Worry about COVID‐19 as a predictor of future insomnia

Received: 17 December 2021 | Revised: 17 January 2022 | Accepted: 30 January 2022 DOI: 10.1111/jsr.13564 RESEARCH ARTICLE Worry about COVID-19 as a predictor of future insomnia Lily A. Brown1 | Gabriella E. Hamlett1 | Yiqin Zhu1 | Joshua F. Wiley2 | Tyler M. Moore1,3 | Grace E. DiDomenico3 | Elina Visoki3 | David M. Greenberg4 | Ruben C. Gur1,3 | Raquel E. Gur1,3 | Ran Barzilay1,3,5 1 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA 2 School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia 3 Lifespan Brain Institute of the Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania, USA 4 Bar Ilan University, Ramat Gan, Israel 5 Children’s Hospital of Philadelphia Department of Child Adolescent Psychiatry and Behavioral Sciences, Philadelphia, Pennsylvania, USA Summary The coronavirus disease 2019 (COVID-19) pandemic resulted in significant increases in insomnia, with up to 60% of people reporting increased insomnia. However, it is unclear whether exposure to risk factors for the virus or worries about COVID-19 are more strongly associated with insomnia. Using a three-part survey over the course of the first 6 months of the pandemic, we evaluated associations between COVID-19 exposures, COVID-19 worries, and insomnia. We hypothesised that COVID-19related worries and exposure to risk of COVID-19 would predict increases in insomnia. Participants (N = 3,560) completed a survey at three time-points indicating their exposures to COVID-19 risk factors, COVID-19-related worries, and insomnia. COVID-19 worry variables were consistently associated with greater insomnia sever- Correspondence Lily A. Brown, 3535 Market Street Suite 600N, Philadelphia, PA 19104, USA. Email: [email protected] ity, whereas COVID-19 exposure variables were not. COVID-19 worries decreased Funding information National Institute of Mental Health, Grant/Award Number: K23MH120437 enced increases in COVID-19 worries also experienced increases in insomnia severity. significantly over time, and there were significant interactions between change in COVID-19 worries and change in insomnia severity over time. Individuals who experiChanges in worry during the COVID-19 pandemic were associated with changes in insomnia; worries about COVID-19 were a more consistent predictor of insomnia than COVID-19 exposures. Evidence-based treatments targeting virus-related worries may improve insomnia during this and future calamities. KEYWORDS anxiety, health-anxiety, psychology, sleep 1 | I NTRO D U C TI O N post-traumatic stress disorder (PTSD), and generalised anxiety disorder (GAD) (Ford & Kamerow, 1989; Ohayon & Shapiro, 2000; Insomnia, the subjective perception of an inability to fall or stay Riemann, 2007; Soldatos, 1994). asleep or early morning awakenings, occurs in 10%–30% of the pop- During the global coronavirus disease 2019 (COVID-19) pan- ulation and can cause significant distress and impairment (Aernout demic, 30%–60% of people reported increased insomnia symptoms, et al., 2021; American Psychiatric Association, 2013; Dopheide, representing a doubling or tripling of risk (Cai et al., 2020; Gao & 2020; Ohayon, 2002; Sivertsen, Hysing, Harvey, & Petrie, 2021). Scullin, 2020; Huang et al., 2020; Pappa et al., 2020; Zhan et al., Insomnia has significant deleterious consequences on physical 2020; Zhao et al., 2020). Given that the COVID-19 pandemic has far- health, including high blood pressure, congestive heart failure, reaching consequences on health, finances, job security, and social diabetes, and stroke (Quan, 2009; Sofi et al., 2014). In addition, connection, each of which are independent predictors of insomnia individuals with insomnia have a significantly elevated risk for psy- (Burgard & Ailshire, 2009; Hamilton et al., 2007; Léger, Guilleminault, chiatric comorbidities, including major depressive disorder (MDD), Bader, Lévy, & Paillard, 2002), it is important to understand which J Sleep Res. 2022;00:e13564. https://doi.org/10.1111/jsr.13564 wileyonlinelibrary.com/journal/jsr © 2022 European Sleep Research Society | 1 of 11 2 of 11 | aspects of the pandemic are associated with insomnia symptoms. BROWN ET AL. 2 | M E TH O DS Identifying the key drivers of insomnia can inform appropriate interventions to reduce insomnia symptoms during the COVID-19 pan- 2.1 | Participants demic and future calamities. The tendency to worry before and in bed is associated with Participants were recruited to complete a survey offered in English sleep interference and other adverse mental health outcomes (Bajaj, or Hebrew through a crowdsourcing website (Barzilay et al., 2020). Blair, Schwartz, Dobbertin, & Blair, 2020; Harvey, 2002). Worries The study was advertised through: (1) the researchers’ social net- about COVID-19 in particular may exacerbate symptoms of insom- works, including emails to colleagues around the world; (2) social nia during the pandemic, as demonstrated by