Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study.

STUDY OBJECTIVES: Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization. METHODS: Four thousand five hundred and twenty individuals (age 35.6 ±â€…9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n = 3221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e. the number of weeks in which at least one service from the patient's general practitioner (GP) was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics. RESULTS: Three hundred and nineteen individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95% CI: 1.9, 4.1] fold higher odds of reporting poor health and with an average of 1.4 [95% CI: 0.7, 2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization. CONCLUSIONS: Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers.

Nighttime smartphone use is associated with sleep problems, which in turn have a bidirectional association with overweight. We aim to investigate whether nighttime smartphone use and sleep are related to overweight and metabolic dysfunction in adult populations. We used data from three population samples (aged 16-89) from the SmartSleep Study, which included survey data (N = 29,838), high-resolution tracking data (N = 3446), follow-up data (N = 1768), and cardiometabolic risk markers (N = 242). Frequent self-reported nighttime smartphone use was associated with 51% higher odds (95% CI: 1.32; 1.70) of overweight compared with no use. Tracked nighttime smartphone use was also associated with overweight. Similar results were found for obesity as an outcome. No consistent associations were found between nighttime smartphone use and cardiometabolic risk markers in a small subsample of healthy young women. Poor sleep quality (vs. good sleep quality) was associated with overweight (OR = 1.19, 85% CI: 1.10; 1.28). Overall, frequent nighttime smartphone use was consistently associated with overweight and a higher BMI across diverse population samples. The bidirectional interplay between nighttime smartphone use, sleep, and overweight may create a vicious circle of metabolic dysfunction over time. Therefore, nighttime smartphone use may be a potential target point for public health interventions to reduce overweight at the population level.

Acute and Long-Term Changes in Blood-Borne Biomarkers in Response to Dynamic Standing in Nonambulant Children With Cerebral Palsy.

PURPOSE: To investigate acute and long-term changes in hormonal and inflammatory biomarkers in nonambulant children with cerebral palsy in response to dynamic standing exercise. METHODS: Fourteen children with severe cerebral palsy were recruited. Anthropometrics and body composition measures were obtained. Physical activity levels before the study were assessed using hip-worn accelerometry. All children underwent a 30-minute dynamic standing exercise using the Innowalk standing aid. Respiratory data during exercise were collected using indirect calorimetry. Blood samples were collected before and after exercise. Blood samples were also obtained after two 16-week exercise protocols, in a resting state. Hormonal and inflammatory metabolites were measured from blood serum/plasma, and acute and long-term changes in biomarker levels were assessed using Wilcoxon signed-rank tests. RESULTS: Of the 14 children at baseline, all had slightly/moderately/severely elevated C-reactive protein and cortisol levels. C-reactive protein levels were decreased following a 30-minute bout of dynamic standing (before exercise: 53 mg/L [interquartile range: 40-201]; after exercise: 39 mg/L [interquartile range: 20-107]; P = .04). CONCLUSIONS: We show that several hormonal and inflammatory biomarkers are dysregulated in children with cerebral palsy. Our preliminary results from a small, but deep-phenotyped prospective cohort indicate acute and long-term alterations of several biomarkers in response to exercise.

Chronic and Transient Loneliness in Western Countries: Risk Factors and Association With Depression. A 2-Year Follow-Up Study.

