https://orcid.org/0000-0002-2743-0372
Abstract
Objectives
Psychological burdens of the COVID-19 pandemic are likely to impact sleep negatively. We investigate prevalence and correlates of disturbed sleep among subscribers to Text4Hope a daily supportive text message program launched in Alberta to support residents to deal with stress, anxiety, and depression.
Methods
A survey link was sent to Text4Hope subscribers to assess demographic and clinical variables, including disturbed sleep, stress, anxiety, and depression using the third question on the Patient Health Questionnaire-9 (PHQ-9), Perceived Stress Scale, Generalised Anxiety Disorder 7-item scale, and PHQ-9, respectively. Data were analysed using univariate and logistic regression analyses.
Results
Overall, 6041 out of 32,805 Text4Hope subscribers completed the survey (18.4% response rate). Prevalence of disturbed sleep was 77.8%. Subscribers aged 41–60 years were twice as likely to present with sleep disturbance compared to individuals ≤25 years (OR 1.89, 95% CI: 1.27–2.81). Individuals with moderate/high anxiety and stress symptoms and those with passive death wish/suicidal ideation had higher probability for sleep disturbance [(OR 4.05, 95% CI: 3.33–4.93), (OR 2.42, 95% CI: 1.99–2.94), and (OR 2.39, 95% CI: 1.69–3.38)], respectively.
Conclusion
As the pandemic continues, more Canadians are likely to develop sleep problems, an important consideration for planning mental health services.
This is the first study to examine the prevalence rates and demographic and clinical correlates of disturbed sleep in a large sample (n = 6041) of Canadians during the COVID-19 pandemic.
Prevalence of disturbed sleep was high at 77.8%.
Individuals aged 41–60 years were twice as likely to present with sleep disturbance compared to individuals ≤25 years (OR 1.89, 95% CI: 1.27–2.81).
Individuals with moderate/high anxiety symptoms, moderate/high stress symptoms, and suicidal ideation/thoughts of self-harm had higher likelihood of developing sleep disturbance, compared to individuals lacking these symptoms [(OR 4.05, 95% CI: 3.33–4.93) and (OR 2.42, 95% CI: 1.98–2.94)], respectively.
As the pandemic continues, with fear of multiple waves, more Canadians are likely to develop sleep problems, an important consideration for planning the provision of mental health services.
KEY POINTS
Introduction
The coronavirus disease (COVID-19) was first reported in China in December 2019 (Cronise et al. Citation2016; Paules et al. Citation2020; Wang et al. Citation2020). This novel disease is characterised by flu-like symptoms such as rhinitis, fever, respiratory problems, and even death; with more clinical features being identified as the scientific community gains a greater understanding of the disease (Chen et al. Citation2020; Hu et al. Citation2020; Sohrabi et al. Citation2020; Su et al. Citation2020; Yin et al. Citation2020). By the beginning of 2020, the virus had spread from China to other parts of the world with human-to-human transmission quickly occurring. By 11 March 2020, the World Health Organisation (WHO) declared COVID-19 a pandemic (Walsh et al. Citation2018). As of 17 May 2020, the WHO reported 4,619,477 confirmed cases of COVID-19 and 31,1847 COVID-19-related deaths in 216 countries, areas, and territories (Yu et al. Citation2018). Canada reported its first case of COVID-19 on 25 January 2020; and as of 18 May 2020, there have been over 77,000 confirmed cases and over 5800 deaths (Shepardson et al. Citation2018).
In the absence of an effective treatment or vaccine, governments around the world have resorted to public health measures such as hand washing, social/physical distancing, lockdown, and quarantine to control the spread of the infection (Jung et al. Citation2020; Roy et al. Citation2020; Ramesh et al. Citation2020). Although a necessity, these measures are associated with a number of stressors, including job loss, financial stress, social isolation, lack of leisure opportunities, and suspension of many elective medical procedures. Thousands of people have become unwell with the virus and thousands have lost family, friends, and colleagues to the virus. Given the stresses, there has been a rise in various mental health issues during this pandemic, including depression, anxiety, suicidal thoughts, and sleep disturbances during (Li et al. Citation2020; Roy et al. Citation2020; Zhang et al. Citation2020). Disturbed sleep is associated with numerous negative consequences, including an overall feeling of poor quality of life (Lucena et al. Citation2020); cardiovascular complications (Manolis et al. Citation2020); increased anxiety and depression (Zvolensky et al. Citation2019); increased workplace injuries (Kling et al. Citation2010); and death (Rockwood et al. Citation2001). In this study, we aimed to evaluate the prevalence and correlates of disturbed sleep symptoms in subscribers of the Text4Hope program during the COVID-19 pandemic. We hypothesised that the prevalence rates for disturbed sleep would be high because sleep disturbance is a common symptom associated with depression, anxiety, and stress which is on the rise because of COVID-19 (Nwachukwu et al. Citation2020). We also hypothesised that several demographic and clinical variables would be correlated with disturbed sleep. Surveys have been used in other jurisdictions including China (Huang and Zhao Citation2020) and Italy (Cellini et al. Citation2020) during this COVID-19 pandemic to examine the quality of sleep at the population level. As far as we are aware, this is the first study to examine sleep disturbances as well as their demographic and clinical correlates of the COVID-19 pandemic amongst Canadians.
