CoChat
Sign Up
Cited by 10

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care

Varidel, Mathew, Hickie, Ian B., Prodan, Ante, Skinner, Adam, Marchant, Roman, Cripps, Sally, Oliveria, Rafael, Chong, Min K., Scott, Elizabeth, Scott, Jan, Iorfino, Frank·2024·npj Mental Health Research
DOI 10.1038/s44184-024-00071-0

Published version of a preprint

Open this paper in CoChat — free account

Abstract

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population ( N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual’s level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s44184-024-00071-0

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 18 references

Research works printed but not linked: 49

  1. Klein, A. et al. Remote digital psychiatry for mobile mental health assessment and therapy: MindLogger platform development study. J. Med. Internet Res. 23, e22369 (2021). · DOI
  2. Jellins, L. Assessment in the digital age: an overview of online tools and considerations for school psychologists and school counsellors. J. Psychol. Couns. Sch. 25, 116–125 (2015). · DOI
  3. Chung, J. & Buchanan, B. A self-report survey: Australian Clinicians’ attitudes towards progress monitoring measures. Aust. Psychol. Soc. 54, 3–12 (2019). · DOI
  4. Kwan, B., Rickwood, D. J. & Telford, N. R. Development and validation of MyLifeTracker: a routine outcome measure for youth mental health. Psychol. Res. Behav. Manag. 11, 67–77 (2018). · DOI
  5. Piwek, L., Ellis, D. A., Andrews, S. & Joinson, A. The rise of consumer health wearables: promises and barriers. PLoS Med. 13, 1–9 (2016). · DOI
  6. Merikangas, K. R. et al. Real-time mobile monitoring of the dynamic associations among motor activity, energy, mood, and sleep in adults with bipolar disorder. JAMA Psychiatry 76, 190–198 (2019). · DOI · Paper page
  7. Trivedi, M. H. & Daly, E. J. Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice. Drug Alcohol Depend. 88, S61–71 (2007). · DOI
  8. Harding, K. J. K., Rush, A. J., Arbuckle, M., Trivedi, M. H. & Pincus, H. A. Measurement-based care in psychiatric practice: a policy framework for implementation. J. Clin. Psychiatry 72, 1136–1143 (2011). · DOI
  9. Parikh, A., Fristad, M. A., Axelson, D. & Krishna, R. Evidence base for measurement-based care in child and adolescent psychiatry. Child Adolesc. Psychiatr. Clin. N. Am. 29, 587–599 (2020). · DOI
  10. Rognstad, K., Wentzel-Larsen, T., Neumer, S. P. & Kjøbli, J. A systematic review and meta-analysis of measurement feedback systems in treatment for common mental health disorders. Adm. Policy Ment. Health. 50, 269–282 (2023). · DOI
  11. de Jong, K. et al. Using progress feedback to improve outcomes and reduce drop-out, treatment duration, and deterioration: a multilevel meta-analysis. Clin. Psychol. Rev. 85, 102002 (2021). · DOI · Paper page
  12. Iorfino, F. et al. Social and occupational outcomes for young people who attend early intervention mental health services: a longitudinal study. Med. J. Aust. 216, 87–93 (2022). · DOI
  13. Hannan, C. et al. A lab test and algorithms for identifying clients at risk for treatment failure. J. Clin. Psychol. 61, 155–163 (2005). · DOI
  14. Hatfield, D., McCullough, L., Frantz, S. H. B. & Krieger, K. Do we know when our clients get worse? An investigation of therapists’ ability to detect negative client change. Clin. Psychol. Psychother. 17, 25–32 (2010). · DOI · Paper page
  15. Graham, S. et al. Artificial Intelligence for mental health and mental illnesses: an overview. Curr. Psychiatry Rep. 21, 116 (2019). · DOI · Paper page
  16. Lutz, W., Schwartz, B. & Delgadillo, J. Measurement-based and data-informed psychological therapy. Annu. Rev. Clin. Psychol. 18, 71–98 (2022). · DOI
  17. Kwan, B. & Rickwood, D. J. A routine outcome measure for youth mental health: clinically interpreting MyLifeTracker. Early Interv. Psychiatry 15, 807–817 (2021). · DOI
  18. Franklin, J. C. et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143, 187–232 (2017). · DOI · Paper page
  19. McHugh, C. M. et al. Predictive modelling of deliberate self-harm and suicide attempts in young people accessing primary care: a machine learning analysis of a longitudinal study. Soc. Psychiatry Psychiatr. Epidemiol. 58, 893–905 (2023). · DOI
  20. Iorfino, F. et al. Predicting self-harm within six months after initial presentation to youth mental health services: a machine learning study. PLoS One 15, 1–16 (2020). · DOI
  21. Iorfino, F. et al. The temporal dependencies between social, emotional and physical health factors in young people receiving mental healthcare: a dynamic Bayesian network analysis. Epidemiol. Psychiatr. Sci. 32, e56 (2023). · DOI
  22. Skinner, A., Osgood, N. D., Occhipinti, J. A., Song, Y. J. C. & Hickie, I. B. Unemployment and underemployment are causes of suicide. Sci. Adv. 9, eadg3758 (2023). · DOI
