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Tag: Digital Phenotyping
03/07/2023

Psychological Phenotypes Correlate with Response to Digital Therapy for Anxiety

Article Excerpt: A patient’s psychological phenotype could be an indication of whether the patient will respond to a digital therapy for anxiety, according to a new report. The study offers insights that could help clinicians offer personalized care to patients with psychological conditions, but it also could explain why some patients respond more strongly than others to the types of therapy often leveraged by prescription digital therapeutics. The findings were published in Scientific Reports. Corresponding author Veronique A. Taylor, Ph.D., M.Sc., of the Brown University School of Public Health, and colleagues, said while personalized medicine has become an important component of other types of healthcare, personalized medicine in mental health has lagged due in part to a lack of research.

Full Article: https://tinyurl.com/2p8tnr2j

Article Source: Managed Healthcare Executive

Posted in: Methodologies & Analytics, Uncategorized // Tagged: ; ; ; ; ; ; ;
03/07/2022

Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia

Raugh IM, James SH, Gonzalez CM, Chapman HC, Cohen AS, Kirkpatrick B, Strauss GP (2021). Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. Journal of Psychiatric Research, 138, 436–443. https://doi.org/10.1016/j.jpsychires.2021.04.022

Digital phenotyping is a method of using mobile technology to collect data from everyday life and can be used as an objective and ecologically valid symptom assessment for schizophrenia. Researchers evaluated levels of adherence, feasibility, and tolerability for active and passive digital phenotyping methods using smartphones and smart band devices. The study included 54 outpatients diagnosed with schizophrenia and 55 demographically matched healthy controls. The participants completed six days of digital phenotyping. Active digital phenotyping included intentional task completion such as signal and event ecological momentary assessments and passive digital phenotyping was collected without participants’ direct input such as geolocation, accelerometry, and ambulatory psychophysiology. The study found that adherence in active digital phenotyping was significantly lower among patients with schizophrenia compared to controls. However, there was no significant different for passive phenotyping. The study’s results also found higher psychosocial functioning predicts greater active recordings adherence. Higher education level, lower positive symptoms, lower negative symptoms, and higher psychosocial functioning were predictors of higher passive recordings adherence. Both groups found digital phenotyping tolerable and feasible.

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ;
09/03/2021

How Machine Learning Systems Help EHR Usabilty, Mitigate Burden

Article Excerpt: Machine learning systems can aid EHR (electronic health record) usability and cut burden for disease phenotyping to support clinical research, according to a recent Mount Sinai study published in the journal Patterns.The machine learning-based algorithm diagnosed patients as accurately as the standard set of disease phenotyping algorithms for conditions like dementia, sickle cell anemia, and multiple sclerosis.

Full Article: https://tinyurl.com/h693cmpw

Article Source: EHR Intelligence

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ;
04/02/2021

Objective digital phenotypes of worry severity, pain severity and pain chronicity in persons living with HIV

Jacobson N, O’Cleirigh C. (2021). Objective digital phenotypes of worry severity, pain severity and pain chronicity in persons living with HIV. The British Journal of Psychiatry. 218(3): 165–167. doi: 10.1192/bjp.2019.168

Researchers recruited adults living with HIV (n = 68) to participate in a study to determine whether objective digital biomarkers developed from participant movement data could accurately predict symptoms of pain and worry in this population. Read More

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ; ;
03/01/2021

A Survey of Computational Methods for Online Mental State Assessment on Social Media

Article Excerpt: Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records.

Full Article: https://tinyurl.com/tsmxxrsz

Article Source: ACM Journals

Posted in: Methodologies & Analytics // Tagged: ; ; ; ; ; ; ; ; ; ;
02/08/2021

Electric Dreams – The Future of Mental Healthcare Is Digital

Article Excerpt: The future of mental healthcare is digital. Developments will change how clinicians diagnose, monitor and manage mental disorders, taking treatment from the clinic into each patient’s mobile device. This goes beyond developing software. There is a concurrent need to address pertinent issues relating to patient confidentiality, safeguards for data privacy, treatment models, regulatory pathways, reimbursement of costs, as well as patient and physician adoption.

Full Article: https://tinyurl.com/y3jwphaq

Article Source: The Straits Times

Posted in: Emerging Technologies // Tagged: ; ; ; ; ; ; ; ; ; ; ; ; ; ;
03/27/2020

Internet searches for opioids predict future emergency department heroin admissions

Young S, Zheng K, Chu L, Humphreys K. (2018). Internet searches for opioids predict future emergency department heroin admissions. Drug and Alcohol Dependence. (190): 166-169. doi: 10.1016/j.drugalcdep.2018.05.009

Researchers analyzed the relationship between volume of geolocated Google searches for prescription and non-prescription opioids (2004–2010) and annual heroin-related Emergency Department (ED) admissions (2005–2011) from The Substance Abuse and Mental Health Services Administration (SAMHSA) to determine whether opioid-related Google search data predicted next-year heroin-related ED admissions. Read More

Posted in: Cutting Edge Literature // Tagged: ; ; ; ; ; ; ; ; ;