Pain Reduction and Improvement in Range of Motion After Daily Consumption of an Açai ( <i>Euterpe oleracea</i> Mart.) Pulp–Fortified Polyphenolic-Rich Fruit and Berry Juice Blend
Bibliographic record
Abstract
Dietary interventions involving antioxidants are of interest for reducing inflammation, improving joint motion, and altering pain perception. We evaluated the effect of oral consumption of a fruit and berry blend on pain and range of motion (ROM). This open-label clinical pilot study involved 14 study participants with limitations in ROM that was associated with pain and affected daily living. Participants included but were not limited to those with age-related osteoarthritis. Study participants consumed 120 mL MonaVie Active® fruit juice, predominantly containing açai pulp (Euterpe oleracea Mart.) and other fruit concentrates, daily for 12 weeks. Study participants were assessed at baseline and 2, 4, 8, and 12 weeks by structured nurse interviews, pain and activities of daily living (ADL) questionnaires, blood samples, and ROM assessment. Pain was scored by using a visual analogue scale. ROM was assessed by using dual digital inclinometry as recommended by American Medical Association guidelines. Consumption of the juice resulted in significant pain reduction, improved ROM measures, and improvement in ADLs. Serum antioxidant status, as monitored by the cell-based antioxidant protection in erythrocytes (CAP-e) assay, was improved within 2 weeks and continued to improve throughout the 12 weeks of study participation (P<.01). The inflammatory marker C-reactive protein was reduced at 12 weeks, but this change did not reach statistical significance. Lipid peroxidation decreased mildly at 12 weeks. The antioxidant status, as measured by the CAP-e bioassay, showed the best correlation with improvements in physical well-being (pain, ROM, and ADL). The significant association among increased antioxidant status, improved ROM, and pain reduction warrants further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".