Efficacy of Tuina Versus the Proprioceptive Neuromuscular Facilitation (PNF) Technique in Patients With Nonspecific Chronic Neck Pain: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Nonspecific chronic neck pain (NCNP), characterized by a long course, a high recurrence rate, and a young age of onset, causes a huge economic burden. Scientific evidence supports the efficacy of tuina, a manual traditional Chinese medicine (TCM) therapy involving manipulation of soft tissues and joints, for NCNP. However, there is little evidence of the effectiveness of proprioceptive neuromuscular facilitation (PNF), a rehabilitative method involving specific patterns of muscle contraction and stretching, in treating NCNP, either alone or in combination with tuina. OBJECTIVE: This study aims to compare the effects of the PNF technique, tuina, and their combination on patients with NCNP and assess whether combined therapy outperforms monotherapies. METHODS: The parallel, double-blind, three-arm clinical randomized controlled trial (RCT) is being conducted at the Beijing University of Chinese Medicine and its affiliated hospitals. Patients will be recruited and randomly assigned to a PNF group, a tuina group, and a combined (PNF+tuina) group in a 1:1:1 ratio. The PNF intervention (PNF stretching and PNF plyometrics) will last for 30 minutes each session. Tuina therapy will also last for 30 minutes each session. The combined group will receive 30 minutes of PNF, followed by 30 minutes of tuina therapy. Participants will receive 4 weeks of treatment, thrice a week, for a total of 12 treatments. Visual Analogue Scale (VAS) and Neck Disability Index (NDI) scores will be used as primary outcome measures. Cervical active joint mobility measured with the MicroFET3 Portable Muscle Strength Test and Joint Mobility Meter and muscle physical properties tested with the Myoton Muscle Tester will be used as secondary outcome measures. Data will be analyzed at baseline, at the end of the intervention, and during the 4 weeks of follow-up using repeated measures ANOVA. The significance level will be 5%. RESULTS: As of May 26, 2025, 43 participants were already recruited and randomly assigned to the three treatment groups (PNF: n=14, 32.6%; tuina: n=15, 34.9%; combined: n=14, 32.5%). All enrolled participants have initiated treatment, with an average adherence rate of 92% and no withdrawals due to adverse events (AEs) or treatment dissatisfaction. The short-term follow-up (end of intervention) for the first cohort was completed on July 30, 2025, with long-term follow-up (1 month postintervention) to be completed by August 31, 2025. The final analysis is projected to include data of all 69 participants by October 2025, with primary results expected to be submitted for publication in December 2025. CONCLUSIONS: Our findings will provide a solid evidence base for clinical approaches to managing NCNP. Moreover, our results will offer valuable insights into the relative efficacy of tuina, PNF, and their combination, shedding light on their potential benefits and helping identify the most effective treatment strategies for NCNP. TRIAL REGISTRATION: International Traditional Medicine Clinical Trial Registry ITMCTR2023000061; https://itmctr.ccebtcm.org.cn/mgt/project/view/346154483678331720/false. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63528.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".