Dataset Information

Social Rhythm Disruption is Associated with Greater Depressive Symptoms in People with Mood Disorders: Findings from a Multinational Online Survey During COVID-19.

ABSTRACT:

Objectives

Societal restrictions imposed to prevent transmission of COVID-19 may challenge circadian-driven lifestyle behaviours, particularly amongst those vulnerable to mood disorders. The overarching aim of the present study was to investigate the hypothesis that, in the routine-disrupted environment of the COVID-19, amongst a sample of people living with mood disorders, greater social rhythm disruption would be associated with more severe mood symptoms.

Methods

We conducted a two-wave, multinational survey of 997 participants (MAge=39.75±13.39,Female=81.6%) who self-reported a mood disorder diagnosis (i.e., major depressive disorder or bipolar disorder). Respondents completed questionnaires assessing demographics, social rhythmicity (The Brief Social Rhythm Scale), depression symptoms (Patient Health Questionnaire-9), sleep quality and diurnal preference (The Sleep, Circadian Rhythms and Mood questionnaire) and stressful life events during the COVID-19 pandemic (The Social Readjustment Rating Scale).

Results

The majority of participants indicated COVID-19-related social disruption had affected the regularity of their daily routines to at least some extent (n = 788, 79.1%). As hypothesised, lower social rhythmicity was associated with greater depressive symptoms when tested cross-sectionally (standardised β = -.25, t = -7.94, P = 0.000) and when tested using a 2-level hierarchical linear model across two time points (b = -0.14, t = -3.46, df = 264, P ≤ 0.001).

Conclusions

These results are consistent with the social zeitgeber hypothesis proposing that mood disorders are sensitive to life events that disrupt social rhythms.

SUBMITTER: Kahawage P

PROVIDER: S-EPMC9096005 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Social Rhythm Disruption is Associated with Greater Depressive Symptoms in People with Mood Disorders: Findings from a Multinational Online Survey During COVID-19.

Kahawage Piyumi P Bullock Ben B Meyer Denny D Gottlieb John J Crowe Marie M Swartz Holly A HA Yatham Lakshmi N LN Inder Maree M Porter Richard J RJ Nierenberg Andrew A AA Meesters Ybe Y Gordijn Marijke M Haarman Bartholomeus C M BCM Murray Greg G

Canadian journal of psychiatry. Revue canadienne de psychiatrie 20220510 11

<h4>Objectives</h4>Societal restrictions imposed to prevent transmission of COVID-19 may challenge circadian-driven lifestyle behaviours, particularly amongst those vulnerable to mood disorders. The overarching aim of the present study was to investigate the hypothesis that, in the routine-disrupted environment of the COVID-19, amongst a sample of people living with mood disorders, greater social rhythm disruption would be associated with more severe mood symptoms.<h4>Methods</h4>We conducted a ...[more]

PMID: 35535550

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Project description:BackgroundDepression is the most prevalent mental health problem. The need for effective treatments for depression far outstrips the availability of trained mental health professionals. Smartphones and other widely available technologies are increasingly being leveraged to deliver treatments for depression. Whether there are patient characteristics that affect the potency of smartphone interventions for depression is not well understood.ObjectiveThis study aimed to evaluate whether patient characteristics including clinical diagnosis, depression severity, psychosis status, and current use of antidepressant medications impact the effects of an evidence-based smartphone intervention on depressive symptoms.MethodsData were collected as part of a 2-arm randomized controlled trial comparing a multimodal smartphone intervention called FOCUS with a clinic-based intervention. Here, we report on 82 participants assigned to 12 weeks of FOCUS treatment. We conducted assessments of depressive symptoms using the Beck Depression Inventory-second edition (BDI-II) at baseline, postintervention (3 months), and follow-up (6 months). We tested for differences in the amount of improvement in BDI-II scores from baseline to posttreatment and 6-month follow-up between each of the following patient subgroups using 2 (group) × 2 (time) mixed effects models: diagnosis (ie, schizophrenia spectrum disorder vs bipolar disorder vs major depressive disorder), depression severity (ie, minimal-mild vs moderate-severe depression), psychosis status (ie, presence vs absence of psychotic symptoms), and antidepressant use (ie, taking antidepressants vs not taking antidepressants).ResultsThe majority of participants were male (60%, 49/82), African American (65%, 53/82), and middle-aged (mean age 49 years), with a high school education or lower (62%, 51/82). There were no differences in patient demographics across the variables that were used to stratify the analyses. There was a significant group × time interaction for baseline depression severity (F1,76.8=5.26, P=.02 [posttreatment] and F1,77.4=6.56, P=.01 [6-month follow-up]). Participants with moderate or severe depression had significant improvements (t42=3.20, P=.003 [posttreatment] and t42=4.20, P<.001 [6-month follow-up]), but participants with minimal or mild depression did not (t31=0.20, P=.84 [posttreatment] and t30=0.43, P=.67 [6-month follow-up]). There were no significant group × time interactions for diagnosis, psychosis status, or antidepressant medication use. Participants with minimal or mild depression had negligible nonsignificant improvements (<1 point on the BDI-II). Reduction in depression in all other groups was larger (range 1.7-6.5 points on the BDI-II).ConclusionsOur results suggest that FOCUS can be deployed to treat moderate to severe depressive symptoms among people with schizophrenia spectrum disorders, bipolar disorder, and major depressive disorder, in concert with antidepressant medications or without them, in both people with and without active psychotic symptoms. The study results are consistent with research on transdiagnostic models in psychotherapy and extend our knowledge about the potential of transdiagnostic mobile health.Trial registrationClinicalTrials.gov NCT02421965; http://clinicaltrials.gov/ct2/show/NCT02421965 (Archived by WebCite at http://www.webcitation.org/76pyDlvAS).

