Burden of neck pain and associated factors among smart phone user students in University of Gondar, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Mobile technology has spread rapidly around the globe. In 2018 the numbers of mobile subscribers in Ethiopia hit 66.2 million. Musculoskeletal complaints related to smartphone use in different body parts have been reported ranging from 8.2% to 89.9%. Neck pain has the highest prevalence rate, which ranges from 17.3% to 67.8%. However, there is limited evidence on the burden of neck pain related to Smartphone usage and no research is done in Ethiopia. Therefore, this study was conducted to determine the burden of neck pain and factors associated with smartphone use in Ethiopia. PURPOSE: The objective of this study was to identify the prevalence and factors associated with neck pain among smartphone users at University of Gondar. METHODS: Institutional based cross-sectional study was conducted from November to December 2019 to determine the prevalence and associated factors of neck pain, with a sample of 845 university student smartphone users at University of Gondar, Ethiopia. A self-administered questionnaire adapted from the Nordic musculoskeletal questionnaire was used to collect data. Independent variables which had a significant association were identified using logistic regression models. Results were reported by using texts and frequency distribution tables. RESULTS: Out of 845 questionnaires distributed, 808 students responded; hence, the response rate was 95.6%. The overall prevalence of neck pain among smart phone users in the past 12 months was 47.4% (95% CI, 44.1-50.9%). Attending 5th year (AOR: 3.907, 95% CI: 1. 952-7.82) and 6th year (AOR: 2.93,95% CI: 1,304-6.59), regular physical exercise (AOR: 2.405, 95% CI: 1.549-3.734), cigarette smoking (AOR: 5.415, 95% CI: 2.685-10.919), residency (AOR: 1.681, 95% CI: 1.181-2.391), break while using smartphone (AOR: 3.253 95% CI: 2.252-4.699), used smartphone > 6 hour per day (AOR: 2.782 (1.528 95% CI: 1.528-5.063), used other devises (AOR: 3.158 95% CI: 2.128-4.689), number of social media used daily (AOR: 2.007 95% CI: 1.228-3.2788), used devise for playing game (AOR: 1.484 95% CI: 1.024-2.15) were factors significantly associated with neck pain. CONCLUSION: The current study depicted that nearly half of the study participants reported neck pain in the past 12 months. Attending last year of university, personal characteristics, use of smart phone for longer period, playing game, not taking break, other electronic device use, increased number of social media use were associated with neck pain among smartphone users.
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 | 0.000 |
| 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".