The correlation between academic stress, sleep quality, and acne severity: a longitudinal cohort study

Objective: To evaluate the relationship between acne severity, sleep quality, and stress levels in undergraduate students and to create a predictive model for acne severity. Methods: This longitudinal cohort study was conducted at Isra University Hyderabad on 150 acne-prone undergraduate students. The Perceived Stress Scale, Sleep Quality Scale, and Comprehensive Acne Severity Scale were used for data collection during the non-examination and examination periods, respectively. Results: The mean age of the study participants was 20.08±1.42 years. A statistically significant rise was observed in perceived stress (p<0.01), sleep quality (p<0.01), and acne severity (p<0.01) during the examination period. A statistically significant association of acne severity was observed with male gender (X2-9.51, p<0.05). A significant positive correlation of acne severity was observed with perceived stress (r=0.95, P<0.01) and sleep quality (r=0.95, P<0.01). Among predictors of acne severity, age (p<0.05), stress score (p<0.05), and sleep score (p<0.05) were found to be significant predictors whereas BMI was found to be non-significant (p>0.05). Conclusion: Acne is correlated with higher stress levels and poor sleep quality. Young age, high stress, and poor sleep are significant predictors of acne severity. This makes acne breakouts common among students, particularly during periods of examinations.

Institutions

Publication Details

Journal
Khyber Medical University Journal
Published
2025-06-30
DOI
https://doi.org/10.35845/kmuj.2025.23614
Primary Topic
Vitamin C and Antioxidants Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The correlation between academic stress, sleep quality, and acne severity: a longitudinal cohort study

Khyber Medical University Journal
Vitamin C and Antioxidants Research
article

The correlation between academic stress, sleep quality, and acne severity: a longitudinal cohort study

article en

Abstract

Objective: To evaluate the relationship between acne severity, sleep quality, and stress levels in undergraduate students and to create a predictive model for acne severity. Methods: This longitudinal cohort study was conducted at Isra University Hyderabad on 150 acne-prone undergraduate students. The Perceived Stress Scale, Sleep Quality Scale, and Comprehensive Acne Severity Scale were used for data collection during the non-examination and examination periods, respectively. Results: The mean age of the study participants was 20.08±1.42 years. A statistically significant rise was observed in perceived stress (p<0.01), sleep quality (p<0.01), and acne severity (p<0.01) during the examination period. A statistically significant association of acne severity was observed with male gender (X2-9.51, p<0.05). A significant positive correlation of acne severity was observed with perceived stress (r=0.95, P<0.01) and sleep quality (r=0.95, P<0.01). Among predictors of acne severity, age (p<0.05), stress score (p<0.05), and sleep score (p<0.05) were found to be significant predictors whereas BMI was found to be non-significant (p>0.05). Conclusion: Acne is correlated with higher stress levels and poor sleep quality. Young age, high stress, and poor sleep are significant predictors of acne severity. This makes acne breakouts common among students, particularly during periods of examinations.

Khyber Medical University Journal
Isra University (PK)
Openalex Percentile: Top 81%
Vitamin C and Antioxidants Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.