Effects of Elastic Band Resistance Exercise on the Working Memory in the Aged PeopleEvidence from fNIRS
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摘要:目的
观察长期弹力带运动改善高龄老年人工作记忆的效果，采用近红外光谱技术探究可能的脑机制。
方法将60名高龄老年人随机分为弹力带组与对照组，弹力带组接受16周、每周3次、每次40 min的弹力带干预，对照组保持原有生活状态；采集实验前后受试者完成工作记忆任务期间的前额叶血流动力学指标。
结果反应时的时间×组别交互效应具有统计学意义（P<0.001）；正确率的时间×记忆负荷交互效应具有统计学意义（P=0.003）；正确反应率的时间×组别交互效应具有统计学意义（P<0.001）；氧合血红蛋白结果显示，9条通道组别×时间×记忆负荷交互效应均具有统计学意义（P<0.002）。
结论规律性中低强度弹力带抗阻运动能改善高龄老年人的工作记忆表现；在低记忆负荷时前额叶激活不明显，中、高记忆负荷时双侧腹外侧前额叶、左背外侧前额叶、左额极区显著激活，这种前额叶激活优化模式可能是弹力带抗阻运动改善工作记忆的脑机制。
Abstract:ObjectiveTo examine the effect of elastic band exercise in improving the working memory in aged people, and to explore the potential brain mechanism by functional Near Infrared Spectroscopy (fNIRS).
MethodsSixty aged people were randomly divided into an elastic band group (EBG) and a control group (CG). EBG received elastic band intervention for 16 weeks, 3 times a week, 40 minutes each time, and CG maintained the original living condition. The subjects' prefrontal cortex hemodynamics during the N-back task were measured before and after the intervention.
ResultsThe time×group interaction of reaction time (RT) was statistically significant (P<0.001). The interaction effect of time×working memory load at accuracy was statistically significant (P=0.003). The interaction effect of time×group at rate-correct score was statistically significant (P<0.001).The results of HbO2 showed that the interaction effect groups×time×working memory load was statistically significant in 9 channels (P<0.002).
ConclusionRegular low-to-morderate-intensity elastic band resistance exercise improved the working memory performance in aged people. The activation of the prefrontal cortex was not significant at low working memory loads, and the activity of the bilateral ventrolateral prefrontal cortex, left dorsolateral prefrontal cortex and left frontopolar cortex area were increased at medium and high memory loads. This optimized prefrontal cortex activation pattern may be the brain mechanism by which resistance exercise improves working memory.
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在智能算法快速发展背景下，学界已开始借助词提取、主题聚类、情感分析、网络分析等自然语言处理方法对现有体育学知识进行挖掘整合，并基于此展开体系构建与话语呈现研究，以回应中国特色体育学“三大体系”与自主知识体系建设。事实上，在自然语言处理过程中，文本分词结果对知识挖掘结果起基础性作用。从现有研究看，体育学知识挖掘研究多只采用jieba、Han LP等基础分词模块对文本进行简单分词，这较难捕捉到一些体育学特色突出的词汇，继而影响体育学知识挖掘的精准度。
词向量（Word Embedding）是一种通过对文本语料展开大数据学习，将其中的词语或短语映射到一个低维、连续、稠密的实数向量空间中，并用一个固定长度对词语进行向量化表示的形式。这可以使相似语义的词语在几何层面距离相近，提升知识挖掘的精准度。需要说明的是，词向量模型的精准性与其所依赖文本语料库的规模与多样性紧密相关，规模大且覆盖广的训练集能够帮助词向量模型更精准地对某些特定词语进行表达。因此，在体育学知识挖掘过程中，基于足够多的体育类文本语料训练而来的词向量模型，可以较敏锐地捕捉到单个词语在各种体育学知识情境中的多重含义，弥补传统分词方法在体育学特色词识别、新词发现、一词多义等文本处理方面的不足，继而有效提升体育学知识挖掘过程中词提取、文本分类、情感分析、网络分析、主题聚类分析、知识图谱构建、跨模态知识融合、知识演化与追踪的精准度。
以体育学知识演化与追踪研究为例：首先，在严格遵循法律、政策、道德与技术规范的前提下，研究者可以借助爬虫技术大规模地获取包含体育类期刊文献、电子图书、政策文件、新闻报道、网络评论、对话访谈等的文本语料作为训练集，并采用词向量训练工具（如Word2Vec）对其进行训练，以获得包含体育学专业术语和语义特性的专属词向量模型。其次，运用余弦相似度计算同一词语在不同时间阶段词向量空间中的距离，通过对比其表示差异，窥见体育学领域中新词出现、旧词消亡、词义扩展或收缩、词语关系变化等情况。在此基础上，利用这些具有时序特征的词向量信息对主题分析方法（如LDA）进行参数优化或预处理改进，以辅助主题模型更精准地挖掘出文本内隐藏的体育学特色词语与潜在主题结构。最后，可以采用t-SNE或UMAP降维技术将词向量组成的文档表示或主题结构分布映射至低维空间，并对体育学知识主题的演进过程进行可视化展示，由此实现对体育学知识发展态势的精准把握。总之，在智能算法时代，学者们应关注词向量等自然语言处理方法在提升体育学知识挖掘精准度上的技术支撑作用，推动体育学领域在知识整合、体系构建、话语呈现、学科建设等方面的研究范式转换。