cross-sectional data media; (3) the University of Pennsylvania and Children’s Hospital of from India (Bajaj et al., 2020), China (Huang et al., 2020; Zhan et al., Philadelphia internal notifications and websites; and (4) organisa- 2020; Zhang et al., 2020), Greece (Voitsidis et al., 2020), and France tional mailing lists. As described in Table 1, participants (N = 3,560) (Kokou-Kpolou, Megalakaki, Laimou, & Kousouri, 2020). In addition, had a mean (range) age of 40 (13–90) years and were mostly women. the association between worry during the pandemic and subsequent The majority identified as White participants, and the majority re- depression is mediated by insomnia (Bajaj et al., 2020), which is in- ported being located in the United States. While recruitment was dependently associated with other negative health outcomes (Balikji open across the lifespan, only 80 participants were aged <18 years et al., 2018; Grandner, Jackson, Pak, & Gehrman, 2012). during completion of the first survey. In addition to information Emerging evidence also suggests that exposure to COVID-19 risk reported in Table 1, participants endorsed self-reported history of factors, such as knowing someone who tested positive for the virus, diagnoses of the following: attention-deficit hyperactivity disorder may account for increases in insomnia. For instance, healthcare (304 participants), anorexia (96), autism spectrum disorder (21), workers who were directly exposed to patients who tested positive bipolar disorder (112), intellectual disability (42), language delay for COVID-19 were at higher risk of insomnia (Li et al., 2020; Zhan (seven), obsessive compulsive disorder (134), personality disorder et al., 2020). However, it is not clear whether actual exposure to risk (28), schizophrenia (four), and PTSD (86). On a single item assessing factors for COVID-19 or worries about the virus are more strongly as- physical health (“Compared to others your age, how would you rate sociated with insomnia symptoms. One study found that individuals your physical health?”) about half of the sample (43%) reported good who were uncertain about whether their family had contracted the health, one-quarter (24%) reported excellent health, one-quarter virus experienced more severe insomnia than individuals who knew (24%) reported average health, and the remainder reported below that their family had contracted the virus (Voitsidis et al., 2020). This average health or preferred not to specify. finding suggests that worries about the virus may be more predictive of insomnia symptoms than exposure to the virus itself, but more research is needed to understand the relative importance of worries 2.2 | Measures about COVID-19 versus exposure to risk factors when predicting insomnia. This research could inform which individuals would ben- 2.2.1 | The ISI efit the most from intervention: those at highest risk of exposure to COVID-19, or those with greater worries about the virus regardless The ISI is a seven-item assessment of insomnia symptoms over the of exposure risk. In addition, research is needed to evaluate longi- prior 2 weeks, with items rated on a scale ranging from 0 (“no prob- tudinal associations between exposure to risk factors, worry, and lems”) to 4 (“very severe”) (Bastien, Vallières, & Morin, 2001). The ISI insomnia over time. is a reliable and valid instrument to determine perceived insomnia This study aimed to investigate the associations among COVID- severity (Bastien et al., 2001). Total scores are categorised as not 19-related exposures, worries about COVID-19, and changes in in- clinically significant (0–7), subthreshold insomnia (8–14), moderate somnia symptoms. We disseminated a survey at three time-points insomnia (15–21), or severe insomnia (22–28). throughout a 6-month period between April and August 2020 using an interactive crowdsourcing research website to measure insomnia symptoms (www.covid19resilience.org). We assessed COVID-19- 2.2.2 | COVID-19-related worries related worries (e.g., worries about contracting COVID-19, dying of COVID-19, family contracting COVID-19, unknowingly infect- Participants were asked to indicate to what degree they were wor- ing others, having COVID-19, and finances); exposures related to ried about a variety of COVID-19-related outcomes on a 0 (“not at COVID-19 (e.g., getting tested for COVID-19, having symptoms of all”) to 4 (“a great deal”) point Likert scale that was developed for COVID-19, knowing someone who tested positive for COVID-19, this survey. Worries included: (1) Contracting COVID-19; (2) Dying knowing someone who died from COVID-19, and job loss during from COVID-19; (3) Family members contracting COVID-19; (4) the pandemic), and insomnia symptoms using the Insomnia Severity Unknowingly infecting others with COVID-19; (5) Currently having Index (ISI). We hypothesised that COVID-19-related worries and ex- COVID-19; and (6) Having significant financial burden because of