The prediction of study-emergent suicidal ideation in bipolar disorder: a pilot study using ecological momentary assessment data BIPOLAR DISORDERS Thompson, W. K., Gershon, A., O'Hara, R., Bernert, R. A., Depp, C. A. 2014; 16 (7): 669-677


Bipolar disorder is associated with idiosyncratic precursors of clinically important states such as suicidal ideation. Ecological momentary assessment (EMA) - high frequency data collection in a subject's usual environment - provides the potential for development of temporal, individualized prediction of risk states. The present study tested the ability of EMA data to predict individual symptom change in clinician-rated suicidal ideation.Thirty-five adults diagnosed with inter-episode bipolar disorder completed daily measures of affect in their home environments using diaries administered over an eight-week assessment timeline. Suicidal ideation was assessed monthly at in-person visits using the Inventory of Depressive Symptomatology-Clinician Rated. We used a novel application of functional linear models (FLMs) to generate prospective predictions of suicidal ideation at in-person clinician assessments based on intensively sampled trajectories of daily affect.Eight instances of suicidal ideation scores > 0 were recorded during the study period on six participants. Utilizing trajectories of negative and positive affect, cross-validated predictions attained 88% sensitivity with 95% specificity for elevated suicidal ideation one week prior to in-person clinician assessment. This model strongly outperformed prediction models using cross-sectional data obtained at study visits alone.Utilizing EMA data with FLM prediction models substantially increases the accuracy of prediction of study-emergent suicidal ideation. Prediction algorithms employing intensively sampled longitudinal EMA data could sensitively detect the warning signs of suicidal ideation to facilitate improved suicide risk assessment and the timely delivery of preventative interventions.

View details for DOI 10.1111/bdi.12218

View details for Web of Science ID 000344373100001