Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study
Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0002-4872-1961
Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
Show others and affiliations
2022 (English)In: BMC Psychiatry, E-ISSN 1471-244X, Vol. 22, no 1, article id 288Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episodes of illness is particularly relevant in bipolar disorder (BD), a mood disorder with high recurrence, disability, and suicide rates. Thus, to understand the dynamic changes involved in episode generation in BD, we propose to extract and interpret individual illness trajectories and patterns suggestive of relapse using passive sensing, nonlinear techniques, and deep anomaly detection. Here we describe the study we have designed to test this hypothesis and the rationale for its design.

METHOD: This is a protocol for a contactless cohort study in 200 adult BD patients. Participants will be followed for up to 2 years during which they will be monitored continuously using passive sensing, a wearable that collects multimodal physiological (heart rate variability) and objective (sleep, activity) data. Participants will complete (i) a comprehensive baseline assessment; (ii) weekly assessments; (iii) daily assessments using electronic rating scales. Data will be analyzed using nonlinear techniques and deep anomaly detection to forecast episodes of illness.

DISCUSSION: This proposed contactless, large cohort study aims to obtain and combine high-dimensional, multimodal physiological, objective, and subjective data. Our work, by conceptualizing mood as a dynamic property of biological systems, will demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.

Place, publisher, year, edition, pages
2022. Vol. 22, no 1, article id 288
Keywords [en]
Bipolar disorder, Episode prediction, Machine learning, Wearable device
National Category
Psychiatry
Identifiers
URN: urn:nbn:se:du-41369DOI: 10.1186/s12888-022-03923-1ISI: 000786345500001PubMedID: 35459150Scopus ID: 2-s2.0-85128645699OAI: oai:DiVA.org:du-41369DiVA, id: diva2:1656255
Available from: 2022-05-05 Created: 2022-05-05 Last updated: 2024-01-17Bibliographically approved

Open Access in DiVA

fulltext(757 kB)134 downloads
File information
File name FULLTEXT01.pdfFile size 757 kBChecksum SHA-512
252bfe2d283623545ef78acf6f2307cb8df6fd0b24e03ab6a5f7195214a8d3c2c9c115f6641148d9257f8c0a47ed7277b09f481c37ae5e0afa72d298c999cac2
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Hintze, Arend

Search in DiVA

By author/editor
Hintze, Arend
By organisation
Microdata Analysis
In the same journal
BMC Psychiatry
Psychiatry

Search outside of DiVA

GoogleGoogle Scholar
Total: 134 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 201 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf