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This study aimed to examine changes in depression and anxiety symptoms from before to during the first 6 months of the COVID‐19 pandemic in a sample of 1,339 adolescents (9–18 years old, 59% female) from three countries. We also examined if age, race/ethnicity, disease burden, or strictness of government restrictions moderated change in symptoms. Data from 12 longitudinal studies (10 U.S., 1 Netherlands, 1 Peru) were combined. Linear mixed effect models showed that depression, but not anxiety, symptoms increased significantly (median increase = 28%). The most negative mental health impacts were reported by multiracial adolescents and those under ‘lockdown’ restrictions. Policy makers need to consider these impacts by investing in ways to support adolescents’ mental health during the pandemic.
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Continuous-time model identification: application on a behavioural (miLife) study
To develop efficient just-in-time personalised treatments, dynamical models are needed that provide a description of how an individual responds to treatment. However, available system identification approaches cannot effectively be applied to most behavioural datasets since, usually, the data collected is subjected to a large amount of noise and time sampling is not uniform. To be able to circumvent these issues, in this paper a new method is proposed for parsimonious system identification of continuous-time systems that does not require specially structured data. The developed algorithm provides an effective way to leverage these ‘non-standard’ datasets to identify continuous time dynamical models that are compati- ble with a-priori information available on the process. The algorithm developed is tested on data obtained from a behavioural study on adolescents and violence. The objective is to model the temporal dynamics of the association between violence exposure and mental health symptoms (depression and anxiety) in day- to-day life among a sample of adolescents at heightened risk for both substance use exposure and problem behaviour. The information extracted from individual models of behaviour such as the maximum burden and the time of fading away of depression/anxiety does differ substantially from person to person. This information has the potential to be useful to design personalised interventions that would have a better chance of succeeding.
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- Award ID(s):
- 1808381
- PAR ID:
- 10186510
- Date Published:
- Journal Name:
- International Journal of Control
- ISSN:
- 0020-7179
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/00207179.2019.1706101