Methods
Design
This was a cross-sectional study examining Text4Hope baseline survey data that were collected one-week after the program (Text4Hope) launched (23 March to 30 March 2020). Text4Hope is a text-messaging program aimed at providing Albertans with support during the early days of the COVID-19 pandemic, in Canada. An advertisement about Text4Hope was provided by Alberta Health Services (AHS) on its official website, and the program was open to public subscription, without any restrictions. Individuals self-subscribed to the program by texting ‘COVID19HOPE’ to a short-code number. Once subscribed, individuals receive one daily text message, at the same time each day, for 12 weeks. Text messages are aligned with a cognitive behavioural framework. The content was written by mental health therapists and study authors (MH, VA), who are practicing psychologists and psychiatrists, respectively.
The baseline survey was administered via Select Survey, an online survey program, by AHS. All Text4Hope subscribers were invited to complete the survey. Individuals were asked demographic questions about their gender, age, ethnicity, highest level of education completed, current employment status, current relationship status, and current housing status.
Respondents were asked to complete a number of standardised questionnaires, including the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al. Citation2001) (for likely major depressive disorder (MDD); PHQ-9 ≥ 10) to assess depression. One of the questions on the PHQ-9, ‘Trouble falling or staying asleep, or sleeping too much,’ was used to screen sleep disturbance during the pandemic and represented the primary outcome. Results from the other scales, including the Perceived Stress Scale (PSS) (Cohen et al. Citation1983) (for moderate to high stress; PSS ≥ 14), and the Generalised Anxiety Disorder 7-item (GAD-7) scale (Spitzer et al. Citation2006) (for likely generalised anxiety disorder (GAD); GAD-7 ≥ 10), were used to assess stress and anxiety, respectively. The study was approved by the University of Alberta Human Research Ethics Board (Pro00086163) and the survey took around 10 minutes to complete. Consent was implied if the participants completed the survey and submitted their responses. Because the population of Alberta is approximately 4.3 million people, the sample size needed to estimate prevalence rates with a confidence level of 99% and a 2% margin of error was 4200 individuals. Based on a similar study (Agyapong et al. Citation2016), the expected response rate was 20%.
Data analysis
Data were analysed using the Statistical Package for Social Sciences (SPSS) version 26 (Gray et al. Citation2017). Descriptive statistics of the demographic and clinical characteristics are described in frequencies and percentages. The main outcome in the current study is the PHQ-9 question related to sleep. This item is coded based on four responses (not at all (0), several days (1), more than half the days (2), and nearly every day (3)) that for the purposes of analysis, it was collapsed into two categories (PHQ-9 ≥ 1) and (PHQ-9 < 1), which reflected endorsing disturbed sleep in the previous two weeks vs. no disturbed sleep in the preceding two weeks. Univariate analyses with chi-square test were used to ascertain the relationship of sociodemographic and clinical variables to the dependent variable (presence of sleep disturbance).
Variables with a statistically significant relationship with the dependent variable on univariate analysis (p ≤ 0.05, two-tailed), or a trend towards significant relationship significance (0.05 ≥ p ≤ 0.1) were entered into a logistic regression model. Correlational analyses were conducted prior to the regression analysis to avoid the high inter-correlations among the predictor variables. Results from the binary logistic regression analysis were reported as odds ratios (OR) and confidence intervals (CI). We presented results in frequencies and percentages, and a two-tailed significance value of p ≤ 0.05 or less was deemed statistically significant. Grossly incomplete responses were excluded from the analysis, and pairwise deletion analysis was run for the rest of the missing responses with no imputation.
