Single‐leg disuse decreases skeletal muscle strength, size, and power in uninjured adults: A systematic review and meta‐analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We aimed to quantify declines from baseline in lower limb skeletal muscle size and strength of uninjured adults following single‐leg disuse. We searched EMBASE, Medline, CINAHL, and CCRCT up to 30 January 2022. Studies were included in the systematic review if they (1) recruited uninjured participants; (2) were an original experimental study; (3) employed a single‐leg disuse model; and (4) reported muscle strength, size, or power data following a period of single‐leg disuse for at least one group without a countermeasure. Studies were excluded if they (1) did not meet all inclusion criteria; (2) were not in English; (3) reported previously published muscle strength, size, or power data; or (4) could not be sourced from two different libraries, repeated online searches, and the authors. We used the Cochrane Risk of Bias Assessment Tool to assess risk of bias. We then performed random‐effects meta‐analyses on studies reporting measures of leg extension strength and extensor size. Our search revealed 6548 studies, and 86 were included in our systematic review. Data from 35 and 20 studies were then included in the meta‐analyses for measures of leg extensor strength and size, respectively (40 different studies). No meta‐analysis for muscle power was performed due to insufficient homogenous data. Effect sizes (Hedges' g av ) with 95% confidence intervals for leg extensor strength were all durations = −0.80 [−0.92, −0.68] ( n = 429 participants; n = 68 aged 40 years or older; n ≥ 78 females); ≤7 days of disuse = −0.57 [−0.75, −0.40] ( n = 151); >7 days and ≤14 days = −0.93 [−1.12, −0.74] ( n = 206); and >14 days = −0.95 [−1.20, −0.70] ( n = 72). Effect sizes for measures of leg extensor size were all durations = −0.41 [−0.51, −0.31] ( n = 233; n = 32 aged 40 years or older; n ≥ 42 females); ≤7 days = −0.26 [−0.36, −0.16] ( n = 84); >7 days and ≤14 days = −0.49 [−0.67, −0.30] ( n = 102); and >14 days = −0.52 [−0.74, −0.30] ( n = 47). Decreases in leg extensor strength (cast: −0.94 [−1.30, −0.59] ( n = 73); brace: −0.90 [−1.18, −0.63] ( n = 106)) and size (cast: −0.61[−0.87, −0.35] ( n = 41); brace: (−0.48 [−1.04, 0.07] ( n = 41)) following 14 days of disuse did not differ for cast and brace disuse models. Single‐leg disuse in adults resulted in a decline in leg extensor strength and size that reached a nadir beyond 14 days. Bracing and casting led to similar declines in leg extensor strength and size following 14 days of disuse. Studies including females and males and adults over 40 years of age are lacking.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it