Clinical and exercise professional opinion on designing a postpartum return-to-running training programme: an international Delphi study and consensus statement
Bibliographic record
Abstract
Returning to running postpartum presents challenges such as musculoskeletal pain and pelvic floor dysfunction for some females, but there is little guidance on developing and progressing postpartum training programmes. This study aims to establish expert consensus recommendations on designing and modifying a postpartum return-to-running training programme, highlight costs and access to qualified professionals as potential barriers and discuss clinical, research and sports policy implications.A three-round Delphi survey of clinical and exercise professionals working with postpartum runners was conducted. Round I consisted of open-ended questions related to designing the training plan, modifications based on biopsychosocial factors, key muscle groups to train and referral and payment sources. Rounds II and III involved Likert-scale voting to identify consensus (≥75% agreement).118 participants completed Round I, 107 completed Round II (response rate 90.6%) and 95 completed Round III (response rate 80.5%). Consensus was reached in 42/47 (89%) statements, including recommendations for a period of relative rest, gradual increases in duration and intensity, starting with a walk-run protocol and incorporating strength training. Training should be modified based on musculoskeletal or pelvic symptoms, sleep, mental health, lactation or energy availability concerns. Cost and access to experienced postpartum running professionals were identified as potential barriers for runners to receive care.Consensus recommendations for a postpartum return-to-running programme include an individualised exercise prescription, gradual increases in physical activity, walk-run protocols and targeted muscle strengthening. Further research and improved access to clinical and exercise professionals are needed to inform and facilitate best practices.
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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 | 0.116 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".