Efficacy of Acupuncture for Mild to Moderate Depression in Older People: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Selective serotonin reuptake inhibitors are first-line antidepressants; however, only approximately 60% of patients can benefit from them. There is insufficient evidence for using acupuncture for symptom relief or for improving tolerance to selective serotonin reuptake inhibitors. Objective: This randomized controlled trial aims to assess the effects of acupuncture combined with citalopram hydrobromide on mild to moderate depression in older people. Methods: This study is a 2-arm, parallel, randomized controlled trial. A total of 132 participants aged 60 to 80 years diagnosed with major depressive disorder were divided into an acupuncture and medication group or a medication group. Participants in both groups take citalopram hydrobromide at a dose of up to 20 mg daily for 12 weeks. The acupuncture and medication group additionally receives 36 sessions of acupuncture treatment over 12 weeks. The primary outcome is the response rate of the 17-item Hamilton Depression Scale at the twelfth week. The secondary outcomes include changes in scores on the 17-item Hamilton Depression Scale and Mini-Mental State Examination at various time points. Adverse events will be recorded in detail. Results: The study commenced on June 30, 2023, and as of October 17, 2024, a total of 132 participants had been enrolled. Data collection has been completed. Currently, data analysis is in progress, with preliminary findings anticipated to be available by October 2025. The findings of this study are expected to be submitted for publication in 2026. Conclusions: This pilot study is expected to provide critical insights into the feasibility of integrating acupuncture with standard medication for managing mild to moderate depression in older people. By generating preliminary evidence on its potential benefits, the study aims to inform the design and sample size estimation of future multicenter trials, potentially advancing nonpharmacological treatment options for depression.
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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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.012 |
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".