the posures both would predict increases in insomnia. COVID-19 pandemic. | BROWN ET AL. 3 of 11 TA B L E 1 Demographics of sample who had Insomnia Severity Index available at Times 1–3 Variable Time 1 N = 3,560 Time 2 N = 1,282 Time 3 N = 944 Age, years, mean (SD) 42.56 (14.24) 40.55 (13.47) 41.25 (14.16) Men 782 (21.97) 230 (17.94) 157 (16.63) Women 2766 (77.70) 1050 (81.90) 786 (83.26) Missing 12 (0.33) 2 (0.15) 1 (0.11) Sex, n (%) Race, n (%) White 3067 (86.15) 1142 (89.08) 855 (90.57) Other 469 (13.17) 135 (10.52) 85 (9.00) Missing 15 (0.42) 5 (0.40) 4 (0.42) 749 (21.04) 321 (25.04) 220 (23.31) Clinical diagnosis, n (%) GAD No GAD 2811 (78.96) 961 (74.96) 724 (76.69) MDD 790 (22.19) 313 (24.41) 218 (23.09) No MDD 2770 (77.81) 969 (75.59) 726 (76.91) Country, n (%) Israel 601 (16.88) 203 (15.83) 146 (15.47) USA 2650 (74.43) 1000 (78.00) 745 (78.92) Other country/missing 399 (11.21) 79 (6.16) 0 (0.00) GAD, generalised anxiety disorder; MDD, major depressive disorder. 2.2.3 | COVID-19-related exposures between May 12 and June 21; Time 3 (T3) occurred between August 25 and September 27, 2020. Participants were asked to rate whether they had experienced the following: (1) Being tested for COVID-19; (2) Having experienced symptoms that they feel may be related to COVID-19; (3) Knowing 2.4 | Data analytic plan anyone who tested positive for COVID-19; (4) Knowing someone who died from COVID-19; and (5) Job loss/reduced pay since the The present analysis was conducted on data from participants who start of the COVID-19 pandemic. These variables were scored as provided their email at T1 (N = 3,560) and consented for future con- “Yes” = 1 (experienced) or “No” = 0 (not experienced). tact, from which 1,282 provided data at T2, and 944 provided data at T3. A total of 672 participants had data at all three time-points. Participants who completed T2 (p < 0.001) were significantly older 2.2.4 | Mental health diagnoses at T1 than participants who did not, but there were no differences in age at T1 based on participants who were missing at T3. Male Participants were asked whether they had received a diagnosis of and White participants were significantly more likely to not com- MDD or GAD prior to the COVID-19 pandemic. plete the T2 and T3 assessments (all p < 0.001), as were participants with a self-reported diagnosis of GAD (p < 0.05) or MDD (only at T2, p < 0.01). Participants in Israel were less likely to complete the 2.3 | Procedure observations at T2 and T3 (all p < 0.01). Therefore, these variables were included as covariates in sensitivity analyses throughout. The study was approved by the Institutional Review Board of the Table S1 provides comparisons between participants who provided University of Pennsylvania. After completion of online informed longitudinal data and those who were lost to follow-up. consent, the survey followed and provided personalised feedback Cross-sectional multiple logistic regression analyses were run to on participants’ responses. The feedback aimed to enhance well- establish the relative influence of COVID-19-related exposures vari- being and was offered as an incentive to participate and complete ables and COVID-19-related worries variables (independent variables) follow-up surveys, which were delivered to those who provided in predicting ISI score (insomnia severity, the dependent variable) at their email address and consent for future contact. The Time 1 (T1) each time-point. These analyses were repeated after controlling for survey occurred from April 6 to May 5, 2020; Time 2 (T2) occurred age, gender, race (White participants, Other race), country (United 4 of 11 | BROWN ET AL. States, Israel), self-reported history of depression, and self-reported at baseline. Because surveys were completed ~1 and 5 months after history of GAD. Then, mixed effects multilevel models were run in baseline, time was coded in months. Mixed effects models included which observations across time were nested within participants, random intercepts and random effects of time and unstructured co- with time centred at T1 to allow for a determination of differences variance matrices. Random slopes and intercepts of key variables from TA B L E 2 Cross-sectional multivariable regression results predicting insomnia severity index severity from coronavirus disease 2019 (COVID-19)-related exposures and COVID-19-related worries Time point B (SE) t p 2 Time 1 (Adjusted R = 0.1133) COVID-19-related exposures Being tested for COVID-19 0.484 (0.326) 1.48 0.138 Experiencing symptoms of COVID-19 0.747 (0.212) 3.52 <0.001 Knowing someone who tested positive for COVID-19 −0.173 (0.193) −0.90 0.370 Knowing someone who died from COVID-19 1.022 (0.307) 3.33 0.001 Job loss/pay reduction due to COVID-19 −0.439 (0.246) −1.79 0.074 COVID-19-related worries Worries about getting COVID-19 0.155 (0.137) 1.13 0.258 Worries about dying from COVID-19 0.236 (0.117) 2.01 0.044 Worries about family contracting COVID-19 