Back

Short-term effects of "Yijinjing Wohu Pushi" posture-voice therapy on speech in Parkinson's disease after STN-DBS.

Sun, X.; Yang, M.; Yan, J.; Wang, L.; Sun, Y.; Wang, Y.; Yan, S.; Li, D.; Niu, C. M.

2023-12-14 rehabilitation medicine and physical therapy
10.1101/2023.12.13.23299527 medRxiv
Show abstract

Hypokinetic dysarthria in Parkinsons Disease (HD-PD) deteriorates patients quality of life by impeding communication and social engagement. Existing treatments like levodopa drugs and deep brain stimulation (DBS) can improve motor symptoms but fall short in addressing speech-related impairments; effective speech therapies tend not to mandate the posture, which potentially hinders vocal performance. Here we first proposed a therapeutic protocol that specified a lunging-and-clawing posture (Wohu Pushi method from Yijinjing) during vocalization training. The protocol aimed to ensure that PD patients could accomplish the training even with moderate motor impairments. A total of 20 HD-PD patients with implanted stimulators in subthalamic nuclei (STN-DBS) participated in one session of training, either following our posture-voice therapy (PVT) or the conventional voice therapy (CVT). Results indicated that the maximum phonation time was significantly elongated in PVT(3.85{+/-}2.81s) but not in CVT(0.46 {+/-} 2.24s), and the formant-distance was significantly increased in PVT(95.80 {+/-} 112.71Hz) compare to CVT(-47.10 {+/-} 84.28Hz). Our results indicated that by demanding appropriate amount of postural maintenance during vocalization, it might facilitate the recovery of speech-related functions. This study warranted larger-scale clinical trials to understand the impact, limitation, and optimization of posture-voice therapy for HD-PD.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.