervention, and seven completed the post-intervention questionnaires. The results of our pilot study showed improvements in depression, hopelessness, physical QoL, and fertility-related stress scores. However, we observed declines in anxiety, mental QoL, and perceived social support measures. Our mixed findings may be due to the unique context of our pilot study. The study took place during the COVID-19 pandemic, where increased stress, anxiety, and isolation, negatively impacted fertility patients. Additionally, the virtual intervention, required by restrictions, may have reduced social interaction and support, worsening one's mental health.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".