Sensory focused exercise improves anxiety in Parkinson’s disease: A randomized controlled trial
Bibliographic record
Abstract
Anxiety has been implicated as one of the greatest influences on quality of life in Parkinson's disease (PD). The etiology of anxiety is unclear, although previous work suggests that anxiety may be linked to sensory deficits that cause uncertainty in movement. Thus, the current study examined whether focusing attention on sensory feedback during goal-based exercise has the potential to provide benefits to anxiety in PD. Thirty-five participants with PD were randomized to either a Sensory Attention Focused Exercise (SAFEx) (i.e. internal focus of attention, n = 18) or Sham Exercise control (i.e. external focus of attention, n = 17) and completed 33 one-hour attention-based exercise sessions over 11-weeks. Before and after the program (pre and post), participants completed the Parkinson Anxiety Scale (PAS) questionnaire. The PAS includes three anxiety sections: persistent, episodic, and avoidance. Changes in the total PAS score and within each section of the PAS were subjected to two-factor mixed repeated measures ANCOVA. Significant group by time interactions demonstrated that from pre to post, total PAS scores (p = 0.007) and episodic anxiety scores (p = 0.010) significantly decreased in the SAFEx group only (ΔTotal PAS = -5.2, F(1,27) = 5.41, p = 0.028, ηp2 = 0.17; ΔEpisodic Score = -1.8, F(1,27) = 6.89, p = 0.014, ηp2 = 0.20). In conclusion, focusing attention on sensory feedback while completing goal-based exercises may provide significant benefits to improving anxiety in PD. As such, sensory attention focused exercise may be a critical adjunct therapy for improving anxiety, and ultimately quality of life in people with PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".