Using Intensive Longitudinal Data to Identify Early Predictors of Suicide-Related Outcomes in High-Risk Adolescents: Practical and Conceptual Considerations

Abstract

Mobile technology offers new possibilities for assessing suicidal ideation and behavior in real- or near-real-time. It remains unclear how intensive longitudinal data can be used to identify proximal risk and inform clinical decision making. In this study of adolescent psychiatric inpatients (N = 32, aged 13-17 years, 75% female), we illustrate the application of a three-step process to identify early signs of suicide-related crises using daily diaries. Using receiver operating characteristic (ROC) curve analyses, we considered the utility of 12 features—constructed using means and variances of daily ratings for six risk factors over the first 2 weeks postdischarge (observations = 360)—in identifying a suicidal crisis 2 weeks later. Models derived from single risk factors had modest predictive accuracy (area under the ROC curve [AUC] 0.46-0.80) while nearly all models derived from combinations of risk factors produced higher accuracy (AUCs 0.80-0.91). Based on this illustration, we discuss implications for clinical decision making and future research.

Keywords

ecological momentary assessment daily diary adolescents suicide attempts short-term suicide risk

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References

Almirall

Compton

S. N.

Gunlicks-Stoessel

Duan

Murphy

S. A.

(2012). Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy. Statistics in Medicine, 31(17), 1887-1902. https://doi.org/10.1002/sim.4512

Bagge

C. L.

Glenn

C. R.

Lee

H. J.

(2013). Quantifying the impact of recent negative life events on suicide attempts. Journal of Abnormal Psychology, 122(2), 359-368. https://doi.org/10.1037/a0030371

Bandura

(1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. https://doi.org/10.1037/0033-295X.84.2.191

Ben-Zeev

Young

M. A.

Depp

C. A.

(2012). Real-time predictors of suicidal ideation: Mmobile assessment of hospitalized depressed patients. Psychiatry Research, 197(1-2), 55-59. https://doi.org/10.1016/j.psychres.2011.11.025

Bolland

McCallum

Lian

Bailey

Rowan

(2001). Hopelessness and violence among inner-city youths. Maternal and Child Health Journal, 5(4), 237-244. https://doi.org/10.1023/A:1013028805470

Centers for Disease Control and Prevention. (2019). Web-based Injury Statistics Query and Reporting System (WISQARS). https://www.cdc.gov/injury/wisqars/index.html

Collins

L. M.

Trail

J. B.

Kugler

K. C.

Baker

T. B.

Piper

M. E.

Mermelstein

R. J.

(2013). Evaluating individual intervention components: Making decisions based on the results of a factorial screening experiment. Translational Behavioral Medicine, 4(3), 238-251. https://doi.org/10.1007/s13142-013-0239-7

Czyz

E. K.

Bohnert

A. S.

King

C. A.

Price

A. M.

Kleinberg

Ilgen

M. A.

(2014). Self-efficacy to avoid suicidal action: Factor structure and convergent validity among adults in substance use disorder treatment. Suicide and Life-Threatening Behavior, 44(6), 698-709. https://doi.org/10.1111/sltb.12101

Czyz

E. K.

Horwitz

A. G.

Arango

King

C. A.

(2018). Short-term change and prediction of suicidal ideation among adolescents: A daily diary study following psychiatric hospitalization. Journal of Child Psychology and Psychiatry, 60(7), 732-741. https://doi.org/10.1111/jcpp.12974

10.

Czyz

E. K.

Horwitz

A. G.

King

C. A.

(2016). Self-rated expectations of suicidal behavior predict future suicide attempts among adolescent and young adult psychiatric emergency patients. Depression & Anxiety, 33(6), 512-519. https://doi.org/10.1002/da.22514

11.

Czyz

E. K.

King

C. A.

Nahum-Shani

(2018). Ecological assessment of daily suicidal thoughts and attempts among suicidal teens after psychiatric hospitalization: Lessons about feasibility and acceptability. Psychiatry Research, 267, 566-574. https://doi.org/10.1016/j.psychres.2018.06.031

12.

Czyz

E. K.

King

C. A.

Biermann

B. J.

(2019). Motivational interviewing-enhanced safety planning for adolescents at high suicide risk: A pilot randomized controlled trial. Journal of Clinical Child & Adolescent Psychology, 48(2), 250-262.

13.

Davidson

C. L.

Anestis

M. D.

Gutierrez

P. M.

(2017). Ecological momentary assessment is a neglected methodology in suicidology. Archives of Suicide Research, 21(1), 1-11. https://doi.org/10.1080/13811118.2015.1004482

14.

