Tag: Covid19

Research design and contributors

On the net surveys have been executed to collect both of those longitudinal and cross-sectional details at a few time details. The 1st study was performed early on through the COVID-19 pandemic from 9th to 19th April 2020 the second from 30th July to 16th August 2020 and the third concerning 1st and 25th December 2020. Throughout the initially time position, Australian point out governments had adopted remarkable actions to reduce the rates of infection which includes social distancing, lockdowns, and travel constraints. During the second time stage, all Australian states except Victoria experienced peaceful constraints because of to minimal case numbers of the an infection. At the time of the 3rd study, most COVID-19 limits were being lifted in all States and Territories as the premiums of infection were being mostly below command [21, 22].

At each and every study, participants (together with new contributors recruited for survey 2) were requested if they would like to take part in foreseeable future data selection chances. Individuals completing at minimum two surveys turned section of a longitudinal cohort while all those who elected to entire only one survey formed the cross-sectional cohort. The surveys ended up anonymous and hosted on the Qualtrics survey system. Australian older people aged 18 years and above have been invited to complete the surveys using paid Facebook advertising, social media (e.g., Twitter) and institutional sources such as electronic mail lists. On line educated consent was provided by all members just after they experienced read the data sheet that outlined the mother nature of their participation, the challenges and gains of participation, and how the knowledge would be utilised. Ethical approval was granted by Central Queensland University’s Human Investigate Ethics Committee (Acceptance range 22332).

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Demographic qualities provided age (several years), gender, several years of education, weekly household profits (< 1000 AUD, 1000 - < 2000 AUD, or ≥ 2000 AUD), and marital status (in a relationship or not). Chronic disease status (Yes/No) was identified using the question “Have you ever been told by a doctor that you have any chronic health problems?”. These included one or a combination of heart disease, high blood pressure, stroke, cancer, depressive disorder, anxiety disorder, psychotic illness, bipolar disorder, diabetes, arthritis, chronic back/neck pain, asthma, COPD, and chronic kidney/renal diseases [23].

Resilience was assessed using the six-item Brief Resilience Scale (BRS). The BRS measures an individuals’ ability to bounce back from an adverse event and focuses on the ability to recover [24]. The BRS is a reliable measure of resilience, with Cronbach’s alpha ranging from 0.80 to 0.91 and a 1 month test-retest reliability (ICC) of 0.69 [24]. The BRS is comprised of six items with three positively worded items (1, 3, and 5) and negatively worded items (2, 4, and 6). For example, a positive item states “I tend to bounce back quickly after hard times” while a negative item states “I have a hard time making it through stressful events”. Responses were provided on a 5-point Likert scale with anchors at 1 (strongly disagree) and 5 (strongly agree). The scale was scored by reverse coding the negative items and then averaging the total score for the six items. Final scores range from 1.0–5.0 with a score of 3.0–4.3 considered a normal level of resilience [25].

Psychological distress was measured using the 21-item Depression, Anxiety and Stress Scale (DASS-21) [26]. The DASS-21 has shown acceptable construct validity and high reliability (Cronbach’s alphas were 0.88, 0.82 and 0.90 for depression, anxiety and stress respectively) in a non-clinical adult population [27]. Each domain has seven items scored on a 4-point Likert scale between 0 (did not apply to me at all) and 3 (applied to me very much, or most of the time). Example items were “I was aware of dryness of my mouth” or “I found myself getting agitated”. A score was calculated for each domain by adding the scores for the relevant items and multiplying by two. Standard cut-points were used to determine whether participants had symptom severity above normal for depression (≥10 points), anxiety (≥8 points), and stress (≥15 points) [26].

Physical activity was assessed using the Active Australia Survey (AAS), which comprises eight items identifying the duration and frequency of walking, and moderate and vigorous (MVPA) physical activities, over the past 7 days. For example, questions about walking are “In the last week, how many times have you walked continuously, for at least 10 minutes, for recreation, exercise or to get to or from places?” and “What do you estimate was the total time that you spent walking in this way in the last week?”. The AAS guidelines were used to calculate total physical activity by summing minutes of walking, minutes of moderate activity, and minutes of vigorous activity (multiplied by 2). Participants were then categorised as meeting the physical activity guidelines (≥150 min of moderate – vigorous (MVPA) per week) or not (< 150 min MVPA per week) [28]. The AAS criterion validity has been found to be acceptable for use in self-administered format, with correlations between self-reported physical activity and weekly pedometer steps, and accelerometry being 0.43 and 0.52 respectively [29].

Analyses

Statistical analysis was undertaken using SAS software v9.4. Two datasets, longitudinal and repeated cross-sectional, were analysed separately. Participants completing at least two surveys were included in the longitudinal dataset. The repeated cross-sectional dataset excluded those in the longitudinal dataset and therefore included only those completing one survey. Descriptive statistics (mean, standard deviation, and percentages) were calculated and are presented for each time point. Changes in resilience scores were examined using general linear mixed models for the longitudinal data, and general linear models for cross-sectional data. In addition to bivariate analyses, estimated changes in resilience scores were also adjusted for age, gender, years of education, weekly household income, relationship status, and chronic disease status. Multiple comparison correction was applied using the simulation option in PROC GLIMMIX.

Associations between resilience scores with physical activity and depression, anxiety, and stress were also examined using general linear mixed models for the longitudinal data and general linear models for the cross-sectional data. Three models were run for both datasets. Model 1 included resilience scores, time and either physical activity, depression, anxiety, or stress. Model 2 included the additional covariates: age, gender, years of education, weekly household income, relationship status, and chronic disease status. To examine whether the observed associations were independent, physical activity, depression, anxiety, and stress were also included in Model 3 together with time and all other covariates.

Due to missing values for the household income variable being higher than 10{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}, analyses were conducted with and without household income as a covariate. As the results between these two analyses did not change the findings, only models including household income are presented. Crude and adjusted differences in resilience scores with 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} confidence intervals are reported. All p-values were two sided and considered significant if < 0.05.