  23. Nock, M. K. et al. Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br. J. Psychiatry 192, 98–105 (2008). · DOI · Paper page
  24. Rizk, M. M. et al. Variability in suicidal ideation is associated with affective instability in suicide attempters with borderline personality disorder. Psychiatry 82, 173–178 (2019). · DOI · Paper page
  25. Cox, R. C., Brown, S. L., Chalmers, B. N. & Scott, L. N. Examining sleep disturbance components as near-term predictors of suicide ideation in daily life. Psychiatry Res. 326, 115323 (2023). · DOI
  26. Oud, J. H. L. & Jansen, R. A. R. G. Continuous time state space modeling. Psychometrika 65, 199–215 (2000). · DOI
  27. Deboeck, P. R., & Preacher, K. J. No need to be discrete: a method for continuous time mediation analysis. Struct. Equ. Modeling 23, 61–75 (2015). · DOI
  28. de Haan-Rietdijk, S., Voelkle, M. C., Keijsers, L. & Hamaker, E. L. Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Front. Psychol. 8, 1–19 (2017). · DOI
  29. Oravecz, Z., Tuerlinckx, F. & Vandekerckhove, J. A hierarchical Ornstein–Uhlenbeck model for continuous repeated measurement data. Psychometrika 74, 396–418 (2009). · DOI
  30. Driver, C. C. & Voelkle, M. C. Hierarchical Bayesian continuous time dynamic modelling. Psychol. Method 23, 774–799 (2018). · DOI
  31. Merton, R. C. & Samuelson, P. A (eds) Continuous-Time Finance: Revised Edition (Basil Blackwell, 1992).
  32. Watanabe, S. A widely applicable bayesian information criterion. J. Mach. Learn. Res. 14, 867–897 (2013).
  33. Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017). · DOI
  34. Miranda, R., Ortin, A., Scott, M. & Shaffer, D. Characteristics of suicidal ideation that predict the transition to future suicide attempts in adolescents. J. Child Psychol. Psychiatry 55, 1288–1296 (2014). · DOI
  35. Adolf, J. K., Loossens, T., Tuerlinckx, F. & Ceulemans, E. Optimal sampling rates for reliable continuous-time first-order autoregressive and vector autoregressive modeling. Psychol. Methods 26, 701–718 (2021). · DOI
  36. Capon, W. et al. Clinical staging and the differential risks for clinical and functional outcomes in young people presenting for youth mental health care. BMC Med. 20, 1–10 (2022). · DOI
  37. Hamaker, E. L., Grasman, R. P. P. P. & Kamphuis, J. H. Modeling BAS dysregulation in bipolar disorder: illustrating the potential of time series analysis. Assessment 23, 436–446 (2016). · DOI
  38. Ryan, O., Haslbeck, J. & Waldorp, L. Non-stationarity in time-series analysis: modeling stochastic and deterministic trends. https://doi.org/10.31234/osf.io/z7ja2 (2023). · DOI
  39. Capon, W. et al. Characterising variability in youth mental health service populations: a detailed and scalable approach using digital technology. Australas. Psychiatry 31, 295–301 (2023). · DOI
  40. Van Spijker, B. A. J. et al. The suicidal ideation attributes scale (SIDAS): community-based validation study of a new scale for the measurement of suicidal ideation. Suicide Life Threat. Behav. 44, 408–419 (2014). · DOI · Paper page
  41. Goldman, H. H., Skodol, A. E. & Lave, T. R. Revising axis V for DSM-IV: a review of measures of social functioning. Am. J. Psychiatry 149, 1148–1156 (1992). · DOI
  42. Busner, J. & Targum, S. D. Global impressions scale: applying a research tool in practice. Psychiatry (Edgmont) 4, 28–37 (2007).
  43. Schuster, T. L., Kessler, R. C. & Aseltine, R. H. Supportive interactions, negative interactions, and depressed mood. Am. J. Community Psychol. 18, 423–438 (1990). · DOI · Paper page
  44. Arnold, L. Stochastic Differential Equations: Theory and Applications. SIAM Review (Wiley-Interscience, 1974). https://doi.org/10.1137/1018036. · DOI
  45. Driver, C. C., Oud, J. H. L. & Voelkle, M. C. Continuous time structural equation modeling with r package ctsem. J. Stat. Softw. 77, 1–35 (2017). · DOI
  46. Gelman, A. et al. Bayesian Data Analysis (Chapman and Hall/CRC, 1995). https://doi.org/10.1201/9780429258411. · DOI · Paper page
  47. Wiqvist, S., Golightly, A., McLean, A. T. & Picchini, U. Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Comput. Stat. Data Anal. 157, 107151 (2021). · DOI
  48. Kruschke, J. K. Doing Bayesian Data Analysis: A tutorial with R, JAGS, and Stan, 2nd Edition. (Elsevier Inc., 2015). · DOI
  49. Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).

Metadata

Authors
Varidel, M., Hickie, I. B., Prodan, A., Skinner, A., Marchant, R., Cripps, S., Oliveria, R., Chong, M. K., Scott, E., Scott, J., & Iorfino, F.
Venue
npj Mental Health Research
Publisher
Springer Science and Business Media LLC
Published
2024-06-07
Volume
3
Issue
1
ISSN
2731-4251
DOI
10.1038/s44184-024-00071-0
Cited by
10
References
18
Language
en
Licence
Funders
  • Medical Research Future Fund (MRFAI000097)
  • National Health and Medical Research Council (511921, GNT2018157)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review