0511 Patterns of Concomitant Over-the-Counter, Natural Product and Prescription Sleep Aid Use: A Population-Based Study
Bibliographic record
Abstract
Abstract Introduction Despite limited evidence, over-the-counter medications (OTC) and natural products (NP) are increasingly combined with prescribed medications (Rx) to manage insomnia symptoms. Self-medication patterns are expected to be heterogenous and may predispose individuals to inappropriate medication-taking behaviors, but little is known about the usage trajectories of sleep aids. This study investigates patterns of concomitant NP, OTC and Rx use in a Canadian population-based sample. Methods Data were derived from a longitudinal study on the natural history of insomnia. Participants were 3416 adults (62% female, Mage = 49.7, MInsomnia Severity Index= 8.4). Self-reported data for OTC, NP and Rx use in the last year (yes/no) was extracted at 0-, 6- and 12-month follow-up. A Latent Class Growth Curve Analysis was conducted to identify patterns of concomitant sleep aid use. Participants also completed a battery of clinical measures including the Ford Insomnia Response to Stress Test, Dysfunctional Beliefs and Attitudes about Sleep scale (16-item), Beck Depression Inventory, Insomnia Severity Index and the Pittsburgh Sleep Quality Index. Preliminary associations between class membership and baseline covariates were evaluated using the χ 2 test or a one-way ANOVA. Sampling weights were applied to all analyses, adjusting for partial non-response. Results Analyses revealed a 6-class solution; each class reflected a preferential agent(s) choice, which remained stable over 12-months: Minimal Use (74.5%), Rx-Dominant (11.3%), NP-Dominant (6.3%), OTC-Dominant (4.3%), Rx-NP-Dominant (2.4%), and Rx-OTC-Dominant (1.1%). Classes with prominent prescribed agent use were older [F(5, 207.6) =27.2, p<0.001], more likely to seek help [χ 2(5, n=2977) =653.1, p< 0.001] and consume alcohol [χ 2(5, n=2968) =49.2, p< 0.001]. Clinically, these individuals reported greater stress reactivity [F(5, 2966) =48.4, p< 0.001], depressive symptoms [F(5,197.4) =32.0, p<0.001], dysfunctional sleep beliefs [F(5, 2987) =54.3, p< 0.001], insomnia severity [F(5, 2983) =88.4, p< 0.001] and poorer sleep quality [F(5, 203.8) =124.2, p< 0.001]. Conclusion A majority of adults used agents minimally. Stability of medication-taking patterns suggests that individuals adopt less sporadic approaches when combining sleep aids than previously assumed. Clinical profiles and sleep aid choice could pre-empt vulnerabilities to inappropriate self-medication. Support Research supported by a grant from the Canadian Institutes of Health Research (MOP#115103)
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".