Design a Rehabilitation Platform to Improve Mild Cognitive Impairment by Integrating Cognitive Training Games with Moderate-intensity Cycling Exercise
Bibliographic record
Abstract
Most studies recognized motor-cognitive dual-task training as an effective treatment to enhance neurological disorders in patients with improved motor and cognitive performance. However, the practical treatment strategy of motor-cognitive dual-task training still need to be confirmed with the clinical evidence-based support. The purpose of this study aimed to propose the optimal strategy of dual-task training using moderate-intensity stationary cycling and multidomain cognitive training for patients with MCI, and compare the post-training performance between motor, cognitive and motor-cognitive dual-task training.Materials and MethodsNineteen patients were recruited from the local rehabilitation centers in Hong Kong and were diagnosed with mild cognitive impairment (MCI). The following inclusion criteria were used: (a) total scores of Montreal Cognitive Assessment, Hong Kong version (HK-MoCA) ranging from 7th to 16th percentile stratified by age and education, (b) be able to follow the instructions and perform cycling independently, (c) free of any other neuromuscular that affect the motor performance, (d) free of any cardiovascular diseases that may have a potential risk of life during cycling. All participants were randomly allocated to the treatments of motor (N=7), cognitive (N=7) and motor-cognitive dual-task training (N=5). In this study, we proposed the rehabilitation platform integrating the stationary cycling (Monark 928E, Monark Sport & Medical, Sweden) with cognitive training games (RehaCom, HASOMED GmbH, Germany). The rehabilitation platform was equipped with optical encoder (HN3806-AB -400N, Electronic Katrangi Trading, Syria), power meter sensors (Vector™, Garmin Ltd, Taiwan) and heart rate sensor (Polar H10, Polar Electro, Finland) for real-time cycling parameters display (i.e., elapsed time, heart rate, RPM, power output, and power ratio between left and right pedals) and cardiovascular intensity monitoring. Last, performing stationary cycling concurrent with multidomain cognitive training operated by the touchscreen is the way of the treatment of motor-cognitive dual-task training. Before training, all subjects were asked to perform the Graded Exercise Test modified from YMCA Sub-Maximal Cycle Ergometer Test to determine the upper and lower boundary of workloads for maintaining the moderate-intensity cycling at pedaling rate of 50 RPM during motor and dual-task training. In this study, the definition of HRmax was calculated: 208-(0.7*age), and the moderate-intensity cycling was defined as the range from 55% to 75% HRmax. After finishing the Graded Exercise Test, all patients received 30 training sessions in the frequency of four sessions weekly. The assessments of MoCA, Trail Making Tests (TMTs), 5-Times Sit to Stand Test (5TSTS), 10-Meter Walk Test (10MWT) and 6-Minute Walk Test (6MWT) were used for evaluation. In addition, the record of grading level of the cognitive training using RehaCom software were also used to quantify the multidomain cognitive performance. Paired t-test and Kruskal-Wallis test were used to examine the outcome differences in cognitive function and functional motor performance within groups between baseline and post-assessments, and the comparisons between groups, respectively. After the Kruskal-Wallis test, the Bonferroni correction was used for post-hoc pairwise comparisons. All comparisons were performed using SPSS 22.0 software (SPSS, IBM, Armonk, New York, U.S.A.), and the statistical significance was set at α = 0.05.ResultsFor cognitive function, significant improvements in MoCA were found in cognitive and dual-task groups, and the significant differences in TMTs – part A and part B were found in cognitive group. For functional motor performance, both motor and dual-task groups showed significant improvements in 5TSTS, 10MWT and 6MWT. The greater improvement of grading level in sustain attention and logical reasoning were found in the typical subjects after receiving dual-task training than cognitive treatment. The greater slopes of grading level in sustain attention and logical reasoning were found in the typical subjects after receiving dual-task training than cognitive treatment.ConclusionsThe treatment strategy of simultaneous dual-task using moderate-intensity stationary cycling and computer-based multidomain cognitive training was proposed. The current results encouraged this treatment to be applied as the routine training in a population of MCI for restriction of cognitive decline or delayed progression to related dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".