Results
Overall, 6041 individuals completed the baseline questionnaire out of 32,805 individuals who subscribed to Text4Hope in the first week of the program, yielding an 18.4% response rate. In the first week of the survey, the likelihood to experience sleep disturbance symptoms (trouble falling or staying asleep or sleeping too much) was 77.8%. Descriptive statistical analysis of the sociodemographic and clinical data was performed by gender (male, female, other) groupings summarised in . The majority of respondents identified as female (86.6%), identified as Caucasian (82.3%), had post-secondary education (85.6%), were employed (72.2%), were married, cohabiting, or partnered (71.6%), and owned a home (66.6%), with 43.2% of respondents in the middle-age category (i.e., 41–60 years old). Moderate to severe stress and anxiety were endorsed by 84.9% and 46.7% of the total respondents, respectively. Approximately one-fifth of participants had self-isolated or quarantined (19.2%) and 14.4% reported passive suicidal ideation.
Table 1. Gender distribution of demographic and clinical characteristics of respondents.
Univariate analysis
A univariate analysis using chi-square indicated that all variables (except education level) were significantly associated with the outcome variable (see ). Respondents who identified as gender diverse, were aged 25 years or less, identified as Indigenous, had less than high school education, were unemployed, single, and renting their home, appeared to have the highest likelihood of presenting disturbed sleep symptoms and thoughts of self-harm compared to individuals with other characteristics within the same demographic groups. Similarly, respondents who had to self-isolate or quarantine; those who experienced moderate to severe symptoms of either stress, depression, or anxiety; those worried about dirt, germs, and viruses and those who washed their hands repeatedly or in a special way due to fears of contamination with dirt, germ, and viruses both before and during the current pandemic; and those who had passive death wish or thoughts of self-harm, all seemed to experience more disturbed sleep symptoms, compared to other respondents in their respective groups.
Table 2. Chi-squared test of association between demographic and clinical characteristics and isolation status and likelihood of experiencing disturbed sleep in the preceding 2 weeks.
Logistic regression
Correlational diagnostics were performed before logistic regression
Analysis to ensure that very strong correlations (Spearman’s correlation coefficient of 0.7 to 1.0 or −0.7 to −1.0) among predictor variables were avoided. Consequently, MDD symptoms which was highly correlated with anxiety symptoms (Spearman’s correlation coefficient of 0.76) was dropped from the regression model. PHQ-9 was used to assess the presence or absence of disturbed sleep and MDD symptoms and as such highly correlated, thus providing further justification to drop MDD symptoms from the model. Therefore, a logistic regression was completed to ascertain the effect of the independent variables, excluding MDD symptoms on the likelihood of participants endorsing sleep disturbance. The regression model was statistically significant (Χ2 (29) = 622.72, p < 0.001) and could explain 21.1% of the variance in sleep disturbance. The model showed an accuracy of 78.2% of all the cases. shows that the largest contribution was made by the ‘significant anxiety symptoms’ variable, with a Wald of 195.4, where experiencing anxiety symptoms was associated with a higher likelihood to develop sleep disturbance among respondents (OR 4.05, 95% CI: 3.33–4.93). Among demographic factors, individuals 41–60 years of age were almost two times more at risk to develop sleep disturbance symptoms during the COVID-19 pandemic, compared to individuals 25 years or less (OR 1.89, 95% CI: 1.27–2.81). For the rest of the independent variables, individuals who expressed moderate to high stress symptoms and those who showed passive death wishes or thoughts of self-harm were significantly more affected, with a two-fold risk of experiencing sleep disturbance (OR 2.42, 95% CI: 1.99–2.94) (OR 2.39, 95% CI: 1.69–3.38), compared to the individuals who lack these symptoms in their respective groups.
Table 3. Logistic regression predicting likelihood of disturbed sleep.