0.470 (0.119) 3.94 <0.001 Worries about infecting others 0.234 (0.099) 2.35 0.019 Worries about having COVID-19 0.543 (0.110) 4.95 <0.001 Worries about finances due to COVID-19 0.719 (0.083) 8.64 <0.001 Time 2 (Adjusted R2 = 0.1493) COVID-19-Related Exposures Being tested for COVID-19 0.470 (0.463) 1.01 0.311 Experiencing symptoms of COVID-19 0.581 (0.324) 1.79 0.073 Knowing someone who tested positive for COVID-19 0.440 (0.303) 1.45 0.147 Knowing someone who died from COVID-19 0.711 (0.425) 1.67 0.095 Job loss/pay reduction due to COVID-19 −0.608 (0.380) −1.60 0.110 COVID-19-related worries Worries about getting COVID-19 −0.108 (0.229) −0.47 0.637 Worries about dying from COVID-19 0.478 (0.206) 2.32 0.020 Worries about family contracting COVID-19 0.797 (0.190) 4.18 <0.001 Worries about infecting others −0.217 (0.159) −1.37 0.172 Worries about having COVID-19 0.992 (0.193) 5.15 <0.001 Worries about finances due to COVID-19 0.962 (0.132) 7.27 <0.001 Being tested for COVID-19 0.372 (0.336) 1.11 0.268 Knowing someone who tested positive for COVID-19 −0.537 (0.371) −1.45 0.148 Knowing someone who died from COVID-19 1.421 (0.415) 3.42 0.001 Job loss/pay reduction due to COVID-19 0.395 (0.453) 0.87 0.383 Time 3 (Adjusted R2 = 0.1284) COVID-19-related exposures COVID-19-related worries Worries about getting COVID-19 −0.112 (0.262) −0.45 0.654 Worries about dying from COVID-19 0.454 (0.226) 2.01 0.045 Worries about family contracting COVID-19 0.459 (0.222) 2.06 0.040 Worries about infecting others 0.155 (0.177) 0.88 0.382 Worries about having COVID-19 0.441 (0.224) 1.97 0.049 Worries about finances due to COVID-19 0.995 (0.156) 6.39 <0.001 | BROWN ET AL. 5 of 11 the cross-sectional analyses (e.g., COVID-19-related worries) were ex- did not change after the inclusion of covariates. Multicollinearity was tracted to allow for an examination of interactions between these key low (mean variance inflation factor [VIF] = 1.46). variables and change over time in ISI severity. Given that there was some evidence for bidirectionality in the At T2, when all exposure and worry variables were entered into a simultaneous model, worries about dying of COVID-19 (p < 0.05), multilevel models (described below), we followed these analyses with worries about family contracting COVID-19 (p < 0.001), worries about a cross-lagged panel analysis to directly test directionality between having COVID-19 (p < 0.001), and financial worries were all significant Worry (calculated as a total score, a sum of all Worry variables to reduce predictors of ISI severity (p < 0.001), whereas all of the exposure vari- the number of analyses) and ISI severity. We followed established pro- ables and worries about getting COVID-19 and infecting others with cedures for this evaluation (Brown et al., 2015, 2018, 2019; Martens COVID-19 were not (all p > 0.073). These results did not change with & Haase, 2006) using MPlus software. This procedure involves testing the inclusion of covariates. Multicollinearity was low (mean VIF = 1.45). a series of four models to evaluate directional associations between At T3, when all exposure and worry variables were entered into two variables. Model 1 included only autoregressive paths, reflecting a simultaneous model, knowing someone who died from COVID-19 stability over time. Model 2 added paths from one construct (sleep at (p < 0.01), worries about dying of COVID-19 (p < 0.05), worries baseline) to the second construct at the subsequent time-point (worry about family contracting COVID-19 (p < 0.05), worries about hav- at 1 month; Figure 2) in addition to the autoregressive paths. Model ing COVID-19 (p < 0.05), and worries about finances were each sig- 3 added paths in the opposite direction to the autoregressive model: nificantly associated with ISI severity (p < 0.001). When covariates worry at baseline predicting sleep at 1 month. Model 4 included bi- were included, only knowing someone who died from COVID-19 directional paths (essentially, this is the combination of Model 2 and (t = 3.32, B = 1.357, SE = 0.409, p < 0.01) and worries about fi- 3). Model fit was evaluated, and chi-square difference tests compared nances remained significantly associated with ISI severity (t = 6.47, the fit of the models. After determining optimal model fit, a follow-up B = 1.015, SE = 0.157, p < 0.001). Of note, the questions assess- “constrained analysis” was conducted to compare model fit wherein ing self-reported symptoms of COVID-19 were not collected at T3. cross-lagged path coefficients were constrained to be equal versus Multicollinearity was low (mean VIF = 1.45). freely estimated. In other words, this model constrained the path from sleep at baseline -> worry at 1 month to be equal to the path from worry at baseline -> sleep at 1 month. Then, model fit was compared 3.2 | Longitudinal analyses between the constrained versus freely estimated model, allowing for a determination of whether constraining the