Literature DB >> 31596769

Using Digital Phenotyping to Accurately Detect Depression Severity.

Nicholas C Jacobson1, Hilary Weingarden, Sabine Wilhelm.   

Abstract

Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder (MDD). The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with MDD. Using over 1 week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855; 95% confidence interval [CI], 0.610-0.950; p = 4.95 × 10) and clinician-rated (r = 0.604; 95% CI, 0.133-0.894; p = 0.017) symptom severity. Pending replication, the present data suggest that the use of passive wearable sensors to inform healthcare decisions holds considerable promise.

Entities:  

Mesh:

Year:  2019        PMID: 31596769     DOI: 10.1097/NMD.0000000000001042

Source DB:  PubMed          Journal:  J Nerv Ment Dis        ISSN: 0022-3018            Impact factor:   2.254


  13 in total

1.  Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments.

Authors:  Nicholas C Jacobson; Sukanya Bhattacharya
Journal:  Behav Res Ther       Date:  2021-12-11

Review 2.  Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient's and practitioner's individual cognitive biases.

Authors:  Stéphane Mouchabac; Ismael Conejero; Camille Lakhlifi; Ilyass Msellek; Leo Malandain; Vladimir Adrien; Florian Ferreri; Bruno Millet; Olivier Bonnot; Alexis Bourla; Redwan Maatoug
Journal:  Dialogues Clin Neurosci       Date:  2022-06-01

3.  Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma.

Authors:  Damien Lekkas; Nicholas C Jacobson
Journal:  Sci Rep       Date:  2021-05-13       Impact factor: 4.379

4.  Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones.

Authors:  Nicholas C Jacobson; Yeon Joo Chung
Journal:  Sensors (Basel)       Date:  2020-06-24       Impact factor: 3.576

5.  Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors.

Authors:  Berta Summers; Nicholas C Jacobson; Sabine Wilhelm
Journal:  J Med Internet Res       Date:  2020-05-29       Impact factor: 5.428

6.  Threats to Global Mental Health From Unregulated Digital Phenotyping and Neuromarketing: Recommendations for COVID-19 Era and Beyond.

Authors:  Hossein Akbarialiabad; Bahar Bastani; Mohammad Hossein Taghrir; Shahram Paydar; Nasrollah Ghahramani; Manasi Kumar
Journal:  Front Psychiatry       Date:  2021-09-14       Impact factor: 4.157

7.  Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities.

Authors:  Jonathan R Olsen; Natalie Nicholls; Fiona Caryl; Juan Orjuela Mendoza; Luc Int Panis; Evi Dons; Michelle Laeremans; Arnout Standaert; Duncan Lee; Ione Avila-Palencia; Audrey de Nazelle; Mark Nieuwenhuijsen; Richard Mitchell
Journal:  SSM Popul Health       Date:  2022-07-16

8.  Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence.

Authors:  Daniel Zarate; Vasileios Stavropoulos; Michelle Ball; Gabriel de Sena Collier; Nicholas C Jacobson
Journal:  BMC Psychiatry       Date:  2022-06-22       Impact factor: 4.144

9.  Digital phenotype of mood disorders: A conceptual and critical review.

Authors:  Redwan Maatoug; Antoine Oudin; Vladimir Adrien; Bertrand Saudreau; Olivier Bonnot; Bruno Millet; Florian Ferreri; Stephane Mouchabac; Alexis Bourla
Journal:  Front Psychiatry       Date:  2022-07-26       Impact factor: 5.435

10.  Payment Reform in the Era of Advanced Diagnostics, Artificial Intelligence, and Machine Learning.

Authors:  James Sorace
Journal:  J Pathol Inform       Date:  2020-02-21
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.