Patients often consider bone marrow aspiration and biopsy to be one of the most painful medical procedures. The effectiveness of non-pharmacological interventions to reduce pain during bone marrow aspiration and biopsy remains unclear.
To synthesize existing evidence regarding the effectiveness of non-pharmacological interventions in mitigating procedural pain among patients undergoing bone marrow aspiration and biopsy.
A systematic review and meta-analysis of randomized controlled trials.
Six electronic databases, including PubMed, EMBASE, CINAHL, PsycINFO, Cochrane Library and Web of Science were searched from inception to July 15, 2023. The risk of bias was assessed using the Cochrane Risk of Bias Tool Version 2.0. Meta-analysis was conducted using STATA 16. The certainty of the evidence was assessed by the GRADE approach.
This meta-analysis included 18 studies derived from 17 articles involving a total of 1017 participants. The pooled results revealed statistically significant pain reduction effects using distraction (SMD: −.845, 95% CI: −1.344 to −.346, p < .001), powered bone marrow biopsy system (SMD: −.266, 95% CI: −.529 to −.003, p = .048), and acupoint stimulation (SMD: −1.016, 95% CI: −1.995 to −.037, p = .042) among patients undergoing bone marrow aspiration and biopsy. However, the pooled results on hypnosis (SMD: −1.228, 95% CI: −4.091 to 1.515, p = .368) showed no significant impact on pain reduction. Additionally, the pooled results for distraction did not demonstrate a significant effect on operative anxiety (MD: −2.942, 95% CI: −7.650 to 1.767, p = .221).
Distraction, powered bone marrow biopsy system and acupoint stimulation are effective in reducing pain among patients undergoing bone marrow aspiration and biopsy.
Not applicable.
This meta-analysis highlights the effectiveness of distraction, powered bone marrow biopsy system and acupoint stimulation for reducing pain in patients undergoing bone marrow biopsy. Healthcare professionals should consider integrating these interventions into pain management practices for these patients.
(PROSPERO): CRD42023422854.
by Jiawei Wu, Peng Ren, Boming Song, Ran Zhang, Chen Zhao, Xiao Zhang
As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.