Depression, Isolation, and Social Connectivity Online (DISCO)
抑郁、孤立和在线社交联系 (DISCO)
基本信息
- 批准号:10612642
- 负责人:
- 金额:$ 189.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-09 至 2025-09-08
- 项目状态:未结题
- 来源:
- 关键词:AddressAdultAgeBehaviorBlack raceCOVID-19COVID-19 impactCOVID-19 pandemicCensusesCessation of lifeCharacteristicsCollaborationsCommunitiesComputing MethodologiesContainmentDataData AnalysesData SetDepressed moodDiseaseElderlyElementsEnrollmentHispanicHouseholdIndividualInformaticsInternetInterventionInvestigationKnowledgeLonelinessLongevityMajor Depressive DisorderMeasuresMental DepressionMental HealthMethodsMood DisordersNatureOutcomePatient Self-ReportPersonsPoliciesPolicy AnalysisPopulationRiskRisk FactorsSARS-CoV-2 infectionSchoolsScientistSeriesSocial BehaviorSocial ImpactsSocial InteractionSocial NetworkSocial isolationSocial supportSubgroupSurveysTimeUnderserved PopulationUnited StatesVariantVirusVulnerable PopulationsWorkbehavior measurementcollegecoronavirus diseasedepressive symptomsdesigndisabilityeconomic impactinnovationminority communitiesmultidisciplinarynovelpandemic diseasepower analysisremote interactionresponsesocialsocial mediastressorunderserved communityvulnerable communityyoung adult
项目摘要
Social isolation represents a risk factor for major depression across the lifespan. The COVID-19 pandemic has
contributed to unprecedented disruption in social networks, as a result both of the disease itself and the
measures required to contain it. Vulnerable and underserved communities have been particularly impacted in
both of these regards, with greater rates of COVID-19 infection as well as greater economic and social impact
of closures and restrictions. It is not surprising, then, that rates of major depressive symptoms in the United
States have approached levels 3 to 4 times those observed before the pandemic. Beginning to address the rise
of social disconnection and its contribution to depression requires a better understanding of the aspects of social
networks most disrupted by the pandemic, and how they relate to depression, especially among individuals in
vulnerable communities. Identifying targets for intervention also requires understanding how online social
behavior may compensate for, or exacerbate, effects of social disconnection. Furthermore, it is necessary to
understand how external factors in a community such as containment policies may contribute to or moderate
social disconnection and depression. To address these critical questions, this study will use data from the Covid
States Project, a 50-state survey conducted approximately every 8 weeks since April 2020, which has enrolled
more than 260,000 unique individuals, including 125,000 from households earning less than $50,000 per year.
Beyond symptoms of depression, the survey asks detailed questions about social networks and social support,
as well as online activity, impact of COVID-19, and a range of other topics. The first aim of the study will
characterize, in well-powered analysis of individual subgroups, the relationship between specific aspects of
social networks and depressed mood, and identify features that may moderate these effects. In aim 2, using an
innovative browser extension, the study will characterize online behavior among 1200 individuals completing the
survey. In aim 3, the study will integrate survey data with longitudinal data at the state and census tract level
regarding pandemic containment policies, mobility, and COVID-19 cases and death. These latter two aims will
provide a novel understanding of how online behavior, and external factors, impact social networks and moderate
their relationship with depression. The study will build on a highly productive collaboration for the past 2 years
between the PI, with expertise in informatics methods for studying mood disorders, and the Northeastern PI, a
computational social scientist with expert in large-scale surveys and investigation of social networks. The
consultants, who have worked closely with the PIs on the survey, bring additional expertise in survey design and
analysis, investigation of time series data including mobility data, and policy analysis. The study will identify
targets for interventions to address social disconnection and its impact on depression, particularly among
vulnerable populations, providing critical guidance regarding where to focus such interventions.
社会孤立是一生中患严重抑郁症的一个风险因素。COVID-19大流行
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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ROY H. Perlis其他文献
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{{ truncateString('ROY H. Perlis', 18)}}的其他基金
Characterization of schizophrenia liability genes in models of human microglial synaptic pruning
人类小胶质细胞突触修剪模型中精神分裂症易感基因的表征
- 批准号:
10736092 - 财政年份:2023
- 资助金额:
$ 189.38万 - 项目类别:
Data-driven subtyping in major depressive disorder
重度抑郁症的数据驱动亚型
- 批准号:
10393687 - 财政年份:2021
- 资助金额:
$ 189.38万 - 项目类别:
Data-driven subtyping in major depressive disorder
重度抑郁症的数据驱动亚型
- 批准号:
10580741 - 财政年份:2021
- 资助金额:
$ 189.38万 - 项目类别:
Data-driven subtyping in major depressive disorder
重度抑郁症的数据驱动亚型
- 批准号:
10211310 - 财政年份:2021
- 资助金额:
$ 189.38万 - 项目类别:
Patient-derived Models of Synaptic Pruning in Schizophrenia
精神分裂症患者衍生的突触修剪模型
- 批准号:
10614930 - 财政年份:2019
- 资助金额:
$ 189.38万 - 项目类别:
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1/2 利用电子健康记录进行精神疾病的药物基因组学研究
- 批准号:
10312110 - 财政年份:2019
- 资助金额:
$ 189.38万 - 项目类别:
Patient-derived Models of Synaptic Pruning in Schizophrenia
精神分裂症患者衍生的突触修剪模型
- 批准号:
9981011 - 财政年份:2019
- 资助金额:
$ 189.38万 - 项目类别:
1/2 Leveraging electronic health records for pharmacogenomics of psychiatric disorders
1/2 利用电子健康记录进行精神疾病的药物基因组学研究
- 批准号:
10064583 - 财政年份:2019
- 资助金额:
$ 189.38万 - 项目类别:
Patient-derived Models of Synaptic Pruning in Schizophrenia
精神分裂症患者衍生的突触修剪模型
- 批准号:
10392927 - 财政年份:2019
- 资助金额:
$ 189.38万 - 项目类别:
Natural language processing for characterizing psychopathology
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- 批准号:
9254614 - 财政年份:2016
- 资助金额:
$ 189.38万 - 项目类别:
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