Discussion
In this cross-sectional study, we looked at the demographic and clinical correlates of disturbed sleep among 6041 Canadians. In our study, the prevalence of disturbed sleep was 77.8%, two to six times higher than prevalence rates of sleep disturbances for the general Canadian population (Ohayon Citation1996; Sutton et al. Citation2001; Morin et al. Citation2011). Thus, although this study did not incorporate baseline levels of sleep disturbance prior to the pandemic, and does not provide definitive evidence of a causative link between the pandemic and sleep disturbance, it can be inferred that the disturbance reported is likely related to the pandemic or its consequences. The rate of sleep disturbance in our study is higher than a prevalence of 18.0% of disturbed sleep reported for a study conducted in China during the pandemic (Huang and Zhao Citation2020). A similar study conducted in Italy (Cellini et al. Citation2020), also showed disturbed sleep patterns during the COVID-19 pandemic among respondents, with a prevalence of 11.9%. A possible explanation for the higher prevalence of sleep disturbance in our study compared to the Chinese and Italian studies may be in differences in the criteria for defining sleep disturbance. Whilst we used the score of one or greater on the third question on the PHQ-9 scale to define disturbed sleep, the Chinese study used the Pittsburgh Sleep Quality Index (Liu et al. Citation1996) and the Italian study used an Italian version of the Pittsburgh Sleep Quality Index to assess sleep quality over the past two weeks (Curcio et al. Citation2013). The high rates of sleep disturbance in our study may also reflect a selection bias, as people who are struggling with mental health and stress may be more likely to subscribe to Text4Hope, and thus show a higher level of sleep disturbance than would a random sample of the population. Individuals who identified as women were slightly more affected than those who identified as men. This finding is similar to reports from other studies (Ohayon Citation1996; Laver et al. Citation2020; McArdle et al. Citation2020). All gender groups experienced significantly disturbed sleep in the preceding two weeks. The ‘other gender’ category represented most of the respondents who expressed trouble falling or staying asleep or sleeping too much in the last two weeks preceding the survey at 89.6%. It is not very clear why the other gender group were more affected. Similar studies in other countries, such as the Italy and China studies mentioned above, did not have a separate ‘other gender’ group; analysis was limited to male and female sexes.
Our study showed that, when controlling for all other variables in the logistic regression model, the only demographic factor that had a significant association with disturbed sleep was age. Individuals who belong to the 41–60-year age group were almost two times more at risk to develop sleep disturbance symptoms during the COVID-19 pandemic, compared to individuals of 25 years or less (OR 1.89, 95% CI: 1.27–2.81). It is not clear why this particular age range was more affected and this seems ironic, given that a previous study during the pandemic suggested that younger age population (≤25 years) had higher prevalence of stress, anxiety, and depression during the pandemic compared to the older population (Nwachukwu et al. Citation2020) and would be expected to have higher proportion of disturbed sleep. The higher burden of mental disorders noted in that study was consistent with a study by Viana and Andrade (Citation2012) which estimated prevalence, age-of-onset, gender distribution, and identified correlates of lifetime psychiatric disorders in the São Paulo Metropolitan Area and reported that younger ages were significant predictors for all mental disorder classes (Viana and Andrade Citation2012). A possible explanation for the high prevalence of disturbed sleep symptoms in the middle-age group is the higher secreted levels of cortisol stress hormone that may induce stress, anxiety, and depression symptoms which include disturbed sleep (Vgontzas et al. Citation2001).
In a previous Canadian study on a general population sample (Sutton et al. Citation2001), sleep difficulties seem to worsen with older age. However, a second study (Morin et al. Citation2011) showed the prevalence of sleep difficulties to be highest in the 41–60 year age group, in accord with our results.
Our study showed that respondents’ ethnicity and level of education were not significantly associated with disturbed sleep in the preceding two weeks. Individuals who expressed moderate to high stress symptoms and those who exhibited passive death wishes or thoughts of self-harm could significantly predict the dependent variable, as those individuals had over twice the risk of experiencing trouble falling or staying asleep or sleeping too much in the preceding two weeks (OR 2.42, 95% CI: 1.99–2.94) (OR 2.39, 95% CI: 1.69–3.38), compared to the individuals who lack these symptoms in their respective groups. The association between high stress and poor sleep has been reported by other authors (Carrillo-Gonzalez et al. Citation2019; Park et al. Citation2019; Bishop et al. Citation2020). Our results show a similar pattern.
Strengths and limitations
This study had a large sample size and was the first study to investigate sleep disturbances in Canada during the COVID-19 pandemic. The response rate in our study of 18.4% was slightly lower than the response rate of 21.7% reported in a similar survey (Agyapong et al. Citation2016). However, our sample size was greater than the sample size of 3693 needed to estimate the prevalence rates for disturbed sleep in our overall sample of 32,805 subscribers or the 4200-sample size needed to estimate the prevalence rates for sleep disturbance in the entire Alberta population with a confidence interval of 99% and a 2% margin of error. Notwithstanding the large sample size, our study is limited by a potential selection bias. First, our study is not representative of the population in Alberta either by age or gender (Statistica Citation2019) and so our findings may not be generalised to the entire population. Second, the high rates of sleep disturbance in our study could be because people who are struggling with mental health and stress may be more likely to subscribe to Text4Hope, and thus show a higher level of sleep disturbance than would a random sample of the population.
