Effects of Applying Technological Exercise Training to Improve the Physical and Cognitive Functions for the Elderly
Bibliographic record
Abstract
[[abstract]]目的:為探討高齡科技運動訓練之介入效益。方法:徵召65歲以上能獨立行走之社區女性高齡者60位，以準實驗設計進行介入：實驗組(30位)接受智能運動地墊訓練，包括有氧運動、肌力訓練、伸展、平衡協調敏捷等單人與雙人或團體多元運動模式，結合認知訓練(包括記憶力、反應力、注意力)；控制組(30位)接受傳統社區團體運動及認知課程訓練。於十週(每週1次、每次120分鐘)的課程前後，以功能性體適能檢測、MoCa (Montreal cognitive assessment)評估量表、雙項任務行走測試(走路加端裝水盤子、走路加算數)、跌倒風險評估問卷，並以SPSS 23.0進行統計分析比較。以描述性統計呈現研究對象基本資料及量測資料，並且以二因子混合設計變異數分析比較組間介入效益之差異。統計顯著水準設定為α=.05。結果發現實驗組在下肢肌力(p=0.004)、下肢柔軟度(p<0.001)、動態平衡與敏捷能力(p<0.001)及認知雙向任務行走能力(p=0.003)有顯著進步，控制組在訓練後MoCA認知分數顯著提升，且控制組的改善顯著優於實驗組(p<0.009)。智能運動地墊訓練能有效改善高齡者的功能性體適能狀況，及雙向任務行走能力。期許未來能夠推廣相關訓練，使高齡者更健康與體能的提升。本研究之成果可作為高齡者訓練課程的參考。 The purpose of this study was to explore the training effects of applying technological exercises on physical and cognitive functions for the elderly. This was a quasi-experimental, non-randomized controlled intervention study. We recruited 60 females aged 65 years or older from local community activity centers who were able to walk independently without any assistive devices. The experimental group (n=30) received smart mat exergames with simultaneous cognitive-physical training while the control group (n=30) underwent a traditional multicomponent exercise intervention focused on physical and cognitive training. Two-hour training session per week over 3 months was applied. Functional fitness, Montreal cognitive assessment (MoCA), dual-task walking with counting and carrying a tray with water, fall risk questionnaire were assessed before and after interventions. Statistical analyses were performed using SPSS 23.0 for Windows. Descriptive statistics were used to determine the participants’demographics and their performances. A two-way mixed design ANOVA was used to determine the pre- and post-test differences between the experimental and control group in all outcome variables of interest. The level of significance was set at α=0.05. The results showed that the experimental group had significant improvements in the lower limb muscle strength (p = 0.004), lower limb flexibility (p<0.001), dynamic balance and agility (p <0.001), and cognitive dual task walking ability (p = 0.003). After training, the MoCA cognitive score was significantly improved, and the improvement in the control group was significantly better than that in the experimental group. Smart mat exergames training can effectively improve the functional fitness of the elderly and their ability to walk. It is hoped that the training can be promoted in the future, so that the elderly can improve their health and physical fitness. The results of this study can be used as a reference for the elderly training courses
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".