Evan M Kleiman1, Brianna J Turner2, Szymon Fedor3, Eleanor E Beale4,5, Rosalind W Picard3, Jeff C Huffman4,5, Matthew K Nock1,4,6. 1. Department of Psychology, Harvard University, Cambridge, MA, USA. 2. Department of Psychology, University of Victoria, Victoria, Canada. 3. Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. 4. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. 5. Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA. 6. Cambridge Computational Clinical Psychology Organization (C3PO), Cambridge, MA, USA.
Abstract
BACKGROUND: To examine whether there are subtypes of suicidal thinking using real-time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible. METHODS: We used smartphone-based real-time monitoring to assess suicidal thoughts four times per day in two samples: Adults who attempted suicide in the past year recruited from online forums (n = 51 participants with a total of 2,889 responses, surveyed over 28 days; ages ranged from 18 to 38 years) and psychiatric inpatients with recent suicidal ideation or attempts (n = 32 participants with a total of 640 responses, surveyed over the duration of inpatient treatment [mean stay = 8.79 days], ages ranged 23-68 years). Latent profile analyses were used to identify distinct phenotypes of suicidal thinking based on the frequency, intensity, and variability of such thoughts. RESULTS: Across both samples, five distinct phenotypes of suicidal thinking emerged that differed primarily on the intensity and variability of suicidal thoughts. Participants whose profile was characterized by more severe, persistent suicidal thoughts (i.e., higher mean and lower variability around the mean) were most likely to have made a recent suicide attempt. CONCLUSIONS: Suicidal thinking has historically been studied as a homogeneous construct, but using newly available monitoring technology we discovered five profiles of suicidal thinking. Key questions for future research include how these phenotypes prospectively relate to future suicidal behaviors, and whether they represent remain stable or trait-like over longer periods.
BACKGROUND: To examine whether there are subtypes of suicidal thinking using real-time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible. METHODS: We used smartphone-based real-time monitoring to assess suicidal thoughts four times per day in two samples: Adults who attempted suicide in the past year recruited from online forums (n = 51 participants with a total of 2,889 responses, surveyed over 28 days; ages ranged from 18 to 38 years) and psychiatric inpatients with recent suicidal ideation or attempts (n = 32 participants with a total of 640 responses, surveyed over the duration of inpatient treatment [mean stay = 8.79 days], ages ranged 23-68 years). Latent profile analyses were used to identify distinct phenotypes of suicidal thinking based on the frequency, intensity, and variability of such thoughts. RESULTS: Across both samples, five distinct phenotypes of suicidal thinking emerged that differed primarily on the intensity and variability of suicidal thoughts. Participants whose profile was characterized by more severe, persistent suicidal thoughts (i.e., higher mean and lower variability around the mean) were most likely to have made a recent suicide attempt. CONCLUSIONS: Suicidal thinking has historically been studied as a homogeneous construct, but using newly available monitoring technology we discovered five profiles of suicidal thinking. Key questions for future research include how these phenotypes prospectively relate to future suicidal behaviors, and whether they represent remain stable or trait-like over longer periods.
Authors: Liat Itzhaky; Ilana Gratch; Hanga Galfalvy; John G Keilp; Ainsley K Burke; Maria A Oquendo; J John Mann; Barbara H Stanley Journal: J Psychiatr Res Date: 2020-03-18 Impact factor: 4.791
Authors: David Mou; Evan M Kleiman; Szymon Fedor; Stuart Beck; Jeff C Huffman; Matthew K Nock Journal: J Psychiatr Res Date: 2018-08-03 Impact factor: 4.791