A LIVE ONLINE EXERCISE PROGRAM FOR OLDER ADULTS’ IMPROVED DEPRESSIVE SYMPTOMS: A PILOT RCT
Bibliographic record
Abstract
Abstract Exercise improves mental health and effectively alleviates cognitive and physical declines. Unfortunately, engagement in physical activity decreases as individuals age and this was likely exacerbated by the COVID-19 pandemic. New technologies to deliver live online home-based group exercise classes may help mitigate mental and physical health declines in older adults while maintaining social connectivity. We evaluated the feasibility of an age-appropriate and ability-modified at-home exercise program via live video stream. The impact on loneliness, anxiety, and depression in older adults were exploratory outcomes. In this two-arm pilot RCT, we randomly assigned sedentary community-dwelling adults (65-80 years) to a waitlist control (CON) or an active group (ACTIVE) of thrice-weekly, 8-wk online live exercise program delivered via Zoom by trained exercise professionals. Attendance was recorded, and participant satisfaction to ACTIVE was assessed. Pre- and post-intervention loneliness, anxiety, and depression were collected using the revised UCLA Loneliness Scale (R-UCLA), the Geriatric Anxiety Inventory (GAI), and the Geriatric Depression Scale (GDS). 32 participants were randomized (ACTIVE: n=16, mean age 70 ± 4, 69% women, 30 ± 5 kg/m2; CON: n=16, mean age 71 ± 5; 88% women; 29 ± 5 kg/m2). Attendance to online classes was >80% and all ACTIVE participants reported being satisfied with the exercise sessions. There was no intervention effect compared to CON on loneliness and anxiety. An effect of the intervention was observed for depression (ACTIVE: -1.94; CON: -0.07; p=0.015). We demonstrated good feasibility, satisfaction, and preliminary efficacy of a live online exercise program on older adults’ mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.006 | 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".