Evidence map›Paper›PMID 37071683›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Mapping the timescale of suicidal thinking.

Daniel D L Coppersmith, Oisín Ryan, Rebecca G Fortgang, Alexander J Millner, Evan M Kleiman, Matthew K Nock

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 3 pooled it
–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

50 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Neural Correlates Involved in Behavioral Metrics of Emotion Regulation and Suicidal Thoughts and Behaviors.Archives of suicide research : official journal of the International Academy for Suicide Research
    Pooled it
  4. Trial
  5. Trial
  6. Trial
  7. Article
  8. Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026
    Article
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  10. Article
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  17. AI-driven complex systems redefine cognitive science.Innovation (Cambridge (Mass.)) · 2026
    Review
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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

6 authors.

Daniel D L CoppersmithDepartment of Psychology, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-9397-3228
Oisín RyanDepartment of Methodology and Statistics, Utrecht University, 3508 TC Utrecht, The Netherlands.ORCID 0000-0003-3698-6396
Rebecca G FortgangDepartment of Psychology, Harvard University, Cambridge, MA 02138.
Alexander J MillnerDepartment of Psychology, Harvard University, Cambridge, MA 02138.ORCID 0000-0001-6092-2857
Evan M KleimanDepartment of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
Matthew K NockDepartment of Psychology, Harvard University, Cambridge, MA 02138.

Funding

Capturing the Structure and Dynamics of Suicidal ThinkingF31MH130055 · NIMH · HARVARD UNIVERSITY · PI COPPERSMITH, DANIEL · 2022 to 2023
$65k
HHS | NIH | National Institute of Mental Health (NIMH) F31MH130055NSF | EDU | Division of Graduate Education (DGE) DGE-1745303
6 · The paper itself

Abstract

This study aims to identify the timescale of suicidal thinking, leveraging real-time monitoring data and a number of different analytic approaches. Participants were 105 adults with past week suicidal thoughts who completed a 42-d real-time monitoring study (total number of observations = 20,255). Participants completed two forms of real-time assessments: traditional real-time assessments (spaced hours apart each day) and high-frequency assessments (spaced 10 min apart over 1 h). We found that suicidal thinking changes rapidly. Both descriptive statistics and Markov-switching models indicated that elevated states of suicidal thinking lasted on average 1 to 3 h. Individuals exhibited heterogeneity in how often and for how long they reported elevated suicidal thinking, and our analyses suggest that different aspects of suicidal thinking operated on different timescales. Continuous-time autoregressive models suggest that current suicidal intent is predictive of future intent levels for 2 to 3 h, while current suicidal desire is predictive of future suicidal desire levels for 20 h. Multiple models found that elevated suicidal intent has on average shorter duration than elevated suicidal desire. Finally, inferences about the within-person dynamics of suicidal thinking on the basis of statistical modeling were shown to depend on the frequency at which data was sampled. For example, traditional real-time assessments estimated the duration of severe suicidal states of suicidal desire as 9.5 h, whereas the high-frequency assessments shifted the estimated duration to 1.4 h.

Indexed as

Models, StatisticalSuicidal IdeationAdultHumansIntentionTime Factorsecological momentary assessmentsuicidal thinkingsuicide

Identifiers

PMID37071683
PMCPMC10151607

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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 OpenQuestion graph.