Associations Between Behavioral Addictions and Mental Health Concerns During the COVID-19 Pandemic: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Purpose of Review The COVID-19 pandemic has promoted behavioral changes and elevated mental distress. Addictive behaviors often increased, generating mental health problems. The present study’s primary aim was to investigate associations between different types of behavioral addictions (including behavioral addictions, related conditions, and phenomena) and different types of mental health problems. The secondary aims were: (i) to identify possible sources of heterogeneity and (ii) to explore potential moderators in associations between different types of behavioral addictions (including behavioral addictions, related conditions, and phenomena) and different types of mental health problems. Recent Findings Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), studies from the period between December 2019 and May 2023 were sought from PubMed, Scopus, ISI Web of Knowledge, and Google Scholar in its first ten pages. The articles’ relevance was screened and evaluated. The included papers’ quality was assessed according to the Newcastle Ottawa Scale. Fisher’s Z scores were computed to present magnitudes of associations and I2 indices were used to estimate levels of heterogeneity in the meta-analysis. Among the 85 included studies (N = 104,425 from 23 countries; mean age = 24.22 years; 60.77% female), most were internet-related behavioral addictions, related conditions, and phenomena (28 studies on social media, 25 on internet, 23 on smartphone, and 12 on gaming). The pooled estimation of the associations showed that higher levels of behavioral addictions, related conditions, and phenomena related to internet use (regardless of type) were associated with more mental health problems (regardless of which type). Moderator analyses showed that almost no variables affected heterogeneity for the founded associations. Summary Most studies of behavioral addictions, related conditions, and phenomena focused on internet-related behaviors, with studies suggesting relationships with specific types of mental health problems during the COVID-19 pandemic. Moreover, associations between behavioral addictions (including behavioral addictions, related conditions, and phenomena) and mental health problems found in the present systematic review and meta-analysis were comparable to the associations identified in studies conducted before the COVID-19 pandemic. How to help people reduce internet-related behavioral addictions, related conditions, and phenomena and address associated mental health concerns are important topics for healthcare providers.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".