Acne-related quality of life and mental health among adolescents: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Acne vulgaris is one of the most common skin conditions worldwide among adolescents. Beyond its physical manifestations, acne can leave invisible psychological scars. OBJECTIVES: We aimed to examine the protective and risk factors of acne-related quality of life and its association with mental health outcomes. METHODS: The analysis included data collected in 2023 from adolescents enrolled in the SEROCoV-KIDS population-based cohort. By combining the Acne Severity and Acne-Specific Quality of Life (Acne-QoL) scales, the following three groups were established: Acne-LowAQoL (adolescents with acne and low acne-related quality of life), Acne-HighAQoL and NoAcne-HighAQoL. We used multinomial and logistic regression to assess the association between health behaviours and mental health outcomes in these groups. RESULTS: Among 335 adolescents [mean age 16.1 years (SD 1.8), 56% female sex], 65 (19.4%) reported experiencing acne while maintaining a high Acne-QoL, 26 (7.7%) reported having acne and a low Acne-QoL, and 244 (72.9%) reported having nearly no acne. Low engagement in physical activity [adjusted odds ratio (aOR) 0.30, 95% confidence interval (CI) 0.12-0.77], addictive use of social media (aOR 3.78, 95% CI 1.60-8.96), and prolonged screen time (aOR 2.99, 95% CI 1.26-7.08) were independently associated with Acne-LowAQoL. Conversely, those from the group, Acne-HighAQoL, reported higher social support (aOR 1.95, 95% CI 1.07-3.54). Adolescents with Acne-LowAQoL showed lower levels of self-esteem, resilience and increased psychological distress. CONCLUSIONS: Among adolescents with acne, physical activity and social support were positively associated with good acne-related quality of life, which translated into better mental health. In contrast, screen time and social media use notably worsened mental health. Dermatologists should incorporate these considerations into clinical practice to ensure effective patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".