Precision Medicine and Suicide: an Opportunity for Digital Health
Purpose of Review
A better understanding of suicide phenomena is needed, and precision medicine is a promising approach toward this aim. In this manuscript, we review recent advances in the field, with particular focus on the role of digital health.
Technological advances such as smartphone-based ecological momentary assessment and passive collection of information from sensors provide a detailed description of suicidal behavior and thoughts. Further, we review more traditional approaches in the field of genetics.
We first highlight the need for precision medicine in suicidology. Then, in light of recent and promising research, we examine the role of smartphone-based information collection using explicit (active) and implicit (passive) means to construct a digital phenotype, which should be integrated with genetic and epigenetic data to develop tailored therapeutic and preventive approaches for suicide.
KeywordsSuicide Attempted suicide Precision medicine Mobile health Big data Ecological momentary assessment
This work was partially funded through ANR (the French National Research Agency) under the “Investissements d’avenir” programme with the reference ANR-16-IDEX-0006, Carlos III (ISCIII PI16/01852), American Foundation for Suicide Prevention (LSRG-1-005-16), Structural Funds of the European Union, MINECO/FEDER (“ADVENTURE”, id. TEC2015-69868-C2-1-R), and MCIU Explora Grant “AMBITION” (id. TEC2017-92552-EXP).
Compliance with Ethical Standards
Conflict of Interest
Maria Luisa Barrigon reports grants from Instituto de Salud Carlos III, American Foundation for Suicide Prevention, and from Structural Funds of the European Union, MINECO/FEDER.
Philippe Courtet reports grants from American Foundation for Suicide Prevention, grants and personal fees from Fondamental Foundation, and personal fees from Janssen.
Maria Oquendo receives royalties for the commercial use of the Columbia-Suicide Severity Rating Scale, and Dr. Oquendo’s family owns stock in Bristol Myers Squibb.
Enrique Baca-García reports grants from Instituto de Salud Carlos III, American Foundation for Suicide Prevention, and from Structural Funds of the European Union, MINECO/FEDER.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance
- 2.Saxena S, Krug EG, Chestnov O, World Health Organization, editors. Preventing suicide: a global imperative. Geneva: World Health Organization; 2014.Google Scholar
- 3.Lee L, Roser M, Ortiz-Ospina E. Suicide [Internet]. Our World in Data. 2018 [cited 2018 Dec 9]. Available from: https://ourworldindata.org/suicide
- 4.Products - Data Briefs - Number 241 - April 2016 [Internet]. [cited 2017 Dec 14]. Available from: https://www.cdc.gov/nchs/products/databriefs/db241.htm
- 8.• Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychological Bulletin. 2017;143:187–232 This meta-analysis critically reviews 50 years of research in suicide factor risk study. PubMedCrossRefPubMedCentralGoogle Scholar
- 9.Schaffer A, Isometsä ET, Azorin J-M, Cassidy F, Goldstein T, Rihmer Z, et al. A review of factors associated with greater likelihood of suicide attempts and suicide deaths in bipolar disorder: part II of a report of the International Society for Bipolar Disorders Task Force on Suicide in Bipolar Disorder. Aust N Z J Psychiatry. 2015;49:1006–20.PubMedPubMedCentralCrossRefGoogle Scholar
- 11.• Torous J, Larsen ME, Depp C, Cosco TD, Barnett I, Nock MK, et al. Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps. Curr Psychiatry Rep. 2018;20:51. This paper is a revision of technological advances in suicide assessment and prevention. Google Scholar
- 13.Reference GH. What is the precision medicine initiative? [Internet]. Genetics Home Reference. [cited 2018 Dec 16]. Available from: https://ghr.nlm.nih.gov/primer/precisionmedicine/initiative
- 14.National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease [Internet]. Washington (DC): National Academies Press (US); 2011 [cited 2018 Dec 8]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK91503/
- 16.• Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Medicine. 2016;8:134. This paper focuses on the importance of a collaborative approach in precision medicine. Google Scholar
