Effects of Transcranial Alternating Current Stimulation Combined with Electroacupuncture on Patients with Attention Deficit after Stroke
Notice bibliographique
Résumé
ObjectiveTo investigate the effects of transcranial alternating current stimulation (tACS) combined with electroacupuncture on attention function and walking ability in stroke patients with attention deficit.MethodsA total of 60 stroke patients with attention deficit treated in the Department of Rehabilitation Medicine at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine from January to December 2023 were randomly assigned to control group and observation group using a random number table generated by SPSS 26.0 statistical software, with 30 cases in each group. One patient in the control group voluntarily withdrew, and one in the observation group dropped out due to early hospital discharge. The control group received tACS treatment and sham EA stimulation, with a tACS treatment frequency of 6 Hz, current of 1 mA, and intervention duration of 20 minutes a time. Sham EA stimulation was applied at Shenting (DU24) and Baihui (DU20) acupoints, with acupuncture needles stimulating only the epidermis without skin penetration or electrical current, and the stimulation duration was 20 minutes a time. The observation group received tACS treatment and EA stimulation. The tACS treatment was the same as that in the control group. EA stimulation was applied at Shenting (DU24) and Baihui (DU20) acupoints, with acupuncture needles inserted at a 30° angle to the scalp. Electroacupuncture used dense-disperse waves with a frequency of 2/10 Hz and current intensity adjusted to the subject's tolerance, and each intervention lasted 20 minutes. Both groups received treatment once daily, five times per week, for a total of two weeks. Before and after treatment, the Montreal Cognitive Assessment (MoCA) was used to assess the cognitive function. The MoCA attention score, trail making test A (TMT-A) and trail making test B (TMT-B) were used to assess the attention function. Functional Ambulation Category Scale (FAC) was used to assess the walking ability. A digital monitoring treadmill was used to assess gait kinematics (hip and knee joint range of motion). The correlation between changes in walking ability scores and cognitive attention function scores was analyzed.Results(1) MoCA total score and attention function score: compared with those before treatment, the MoCA score and MoCA attention score in both groups after treatment increased significantly (P<0.05), and the TMT-A score and TMT-B score decreased significantly (P<0.05). Compared with the control group, the MoCA score and MoCA attention score in the observation group after treatment were significantly higher (P<0.05), and the TMT-B score was significantly lower (P<0.05). (2) FAC score and hip/knee joint range of motion: compared with those before treatment, the FAC score, hip joint range of motion and knee joint range of motion in both groups after treatment increased significantly (P<0.05). Compared with the control group, the FAC score and hip joint range of motion in the observation group after treatment were significantly higher (P<0.05), while the difference was not statistically significant in knee joint range of motion (P>0.05). (3) Correlation between walking ability difference and cognitive attention difference: there was positive correlation between the FAC difference and the MoCA score difference (r=0.333, P<0.05), and the FAC difference was positively correlated with the difference in MoCA attention score (r=0.308, P<0.05); the difference in hip joint range of motion was positively correlated with the difference in MoCA score (r=0.425, P<0.05), with the difference in MoCA attention score (r=0.442, P<0.05), and with the difference in TMT-A score (r=0.2931, P<0.05).ConclusiontACS combined with EA can improve the attention function and walking ability of stroke patients with attention deficit, and the walking ability improvement is closely related to cognitive and attentional functions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».