» Articles » PMID: 34089619

Patterns of Concomitant Prescription, Over-the-counter and Natural Sleep Aid Use over a 12-month Period: a Population Based Study

Overview
Journal Sleep
Specialty Psychiatry
Date 2021 Jun 5
PMID 34089619
Citations 2
Authors
Affiliations
Soon will be listed here.
Abstract

Study Objectives: Concomitant patterns of sleep aid use may provide insight for understanding the transition to chronic sleep medication use. Therefore, we sought to characterize the trajectories of concomitant natural product (NP), over-the-counter (OTC), and prescribed (Rx) sleep aid use in a population-based sample over 12-months.

Methods: Self-reported data on the use of NP, OTC, and Rx sleep aids were extracted from a Canadian longitudinal study on the natural history of insomnia (N = 3416, M age = 49.7 ± 14.7 years old; 62% women) at baseline, 6-month, and 12-month. Latent class growth modeling was used to identify latent class trajectories using MPlus Version 7. Participants completed a battery of clinical measures: Ford Insomnia Response to Stress Test, abbreviated Dysfunctional Beliefs and Attitudes about Sleep Scale, Beck Depression Inventory, Insomnia Severity Index and, the Pittsburgh Sleep Quality Index. Associations between class membership and baseline covariates were evaluated.

Results: Concurrent sleep aid use fell into six distinct latent class trajectories over a 12-month period: 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%). The three latent classes with prominent prescribed agent use predicted greater incidence of healthcare professional consultations for their sleep (p < 0.05), poorer sleep quality (p < 0.001), elevated dysfunctional sleep beliefs (p < 0.001), and sleep reactivity (p < 0.001). Compared to the other four latent classes, clinical profiles of Rx-NP-dominant and Rx-OTC-dominant groups endorsed greater severity across measures.

Conclusions: Patterns of sleep aid use may provide insight for identifying individuals who may be vulnerable to inappropriate self-medicating practices.

Citing Articles

Reliability and validity of the cancer-related dysfunctional beliefs and attitudes about sleep scale in cancer patients.

Gao Z, Gao Z, Zheng C, Ma J, Zhao Y, Zhang L BMC Psychiatry. 2024; 24(1):144.

PMID: 38378492 PMC: 10880202. DOI: 10.1186/s12888-024-05580-y.


Mapping the insomnia patient journey in Europe and Canada.

ORegan D, Garcia-Borreguero D, Gloggner F, Wild I, Leontiou C, Ferini-Strambi L Front Public Health. 2023; 11:1233201.

PMID: 37711247 PMC: 10497771. DOI: 10.3389/fpubh.2023.1233201.

References
1.
Curran P, Obeidat K, Losardo D . Twelve Frequently Asked Questions About Growth Curve Modeling. J Cogn Dev. 2011; 11(2):121-136. PMC: 3131138. DOI: 10.1080/15248371003699969. View

2.
Julian L . Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res (Hoboken). 2012; 63 Suppl 11:S467-72. PMC: 3879951. DOI: 10.1002/acr.20561. View

3.
Davidson J, Feldman-Stewart D, Brennenstuhl S, Ram S . How to provide insomnia interventions to people with cancer: insights from patients. Psychooncology. 2007; 16(11):1028-38. DOI: 10.1002/pon.1183. View

4.
Morin C, Vallieres A, Ivers H . Dysfunctional beliefs and attitudes about sleep (DBAS): validation of a brief version (DBAS-16). Sleep. 2007; 30(11):1547-54. PMC: 2082102. DOI: 10.1093/sleep/30.11.1547. View

5.
Morin C, Belleville G, Belanger L, Ivers H . The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. 2011; 34(5):601-8. PMC: 3079939. DOI: 10.1093/sleep/34.5.601. View