Because of the cross-sectional nature of the study, it is difficult to draw a causal relationship between the variables and disturbed sleep. Also, as this was a cross sectional study, we did not have baseline values to compare our results with. Additionally, the study did not elaborate upon subscribers’ pre-existing mental disorders which would have helped to establish epidemiological incidence, rather than the prevalence of such conditions. Also, although we included data on the ethnicity of subscribers, we did not gather information on their cultural identity. We also did not collect information on the COVID-19-related death rates in Alberta given we did not have a comparison group. Both the cultural identity of subscribers and the relative COVID-19-related death rates could potentially influence disturbed sleep among subscribers. Finally, we relied on self-rated scales to assess sleep disturbance, stress, anxiety, and depression symptomatology. While such scales are perceived valid tools for the assessment of symptom severity (Kroenke et al. Citation2001), they may potentially overestimate the levels of these conditions when compared to the structured method of clinical interviews using the Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-5) for diagnosis (American Psychiatric Association Citation2020).
Conclusion and clinical implications
Our study shows an increase in disturbed sleep during the COVID-19 pandemic. We also found that individuals with moderate/high anxiety and stress symptoms had higher likelihood to develop sleep disturbance, compared to individuals lacking these symptoms. Given that the pandemic and lockdown are still ongoing, with some scientists predicting a possible second wave of the pandemic (Ali Citation2020; Xu and Li Citation2020), there is a need for more resources to be put in place for people with sleep disturbances and other difficulties during this pandemic. For instance, increasing access to cognitive behavioural therapy for insomnia (Sadler et al. Citation2018; Dewald-Kaufmann et al. Citation2019) is known to improve sleep problems. Some of this can be delivered via telemedicine (Ritterband et al. Citation2017), although, given the very high prevalence rates for disturbed sleep amongst the populations, not enough human resources may be available to address this issue speedily at the population level. Furthermore, with the finding that high and moderate stress, anxiety, and depression predict disturbed sleep, it may be more useful to address the burden of stress, anxiety, and depression that exist at the population level as a way of improving the sleep of individuals. Innovative and cost-effective interventions, such as supportive text messaging, which are geographic location independent, free to the end user and do not require expensive data plans, and can reach thousands of people simultaneously, may be useful in mitigating stress, anxiety, and depression during pandemics, thereby improving sleep (Agyapong et al. Citation2013a, Citation2013b, Citation2015, Citation2017; Agyapong Citation2020; O’Reilly et al. Citation2019). Supportive text messages are associated with positive outcomes, including reduction of depressive and anxiety symptoms and achieve high user satisfaction amongst subscribers in previous research. For example, in randomised controlled trials, patients with depression who received daily supportive text messages showed symptom reduction on standardised self-report compared to a similar patient group not receiving messages (with large effect sizes: Cohen’s d = 0.85, Cohen’s d = 0.67) (Agyapong et al. Citation2012, Citation2017). In two user satisfaction surveys, over 80% of subscribers reported that a supportive text messaging program improved their mental health (Agyapong et al. Citation2013, Citation2016). Subscribers reported text messages made them feel more hopeful about managing issues (82%), in charge of managing depression and anxiety (77%), connected to a support system (75%), and improved their overall mental wellbeing (83%) (Agyapong et al. Citation2016).
Authors contribution
The study was conceived and designed by VIOA. FO and RS drafted the initial manuscript. AG, WV, and SS contributed to data collection. All authors contributed to study design, reviewing and revising the initial draft manuscript and approved of the final draft prior to submission.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
References
- Agyapong V. 2020. Coronavirus disease pandemic: health system and community response to a text message (Text4Hope) program supporting mental health in Alberta. Disaster Med Public Health Prep. 14:E5–E6.
