A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

Rebekka Büscher; Tanita Winkler; Jacopo Mocellin; Stephanie Homan; Natasha Josifovski; Marketa Ciharova; Ward van Breda; Sam Kwon; Mark E. Larsen; John Torous; Joseph Firth; Lasse B. Sander

doi:10.1038/s44184-024-00089-4

A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

Rebekka Büscher^*, Tanita Winkler, Jacopo Mocellin, Stephanie Homan, Natasha Josifovski, Marketa Ciharova, Ward van Breda, Sam Kwon, Mark E. Larsen, John Torous, Joseph Firth, Lasse B. Sander

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.

Original language	English
Article number	42
Pages (from-to)	1-10
Number of pages	10
Journal	npj Mental Health Research
Volume	3
Early online date	23 Sept 2024
DOIs	https://doi.org/10.1038/s44184-024-00089-4
Publication status	Published - 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Funding

Funders	Funder number
Angelini Pharma
UK Research and Innovation Future Leaders
Bayer
Nutritional Medicine Institute
Gillian Kenny Associates
Not added	MR/T021780/1

Access to Document

10.1038/s44184-024-00089-4

Persistent URL (handle)

Persistent link

Cite this

@article{57f2989af54e40b38f68e330a481d486,

title = "A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors",

abstract = "Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.",

author = "Rebekka B{\"u}scher and Tanita Winkler and Jacopo Mocellin and Stephanie Homan and Natasha Josifovski and Marketa Ciharova and \{van Breda\}, Ward and Sam Kwon and Larsen, \{Mark E.\} and John Torous and Joseph Firth and Sander, \{Lasse B.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

doi = "10.1038/s44184-024-00089-4",

language = "English",

volume = "3",

pages = "1--10",

journal = "npj Mental Health Research",

issn = "2731-4251",

publisher = "Nature Publishing Group",

}

TY - JOUR

T1 - A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

AU - Büscher, Rebekka

AU - Winkler, Tanita

AU - Mocellin, Jacopo

AU - Homan, Stephanie

AU - Josifovski, Natasha

AU - Ciharova, Marketa

AU - van Breda, Ward

AU - Kwon, Sam

AU - Larsen, Mark E.

AU - Torous, John

AU - Firth, Joseph

AU - Sander, Lasse B.

PY - 2024

Y1 - 2024

N2 - Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.

AB - Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.

UR - https://www.scopus.com/pages/publications/105004850164

UR - https://www.scopus.com/inward/citedby.url?scp=105004850164&partnerID=8YFLogxK

U2 - 10.1038/s44184-024-00089-4

DO - 10.1038/s44184-024-00089-4

M3 - Article

AN - SCOPUS:105004850164

SN - 2731-4251

VL - 3

SP - 1

EP - 10

JO - npj Mental Health Research

JF - npj Mental Health Research

M1 - 42

ER -

A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

Abstract

Bibliographical note

Funding

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this