Abstract
Background
COVID19, and associated lockdown restrictions, have impacted on people’s daily lives. Understanding the mental health and wellbeing implications of these impacts has been identified as a public health research priority.
Aims
Building on an earlier cross-sectional study, the current study sought to investigate whether capability-based quality of life changed during the first 5-months of lock-down restrictions in the UK, and whether capability-based quality of life was predictive of future levels of depression and anxiety.
Methods
An initial convenience sample of 594 participants were followed up at three different timepoints spanning a 20-week time-period between March 2020 and August 2020. Participants provided demographic information and completed the Oxford Capabilities Questionnaire – Mental Health (OxCAP-MH), and the Hospital Anxiety and Depression Scale (HADS).
Results
The mean scores indicated that levels of both depression and anxiety decreased across the three timepoints, whereas capability-based QoL (as assessed by the OxCAP-MH) decreased over time. Capability-based QoL predicted additional levels of variance in both depression and anxiety levels when time and sociodemographic factors were controlled for. Cross-lagged panel model analyses indicated that capability-based QoL over a month into lockdown restrictions predicted levels of depression and anxiety 5 months into the restrictions.
Conclusions
The study findings suggest that the capability-limiting impact of public health emergencies and related lockdown restrictions are important for understanding peoples’ levels of depression and anxiety. The implications that the findings have for the provision of support in the context of public health emergencies and associated restrictions are discussed.
COVID19, and associated lockdown restrictions, have impacted on people’s daily lives. Understanding the mental health and wellbeing implications of these impacts has been identified as a public health research priority.
Aims
Building on an earlier cross-sectional study, the current study sought to investigate whether capability-based quality of life changed during the first 5-months of lock-down restrictions in the UK, and whether capability-based quality of life was predictive of future levels of depression and anxiety.
Methods
An initial convenience sample of 594 participants were followed up at three different timepoints spanning a 20-week time-period between March 2020 and August 2020. Participants provided demographic information and completed the Oxford Capabilities Questionnaire – Mental Health (OxCAP-MH), and the Hospital Anxiety and Depression Scale (HADS).
Results
The mean scores indicated that levels of both depression and anxiety decreased across the three timepoints, whereas capability-based QoL (as assessed by the OxCAP-MH) decreased over time. Capability-based QoL predicted additional levels of variance in both depression and anxiety levels when time and sociodemographic factors were controlled for. Cross-lagged panel model analyses indicated that capability-based QoL over a month into lockdown restrictions predicted levels of depression and anxiety 5 months into the restrictions.
Conclusions
The study findings suggest that the capability-limiting impact of public health emergencies and related lockdown restrictions are important for understanding peoples’ levels of depression and anxiety. The implications that the findings have for the provision of support in the context of public health emergencies and associated restrictions are discussed.
| Original language | English |
|---|---|
| Article number | 451 |
| Number of pages | 11 |
| Journal | BMC Public Health |
| Volume | 23 |
| DOIs | |
| Publication status | Published - 08 Mar 2023 |
Keywords
- Research
- COVID19
- Mental health
- Lockdown
- Depression
- Anxiety
- Capability approach
- Quality of life
Access to Document
- A longitudinal study of capability-based quality of life and mental health in the first 5-months of lockdown restrictions in the UK
Copyright 2023 The authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.