- Agyapong VI, Ahern S, McLoughlin DM, Farren CK. 2012. Supportive text messaging for depression and comorbid alcohol use disorder: single-blind randomised trial. J Affect Disord. 141(2-3):168–176. doi:10.1016/j.jad.2012.02.040
- Agyapong VIO, Juhás M, Ohinmaa A, Omeje J, Mrklas K, Suen VYM, Dursun SM, Greenshaw AJ. 2017. Randomized controlled pilot trial of supportive text messages for patients with depression. BMC Psychiatry. 17(1):286. doi:10.1186/s12888-017-1448-2
- Agyapong VIO, McLoughlin DM, Farren CK. 2013a. Six-months outcomes of a randomised trial of supportive text messaging for depression and comorbid alcohol use disorder. J Affect Disord. 151(1):100–104. doi:10.1016/j.jad.2013.05.058
- Agyapong VIO, Milnes J, McLoughlin DM, Farren CK. 2013b. Perception of patients with alcohol use disorder and comorbid depression about the usefulness of supportive text messages. Technol Health Care. 21(1):31–39. doi:10.3233/THC-120707
- Agyapong VIO, Mrklas K, Juhás M, Omeje J, Ohinmaa A, Dursun SM, Greenshaw AJ. 2016. Cross-sectional survey evaluating Text4Mood: mobile health program to reduce psychological treatment gap in mental healthcare in Alberta through daily supportive text messages. BMC Psychiatry. 16(1):378. doi:10.1186/s12888-016-1104-2
- Agyapong VIO, Mrklas K, Suen VYM, Rose MS, Jahn M, Gladue I, Kozak J, Leslie M, Dursun S, Ohinmaa A, et al. 2015. Supportive text messages to reduce mood symptoms and problem drinking in patients with primary depression or alcohol use disorder: protocol for an implementation research study. JMIR Res Protoc. 4(2):e55. doi:10.2196/resprot.4371
- Ali I. 2020. COVID-19: are we ready for the second wave? Disaster Med Public Health Prep. 14:E16–E18.
- American Psychiatric Association. 2020. Diagnostic and statistical manual of mental disorders (DSM–5). [accessed 4 Oct]. https://www.psychiatry.org/psychiatrists/practice/dsm
- Bishop TM, Walsh PG, Ashrafioun L, Lavigne JE, Pigeon WR. 2020. Sleep, suicide behaviors, and the protective role of sleep medicine. Sleep Med. 66:264–270. doi:10.1016/j.sleep.2019.07.016
- Carrillo-Gonzalez A, Camargo-Mendoza M, Cantor-Cutiva LC. 2019. Relationship between sleep quality and stress with voice functioning among college professors: a systematic review and meta-analysis. J Voice. doi:10.3390/ijerph17062032
- Cellini N, Canale N, Mioni G, Costa S. 2020. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy. J Sleep Res. 29:e13074.
- Chen Q, Zheng Z, Zhang C, Zhang X, Wu H, Wang J, Wang S, Zheng C. 2020. Clinical characteristics of 145 patients with corona virus disease 2019 (COVID-19) in Taizhou, Zhejiang, China. Infection. 48(4):543–551. doi:10.1007/s15010-020-01432-5
- Cohen S, Kamarck T, Mermelstein R. 1983. A global measure of perceived stress. J Health Soc Behav. 24(4):385–396. doi:10.2307/2136404
- Cronise R, Teixeira C, Rogers ES, Harrington S. 2016. The peer support workforce: results of a national survey. Psychiatr Rehabil J. 39(3):211–221. doi:10.1037/prj0000222
- Curcio G, Tempesta D, Scarlata S, Marzano C, Moroni F, Rossini PM, Ferrara M, De Gennaro L. 2013. Validity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI). Neurol Sci. 34(4):511–519. doi:10.1007/s10072-012-1085-y
- Dewald-Kaufmann J, de Bruin E, Michael G. 2019. Cognitive behavioral therapy for insomnia (CBT-i) in school-aged children and adolescents. Sleep Med Clin. 14(2):155–165. doi:10.1016/j.jsmc.2019.02.002
- Gray M, Davies K, Butcher L. 2017. Finding the right connections: peer support within a community-based mental health service. Int J Soc Welfare. 26(2):188–196. doi:10.1111/ijsw.12222
- Hu Y, Sun J, Dai Z, Deng H, Li X, Huang Q, Wu Y, Sun L, Xu Y. 2020. Prevalence and severity of corona virus disease 2019 (COVID-19): a systematic review and meta-analysis. J Clin Virol. 127:104371. doi:10.1016/j.jcv.2020.104371
- Huang Y, Zhao N. 2020. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res. 288:112954. doi:10.1016/j.psychres.2020.112954
- Jung F, Krieger V, Hufert FT, Kupper JH. 2020. How we should respond to the Coronavirus SARS-CoV-2 outbreak: a German perspective. Clin Hemorheol Microcirc. 74:363–372.