INTRODUCTION: Our aim was to test risk factors for chronic and transient loneliness as well as cross-sectional and longitudinal associations of courses of loneliness with depression. METHODS: Responses from participants in Wave 5 (T1, 2013) and Wave 6 (T2, 2015) of The Survey of Health, Ageing and Retirement in Europe (SHARE) (N = 45,490) were analyzed. The existence of clinically significant symptoms of depression was defined as reporting a value greater than or equal to 4 on the Euro-D scale. Loneliness was measured through the 3-item UCLA loneliness scale and a single question. Both measures were tested in separate regression models to identify risk factors for transient (loneliness at T1) and chronic (loneliness at T1 and T2) loneliness as well as their associations with depression. RESULTS: Chronic loneliness was observed in 47%-40% of the cases of loneliness, according to the UCLA scale and the single question, respectively. Risk factors for chronic loneliness in both models were being female, not being married, having a low educational level, having poor mental and physical health, being limited in activities, having a poor social network, and living in a culturally individualistic country. Risk factors for transient loneliness were less robust and no significant effects were found for variables such as sex and physical health in both models, education level in the UCLA measure model, and social network size in the single question model. Chronic loneliness also showed a strong association with depression in the cross-sectional model and a marked one in the longitudinal model. CONCLUSION: The courses of loneliness are relevant in the study of its risk factors and association with depression.

Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities.

The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.

Nighttime smartphone use, sleep quality, and mental health: investigating a complex relationship.

STUDY OBJECTIVES: This study investigated the complex relationship between nighttime smartphone use, sleep, and mental health among adult populations in Denmark. METHODS: Data from three interconnected samples (aged 16-89 years) from the SmartSleep Study included 5798 individuals with survey and register data; 4239 individuals also provided high-resolution smartphone tracking data. Logistic regression models and causal discovery algorithms, which suggest possible causal pathways consistent with the underlying data structure, were used to infer the relationship between self-reported and tracked nighttime smartphone use, self-reported sleep quality, mental health indicators, and register-based psychotropic medication use. RESULTS: Frequent self-reported nighttime smartphone use was associated with high perceived stress (OR: 2.24, 95% CI = 1.42 to 3.55) and severe depressive symptoms (OR: 2.96, 95% CI = 2.04 to 4.28). We found no clear associations between tracked nighttime smartphone use and mental health outcomes, except for the cluster that used their smartphones repeatedly during the sleep period, which was associated with severe depressive symptoms (OR = 1.69, 95% CI = 1.24 to 2.31). Poor sleep quality (vs. good sleep quality) was associated with high perceived stress (OR = 5.07, 95% CI = 3.72 to 6.90), severe depressive symptoms (OR = 9.67, 95% CI = 7.09 to 13.19), and psychotropic medication use (OR = 2.13, 95% CI = 1.36 to 3.35). The causal discovery models suggest that nighttime smartphone use affects mental health through both problematic smartphone use and poor sleep quality. CONCLUSION: The complex relationship between nighttime smartphone use, sleep, and poor mental health may create a vicious circle over time, and nighttime smartphone use may constitute a potential leverage point for public health interventions aimed at improving sleep and mental health.

Saving time and money in biomedical publishing: the case for free-format submissions with minimal requirements.

BACKGROUND: Manuscript preparation and the (re)submission of articles can create a significant workload in academic jobs. In this exploratory analysis, we estimate the time and costs needed to meet the diverse formatting requirements for manuscript submissions in biomedical publishing. METHODS: We reviewed 302 leading biomedical journals' submission guidelines and extracted information on the components that tend to vary the most among submission guidelines (the length of the title, the running title, the abstract, and the manuscript; the structure of the abstract and the manuscript, number of items and references allowed, whether the journal has a template). We estimated annual research funding lost due to manuscript formatting by calculating hourly academic salaries, the time lost to reformatting articles, and quantifying the total number of resubmissions per year. We interviewed several researchers and senior journal editors and editors-in-chief to contextualize our findings and develop guidelines that could help both biomedical journals and researchers work more efficiently. RESULTS: Among the analyzed journals, we found a huge diversity in submission requirements. By calculating average researcher salaries in the European Union and the USA, and the time spent on reformatting articles, we estimated that ~ 230 million USD were lost in 2021 alone due to reformatting articles. Should the current practice remain unchanged within this decade, we estimate ~ 2.5 billion USD could be lost between 2022 and 2030-solely due to reformatting articles after a first editorial desk rejection. In our interviews, we found alignment between researchers and editors; researchers would like the submission process alignment between researchers and editors; researchers would like the submission process to be as straightforward and simple as possible, and editors want to easily identify strong, suitable articles and not waste researchers' time. CONCLUSIONS: Based on the findings from our quantitative analysis and contextualized by the qualitative interviews, we conclude that free-format submission guidelines would benefit both researchers and editors. However, a minimum set of requirements is necessary to avoid manuscript submissions that lack structure. We developed our guidelines to improve the status quo, and we urge the publishers and the editorial-advisory boards of biomedical journals to adopt them. This may also require support from publishers and major international organizations that govern the work of editors.