作者贡献声明：蔡治东：设计论文框架，收集、分析实验数据，撰写、修改论文；作者贡献声明：江婉婷：收集、核实数据，修改论文；作者贡献声明：王兴：指导实验设计，修改论文。 -
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图 3 便携式近红外脑成像系统Brite24与ROIs划分
注：图3（a）中，T表示发射光极，R表示接收光极；图3（b）中，红色表示DLPFC，黄色表示VLPFC，绿色表示FPA。
Figure 3. Portable near infrared brain imaging system Brite24 and ROI setup
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图 4 实验组、对照组前后测行为学结果
注：*表示P<0.05。
Figure 4. Results of behavioral data between experimental group and control group before and after experiment
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图 5 弹力带抗阻运动对老年人HbO2信号的影响
注：通道颜色代表HbO2重复测量方差分析F值，白点代表无差异通道。
Figure 5. Effect of elastic band resistance exercise on HbO2 of the elderly
表 1 受试者基线特征
Table 1 Participant characteristics at baseline
指标 实验组（n=25） 对照组（n=27） t或χ2 P 年龄/岁 83.64±6.53 82.62±7.81 0.50 0.62 性别（男/女） 9/16 11/15 2.69* 0.11 身高/cm 158.84±8.05 158.85±6.30 −0.06 1.00 体质量/kg 58.72±10.08 61.01±9.08 −0.87 0.39 BMI/（kg·m−2） 23.18±2.82 24.25±4.02 −1.11 0.27 受教育年限 9.68±5.10 8.22±4.29 1.12 0.27 MoCA/分 22.64±4.42 21.93±5.06 1.08 0.28 体力活动水平/METs 1443.27±182.46 1310.41±159.03 0.56 0.58 6 min步行实验/m 413.63±152.19 411.28±154.66 0.09 0.93 RT0/ms 633.52±134.64 621.17±96.93 0.54 0.60 RT1/ms 778.16±168.57 752.37±196.19 0.66 0.51 RT2/ms 1005.61±229.81 916.58±279.16 1.20 0.24 ACC0 0.94±0.03 0.95±0.04 −0.26 0.79 ACC1 0.74±0.12 0.78±0.25 −0.49 0.63 ACC2 0.72±0.11 0.71±0.14 0.43 0.67 RCS0/（次·ms−1） 1.50±0.35 1.57±0.28 −0.78 0.44 RCS1/（次·ms−1） 1.02±0.39 1.14±0.45 −0.89 0.38 RCS2/（次·ms−1） 0.75±0.28 0.84±0.35 −1.06 0.29 注：数据为均值±标准差（性别除外）；*表示χ2值；RT表示反应时，ACC表示正确率，RCS表示正确反应率。 
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表 2 实验前后N-back反应时的描述性统计结果
Table 2 Descriptive statistic results of N-back reaction time before and after experiment
记忆负荷 实验组（均值±标准差） 对照组（均值±标准差） 前测 后测 前测 后测 0-back 633.52±134.64 564.16±95.72 621.17±96.93 657.85±156.24 1-back 778.16±168.57 679.47±173.46 752.37±196.19 796.69±163.07 2-back 1005.61±229.81 866.83±268.14 916.58±279.16 985.31±292.49
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表 3 实验前后N-back正确率的描述性统计结果
Table 3 Descriptive statistic results of N-back accuracy before and after experiment
记忆负荷 实验组（均值±标准差） 对照组（均值±标准差） 前测 后测 前测 后测 0-back 0.94±0.03 0.96±0.05 0.95±0.04 0.95±0.05 1-back 0.74±0.12 0.89±0.13 0.78±0.25 0.84±0.26 2-back 0.72±0.11 0.77±0.16 0.71±0.14 0.74±0.13
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表 4 实验前后N-back RCS的描述性统计结果
Table 4 Descriptive statistics of N-back RCS before and after experiment
记忆负荷 实验组（均值±标准差） 对照组（均值±标准差） 前测 后测 前测 后测 0-back 1.50±0.35 1.72±0.27 1.57±0.28 1.51±0.33 1-back 1.02±0.39 1.37±0.36 1.14±0.45 1.14±0.43 2-back 0.75±0.28 0.97±0.32 0.84±0.35 0.83±0.31
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表 5 HbO2重复测量方差分析结果
Table 5 Results of repeated measurement analysis of variance of HbO2
通道 激活脑区 效应类型 F P 偏η2 2 R-VLPFC G×T×L 8.249 0.001 0.161 6 R-VLPFC G×T×L 7.184 0.001 0.143 8 R-DLPFC G×T×L 7.419 0.001 0.147 16 L-FPA G×T×L 8.606 <0.001 0.167 17 L-DLPFC G×T×L 7.804 0.001 0.154 18 L-VLPFC G×T×L 10.932 <0.001 0.203 20 L-FPA G×T×L 11.55 <0.001 0.212 21 L-DLPFC G×T×L 9.742 <0.001 0.185 22 L-VLPFC G×T×L 12.936 <0.001 0.231 注：G表示组别，T表示时间，L表示记忆负荷。
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