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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".