- 23.NIMH Research Domain Criteria (RDoC) [Internet]. [cited 2018 Dec 8]. Available from: https://www.nimh.nih.gov/research-priorities/rdoc/index.shtml
- 26.Poushter J. Smartphone ownership and Internet usage continues to climb in emerging economies [Internet]. Pew Research Center’s Global Attitudes Project. 2016 [cited 2018 May 27]. Available from: http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
- 27.• Kleiman EM, Turner BJ, Fedor S, Beale EE, Huffman JC, Nock MK. Examination of real-time fluctuations in suicidal ideation and its risk factors: results from two ecological momentary assessment studies. J Abnorm Psychol. 2017;126:726–38 This is one of the six recent studies using smartphone-based EMA in the study of suicide. PubMedCrossRefPubMedCentralGoogle Scholar
- 33.Berrouiguet S, Ramírez D, Barrigón ML, Moreno-Muñoz P, Camacho RC, Baca-García E, et al. Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth. 2018;6:e197.PubMedPubMedCentralCrossRefGoogle Scholar
- 38.We’re more honest with our phones than with our doctors. The New York Times [Internet]. 2016 Mar 23 [cited 2016 Apr 21]; Available from: http://www.nytimes.com/interactive/2016/03/26/magazine/100000004288446.embedded.html
- 45.• Kleiman EM, Coppersmith DDL, Millner AJ, Franz PJ, Fox KR, Nock MK. Are suicidal thoughts reinforcing? A preliminary real-time monitoring study on the potential affect regulation function of suicidal thinking. J Affect Disord. 2018;232:122–6. This is one of the six recent studies using smartphone-based EMA in the study of suicide. PubMedCrossRefPubMedCentralGoogle Scholar
- 47.• Czyz EK, King CA, Nahum-Shani I. Ecological assessment of daily suicidal thoughts and attempts among suicidal teens after psychiatric hospitalization: lessons about feasibility and acceptability. Psychiatry Res. 2018;267:566–74. This is one of the six recent studies using smartphone-based EMA in the study of suicide. PubMedPubMedCentralCrossRefGoogle Scholar
- 48.• Hallensleben N, Glaesmer H, Forkmann T, Rath D, Strauss M, Kersting A, et al. Predicting suicidal ideation by interpersonal variables, hopelessness and depression in real-time. An ecological momentary assessment study in psychiatric inpatients with depression. Eur Psychiatry. 2018;56:43–50. This is one of the six recent studies using smartphone-based EMA in the study of suicide. PubMedCrossRefPubMedCentralGoogle Scholar
- 51.• Reinertsen E, Clifford GD. A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiol Meas. 2018;39:05TR01. This is a comprehensive review of recent studies using sensors for monitoring neuropsychiatric illnesses. PubMedPubMedCentralCrossRefGoogle Scholar
- 55.Evidence-Based Behavior [Internet]. Evidence-based behavior. [cited 2018 Dec 22]. Available from: https://eb2.tech/
- 58.• Barnett I, Torous J, Staples P, Keshavan M, Onnela J-P. Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data. J Am Med Inform Assoc. 2018;25:1669–74. Here, authors highlight the importance of data analyses. PubMedPubMedCentralCrossRefGoogle Scholar
- 59.Peis-Aznarte I, Olmos PM. Vera-Varela C. Barrigón ML: Courtet P, Baca-Garcia E, et al. Deep sequential models for suicidal ideation from multiple source data. Enviado para publicación; 2018.Google Scholar
- 64.O’Connor RC, Portzky G. Looking to the future: a synthesis of new developments and challenges in suicide research and prevention. Front Psychol [Internet]. 2018 [cited 2018 Dec 22];9. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02139/full
- 65.• Niculescu AB, Le-Niculescu H, Levey DF, Phalen PL, Dainton HL, Roseberry K, et al. Precision medicine for suicidality: from universality to subtypes and personalization. Molecular Psychiatry. 2017;22:1250–73 This paper provides an approach to precision medicine in suicide from genetics. PubMedPubMedCentralCrossRefGoogle Scholar
- 66.The Emory Healthy Aging Study | Emory University [Internet]. Emory | Healthy Aging Study. [cited 2018 Dec 23]. Available from: https://healthyaging.emory.edu/
- 67.McKernan LC, Clayton EW, Walsh CG. Protecting life while preserving liberty: ethical recommendations for suicide prevention with artificial intelligence. Front Psychiatry [Internet]. 2018 [cited 2018 Dec 23];9. Available from: https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00650/full