Project description:Rest-activity rhythm (RAR) disturbances are associated with mood disorders. But there remains a need to identify the particular RAR profiles associated with psychiatric symptom dimensions. Establishing such profiles would support the development of tools that track the 24-h sleep-wake phenotypes signaling clinical heterogeneity. We used data-driven clustering to identify RAR profiles in 145 adults aged 36-82 years (mean = 60, standard deviation = 9). Then we evaluated psychiatric symptom dimensions (including positive and negative affect, depressive, manic-hypomanic, panic-agoraphobic, and substance use symptoms) associated with these empirically-derived RAR profiles. Clustering identified three sub-groups characterized, on average, by: (1) earlier and more robust RARs ("earlier/robust," n = 55, 38%); (2) later and irregular RARs ("later/irregular," n = 31, 21%); and (3) later RARs and a narrower active period ("later/narrower," n = 59, 41%). Compared with the "earlier/robust" group: the "later/irregular" group had higher levels of lifetime manic-hypomanic symptoms (β (standard error) = 0.80 (0.22) higher standardized symptom units, p = 0.0004) and lifetime depression symptoms (β (standard error) = 0.73 (0.21) higher standardized symptom units, p = 0.0009); the "later/narrower" group had more lifetime depression symptoms (β (standard error) = 0.48 (0.18) higher standardized symptom units, p = 0.0076). These associations persisted after adjustments for sleep continuity and duration, suggesting that RARs are distinct behavioral correlates of clinical heterogeneity. Longitudinal studies are needed to confirm whether RAR characteristics contribute to the risk of manic and/or depressive episodes, and whether they reflect the consequences of psychiatric disturbance (e.g., on quality of life or disability). Opportunities to monitor or intervene on objectively-assessed RARs could facilitate better mental health related outcomes.

Project description:BackgroundSeveral studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.ObjectiveThe objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.MethodsWe conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.ResultsA total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).ConclusionsSeveral studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.

Project description:BackgroundResearch suggests that depressive disorders exist on a continuum, with subthreshold symptoms causing considerable population burden and increasing individual risk of developing major depressive disorder. An alternative strategy to professional treatment of subthreshold depression is population promotion of effective self-help interventions that can be easily applied by an individual without professional guidance. The evidence for self-help interventions for depressive symptoms is reviewed in the present work, with the aim of identifying promising interventions that could inform future health promotion campaigns or stimulate further research.MethodsA literature search for randomised controlled trials investigating self-help interventions for depressive disorders or depressive symptoms was performed using PubMed, PsycINFO and the Cochrane Database of Systematic Reviews. Reference lists and citations of included studies were also checked. Studies were grouped into those involving participants with depressive disorders or a high level of depressive symptoms, or non-clinically depressed participants not selected for depression. A number of exclusion criteria were applied, including trials with small sample sizes and where the intervention was adjunctive to antidepressants or psychotherapy.ResultsThe majority of interventions searched had no relevant evidence to review. Of the 38 interventions reviewed, the ones with the best evidence of efficacy in depressive disorders were S-adenosylmethionine, St John's wort, bibliotherapy, computerised interventions, distraction, relaxation training, exercise, pleasant activities, sleep deprivation, and light therapy. A number of other interventions showed promise but had received less research attention. Research in non-clinical samples indicated immediate beneficial effects on depressed mood for distraction, exercise, humour, music, negative air ionisation, and singing; while potential for helpful longer-term effects was found for autogenic training, light therapy, omega 3 fatty acids, pets, and prayer. Many of the trials were poor quality and may not generalize to self-help without professional guidance.ConclusionA number of self-help interventions have promising evidence for reducing subthreshold depressive symptoms. Other forms of evidence such as expert consensus may be more appropriate for interventions that are not feasible to evaluate in randomised controlled trials. There needs to be evaluation of whether promotion to the public of effective self-help strategies for subthreshold depressive symptoms could delay or prevent onset of depressive illness, reduce functional impairment, and prevent progression to other undesirable outcomes such as harmful use of substances.

Dataset Information

Social Rhythm Disruption is Associated with Greater Depressive Symptoms in People with Mood Disorders: Findings from a Multinational Online Survey During COVID-19.

Objectives

Methods

Results

Conclusions

Publications

Social Rhythm Disruption is Associated with Greater Depressive Symptoms in People with Mood Disorders: Findings from a Multinational Online Survey During COVID-19.

Similar Datasets

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