paths to be equal wors- Given that worry variables were consistently associated with ISI sever- ened model fit (an indication that one direction is stronger than the ity in multivariable cross-sectional analyses, whereas exposure variables other, providing evidence for unidirectionality as opposed to bidirec- were less consistently associated with ISI severity, longitudinal models tionality). As we discuss elsewhere (Brown et al., 2015), the correlation matrix must be imported for this constrained analysis, which was calculated using full-information maximum likelihood to account for missing data using corFiml in the “psych” package in R (Revelle, 2013). The results of this constrained analyses must be interpreted with caution, although the comparison between the constrained and unconstrained model can provide useful information about strength of directionality. TA B L E 3 Change in coronavirus disease 2019 (COVID-19) worry variables over time Worries B (95% CI) z p Contracting COVID-19 Time −0.018 (−0.0300, −0.005) −2.74 0.006 Intercept 1.792 (1.769, 1.815) 153.58 <0.001 Time −0.026 (−0.038, −0.015) −4.63 <0.001 Intercept 1.129 (1.104, 1.154) 89.37 <0.001 Dying from COVID-19 3 | R E S U LT S 3.1 | Cross-sectional multivariable results When all T1 COVID-19-related exposures and worries were entered into a simultaneous model, experiencing symptoms of COVID-19 Currently having COVID-19 Time −0.044(−0.057, −0.032) −6.83 <0.001 Intercept 0.878(.856,.900) 78.55 <0.001 Family contracting COVID-19 (p < 0.001), knowing someone who died from COVID-19 (p < 0.01), Time 0.025 (−0.038, −0.012) −3.84 <0.001 worries about dying of COVID-19 (p < 0.01), worries about family con- Intercept 2.494 (2.469, 2.517) 206.19 <0.001 tracting COVID-19 (p < 0.001), worries about infecting others with COVID-19 (p < 0.05), worries about having COVID-19 (p < 0.001), and financial worries were each significantly associated with insomnia severity (p < 0.001), whereas being tested for COVID-19 (p = 0.138), knowing someone who tested positive for COVID-19 (p = 0.370), experiencing job loss (p = 0.074), and worries about getting COVID-19 (p = 0.258) were not associated with ISI severity (Table 2). These results Infecting others with COVID-19 Time −0.017 (−0.0329, −0.003) −2.28 0.023 Intercept 2.129 (2.103,2.155) <0.001 160.37 Financial burden due to COVID-19 Time −0.044 (−0.059, −0.0289) −5.8 <0.001 Intercept 1.428 (1.402,1.456) <0.001 102.75 6 of 11 | BROWN ET AL. were run to evaluate the change in worry variables as predictors of the examples). However, a reverse multilevel model also demonstrated that change in ISI severity. There were significant reductions in all worry var- there were significant interactions between Time and the Slope of ISI iables over time (Table 3). Across all participants, a multilevel model with on all of the Worry variables (Table 5, all p ≤ 0.05). observations nested within participants and a random intercept and slope revealed that there was not a significant change in ISI over time (p = 0.522). However, there were significant interactions between Time 3.3 | Cross-lagged panel analysis and the Slope of all Worry variables (Table 4, all p ≤ 0.01). Specifically, individuals who experienced increases in Worries about COVID-19 Both Model 2 (ISI – > Worry Total Score, Figure 2b, chi-squared also experienced increases in ISI severity over time (see Figure 1a,b, for (2) = 13.59, p < 0.01) and Model 3 (Worry Total Score – > ISI, TA B L E 4 Longitudinal analyses exploring change in Insomnia Severity Index by change in worries B (95% CI) z p Time 0.023 (−0.046, 0.093) 0.66 0.509 Slope of Worries-Getting COVID-19 −0.241 (−7.394, 6.912) −0.07 0.947 Time × Slope of Worries-Getting COVID-19 2.250 (1.001, 3.498) 3.53 <0.001 Intercept of Worries-Getting COVID-19 1.725 (1.238, 2.211) 6.95 <0.001 Intercept 4.645 (3.857, 5.432) 11.56 <0.001 Time 0.070 (−0.016, 0.156) 1.60 0.110 Slope of Worries-Dying from COVID-19 118.684 (32.035, 204.868) 2.69 0.007 Time × Slope of Worries-Dying from COVID-19 3.354 (1.164, 5.544) 3.00 0.003 Intercept of Worries-Dying from COVID-19 5.995 (2.680, 9.309) 3.54 <0.001 Intercept 4.151 (2.670, 5.613) 5.57 <0.001 Time 0.086 (−0.004, 0.176) 1.87 0.062 Slope of Worries-Family getting COVID-19 −5.735 (−18.583, 7.113) −0.87 0.382 Time × Slope of Worries-Family getting COVID-19 4.224 (1.746, 6.702) 3.34 0.001 Intercept of Worries-Family getting COVID-19 1.720 (1.333, 2.107) 8.72 <0.001 Intercept 3.338 (2.588, 4.087) 8.73 <0.001 Time 0.037 (−0.038, 0.112) 0.97 0.33 Slope of Worries-Infecting others w/COVID-19 −2.609 (−10.574, 5.357) −0.64 0.521 ISI ISI ISI ISI Time × Slope of Worries-Infect others w/COVID-19 3.195 (1.377, 5.014) 3.44 0.001 Intercept of Worries-Infect others w/COVID-19 1.462 (1.200, 1.723) 10.94 <0.001 Intercept 4.615 (4.089, 5.141) 17.2 <0.001 Time 0.094 (0.022, 0.187) 2.01 0.045 Slope of Worries-Having COVID-19 10.179 (−5.612, 25.971) 1.26 0.206 ISI Time × Slope of Worries-Having