Exercise Group In a Geriatric Psychiatry Clinic - Improving Physical Strength and Mental Health
Bibliographic record
Abstract
INTRODUCTION: Exercise has wide range of health benefits, more than any other single intervention. It improves depression and anxiety symptoms, attention and other cognitive functions. Exercise is recommended for common chronic diseases, such as diabetes, hypertension, coronary heart disease, osteoporosis, insomnia and more. Benefits to exercise include low cost, social interactions, no drug interactions or drug metabolism, improved physical health, and positive stigma. METHODS: We designed an exercise group for patients followed at our geriatric-psychiatry clinic. We recruited older adults age ≥ 60 with any type of major mental disorder, followed at the tertiary-care geriatric psychiatry clinic, Jewish General Hospital, Montreal. INTERVENTION OF EXERCISE GROUP: Aerobic and anaerobic exercise, for 50 minutes, twice a week, total of 12 weeks, at medium intensity which gradually increased. Groups included 6-12 participants every 3 months and led by an exercise instructor. We ran four groups during 2017-2018. RESULTS: We had 24 individual participants in 4 groups, among them we had 9 patients who joind the group during the first 4 weeks of the 12-week session, completed at least 75% of the exercise sessions, and completed both pre- and post questionnaire (PHQ-9) were included in the final analysis. QUANTITATIVE RESULTS: We used the PHQ-9 to examine depressive symptoms, analysis by Wilcoxon signed rank test. Pre and post intervention analysis showed improvement in depressive symptoms with a significant P=0.03 QUALITATIVE RESULTS: We conducted a focus group at the end of the fourth group. Repeated themes were: feeling more confident, stronger, more energetic, and more calm. Most patients described the exercise environment as positive, non-judgmental, and supportive. Quality of sleep during the night was not improved, though most patients mentioned feeling much less sleepy during the daytime. CONCLUSIONS: Exercise could potentially help number of outcomes for older adults with mental illness. It has been shown to significantly improve depressive symptoms, and qualitative results show improvement in general well-being. Our results and future research in this field will help establish an evidence base to tailor this promising intervention to this vulnerable population of older adults with mental illness.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".