Ebesutani

Regan

Smith

Reise

Higa-McMillan

Chorpita

B. F.

(2012). The 10-item positive and negative affect schedule for children, child and parent shortened versions: Application of item response theory for more efficient assessment. Journal of Psychopathology and Behavioral Assessment, 34(2), 191-203. https://doi.org/10.1007/s10862-011-9273-2

15.

Franklin

J. C.

Ribeiro

J. D.

Fox

K. R.

Bentley

K. H.

Kleiman

E. M.

Huang

Musacchio

K. M.

Jaroszewski

A. C.

Chang

B. P.

Nock

M. K.

(2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187-232. https://doi.org/10.1037/bul0000084

16.

Glenn

C. R.

Nock

M. K.

(2014). Improving the short-term prediction of suicidal behavior. American Journal of Preventive Medicine, 47(3, Suppl. 2), S176-S180. https://doi.org/10.1016/j.amepre.2014.06.004

17.

Hallensleben

Spangenberg

Forkmann

Rath

Hegerl

Kersting

Kallert

T. W.

Glaesmer

(2018). Investigating the dynamics of suicidal ideation. Crisis, 39(1), 65-69. https://doi.org/10.1027/0227-5910/a000464

18.

Husky

Olié

Guillaume

Genty

Swendsen

Courtet

. (2014). Feasibility and validity of ecological momentary assessment in the investigation of suicide risk. Psychiatry Research, 220(1-2), 564-570. https://doi.org/10.1016/j.psychres.2014.08.019

19.

Huth-Bocks

A. C.

Kerr

D. C.

Ivey

A. Z.

Kramer

A. C.

King

C. A.

(2007). Assessment of psychiatrically hospitalized suicidal adolescents: Self-report instruments as predictors of suicidal thoughts and behavior. Journal of the American Academy of Child & Adolescent Psychiatry, 46(3), 387-395. https://doi.org/10.1097/chi.0b013e31802b9535

20.

Joiner

(2005). Why people die by suicide. Harvard University Press.

21.

Kennard

B. D.

Goldstein

Foxwell

A. A.

McMakin

D. L.

Wolfe

Biernesser

Moorehead

Douaihy

Zullo

Wentroble

Owen

Zelazny

Iyengar

Porta

Brent

(2018). As Safe as Possible (ASAP): A brief app-supported inpatient intervention to prevent postdischarge suicidal behavior in hospitalized, suicidal adolescents. American Journal of Psychiatry, 175(9), 864-872. https://doi.org/10.1176/appi.ajp.2018.17101151

22.

Kleiman

E. M.

Turner

B. J.

Fedor

Beale

E. E.

Huffman

J. C.

Nock

M. K.

(2017). Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studies. Journal of Abnormal Psychology, 126(6), 726-738. https://doi.org/10.1037/abn0000273

23.

Klonsky

E. D.

May

A. M

. (2015). The Three-Step Theory (3ST): A new theory of suicide rooted in the “ideation-to-action” framework. International Journal of Cognitive Therapy, 8(2), 114-129. https://doi.org/10.1521/ijct.2015.8.2.114

24.

Lei

Nahum-Shani

Lynch

Oslin

Murphy

S. A.

(2012). A “SMART” design for building individualized treatment sequences. Annual Review of Clinical Psychology, 8, 21-48. https://doi.org/10.1146/annurev-clinpsy-032511-143152

25.

Millner

A. J.

Lee

M. D.

Nock

M. K.

(2017). Describing and measuring the pathway to suicide attempts: A preliminary study. Suicide and Life-Threatening Behavior, 47(3), 353-369. https://doi.org/10.1111/sltb.12284

26.

Mohr

D. C.

Zhang

Schueller

S. M.

(2017). Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23-47. https://doi.org/10.1146/annurev-clinpsy-032816-044949

27.

Nahum-Shani

Qian

Almirall

Pelham

W. E.

Gnagy

Fabiano

G. A.

Waxmonsky

J. G.

Murphy

S. A.

(2012). Experimental design and primary data analysis methods for comparing adaptive interventions. Psychological Methods, 17(4), 457-477. https://doi.org/10.1037/a0029372

28.

Nahum-Shani

Smith

S. N.

Spring

B. J.

Collins

L. M.

Witkiewitz

Tewari

Murphy

S. A.

(2017). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446-462. https://doi.org/10.1007/s12160-016-9830-8

29.

National Action Alliance for Suicide Prevention. (2014). Research prioritization task force: A prioritized research agenda for suicide prevention: an action plan to save lives. National Institute of Mental Health and Research Prioritization Task Force.

30.

Pencina

M. J.

D’Agostino

R. B.

Sr. D’Agostino

R. B.

Jr. Vasan

R. S.

(2008). Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine, 27(2), 157-172. https://doi.org/10.1002/sim.2929

31.

