Prevalence and demographic, substance use, and mental health correlates of fasting among U.S. college students
Bibliographic record
Abstract
BACKGROUND: Fasting is an unhealthy behavior that has been frequently used as part of weight loss attempts. To date, little research has been conducted to determine the prevalence and substance use and mental health correlates of fasting among college students. Therefore, the aim of this study was to estimate the prevalence and associations between any (≥ 1 time) and regular (≥ 13 times) occurrences of fasting in the past 4 weeks and substance use and mental health correlates among a large sample of college students from 2016 to 2020. METHODS: Data from four academic survey years (2016-2020; N = 8255) of the national (USA) Healthy Minds Study were analyzed. Unadjusted prevalence of any and regular fasting by survey year and gender was estimated. Multiple logistic regression analyses were conducted to estimate the associations between any and regular fasting and the demographic (age, body mass index, race/ethnicity, sexual orientation, highest parental education), substance use (cigarette use, marijuana use, other illicit drug use, alcohol use), and mental health (depression, anxiety, eating disorder symptoms, suicidal ideation, non-suicidal self-injury) correlates. RESULTS: Any fasting in the past 4 weeks was common among both men (14.77%) and women (18.12%) and significantly increased from 2016 (10.30%) to 2020 (19.81%) only among men. Regular fasting significantly increased among both men and women from 2016 (men: 1.46%; women: 1.79%) to 2020 (men: 3.53%; women: 6.19%). Among men and women, both any and regular fasting in the past 4 weeks were associated with higher odds of all mental health symptoms, including a positive depression, anxiety, and eating disorder screen, suicidal ideation, and non-suicidal self-injury. Among women, but not men, any and regular fasting in the past 4 weeks were associated with higher odds of marijuana use and other illicit drug use (e.g., cocaine, ecstasy). CONCLUSIONS: The results from this study underscore both the high and increasing prevalence of fasting among a national sample of college students, as well as the substance use and mental health symptoms associated with this behavior. Healthcare professionals both on and off campus should consider screening for fasting behaviors among college students and provide appropriate intervention when 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".