- Kling RN, McLeod CB, Koehoorn M. 2010. Sleep problems and workplace injuries in Canada. Sleep. 33(5):611–618. doi:10.1093/sleep/33.5.611
- Kroenke K, Spitzer RL, Williams JB. 2001. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 16(9):606–613. doi:10.1046/j.1525-1497.2001.016009606.x
- Laver KE, Spargo C, Saggese A, Ong V, Crotty M, Lovato N, Stevens D, Vakulin A. 2020. Sleep disturbance and disorders within adult inpatient rehabilitation settings: a systematic review to identify both the prevalence of disorders and the efficacy of existing interventions. J Am Med Dir Assoc. 21:1824–1832.e2. doi:10.1016/j.jamda.2020.03.002
- Li S, Wang Y, Xue J, Zhao N, Zhu T. 2020. The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. Int J Environ Res Public Health. 17(6):2032. doi:10.3390/ijerph17062032
- Liu X, Tang M, Hu L. 1996. Reliability and validity of the Pittsburgh sleep quality index. Chin J Psychiatry. 29:103–107.
- Lucena L, Polesel DN, Poyares D, Bittencourt L, Andersen ML, Tufik S, Hachul H. 2020. The association of insomnia and quality of life: Sao Paulo epidemiologic sleep study (EPISONO). Sleep Health. 6(5):629–635. doi:10.1016/j.sleh.2020.03.002
- Manolis TA, Manolis AA, Apostolopoulos E, Melita H, Manolis AS. 2020. Cardiovascular complications of sleep disorders: a better night’s sleep for a healthier heart/from bench to bedside. Curr Vasc Pharmacol. 19(2):210–232.
- McArdle N, Ward SV, Bucks RS, Maddison K, Smith A, Huang R-C, Pennell CE, Hillman DR, Eastwood PR. 2020. The prevalence of common sleep disorders in young adults: a descriptive population-based study. Sleep. 43(10):zsaa072. doi:10.1093/sleep/zsaa072
- Morin CM, LeBlanc M, Belanger L, Ivers H, Merette C, Savard J. 2011. Prevalence of insomnia and its treatment in Canada. Can J Psychiatry. 56(9):540–548. doi:10.1177/070674371105600905
- Nwachukwu I, Nkire N, Shalaby R, Hrabok M, Vuong W, Gusnowski A, Surood S, Urichuk L, Greenshaw AJ, Agyapong VIO, et al. 2020. COVID-19 pandemic: age-related differences in measures of stress, anxiety and depression in Canada. IJERPH. 17(17):6366. doi:10.3390/ijerph17176366
- O’Reilly H, Hagerty A, O’Donnell S, Farrell A, Hartnett D, Murphy E, Kehoe E, Agyapong V, McLoughlin DM, Farren C, et al. 2019. Alcohol use disorder and comorbid depression: a randomized controlled trial investigating the effectiveness of supportive text messages in aiding recovery. Alcohol Alcohol. 54(5):551–558. doi:10.1093/alcalc/agz060
- Ohayon M. 1996. Epidemiological study on insomnia in the general population. Sleep. 19(3 Suppl):S7–S15. doi:10.1093/sleep/19.suppl_3.s7
- Park WS, Yang KI, Kim H. 2019. Insufficient sleep and suicidal ideation: a survey of 12,046 female adolescents. Sleep Med. 53:65–69. doi:10.1016/j.sleep.2018.08.025
- Paules CI, Marston HD, Fauci AS. 2020. Coronavirus infections-more than just the common cold. JAMA. 323(8):707. doi:10.1001/jama.2020.0757
- Ramesh N, Siddaiah A, Joseph B. 2020. Tackling Corona Virus Disease 2019 (COVID 19) in Workplaces. Indian J Occup Environ Med. 24(1):16–18. doi:10.4103/ijoem.IJOEM_49_20
- Ritterband LM, Thorndike FP, Ingersoll KS, Lord HR, Gonder-Frederick L, Frederick C, Quigg MS, Cohn WF, Morin CM. 2017. Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: a randomized clinical trial. JAMA Psychiatry. 74(1):68–75. doi:10.1001/jamapsychiatry.2016.3249
- Rockwood K, Davis HS, Merry HR, MacKnight C, McDowell I. 2001. Sleep disturbances and mortality: results from the Canadian Study of Health and Aging. J Am Geriatr Soc. 49(5):639–641. doi:10.1046/j.1532-5415.2001.49125.x
- Roy D, Tripathy S, Kar SK, Sharma N, Verma SK, Kaushal V. 2020. Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian J Psychiatr. 51:102083. doi:10.1016/j.ajp.2020.102083
- Sadler P, McLaren S, Klein B, Harvey J, Jenkins M. 2018. Cognitive behavior therapy for older adults with insomnia and depression: a randomized controlled trial in community mental health services. Sleep. 41(8). doi:10.1093/sleep/zsy104
- Shepardson RL, Johnson EM, Possemato K, Arigo D, Funderburk JS. 2018. Perceived barriers and facilitators to implementation of peer support in Veterans Health Administration Primary Care-Mental Health Integration settings. Psychol Serv. 16:433–444.
