Effects of prolonged engagement in alternating video games and smartphone use on muscle endurance and physical activity intentions in young healthy adults
Bibliographic record
Abstract
Recent studies suggest a negative effect of acute smartphone use or video games on decision-making, psychomotor and maximal force production performances. This study aimed to test the effect of alternating video games and smartphone scrolling on social networks on subsequent muscle endurance performance and physical activity intention in young adults.Thirty-eight young adults (13 female; 19±1 y.o.) participated in this study. Participants engaged either in 90 minutes, alternating between video gaming and scrolling on smartphone (VGSS - experimental condition) or watching documentaries (control condition). Participants rated the perceived workload of each task and their mental fatigue, motivation and boredom during the VGSS or documentary watching. Cognitive performance and physical activity intention were measured pre-post VGSS and documentary watching. Then, participants performed a knee extensors endurance task until exhaustion at 20% of their maximal voluntary peak force, to measure muscle endurance performance. Participants perceived the VGSS as more demanding than the documentary condition. However, only a slight increase in perceived mental fatigue was highlighted and no difference between conditions in cognitive performance was observed. Muscle endurance performance did not differ after VGSS or documentary and physical activity intention decreased regardless of the condition.Engaging in VGSS did not induce a specific state of mental fatigue, thus explaining the absence of impaired muscle endurance performance, compared to watching a documentary. A negative impact on physical activity intentions was observed following both VGSS and documentary watching, suggesting that simply sitting in front of a screen impairs intentions to be active.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".