COVID-19 2.679 (1.094, 4.264) 3.31 0.001 Intercept of Worries-Having COVID-19 3.141 (1.972, 4.310) 5.27 <0.001 Intercept 5.507 (5.081, 5.935) 25.27 <0.001 ISI Time 0.041 (−0.041, 0.122) 0.97 0.33 Slope of Worries-Finances due to COVID-19 9.715 (3.181, 16.249) 2.91 0.004 Time × Slope of Worries-Finances due to COVID-19 1.582 (0.382, 2.782) 2.58 0.01 Intercept of Worries-Finances due to COVID-19 2.101 (1.735, 2.467) 12.08 <0.001 Intercept 5.277 (4.919, 5.635) 28.87 <0.001 COVID-19, coronavirus disease 2019; ISI, Insomnia Severity Index. | BROWN ET AL. 7 of 11 Figure 2c, chi-squared (2) = 17.81, p < 0.001) significantly improved which sleep predicted COVID-19) was also observed but was not as model fit relative to Model 1 (Autoregressive Model; column 5, consistent as the prediction from worries to insomnia. These find- Table 6, Figure 2a). Model 4, which contained bidirectional paths ings suggest that the interpretation of risk about COVID-19, rather between ISI and Worry Total Score, significantly improved model fit than exposure to risk factors for COVID-19 itself, influenced insom- above and beyond Model 2 (chi-squared (2) = 16.70, p < 0.001) and nia severity over time and there was some evidence to support uni- 3 (chi-squared (2) = 12.38, p < 0.01; column 6, Table 6, Figure 2d). directionality for this association. However, the constrained model had significantly worsened fit The findings from this study are consistent with prior cross- relative to the model with freely estimated paths (chi-squared sectional studies. Specifically, in a Greek sample, worries about (2) = 25.90, p < 0.001; column 7, Table 6). Examination of param- COVID-19 were associated with elevations in insomnia sever- eter estimates from Model 4 revealed that the strength of the as- ity (Voitsidis et al., 2020). In this prior study, participants who re- sociation from ISI – > Worry was larger than from Worry – > ISI. ported “not knowing” whether they or their loved ones contracted However, the only significant cross-lagged paths were earlier in the COVID-19 had more severe insomnia than individuals who were cer- longitudinal model (from baseline to one-month, and not from one- tain that they or their loved ones had contracted the virus (Voitsidis to four-months). et al., 2020). This is notable in that it provides further evidence that worries about the virus (or uncertainty in contemporary research, which may lead to worries) may account for more variance in sleep 4 | DISCUSSION disruptions than exposure to the risk of the virus per se. Similarly, in China, COVID-related stress was associated with worsened insom- Across three observations over the initial phase of the COVID-19 nia symptoms during the pandemic (Yun et al., 2020; Zhang et al., pandemic in the United States (April–August 2020), greater sever- 2020), as was increased non-specific (i.e., generalised) worry (Huang ity of COVID-19-related worries was consistently associated with et al., 2020). France also had cross-sectional research demonstrat- elevations in insomnia symptom severity over and above COVID- ing an association between COVID-19-related worries and insomnia 19-related exposure. In contrast, COVID-19-related exposures were (Zhao et al., 2020). The present study replicates these findings in inconsistently associated with insomnia severity after controlling a sample of individuals primarily from the United States and Israel, for COVID-19-related worries. This pattern of findings held after adding a key longitudinal perspective on the dynamics of worries controlling for a variety of demographic factors as well as a self- and sleep over time during a chronic global stressor. reported history of GAD and MDD. In longitudinal analysis, the Consistent with at least one prior study (Huang et al., 2020), in- extent of change in COVID-19-related worries over time was associ- somnia symptoms did not significantly change over time in this study ated with the extent of change in insomnia severity over time. As over the entire sample. Some other studies have found evidence for worries about COVID-19 became more severe, insomnia worsened increased insomnia and anxiety during the pandemic (Cai et al., 2020; over time. Among individuals who reported lessened severity of Gao & Scullin, 2020). Our study potentially explains this discrepancy. COVID-19 worries, insomnia improved. The opposite direction (in Specifically, individuals with heightened COVID-19-related worries F I G U R E 1 (a) Change in Insomnia Severity Index (ISI) by change in worries about getting COVID-19. Lines indicate participants who were at the mean in change in worries about getting COVID-19 over time (“Mean Worries-Getting”), 1 standard deviation (SD) below the mean in changes in worries about getting COVID-19 over time (“−1 SD Worries-Getting”), or 1 SD above the mean in changes in worries about getting COVID-19 over time (“+1 SD Worries-Getting”). (b) Change in ISI by change in worries