Neck pain, disability and mobile phone usage among physiotherapy students – a cross-sectional study
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Modern mobile devices have become tools used for educational, research, business or recreational purposes. Incorrect position during excessive use of a smartphone can lead to biomechanical changes, the most visible of which is the position of the head in protraction, characterized by the protrusion of the head and neck forward in relation to the shoulder girdle and trunk. The aim of the study was to analyze the relationship between disability, neck pain (NP), use of phones before bedtime, and hours of using smartphones. MATERIAL AND METHODS: The study involved 146 physiotherapy students aged 18-26. Students were asked to complete pain surveys (VAS pain scale), and Neck Disability Index (NDI). Participants were also asked if their pain lasted longer than 3 months, how long they used their smartphone during the day, and whether they used it before bedtime. RESULTS: Statistically significant differences were found between groups with and without neck pain regarding NDI score (p<0.001). Participants who suffered from neck pain longer than 3 months had greater NDI scores (p=0.03), greater intensity of symptoms (p=0.04), greater problems with reading (p<0.01) and driving (p=0.04) than participants who experienced pain for less than 3 months. Using phones before bedtime was related to problems with focusing (p<0.01). There were statistically significant correlations between the time of phone use and disability in terms of reading (p=0.04), focusing (p<0.001), work (p<0.001) and sleeping (p=0.02). CONCLUSIONS: Dysfunctions associated with pain in the cervical section may affect the learning abilities of students and thus the acquisition of professional competencies. Not using a smartphone before bedtime is recommended, as it causes poorer concentration. The longer the time spent using the phone, the more significant the disability.
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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 | 0.001 |
| 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".