Evidence map›Paper›PMID 33064759›Full record

ArticlePloS one2020

Identifying emerging mental illness utilizing search engine activity: A feasibility study.

Michael L Birnbaum, Hongyi Wen, Anna Van Meter, Sindhu K Ernala, Asra F Rizvi, Elizabeth Arenare, Deborah Estrin, Munmun De Choudhury, John M Kane

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Michael L BirnbaumThe Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America.ORCID 0000-0002-4285-7868
Hongyi WenCornell Tech, Cornell University, New York, NY, United States of America.
Anna Van MeterThe Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America.ORCID 0000-0003-0012-206X
Sindhu K ErnalaGeorgia Institute of Technology, Atlanta, GA, United States of America.
Asra F RizviThe Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America.
Elizabeth ArenareThe Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America.
Deborah EstrinCornell Tech, Cornell University, New York, NY, United States of America.
Munmun De ChoudhuryGeorgia Institute of Technology, Atlanta, GA, United States of America.
John M KaneThe Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States of America.

Funding

Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with SchizophreniaR01MH117172 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2019 to 2022
$2.6M
Social Media Signals for Post-traumatic Stress and Anxiety in Crisis-Inflicted CommunitiesR01GM112697 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI DE CHOUDHURY, MUNMUN · 2014 to 2018
$1.4M
NIGMS NIH HHS R01 GM112697NIMH NIH HHS R01 MH117172
6 · The paper itself

Abstract

Mental illness often emerges during the formative years of adolescence and young adult development and interferes with the establishment of healthy educational, vocational, and social foundations. Despite the severity of symptoms and decline in functioning, the time between illness onset and receiving appropriate care can be lengthy. A method by which to objectively identify early signs of emerging psychiatric symptoms could improve early intervention strategies. We analyzed a total of 405,523 search queries from 105 individuals with schizophrenia spectrum disorders (SSD, N = 36), non-psychotic mood disorders (MD, N = 38) and healthy volunteers (HV, N = 31) utilizing one year's worth of data prior to the first psychiatric hospitalization. Across 52 weeks, we found significant differences in the timing (p<0.05) and frequency (p<0.001) of searches between individuals with SSD and MD compared to HV up to a year in advance of the first psychiatric hospitalization. We additionally identified significant linguistic differences in search content among the three groups including use of words related to sadness and perception, use of first and second person pronouns, and use of punctuation (all p<0.05). In the weeks before hospitalization, both participants with SSD and MD displayed significant shifts in search timing (p<0.05), and participants with SSD displayed significant shifts in search content (p<0.05). Our findings demonstrate promise for utilizing personal patterns of online search activity to inform clinical care.

Indexed as

AdolescentAdultCase-Control StudiesFeasibility StudiesFemaleHospitalizationHumansInternetMalePsychotic DisordersSchizophreniaSearch EngineYoung Adult

Identifiers

PMID33064759
PMCPMC7567375

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.