Natural course of behavioral addictions: a 5-year longitudinal study
Bibliographic record
Abstract
BACKGROUND: Resolving the theoretical controversy on the labeling of an increasing number of excessive behaviors as behavioral addictions may also be facilitated by more empirical data on these behavioral problems. For instance, an essential issue to the classification of psychiatric disorders is information on their natural course. However, longitudinal research on the chronic vs. episodic nature of behavioral addictions is scarce. The aim of the present study, therefore, was to provide data on prevalence, substance use comorbidity, and five-year trajectories of six excessive behaviors-namely exercising, sexual behavior, shopping, online chatting, video gaming, and eating. METHODS: Analyses were based on the data of the Quinte Longitudinal Study, where a cohort of 4,121 adults from Ontario, Canada was followed for 5 years (2006 to 2011). The response rate was 21.3%, while retention rate was 93.9%. To assess the occurrence of each problem behavior, a single self-diagnostic question asked people whether their over-involvement in the behavior had caused significant problems for them in the past 12 months. To assess the severity of each problem behavior reported, the Behavioral Addiction Measure was administered. A mixed design ANOVA was used to investigate symptom trajectories over time for each problem behavior and whether these symptom trajectories varied as a function of sex. RESULTS: The large majority of people reported having problematic over-involvement for just one of these behaviors and just in a single time period. A main effect of time was found for each problem behavior, indicating a moderately strong decrease in symptom severity across time. The time x sex interaction was insignificant in each model indicating that the decreasing trend is similar for males and females. The data also showed that help seeking was very low in the case of excessive sexual behavior, shopping, online chatting, and video gaming but substantially more prevalent in the case of excessive eating and exercising. CONCLUSIONS: The present results indicate that self-identified excessive exercising, sexual behavior, shopping, online chatting, video gaming, and/or eating tend to be fairly transient for most people. This aspect of the results is inconsistent with conceptualizations of addictions as progressive in nature, unless treated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".