about Family contracting COVID-19. The lines indicate participants who were at the mean in change in worries about family getting COVID-19 over time (“Mean Worries-Family”), 1 SD below the mean in changes in worries about family getting COVID-19 over time (“−1 SD Worries-Family”), or 1 SD above the mean in changes in worries about family getting COVID-19 over time (“+1 SD Worries-Family”) 8 of 11 | BROWN ET AL. TA B L E 5 Longitudinal analyses exploring change in Worries by change in Insomnia Severity Index B (95% CI) z p Worries about getting COVID-19 Time −0.021 (−0.034, −0.009) −3.27 0.001 ISI Slope −0.173 (−0.367, 0.023) −1.73 0.083 Time × ISI Slope 0.065 (0.020, 0.110) 2.85 0.004 ISI Intercept 0.044 (0.033, 0.054) 8.19 <0.001 Intercept 1.494 (1.410, 1.579) 34.81 <0.001 TA B L E 5 (Continued) B (95% CI) z p Time × ISI Slope 0.057 (0.005, 0.109) 2.15 0.032 ISI Intercept 0.077 (0.064, 0.089) 12.06 <0.001 Intercept 0.784 (0.683, 0.884) 15.30 <0.001 COVID-19, coronavirus disease 2019; ISI, Insomnia Severity Index. experienced worsened insomnia, whereas individuals without these Worries about dying of COVID-19 worries were protected from sleep disruption. However, prior studies did not measure changes in COVID-19-related worries over time, Time −0.028 (−0.039, −0.016) −4.72 <0.001 ISI Slope −0.170 (−0.384, 0.045) −1.55 0.121 Time × ISI Slope 0.077 (0.038,0.116) 3.84 <0.001 nia symptoms. One prior study reported that being infected with ISI Intercept 0.047 (0.036, 0.059) 8.04 <0.001 COVID-19 was associated with an increased risk of clinically signif- Intercept 0.786 (0.693, 0.879) 16.61 <0.001 nor the impact of changes in COVID-19-related worries on insomnia. Our study adds to the literature in this regard. In contrast to some prior studies (Sofi et al., 2014), being tested for COVID-19 did not emerge as an important predictor of insom- icant insomnia, but the odds ratio reported in their study actually Worries about family contracting COVID-19 indicated a decreased risk of insomnia (Kokou-Kpolou et al., 2020). In the present study, some exposure variables were associated with Time −0.024 (−0.037, −0.012) −3.73 <0.001 ISI Slope −0.068 (−0.274, 0.138) −0.64 0.520 Time × ISI Slope 0.064 (0.020, 0.108) 2.85 0.004 ISI Intercept 0.058 (0.047, 0.069) 10.17 <0.001 ated with insomnia severity at all time-points regardless of covariate Intercept 2.061 (1.972, 2.151) 45.09 <0.001 inclusion. In multivariable models, worry variables remained consis- insomnia severity at a given time-point, which suggests that under certain circumstances these risk factors may be important to consider. In particular, knowing someone who died from COVID-19 was consistently associated with increased severity of insomnia at all time-points. However, this variable did not remain significant in all multivariable models. In contrast, all worry variables were associ- Worries about infecting others with COVID-19 tently associated with ISI severity over and above the influence of Time −0.017 (−0.033, −0.002) −2.19 0.028 exposure variables. ISI Slope −0.188 (−0.413, 0.037) −1.64 0.101 help individuals manage their COVID-19-related worries, although Time × ISI Slope 0.073 (0.020, 0.125) 2.71 0.007 ISI Intercept 0.049 (0.036, 0.061) 7.78 <0.001 Intercept 1.764 (1.667, 1.863) 35.29 <0.001 Clinically, these findings suggest that it might be worthwhile to more research is needed. Existing research suggests that healthcare workers who are directly exposed to COVID-19 patients are at higher risk of insomnia (Li et al., 2020; Zhan et al., 2020). However, the present study suggests that increased risk of insomnia may be Worries about having COVID-19 attributable to worries about COVID-19 as opposed to direct exposure to risk. This series of findings has implications for cogni- Time −0.039 (−0.052, −0.026) −5.87 <0.001 ISI Slope −0.141 (−0.320, 0.039) −1.53 0.125 Time × ISI Slope 0.062 (0.016, 0.108) 2.63 0.008 ISI Intercept 0.047 (0.037, 0.056) 9.75 <0.001 be worthwhile to offer strategies to improve their worry (or ability Intercept 0.496 (0.420, 0.573) 12.71 <0.001 to cope with worry), with the hope that this might reduce insomnia −0.035 (−0.050, −0.020) −4.54 <0.001 0.161 (−0.072, 0.394) 1.35 worry (Covin, Ouimet, Seeds, & Dozois, 2008) that also results in Worries about finances during COVID-19 Time ISI Slope tive behavioural therapy, an evidence-based treatment for chronic significant improvements in insomnia symptoms (Harvey & Tang, 2003). Because healthcare workers and community members cannot completely reduce their risk of contracting COVID-19, it might symptoms. There are several notable limitations of this study. First, the majority of respondents were White participants and women, 0.176 which is important as some studies have found that insomnia was elevated among women during the pandemic (Kang et al., 2020; Li et al., 2020). COVID-19 and COVID-19-related insomnia have | 9 of 11 and it will be important to study the