Retracted: Effects of Tai Chi on Cognitive Function in Older Adults With Type 2 Diabetes Mellitus: Randomized Controlled Trial Using Wearable Devices in a Mobile Health Model
Post-publication record
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Bibliographic record
Abstract
Background: Telemedicine is an effective and promising strategy, especially for the initial stages of a home-based therapeutic exercise program. Objective: The objectives of this study were 2-fold: first, to assess whether Tai Chi practice combined with wearable device-based monitoring improves cognitive function in this population, and second, to explore the underlying mechanisms for any improvements observed, including changes in physical activity levels and sleep patterns. Methods: The study was a randomized controlled trial in which participants were randomized (1:1:1) to receive usual care, fitness walking, or Tai Chi exercise. All indicators were assessed at baseline and 12-week follow-up. The usual care includes traditional diabetes education. Participants in the fitness walking group performed walking exercises on a treadmill under the supervision of a researcher 3 times a week for 12 weeks. Participants in the Tai Chi group practiced 24-style Simplified Tai Chi through live video streaming under the guidance of professors and professionals. In this 12-week program, participants underwent continuous glucose monitoring (CGM) using Guardian Sensors 3, CGM sensors attached to the upper arm. All participants carried bracelets to record their heart rate, sleep parameters, and steps. The primary outcome was the Montreal Cognitive Assessment (MoCA) at 12 weeks. Secondary outcomes included other cognitive subdomain tests and blood metabolic indices. The MoCA is a tool designed for rapid screening for mild cognitive impairment (MCI) and early dementia, with the core advantage of being more sensitive to early cognitive problems. The MoCA has a total score of 30. Lower scores may indicate the presence of cognitive dysfunction. Results: After 12 weeks of intervention, the Tai Chi exercise group showed a significant improvement in MoCA scores from baseline (mean difference 23.83, 95% CI 17.79-25.66 vs 21.42, 95% CI 17.11-24.74; P=.03). The fitness walking exercise group showed an improvement in MoCA scores (22.94, 95% CI 18.05-23.98 vs 21.58, 95% CI 17.35-24.12; P.08), but this did not reach statistical significance. Furthermore, there was a statistical difference in the improvement of MoCA scores between the Tai Chi and fitness walking groups (2.65, 95% CI 0.34-4.41 vs 1.44, 95% CI 0.89-2.87; P<.05). The usual care group showed the least change in score at both points (0.23, 95% CI -0.02 to 1.39; P=.83). Compared with the MQ in the fitness walking group (91.93, 95% CI 77.83-97.47) vs 88.62, 95% CI 77.14-95.84; P=.45), Trail Making Test Part B (TMT-B) (220.81, 95% CI 210.03-233.49 vs 223.66, 95% CI 215.04-230.27; P=.33), the Tai Chi group was more effective in improving the MQ (99.23, 95% CI 80.55-107.69 vs 89.23, 95% CI 78.16-96.08; P=.001), TMT-B (207.33, 95% CI 200.26-220.82 vs 225.58, 95% CI 214.12-234.94; P=.001) scores, and there were significant differences between the two groups. Conclusions: In summary, this study demonstrated that web-based exercise therapy for patients may enhance the effectiveness of exercise therapy in improving cognitive function among older individuals with type 2 diabetes mellitus. Tai Chi has significant advantages in improving cognitive function and sleep quality, while fitness walking, although also beneficial, is relatively weak in these areas.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".