A LIVE ONLINE EXERCISE INTERVENTION FOR OLDER ADULTS: A MIXED METHODS RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
Abstract Regular physical activity (PA) and exercise alleviate mental health and age-related physical declines in older adults; however, strategies are needed to help older adults engage in PA and exercise. Live online video may be a viable mode of delivery for exercise programs. Thirty-one sedentary community-dwelling older adults (65-80 years) were randomized to an 8-week live online exercise program (ACTIVE, n=16; age 70±4 years; 69% women) or waitlist control (CON, n=15; age 72±5 years; 87% women) group. Quantitative effectiveness data were collected pre-and post-intervention and included accelerometry-derived PA levels and mental health (depression, anxiety, and loneliness). Feasibility outcomes included attendance and participant satisfaction. Semi-structured interviews were optional for all participants after completing the online exercise program (n=22; ACTIVE=12; CON=10; 73% women). The ACTIVE group attended 97% of the online classes, and 98% reported satisfaction with the program. Post-intervention, there were no differences between groups on daily steps (ACTIVE=4336 ± 2638 steps/d; CON=3125 ± 1250 steps/d), daily activity energy expenditure (ACTIVE=227 ± 249 kcal/d; CON=119 ± 166 kcal/d), and moderate-to-vigorous physical activity (ACTIVE=42 ± 44 min/d; CON=23 ± 30 min/d). An effect of the intervention was observed in the ACTIVE group for symptoms of depression (3.9±2.4 to 2.0±1.7; p=0.015). There was no intervention effect compared to CON on anxiety or loneliness. Common themes identified from qualitative interviews included satisfaction with the program and perceived health improvements. A live online exercise program was a feasible approach to delivering physical activity to older adults.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".