TY - JOUR
T1 - Association between mental health symptoms and behavioral performance in younger vs. older online workers
AU - Mills-Finnerty, Colleen
AU - Staggs, Halee
AU - Hogoboom, Nichole
AU - Naparstek, Sharon
AU - Harvey, Tiffany
AU - Beaudreau, Sherry A.
AU - O’Hara, Ruth
N1 - Publisher Copyright:
Copyright © 2023 Mills-Finnerty, Staggs, Hogoboom, Naparstek, Harvey, Beaudreau and O’Hara.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Background: The COVID-19 pandemic has been associated with increased rates of mental health problems, particularly in younger people. Objective: We quantified mental health of online workers before and during the COVID-19 pandemic, and cognition during the early stages of the pandemic in 2020. A pre-registered data analysis plan was completed, testing the following three hypotheses: reward-related behaviors will remain intact as age increases; cognitive performance will decline with age; mood symptoms will worsen during the pandemic compared to before. We also conducted exploratory analyses including Bayesian computational modeling of latent cognitive parameters. Methods: Self-report depression (Patient Health Questionnaire 8) and anxiety (General Anxiety Disorder 7) prevalence were compared from two samples of Amazon Mechanical Turk (MTurk) workers ages 18–76: pre-COVID 2018 (N = 799) and peri-COVID 2020 (N = 233). The peri-COVID sample also completed a browser-based neurocognitive test battery. Results: We found support for two out of three pre-registered hypotheses. Notably our hypothesis that mental health symptoms would increase in the peri-COVID sample compared to pre-COVID sample was not supported: both groups reported high mental health burden, especially younger online workers. Higher mental health symptoms were associated with negative impacts on cognitive performance (speed/accuracy tradeoffs) in the peri-COVID sample. We found support for two hypotheses: reaction time slows down with age in two of three attention tasks tested, whereas reward function and accuracy appear to be preserved with age. Conclusion: This study identified high mental health burden, particularly in younger online workers, and associated negative impacts on cognitive function.
AB - Background: The COVID-19 pandemic has been associated with increased rates of mental health problems, particularly in younger people. Objective: We quantified mental health of online workers before and during the COVID-19 pandemic, and cognition during the early stages of the pandemic in 2020. A pre-registered data analysis plan was completed, testing the following three hypotheses: reward-related behaviors will remain intact as age increases; cognitive performance will decline with age; mood symptoms will worsen during the pandemic compared to before. We also conducted exploratory analyses including Bayesian computational modeling of latent cognitive parameters. Methods: Self-report depression (Patient Health Questionnaire 8) and anxiety (General Anxiety Disorder 7) prevalence were compared from two samples of Amazon Mechanical Turk (MTurk) workers ages 18–76: pre-COVID 2018 (N = 799) and peri-COVID 2020 (N = 233). The peri-COVID sample also completed a browser-based neurocognitive test battery. Results: We found support for two out of three pre-registered hypotheses. Notably our hypothesis that mental health symptoms would increase in the peri-COVID sample compared to pre-COVID sample was not supported: both groups reported high mental health burden, especially younger online workers. Higher mental health symptoms were associated with negative impacts on cognitive performance (speed/accuracy tradeoffs) in the peri-COVID sample. We found support for two hypotheses: reaction time slows down with age in two of three attention tasks tested, whereas reward function and accuracy appear to be preserved with age. Conclusion: This study identified high mental health burden, particularly in younger online workers, and associated negative impacts on cognitive function.
KW - Bayesian analysis
KW - COVID-19
KW - anxiety
KW - behavior and cognition
KW - computational modeling
KW - depression
UR - http://www.scopus.com/inward/record.url?scp=85152788714&partnerID=8YFLogxK
U2 - 10.3389/fpsyt.2023.995445
DO - 10.3389/fpsyt.2023.995445
M3 - Article
C2 - 37065893
AN - SCOPUS:85152788714
SN - 1664-0640
VL - 14
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 995445
ER -