Pencina

M. J.

D’agostino

R. B.

Pencina

K. M.

Janssens

A. C. J.

Greenland

(2012). Interpreting incremental value of markers added to risk prediction models. American Journal of Epidemiology, 176(6), 473-481. https://doi.org/10.1093/aje/kws207

32.

Pettit

J. W.

Silverman

W. K.

Rey

Marin

Jaccard

(2016). Moving to second-stage treatments faster: Identifying midtreatment tailoring variables for youth with anxiety disorders. Journal of Clinical Child & Adolescent Psychology, 45(4), 457-468. https://doi.org/10.1080/15374416.2015.1038824

33.

Posner

Brown

G. K.

Stanley

Brent

D. A.

Yershova

K. V.

Oquendo

M. A.

… Mann

J. J.

(2011). The Columbia–Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. American journal of psychiatry, 168(12), 1266-1277.

34.

Reinertsen

Clifford

G. D.

(2018). A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement, 39(5), Article 05TR01. https://doi.org/10.1088/1361-6579/aabf64

35.

Rice

M. E.

Harris

G. T.

(2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29(5), 615-620. https://doi.org/10.1007/s10979-005-6832-7

36.

Robin

Turck

Hainard

Tiberti

Lisacek

Sanchez

J. C.

Müller

(2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, Article 77. https://doi.org/10.1186/1471-2105-12-77

37.

Rudd

M. D.

Berman

A. L.

Joiner

T. E.

Nock

M. K.

Silverman

M. M.

Mandrusiak

. . .Witte

(2006). Warning signs for suicide: Theory, research, and clinical applications. Suicide & Life-Threating Behavior, 36, 255–262.

38.

Runeson

Odeberg

Pettersson

Edbom

Adamsson

I. J.

Waern

(2017). Instruments for the assessment of suicide risk: A systematic review evaluating the certainty of the evidence. PLOS ONE, 12(7), Article e0180292. https://doi.org/10.1371/journal.pone.0180292

39.

Schneider

Stone

A. A.

(2016). Ambulatory and diary methods can facilitate the measurement of patient-reported outcomes. Quality of Life Research, 25(3), 497-506. https://doi.org/10.1007/s11136-015-1054-z

40.

Schueller

S. M.

Aguilera

Mohr

D. C.

(2017). Ecological momentary interventions for depression and anxiety. Depression & Anxiety, 34(6), 540-545. https://doi.org/10.1002/da.22649

41.

Shiffman

Stone

A. A.

Hufford

M. R.

(2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1-32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415

42.

Shneidman

E. S.

(1993). Suicide as psychache: A clinical approach to self-destructive behavior. Jason Aronson.

43.

Steidtmann

Manber

Blasey

Markowitz

J. C.

Klein

D. N.

Rothbaum

B. O.

Thase

M. E.

Kocsis

J. H.

Arnow

B. A.

(2013). Detecting critical decision points in psychotherapy and psychotherapy+ medication for chronic depression. Journal of Consulting and Clinical psychology, 81(5), 783-792. https://doi.org/10.1037/a0033250

44.

Stone

A. A.

Broderick

J. E.

Schneider

Schwartz

J. E.

(2012). Expanding options for developing outcome measures from momentary assessment data. Psychosomatic Medicine, 74(4), 387-397. https://doi.org/10.1097/PSY.0b013e3182571faa

45.

Tibshirani

R. J.

(2014). In praise of sparsity and convexity. In Lin

Genest

Banks

D. L.

Molenberghs

Scott

D. W.

Wang

J.-L.

(Eds.), Past, present, and future of statistical science (pp. 497-505). CRC. https://doi.org/10.1201/b16720

46.

Torous

Larsen

M. E.

Depp

Cosco

T. D.

Barnett

Nock

M. K.

Firth

(2018). Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: A review of current progress and next steps. Current Psychiatry Reports, 20(7), Article 51. https://doi.org/10.1007/s11920-018-0914-y

47.

Van Orden

K. A.

Witte

T. K.

Cukrowicz

K. C.

Braithwaite

S. R.

Selby

E. A.

Joiner

T. E.

Jr. (2010). The interpersonal theory of suicide. Psychological Review, 117(2), 575-600. https://doi.org/10.1037/a0018697

48.

Wenzel

Beck

A. T.

(2008). A cognitive model of suicidal behavior: Theory and treatment. Applied and Preventive Psychology, 12(4), 189-201. https://doi.org/10.1016/j.appsy.2008.05.001

49.

C. F. J.

Hamada

M. S.

(2009). Experiments: Planning, analysis, and parameter design optimization (2nd ed.). Wiley.

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