Background: Despite several studies having correlated Alzheimer's disease with mental health conditions, the extent to which they have been incorporated into Alzheimer's disease clinical trials remains unclear.
Objective: This study aimed to assess the temporal trends in mental health-related terminology in Alzheimer's disease clinical trials as a proxy measure of research interest. Additionally, it sought to determine the effect of the COVID-19 pandemic on the frequency of these terms through pre-pandemic and post-pandemic trend assessment.
Methods: In this retrospective descriptive analysis, we included 2243 trials with a start date between 1988 and 2022 by searching for the keyword "Alzheimer Disease" in the U.S. National Library of Medicine ClinicaTrials.gov database. A Python program was created to extract and count the frequency of four mental health terms (loneliness, depression, anxiety, and distress) by year and trial status (e.g., completed, active, recruiting). Binary logistic regression analyses were conducted to examine the yearly patterns in the appearance of the four mental health terms. A multivariable logistic regression analysis was performed to identify trial characteristics associated with each mental health term.
Results: Our results depicted a statistically significant increasing trend in three (i.e., loneliness, anxiety, distress) of the four mental health conditions by year. A comparison between pre-pandemic and post-pandemic trials showed an increase in the mention of the same three words over time.
Interpretation: These results may suggest a growing awareness of mental health conditions and a greater interest in considering these conditions in Alzheimer's disease trials, particularly after the onset of COVID-19. Future researchers should conduct more in-depth analyses to examine how mental health variables are operationalized in these trials, with consideration for their subsequent success.
Copyright: © 2024 Golrokhian-Sani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.