PHYSICAL TRAINING FOR PARKINSON’S DISEASE: PILOT STUDY
Bibliographic record
Abstract
Jacilyn Olson, Linda Sealy-Holtz, Chris Ahrens & Kyle Covey University of Central Oklahoma, Edmond, Oklahoma. Due to the variability of symptoms caused by Parkinson’s disease (PD), many different types of therapies are employed to combat this chronic condition, often placing a great time burden on patients and their families. Targeted exercise is important in order to maintain functional ability and improve muscular strength and endurance. Purpose: Therefore, the primary purpose of this study was to report changes in pre-to-post testing outcomes on measures of fitness and balance as the result of a combined physical fitness and speech therapy treatment program for individuals with PD. METHODS: A group of eight individuals with PD (58-82yrs) volunteered for this study. Participants’ initial functional fitness was measured by performance on the Senior Fitness Test (SFT). Initial balance was measured using the MINI-BEST Test (MBT). After initial measurement, a group training program consisting of 60-minute sessions, three times a week for four weeks was administered. Protocol consisted of a warm up, strength and endurance exercises, static and dynamic balance training, and flexibility/cool down. Voice training was administered simultaneously. Modifications were included for individuals to maintain own pace while partaking in group activities. Upon program completion, the SFT and MBT were again assessed to monitor progress. RESULTS: See Table 1 (attached). CONCLUSION: Subjects showed improvement in measurements of fitness and balance. More research is needed to determine how much of these changes can be attributed to the combination of voice and physical fitness training as opposed to training for voice and physical fitness separately. Based on the statistical data and participant feedback, this research will continue to be explored. Supported by UCO Interdisciplinary Grant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".