About: Digital health data-driven approaches to understand human behavior

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Digital health data-driven approaches to understand human behavior Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Digital health data-driven approaches to understand human behavior
Creator	Marsch, Lisa
Source	Medline; PMC
abstract	Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals’ behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.
has issue date	2020-07-12(xsd:dateTime)
bibo:doi	10.1038/s41386-020-0761-5
bibo:pmid	32653896
has license	cc-by
sha1sum (hex)	07d09068a7844bb823d5ada9940eed48a42285de
schema:url	https://doi.org/10.1038/s41386-020-0761-5
resource representing a document's title	Digital health data-driven approaches to understand human behavior
has PubMed Central identifier	PMC7359920
has PubMed identifier	32653896
schema:publication	Neuropsychopharmacology
resource representing a document's body	covid:07d09068a7844bb823d5ada9940eed48a42285de#body_text
is schema:about of	named entity 'And' named entity 'digital health' named entity 'opportunities' named entity 'Digital health' named entity 'METHODS' named entity 'DATA CAPTURE' named entity 'CLINICAL' named entity 'SCIENTIFIC LITERATURE' named entity 'TIMELY' named entity 'DIAGNOSTIC CLASSIFICATION' named entity 'INDIVIDUALS' named entity 'LINE' named entity 'HEALTH DATA' named entity 'OUR' named entity 'DISCOVERY SCIENCE' named entity 'RESEARCH' named entity 'DIAGNOSIS' named entity 'DISCUSSION' named entity 'HAVE' named entity 'psychiatric disorders' named entity 'improved' named entity 'timely' named entity 'manuscript' named entity 'health' named entity 'health' named entity 'evaluating' named entity 'health behavior' named entity 'digital health' named entity 'psychiatric disorders' named entity 'health data' named entity 'assess' named entity 'National Institute on Drug Abuse' named entity 'brain functioning' named entity 'reduced affect' named entity 'mental disorders' named entity 'GPS' named entity 'EMA' named entity 'data analytics' named entity 'digital health' named entity 'quality of life' named entity 'empirically based' named entity 'biomarkers' named entity 'smartphone' named entity 'human behavior' named entity 'anxiety' named entity 'Bluetooth' named entity 'search engine' named entity 'health outcomes' named entity 'digital health' named entity 'smartphone' named entity 'diagnostic process' named entity 'smartphone' named entity 'theoretical models' named entity 'digital health' named entity 'health behavior' named entity 'health behavior' named entity 'Grade Point Average' named entity 'deep-learning' named entity 'health and wellness' named entity 'depressive symptoms' named entity 'schizophrenia' named entity 'health outcomes' named entity 'empirically based' named entity 'National Institutes of Health' named entity 'social media' named entity 'digital phenotyping' named entity 'bipolar disorder' named entity 'digital health' named entity 'digital health' named entity 'pilot study' named entity 'control group' named entity 'drug craving' named entity 'disease-specific' named entity 'health behavior' named entity 'cardiovascular disease' named entity 'psychotic' named entity 'negative mood' named entity 'social interactions' named entity 'personality traits' named entity 'field of psychiatry' named entity 'real-world'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software