Literature DB >> 28855245

The promise of digital mood tracking technologies: are we heading on the right track?

Gin S Malhi1,2,3, Amber Hamilton1,2,3, Grace Morris1,2,3, Zola Mannie1,2,3, Pritha Das1,2,3, Tim Outhred1,2,3.   

Abstract

The growing understanding that mood disorders are dynamic in nature and fluctuate over variable epochs of time has compelled researchers to develop innovative methods of monitoring mood. Technological advancement now allows for the detection of minute-to-minute changes while also capturing a longitudinal perspective of an individual's illness. Traditionally, assessments of mood have been conducted by means of clinical interviews and paper surveys. However, these methods are often inaccurate due to recall bias and compliance issues, and are limited in their capacity to collect and process data over long periods of time. The increased capability, availability and affordability of digital technologies in recent decades has offered a novel, non-invasive alternative to monitoring mood and emotion in daily life. This paper reviews the emerging literature addressing the use of digital mood tracking technologies, primarily focusing on the strengths and inherent limitations of using these new methods including electronic self-report, behavioural data collection and wearable physiological biosensors. This developing field holds great promise in generating novel insights into the mechanistic processes of mood disorders and improving personalised clinical care. However, further research is needed to validate many of these novel approaches to ensure that these devices are indeed achieving their purpose of capturing changes in mood. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  Health Informatics; Information Technology; Mental Health; Psychiatry

Mesh:

Year:  2017        PMID: 28855245     DOI: 10.1136/eb-2017-102757

Source DB:  PubMed          Journal:  Evid Based Ment Health        ISSN: 1362-0347


  10 in total

1.  A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder.

Authors:  Kaela Van Til; Melvin G McInnis; Amy Cochran
Journal:  Bipolar Disord       Date:  2019-10-25       Impact factor: 6.744

Review 2.  Using ambulatory assessment to measure dynamic risk processes in affective disorders.

Authors:  Jonathan P Stange; Evan M Kleiman; Robin J Mermelstein; Timothy J Trull
Journal:  J Affect Disord       Date:  2019-08-19       Impact factor: 4.839

3.  A Longitudinal Analysis of First Professional Year Pharmacy Student Well-being.

Authors:  Nicholas E Hagemeier; Tucker S Carlson; Chelsea L Roberts; Morgan Thomas
Journal:  Am J Pharm Educ       Date:  2020-07       Impact factor: 2.047

4.  Let your fingers do the talking: Passive typing instability predicts future mood outcomes.

Authors:  Jonathan P Stange; John Zulueta; Scott A Langenecker; Kelly A Ryan; Andrea Piscitello; Jenna Duffecy; Melvin G McInnis; Pete Nelson; Olusola Ajilore; Alex Leow
Journal:  Bipolar Disord       Date:  2018-03-08       Impact factor: 6.744

5.  A digital self-report survey of mood for bipolar disorder.

Authors:  Tijana Sagorac Gruichich; Juan Camilo David Gomez; Gabriel Zayas-Cabán; Melvin G McInnis; Amy L Cochran
Journal:  Bipolar Disord       Date:  2021-02-26       Impact factor: 6.744

6.  Emotional valence sensing using a wearable facial EMG device.

Authors:  Wataru Sato; Koichi Murata; Yasuyuki Uraoka; Kazuaki Shibata; Sakiko Yoshikawa; Masafumi Furuta
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

7.  Remote Measurement in Rheumatoid Arthritis: Qualitative Analysis of Patient Perspectives.

Authors:  Katie M White; Alina Ivan; Ruth Williams; James B Galloway; Sam Norton; Faith Matcham
Journal:  JMIR Form Res       Date:  2021-03-09

8.  User experience and acceptance of patients and healthy adults testing a personalized self-management app for depression: A non-randomized mixed-methods feasibility study.

Authors:  Gwendolyn Mayer; Svenja Hummel; Neele Oetjen; Nadine Gronewold; Stefan Bubolz; Kim Blankenhagel; Mathias Slawik; Rüdiger Zarnekow; Thomas Hilbel; Jobst-Hendrik Schultz
Journal:  Digit Health       Date:  2022-04-07

9.  Mood Monitoring Over One Year for People With Chronic Obstructive Pulmonary Disease Using a Mobile Health System: Retrospective Analysis of a Randomized Controlled Trial.

Authors:  Maxine E Whelan; Carmelo Velardo; Heather Rutter; Lionel Tarassenko; Andrew J Farmer
Journal:  JMIR Mhealth Uhealth       Date:  2019-11-22       Impact factor: 4.773

10.  Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories.

Authors:  Yaron Sela; Lorena Santamaria; Yair Amichai-Hamburge; Victoria Leong
Journal:  Sensors (Basel)       Date:  2020-10-12       Impact factor: 3.576

  10 in total

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