Variations in sociodemographic and health-related factors are linked to distinct clusters of individuals with depression based on the PHQ-9 instrument: NHANES 2007-2018.

BACKGROUND: Depression is a heterogeneous disease. Identification of latent depression subgroups and differential associations across these putative groups and sociodemographic and health-related factors might pave the way toward targeted treatment of individuals. METHODS: We used model-based clustering to identify relevant subgroups of 2900 individuals with moderate to severe depression (defined as scores ≥10 on the PHQ-9 instrument) from the NHANES cross-sectional survey. We used ANOVA and chi-squared tests to assess associations between cluster membership and sociodemographics, health-related variables, and prescription medication use. RESULTS: We identified six latent clusters of individuals, three based on depression severity and three differentially loaded by somatic and mental components of the PHQ-9. The Severe mental depression cluster had the most individuals with low education and income (P < 0.05). We observed differences in the prevalence of numerous health conditions, with the Severe mental depression cluster showing the worst overall physical health. We observed marked differences between the clusters regarding prescription medication use: the Severe mental depression cluster had the highest use of cardiovascular and metabolic agents, while the Uniform severe depression cluster showed the highest use of central nervous system and psychotherapeutic agents. LIMITATIONS: Due to the cross-sectional design we cannot make conclusions about causal relationships. We used self-reported data. We did not have access to a replication cohort. CONCLUSIONS: We show that socioeconomic factors, somatic diseases, and prescription medication use are differentially associated with distinct and clinically relevant clusters of individuals with moderate to severe depression.

Assessing racial bias in type 2 diabetes risk prediction algorithms.

Risk prediction models for type 2 diabetes can be useful for the early detection of individuals at high risk. However, models may also bias clinical decision-making processes, for instance by differential risk miscalibration across racial groups. We investigated whether the Prediabetes Risk Test (PRT) issued by the National Diabetes Prevention Program, and two prognostic models, the Framingham Offspring Risk Score, and the ARIC Model, demonstrate racial bias between non-Hispanic Whites and non-Hispanic Blacks. We used National Health and Nutrition Examination Survey (NHANES) data, sampled in six independent two-year batches between 1999 and 2010. A total of 9,987 adults without a prior diagnosis of diabetes and with fasting blood samples available were included. We calculated race- and year-specific average predicted risks of type 2 diabetes according to the risk models. We compared the predicted risks with observed ones extracted from the US Diabetes Surveillance System across racial groups (summary calibration). All investigated models were found to be miscalibrated with regard to race, consistently across the survey years. The Framingham Offspring Risk Score overestimated type 2 diabetes risk for non-Hispanic Whites and underestimated risk for non-Hispanic Blacks. The PRT and the ARIC models overestimated risk for both races, but more so for non-Hispanic Whites. These landmark models overestimated the risk of type 2 diabetes for non-Hispanic Whites more severely than for non-Hispanic Blacks. This may result in a larger proportion of non-Hispanic Whites being prioritized for preventive interventions, but it also increases the risk of overdiagnosis and overtreatment in this group. On the other hand, a larger proportion of non-Hispanic Blacks may be potentially underprioritized and undertreated.

What factors explain the changes in major depressive disorder symptoms by age group during the COVID-19 pandemic? A longitudinal study.

