The Change in Exergaming From Before to During the COVID-19 Pandemic Among Young Adults: Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Exergaming may be an important option to support an active lifestyle, especially during pandemics. OBJECTIVE: Our objectives were (1) to explore whether change in exergaming status (stopped, started or sustained exergaming, or never exergamed) from before to during the COVID-19 pandemic was related to changes in walking, moderate-to-vigorous physical activity (MVPA) or meeting MVPA guidelines and (2) to describe changes among past-year exergamers in minutes per week exergaming from before to during the pandemic. METHODS: A total of 681 participants (mean age 33.6; SD 0.5 years; n=280, 41% male) from the 22-year Nicotine Dependence in Teens (NDIT) study provided data on walking, MVPA, and exergaming before (2017 to 2020) and during (2021) the COVID-19 pandemic. Physical activity (PA) change scores were described by change in exergaming status. RESULTS: We found that 62.4% (n=425) of the 681 participants never exergamed, 8.2% (n=56) started exergaming during the pandemic, 19.7% (n=134) stopped exergaming, and 9.7% (n=66) sustained exergaming. Declines were observed in all 3 PA indicators in all 4 exergaming groups. The more salient findings were that (1) participants who started exergaming during COVID-19 reported the highest MVPA levels before and during the pandemic and declined the least (mean -35 minutes/week), (2) sustained exergamers reported the lowest MVPA levels during the pandemic (median 66 minutes/week) and declined the most in MVPA (mean change of -92 minutes/week) and in meeting MVPA guidelines (-23.6%). During the pandemic, starting exergamers reported 85 minutes of exergaming per week and sustained exergamers increased exergaming by a median 60 minutes per week. CONCLUSIONS: Although starting and sustaining exergaming did not appear to help exergamers maintain prepandemic PA levels, exergaming can contribute a substantial proportion of total PA in young adults and may still represent a useful option to promote PA during pandemics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".