Evidence map›Paper›PMID 40340828›Full record

ArticleBMC psychiatry2025

Subgrouping suicidal ideations: an ecological momentary assessment study in psychiatric inpatients.

Stephanie Homan, Zachary Roman, Anja Ries, Prabhakaran Santhanam, Sofia Michel, Anna-Marie Bertram, Nina Klee, Carlo Berther, Sarina Blaser, Marion Gabi and 10 more

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Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

20 authors.

Stephanie HomanDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland. stephanie.homan@psychologie.uzh.ch.
Zachary RomanQuantitative Methods of Intervention and Evaluation, University of Zurich, Zurich, Switzerland.
Anja RiesExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Prabhakaran SanthanamDepartment of Management, Technology, and Economics at ETH Zurich, Zurich, Switzerland.
Sofia MichelDepartment of Psychology, University of Bern, Bern, Switzerland.
Anna-Marie BertramDepartment of Psychology, University of Basel, Basel, Switzerland.
Nina KleeExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Carlo BertherExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Sarina BlaserExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Marion GabiExperimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland.
Philipp HomanDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Hanne ScheererDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Michael CollaDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Stefan VetterDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Sebastian OlbrichDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Erich SeifritzDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
Isaac Galatzer-LevyPsychiatry, New York University School of Medicine, New York, NY, USA.
Tobias KowatschDepartment of Management, Technology, and Economics at ETH Zurich, Zurich, Switzerland.
Urte ScholzApplied Social and Health Psychology, Department of Psychology, University of Zurich, Zurich, Switzerland.
Birgit KleimDepartment of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10DL12_183251
6 · The paper itself

Abstract

backgroundSuicidal ideation (SI) is one of the strongest predictors of suicide attempts, yet reliable prediction models for suicide risk remain scarce. A key challenge is that SI can fluctuate over time, potentially reflecting different subgroups that may offer important insights for suicide risk prediction. This study aims to build upon previous approaches that averaged SI trajectories by adopting a method that respects the temporal nature of SI.

methodsFirst, we applied longitudinal clustering to ecological momentary assessment (EMA) data on SI, with five daily assessments over 28 days from 51 psychiatric patients (61% female, mean age = 35.26, SD = 12.54). We used the KmlShape algorithm, which takes raw SI scores and the measurement occasion index as input. Second, we regressed each identified subgroup against established clinical risk factors for SI, including a history of suicidal thoughts and behaviors, hopelessness, depression diagnosis, anxiety disorder diagnosis, and history of abuse.

resultsFour distinct subgroups with unique SI patterns were identified: (1) "High SI, moderate variability" (high mean, medium variability, high maximum); (2) "Lowest SI, lowest variability" (lowest mean, lowest variability, lowest maximum); (3) "Low SI, moderate variability" (low mean, medium variability, high maximum); and (4) "Highest SI, highest variability" (highest mean, highest variability, highest maximum). Furthermore, these subgroups were significantly associated with clinical characteristics. For instance, the subgroup with the least severe SI ("lowest SI, lowest variability") showed the lowest levels of hopelessness (beta = -0.95, 95% CI = -1.04, -0.86), whereas the subgroup with the most severe SI ("highest SI, highest variability") exhibited the highest levels of hopelessness (beta = 0.84, 95% CI = 0.72, 0.95).

conclusionApplying longitudinal clustering to EMA data from patients with SI enables the identification of well-defined and distinct SI subgroups with clearer clinical characteristics. This approach is a crucial step toward a deeper understanding of SI and serves as a foundation for enhancing prediction and prevention efforts.

trial registration10DL12_183251.

Indexed as

Ecological Momentary AssessmentInpatientsMental DisordersSuicidal IdeationAdultFemaleHumansLongitudinal StudiesMaleMiddle AgedRisk FactorsSuicide, AttemptedLongitudinal clustering analysisRisk factorsSubgroupsSuicidal ideationSuicide risk

Identifiers

PMID40340828
PMCPMC12063377

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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.