Social Media Use and Jaw Motor Activity: Insights From Electromyography and Self‐Report Data
Bibliographic record
Abstract
BACKGROUND: Oral behaviors (OBs) are recognized risk factors for temporomandibular disorders. Social media use has been reported to be linked with stress, a key regulator of jaw motor activity. Whether social media use increases the incidence of OBs remains undetermined. OBJECTIVE: To investigate the impact of social media use on jaw motor activity. METHODS: Seventy-two individuals (36 females, 36 males; 22.4 ± 2.8 years) completed an online survey assessing their screen habits and OBs using the oral behaviour checklist (OBC). A subset of 30 healthy participants (15 females, 15 males; 22.2 ± 2.0 years) completed an in-person experimental session involving three 30-min tasks (silent reading, using social media and watching a documentary on TV). The electromyographic (EMG) activity of the right masseter was measured throughout the session. OBs were identified as events with EMG amplitude exceeding 10% of the participants' maximum voluntary contraction (MVC) lasting at least 2 s. General linear and mixed-effect models were used to test relationships between screen habits, OBC scores, and task-related EMG metrics. RESULTS: Mean ± SD daily social media use was 154 ± 73 min on weekdays and increased to 185 ± 85 min on weekends (p < 0.001). Time spent on social media was a significant but weak predictor of OBC scores (B = 0.042, SE = 0.014, t = 3.009; 95% CI = 0.014-0.070; p = 0.004). No significant differences in OB frequency, duration, or amplitude were observed across experimental tasks (all p > 0.05). CONCLUSION: While social media use was modestly associated with self-reported OBs, it did not significantly affect jaw motor activity in a controlled experimental setting. Further studies using ambulatory EMG recordings are recommended.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".