OLDER ADULT PERSPECTIVES ABOUT ONLINE EXERCISE CLASSES DURING THE COVID-19 PANDEMIC AT MULTIPLE TIME POINTS
Bibliographic record
Abstract
Abstract Engaging in physical activity can bring health benefits for older adults. However, during the pandemic the availability of in-person exercise classes has been sporadic. As such, online exercise programs have become more common. This research had the goal of exploring the uptake of online exercise programs by older adults in Manitoba, Canada in the first few months in the pandemic and then more than 1.5 years into the pandemic. Older adults (65 years and older) were recruited via emails from a variety of community organizations. Participants completed anonymous online surveys in summer 2020 (n=678) and fall 2021 (n=570). Less than 50% of respondents reported participating in online exercise classes during the pandemic in both surveys. For both surveys, pre-recorded classes were the most common, however, this decreased from 80% in the first survey, to 57% in the second survey. Conversely, live classes where the instructor could see the participants increased from 17% in the 2020 survey, to 47% in the 2021 survey. Additionally, platform use shifted from YouTube as the most popular in the first survey, to Zoom in the second survey. Most of the online classes originated from their local communities. Of those who participated in online exercise early and later in the pandemic, about two thirds reported that they would continue online exercise classes outside of the pandemic. A major reason for not participating was because they enjoy the social aspect of in-person classes. The perspectives of the study participants will be valuable for policymakers, programmers, and instructors.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".