- Sohrabi C, Alsafi Z, O'Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R. 2020. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. 76:71–76. doi:10.1016/j.ijsu.2020.02.034
- Spitzer RL, Kroenke K, Williams JB, Löwe B. 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 166(10):1092–1097. doi:10.1001/archinte.166.10.1092
- Statistica. 2019. Population of Alberta, by age and sex. [accessed 6 Aug 2020]. https://www.statista.com/statistics/605969/population-of-alberta-by-age-and-sex/
- Su L, Ma X, Yu H, Zhang Z, Bian P, Han Y, Sun J, Liu Y, Yang C, Geng J, et al. 2020. The different clinical characteristics of corona virus disease cases between children and their families in China - the character of children with COVID-19. Emerg Microbes Infect. 9(1):707–713. doi:10.1080/22221751.2020.1744483
- Sutton DA, Moldofsky H, Badley EM. 2001. Insomnia and health problems in Canadians. Sleep. 24(6):665–670. doi:10.1093/sleep/24.6.665
- Vgontzas AN, Bixler EO, Lin H-M, Prolo P, Mastorakos G, Vela-Bueno A, Kales A, Chrousos GP. 2001. Chronic insomnia is associated with nyctohemeral activation of the hypothalamic-pituitary-adrenal axis: clinical implications. J Clin Endocrinol Metab. 86(8):3787–3794. doi:10.1210/jcem.86.8.7778
- Viana MC, Andrade LH. 2012. Lifetime Prevalence, age and gender distribution and age-of-onset of psychiatric disorders in the São Paulo Metropolitan Area, Brazil: results from the São Paulo Megacity Mental Health Survey. Braz J Psychiatry. 34(3):249–260. doi:10.1016/j.rbp.2012.03.001
- Walsh PE, McMillan SS, Stewart V, Wheeler AJ. 2018. Understanding paid peer support in mental health. Disabil Soc. 33(4):579–597. doi:10.1080/09687599.2018.1441705
- Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, et al. 2020. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 323(11):1061. doi:10.1001/jama.2020.1585
- Wang S, Xie L, Xu Y, Yu S, Yao B, Xiang D. 2020. Sleep disturbances among medical workers during the outbreak of COVID-2019. Occup Med (Lond). 19:210–232.
- Xu S, Li Y. 2020. Beware of the second wave of COVID-19. Lancet. 395(10233):1321–1322. doi:10.1016/S0140-6736(20)30845-X
- Yin R, Feng W, Wang T, Chen G, Wu T, Chen D, Lv T, Xiang D. 2020. Concomitant neurological symptoms observed in a patient diagnosed with coronavirus disease 2019. J Med Virol. 92(10):1782–1784. doi:10.1002/jmv.25888
- Yu S, Kowitt SD, Fisher EB, Li G. 2018. Mental health in China: stigma, family obligations, and the potential of peer support. Community Ment Health J. 54(6):757–764. doi:10.1007/s10597-017-0182-z
- Zhang W-R, Wang K, Yin L, Zhao W-f, Xue Q, Peng M, Min B-Q, Tian Q, Leng H-X, Du J-L, et al. 2020. Mental health and psychosocial problems of medical health workers during the COVID-19 epidemic in China. Psychother Psychosom. 89(4):242–249. doi:10.1159/000507639
- Zvolensky MJ, Kauffman BY, Bogiaizian D, Viana AG, Bakhshaie J, Peraza N. 2019. Worry among Latinx college students: relations to anxious arousal, social anxiety, general depression, and insomnia. J Am Coll Health. doi:10.1080/07448481.2019.1686004