Is the time spent on smartphones associated with an increased risk of high blood pressure? A Review
Bibliographic record
Abstract
Smartphone use is associated with poor sleep quality, rumination, anxiety, sedentarism, forward posture, and social isolation. In turn, these health outcomes had also been linked to either increased or decreased levels of blood pressure. We aimed to review the extant literature that studied the acute and chronic effects of smartphone use on blood pressure to determine whether a direct link has found either a positive or negative correlation between smartphone use and hypertension. We searched Medline, Embase, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), OpenGrey.eu, and reference lists of included studies. We also used Google Scholar for article chasing. Because this is the first review that we are aware of, our search included any study design. We found five studies: four cross-sectional in adolescents (12-18 years) and a case-crossover study in university students (18-30 years). None of the studies reported the exposure as a protective factor for hypertension, two of them reported no difference between compared groups, and three reported higher levels of blood pressure associated with increased smartphone use. The pooled evidence is generally of low‐quality and not able to adequately answer the question as to what the effect of smartphone use in blood pressure is. Good‐quality trials addressing this question are needed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".