Efficacy of Tuina Versus the Proprioceptive Neuromuscular Facilitation (PNF) Technique in Patients With Nonspecific Chronic Neck Pain: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,004 |
| Méta-épidémiologie (sens large) | 0,017 | 0,009 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,010 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».