Evaluating the effectiveness of aquatic therapy on mobility, balance, and level of functional independence in stroke rehabilitation: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To meta-analyze and systematically review the effectiveness of aquatic therapy in improving mobility, balance, and functional independence after stroke. Data Sources: Articles published in Medline, Embase, CINAHL, PsycINFO, and Scopus up to 20 August 2019. Study Selection: Studies met the following inclusion criteria: (1) English, (2) adult stroke population, (3) randomized or non-randomized prospectively controlled trial (RCT or PCT, respectively) study design, (4) the experimental group received >1 session of aquatic therapy, and (5) included a clinical outcome measure of mobility, balance, or functional independence. Data Extraction: Participant characteristics, treatment protocols, between-group outcomes, point measures, and measures of variability were extracted. Methodological quality was assessed using Physiotherapy Evidence Database (PEDro) tool, and pooled mean differences (MD) ± standard error and 95% confidence intervals (CI) were calculated for Functional Reach Test (FRT), Timed Up and Go Test (TUG), gait speed, and Berg Balance Scale (BBS). Data Synthesis: Nineteen studies (17 RCTs and 2 PCTs) with a mean sample size of 36 participants and mean PEDro score of 5.6 (range 4–8) were included. Aquatic therapy demonstrated statistically significant improvements over land therapy on FRT (MD = 3.511 ± 1.597; 95% CI: 0.381–6.642; P = 0.028), TUG (MD = 2.229 ± 0.513; 95% CI: 1.224–3.234; P < 0.001), gait speed (MD = 0.049 ± 0.023; 95% CI: 0.005–0.094; P = 0.030), and BBS (MD = 2.252 ± 0.552; 95% CI: 1.171–3.334; P < 0.001). Conclusions: While the effect of aquatic therapy on mobility and balance is statistically significant compared to land-based therapy, the clinical significance is less clear, highly variable, and outcome measure dependent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".