BACKGROUND: Data collected during the COVID-19 pandemic suggest an increase in major depressive disorder (MDD) among younger adults. The current study aims to assess the association of age groups and MDD risk before and during the COVID-19 pandemic and quantify the effect of potential mediating variables such as loneliness, social support, resilience, and socioeconomic factors. METHODS: A representative sample of Spanish adults was interviewed before (2019, N = 1880) and during (2020, N = 1103) the COVID-19 pandemic. MDD was assessed using the CIDI, loneliness through the UCLA scale, social support through the OSSS-3, resilience with the 6-BRS, and worsened economic circumstances and unemployment through a single question. Mixed-models were used to study changes in MDD by age group. Regression models were constructed to quantify the association between age and potential mediators, as well as their mediating effect on the association between age group and MDD. RESULTS: Among the younger age cohorts (18-29 and 30-44 years) the probability of having MDD during the pandemic increased from 0.04 (95 % CI: 0.002-0.09) to 0.25 (0.12-0.39) and from 0.02 (-0.001-0.03) to 0.11 (0.04-0.17), respectively. Some 36.6 % of the association between age and risk of MDD during the pandemic was explained by loneliness (12.0 %), low resilience (10.7 %), and worsened economic situation (13.9 %). LIMITATIONS: Reliance on self-report data and generalizability of the findings limited to the Spanish population. CONCLUSIONS: Strategies to decrease the impact of a pandemic on depressive symptoms among young adults should address loneliness, provide tools to improve resilience, and enjoy improved financial support.

Obsessive-Compulsive Disorder During the COVID-19 Pandemic-A Systematic Review.

Background: The COVID-19 pandemic and its associated restrictions may contribute to a deterioration in mental health; individuals with obsessive-compulsive disorder (OCD) may be particularly affected. This systematic review aimed to investigate the effects of the current pandemic on people diagnosed with OCD, and whether pandemics may affect the development of OCD symptoms. Methods: We conducted a systematic search using NCBI PubMed, SCOPUS, and Google Scholar on February 9, 2021. Research articles related to OCD and COVID-19 or other pandemics were attempted to be identified using pre-defined search terms. Case reports, clinical guidelines, letters, and clinical research articles including ≥100 participants were included; reviews were excluded. The systematic review adheres to PRISMA guidelines and the Newcastle-Ottawa Scale was used to assess the quality of the included clinical research articles. Results: A total of 79 articles were included in the full-text assessment. Of these, 59 were clinical research articles, two were clinical guidelines, six were case reports, and 12 were letters. The research articles examined OCD symptoms in adult patients with diagnosed OCD, the general population, pregnant women, healthcare workers, students, and young adults, children, and adolescents. Only one study on OCD in previous pandemics was identified. Conclusion: This systematic review found that people both with and without diagnosed OCD prior to the pandemic generally experienced a worsened landscape of symptoms of OCD during the COVID-19 pandemic. However, the responses are heterogeneous and many factors other than the pandemic seemed to affect the development of OCD symptoms. To prevent the impairment of symptoms and the development of new cases, close monitoring of patients with OCD and education of the general public is essential. Literature is still limited; thus, multinational and cross-cultural, longitudinal studies are warranted to gain further insights on this topic.

Housing environment and mental health of Europeans during the COVID-19 pandemic: a cross-country comparison.

Many studies have investigated the impact of the COVID-19 pandemic on mental health. Throughout the pandemic, time spent at home increased to a great extent due to restrictive measures. Here we set out to investigate the relationship between housing conditions and the mental health of populations across European countries. We analyzed survey data collected during spring 2020 from 69,136 individuals from four cohorts from Denmark, France, and the UK. The investigated housing conditions included household density, composition, and crowding, access to outdoor facilities, dwelling type, and urbanicity. The outcomes were loneliness, anxiety, and life satisfaction. Logistic regression models were used, and results were pooled using random-effects meta-analysis. In the meta-analysis, living alone was associated with higher levels of loneliness (OR = 3.08, 95% CI 1.87-5.07), and lower life satisfaction (OR = 1.27, 95% CI 1.05-0.55), compared to living with others. Not having access to an outdoor space and household crowding were suggestively associated with worse outcomes. Living in crowded households, living alone, or lacking access to outdoor facilities may be particularly important in contributing to poor mental health during a lockdown. Addressing the observed fundamental issues related to housing conditions within society will likely have positive effects in reducing social inequalities, as well as improving preparedness for future pandemics.

Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults.

STUDY OBJECTIVES: The early detection of mental disorders is crucial. Patterns of smartphone behavior have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behavior to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms. METHODS: The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4522 Danish adults and tested in an independent test set comprising of 1885 adults. The prediction models utilized comprehensive information on subjective smartphone behavior, objective night-time smartphone behavior, and self-reported sleep behavior. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model. RESULTS: Neither subjective nor objective smartphone behavior was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC = 0.75, 95% CI: 0.72-0.78) for perceived stress and (AUC = 0.83, 95%CI: 0.80-0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication. CONCLUSIONS: Sleep behavior is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behavior.

Time trends in mental health indicators during the initial 16 months of the COVID-19 pandemic in Denmark.

BACKGROUND: The COVID-19 pandemic and its associated national lockdowns have been linked to deteriorations in mental health worldwide. A number of studies analysed changes in mental health indicators during the pandemic; however, these studies generally had a small number of timepoints, and focused on the initial months of the pandemic. Furthermore, most studies followed-up the same individuals, resulting in significant loss to follow-up and biased estimates of mental health and its change. Here we report on time trends in key mental health indicators amongst Danish adults over the course of the pandemic (March 2020 - July 2021) focusing on subgroups defined by gender, age, and self-reported previously diagnosed chronic and/or mental illness. METHODS: We used time-series data collected by Epinion (N=8,261) with 43 timepoints between 20 March 2020 and 22 July 2021. Using a repeated cross-sectional study design, independent sets of individuals were asked to respond to the Copenhagen Corona-Related Mental Health questionnaire at each timepoint, and data was weighted to population proportions. The six mental health indicators examined were loneliness, anxiety, social isolation, quality of life, COVID-19-related worries, and the mental health scale. Gender, age, and the presence of previously diagnosed mental and/or chronic illness were used to stratify the population into subgroups for comparisons. RESULTS: Poorer mental health were observed during the strictest phases of the lockdowns, whereas better outcomes occurred during reopening phases. Women, young individuals (<34 yrs), and those with a mental- and/or chronic illness demonstrated poorer mean time-series than others. Those with a pre-existing mental illness further had a less reactive mental health time-series. The greatest differences between women/men and younger/older age groups were observed during the second lockdown. CONCLUSIONS: People with mental illness have reported disadvantageous but stable levels of mental health indicators during the pandemic thus far, and they seem to be less affected by the factors that result in fluctuating time-series in other subgroups.

Organizational Justice and Long-term Metabolic Trajectories: A 25-Year Follow-up of the Whitehall II Cohort.

CONTEXT: Organizational justice has been linked to lower risk of several chronic conditions among employees, but less is known about the long-term mechanisms underlying this risk reduction. OBJECTIVE: To assess whether self-reported organizational justice is associated with individual and composite long-term metabolic trajectories. DESIGN: Twenty-five-year follow-up of the Whitehall II prospective cohort study. SETTING: Middle-aged public servants from the United Kingdom. PARTICIPANTS: Data on 8182 participants were used. MAIN OUTCOME MEASURES: Levels of 11 anthropometric, glycemic, lipid, and blood pressure biomarkers were measured at 5 timepoints (1991-2013). We used generalized estimating equations and group-based trajectory modeling to investigate the relationship between organizational justice and biomarker trajectories. RESULTS: High vs low organizational justice were associated with lower waist (-1.7 cm) and hip (-1 cm) circumference, body mass index (-0.6 kg/m2), triglycerides (-1.07 mmol/L), and fasting insulin (-1.08 µIU/mL) trajectories. Two latent metabolic trajectory clusters were identified: a high- and a low-risk cluster. High organizational justice (vs low) were associated with belonging to the low-risk cluster (pooled odds ratio = 1.47). The low-risk cluster demonstrated lower baseline levels of most biomarkers and better glycemic control, whereas the high-risk cluster showed higher baseline levels of most biomarkers, glycemic deterioration, but also greater improvements in lipid levels over time. CONCLUSIONS: People with high organizational justice had more favorable long-term cardiometabolic biomarker patterns than those with low organizational justice, indicating a potential mechanism contributing to the lower risk of chronic diseases in the first group. Further intervention studies are warranted to determine whether improvement of organizational justice might improve long-term health.

