Association of Smartphone Overuse and Neck Pain: A Systematic Review and Meta-Analysis of Over 10,000 individuals. (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Smartphone overuse is associated with both psychological and physical health problems, including depression and musculoskeletal disorders. However, the association between smartphone overuse and neck pain remains unclear. </sec> <sec> <title>OBJECTIVE</title> We performed a meta-analysis to examine the relation between smartphone overuse and neck pain, and to identify high-risk usage patterns. </sec> <sec> <title>METHODS</title> PubMed, Embase and Cochrane CENTRAL databases were searched for relevant studies published up to August 16, 2023, using keywords including "smartphone" and "neck pain". Prospective, retrospective, and case-controlled studies assessing the correlation between smartphone overuse and neck pain were eligible. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Meta-regression and sensitivity analysis using the leave-one-out approach were performed to test the robustness of the results. </sec> <sec> <title>RESULTS</title> Seven retrospective studies including 10,715 participants were included in the systematic review and meta-analysis. The mean age of the participants ranged from 19.9 to 42.9 years. The meta-analysis revealed that compared to participants without smartphone overuse, those who overused a smartphone had a significantly higher risk of neck pain (pooled adjusted odds ratio [aOR] = 2.34, 95% CI: 1.44-3.82). A significant correlation between increasing age and higher ORs for neck pain was found (coefficient = 0.051, P < 0.001). </sec> <sec> <title>CONCLUSIONS</title> These results indicate a significant association between smartphone overuse and increased risk of neck pain, with risk escalating with age. Our findings underscore the necessity of addressing smartphone overuse as a health concern, especially considering its growing prevalence in modern society. </sec>
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".