effect of worry on insomnia among Black participants during the COVID-19 pandemic. Second, as is to be expected by a study that did not pay participants for survey completion, only about one-third of participants completed all three study assessments. Third, while we attempted to advertise the survey for international completion, most participants reported completing the survey in either the United States or Israel. Similarly, while we attempted to recruit across the lifespan, most participants were adults. Fourth, to allow for a larger sample, all data were collected using self-report and not clinicianrated assessments. Therefore, these findings should be replicated in a more diverse sample and with collateral information from trained clinical interviewers before strong conclusions are drawn. Fifth, participants were not followed after August 2020, which covers the first critical months of the global COVID-19 pandemic but worry and insomnia may have decreased since that time with more knowledge available about the disease and improved access to vaccinations. Finally, we did not assess self-reported diagnoses of sleep disorders or of medications, which is an important limitation. Despite these limitations, our findings are consistent with other cross-sectional studies from across the world, which increase confidence in the results. In summary, this is the first longitudinal study to demonstrate that changes in worry during the COVID-19 pandemic were associated with changes in insomnia. It is also the first study to demonstrate that worries about COVID-19 is a more consistent predictor of insomnia than a variety of COVID-19-related exposures, such as being tested for COVID-19 or knowing someone who tested positive for COVID-19. Results point to the importance of offering evidencebased treatment for help-seeking individuals who report high levels of COVID-19-related worries, regardless of their level of risk of exposure to the virus. Given myriad deleterious health consequences of insomnia, offering evidence-based treatments for worry during the pandemic may reduce insomnia severity and consequently improve functioning. C O N FL I C T O F I N T E R E S T Dr Ran Barzilay serves on the scientific board and reports stock ownership in Taliaz Health, with no conflict of interest relevant to this work. All other authors have no conflicts of interest to disclose. * indicates p < 0.01, ** indicates p < 0.001. Note: AIC, Akaike information criterion; CFI, comparative fit index; df, degrees of freedom; RMSEA, root mean square error of approximation; SRMR, standardised root mean residual; TLI, Tucker–Lewis index. 0.01 0.01 1.1 -25.3 90158.5 0.997 0.999 25.90** 4.69 25.17** 1.13 5.99 4 Full model with constraints 0.02 0.00 0.00 0.03 9.6 -26.4 -16.8 90167.0 90157.4 1.002 0.986 0.996 1.000 - 12.38* 30.38** 17.81** 1.125 1.157 1.38 13.45 4 2 Full model Worry -> Sleep 0.02 0.02 0.04 26.4 15.5 -10.9 90183.8 90172.9 0.979 0.975 0.989 0.994 - 30.38** 16.70** 13.59* 1.14 1.161 18.13 31.55 6 Auto-regressive 4 df Sleep -> Worry 0.02 disproportionately impacted Black Americans (Cheng et al., 2022) Model chi-squared Scale correlation factor TA B L E 6 Cross-lagged panel analysis fit indices Satorra– Bentler scaled chi-squared from base model Satorra–Bentler scaled chisquared from full model Constrained versus freely estimated model difference (using correlation matrix) CFI TLI AIC AIC difference from base model AIC difference from full model SRMR RMSEA BROWN ET AL. AU T H O R C O N T R I B U T I O N S Lily A. Brown developed the study concept. Data collection for the study was led by Ran Barzilay. Lily A. Brown and Yiqin Zhu performed the data analysis and interpretation. Gabriella E. Hamlett and Lily A. Brown drafted the manuscript, and all the authors provided critical revisions, including contributing to interpreting the results and grounding the study in the extant literature. All of the authors approved the final manuscript for submission. DATA AVA I L A B I L I T Y S TAT E M E N T Data are available upon request. 10 of 11 | BROWN ET AL. F I G U R E 2 Results of a cross-lagged panel analysis to directly test directionality between Worry (calculated as a total score, a sum of all Worry variables to reduce the number of analyses) and Insomnia Severity Index severity. (a) Model 1 includes only the autoregressive paths. 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Psychiatry Research, 292, 113304. https://doi.org/10.1016/j.psychres.2020.113304 S U P P O R T I N G I N FO R M AT I O N Additional supporting information may be found in the online version of the article at the publisher’s website. How to cite this article: Brown, L. A., Hamlett, G. E., Zhu, Y., Wiley, J. F., Moore, T. M., DiDomenico, G. E., Visoki, E., Greenberg, D. M., Gur, R. C., Gur, R. E., & Barzilay, R. (2022). Worry about COVID-19 as a predictor of future insomnia. Journal of Sleep Research, 00, e13564. https://doi. org/10.1111/jsr.13564