A prospective study of the relationships between movement and glycemic control during day and night in pregnancy.

Both disturbed sleep and lack of exercise can disrupt metabolism in pregnancy. Accelerometery was used to objectively assess movement during waking (physical activity) and movement during sleeping (sleep disturbance) periods and evaluated relationships with continuous blood glucose variation during pregnancy. Data was analysed prospectively. 15-women without pre-existing diabetes mellitus wore continuous glucose monitors and triaxial accelerometers from February through June 2018 in Sweden. The relationships between physical activity and sleep disturbance with blood glucose rate of change were assessed. An interaction term was fitted to determine difference in the relationship between movement and glucose variation, conditional on waking/sleeping. Total movement was inversely related to glucose rate of change (p < 0.001, 95% CI (- 0.037, - 0.026)). Stratified analyses showed total physical activity was inversely related to glucose rate of change (p < 0.001, 95% CI (- 0.040, - 0.028)), whereas sleep disturbance was not related to glucose rate of change (p = 0.07, 95% CI (< - 0.001, 0.013)). The interaction term was positively related to glucose rate of change (p < 0.001, 95% CI (0.029, 0.047)). This study provides temporal evidence of a relationship between total movement and glycemic control in pregnancy, which is conditional on time of day. Movement is beneficially related with glycemic control while awake, but not during sleep.

Mental health indicators in pregnant women compared with women in the general population during the coronavirus disease 2019 pandemic in Denmark.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic and the associated regulations issued to minimize risk of disease transmission seem to have had an impact on general mental health in most populations, but it may have affected pregnant women even more because of pregnancy-related uncertainties, limited access to healthcare resources, and lack of social support. We aimed to compare the mental health response among pregnant women with that in similarly aged women from the general population during the first wave of the COVID-19 pandemic. MATERIAL AND METHODS: From April 14 to July 3, 2020, 647 pregnant women in their second trimester were enrolled in this study. For comparison, 858 women from the general Danish population (20-46 years) were sampled from an ongoing observational study. Participants responded to a questionnaire including six mental health indicators (concern level, perceived social isolation, quality of life, anxiety, mental health, and loneliness). Loneliness was measured using the UCLA Three-item Loneliness Scale and anxiety by the Common Mental Health Disorder Questionnaire 4-item Anxiety Subscale. RESULTS: The pregnant women had better scores during the entire study period for all mental health indicators, and except for concerns, social isolation, and mental health, the differences were also statistically significant. Pregnant women were more concerned about becoming seriously ill (40.2% vs. 29.5%, p < 0.001), whereas the general population was more concerned about economic consequences and prospects. Many pregnant women reported negative feelings associated with being pregnant during the COVID-19 pandemic and concerns regarding social isolation and regulation-imposed partner absence during hospital appointments and childbirth. All mental health indicators improved as Denmark began to reopen after the first wave of the pandemic. CONCLUSIONS: Pregnant women exhibited lower rates of poor mental health compared with the general population. However, they were more concerned about becoming seriously ill, expressed negative feelings about being pregnant during the pandemic, and were worried about the absence of their partner due to imposed regulations. These finding may be taken into account by policy-makers during pandemics to balance specific preventive measures over the potential mental health deterioration of pregnant women.