Use of ecological momentary assessment via wearable devices for detecting acute suicide risk in psychiatric inpatients

초록

Background Individuals with acute psychiatric disorders attempt suicide or engage in self-harm even during inpatient hospitalization. Despite ongoing clinical monitoring, accurately identifying fluctuating suicide risk remains challenging in acute psychiatric inpatient settings. Recently, ecological momentary assessment (EMA) has been increasingly investigated as a promising approach for predicting suicide risk by capturing dynamic changes in patients' mental states. This study investigated whether wearable-derived passive EMA features of sleep and activity, combined with baseline clinical variables, could support daily morning triage for acute suicide risk in psychiatric inpatients admitted to a closed ward.Objective This exploratory pilot study evaluated whether wearable-derived sleep and activity features could complement baseline clinical variables for daily morning risk triage among psychiatric inpatients in a closed ward.Methods We conducted a prospective observational pilot study of 87 enrolled psychiatric inpatients. Of these, 84 had valid Columbia-Suicide Severity Rating Scale (C-SSRS) assessments, and 69 contributed 151 assessment-linked records with both valid C-SSRS labels and temporally aligned wearable-derived sleep/activity features. Participants wore Fitbit Sense devices throughout hospitalization to collect passive sleep and activity data. Physical activity was summarized into 14 non-overlapping 2-hour windows spanning the previous day and assessment morning, ending at the 10:00 AM C-SSRS assessment. These features were combined with baseline clinical variables, including demographics and baseline C-SSRS score, to develop an L1-penalized logistic regression (LASSO) model. Performance was evaluated using recall (sensitivity) and the F2-score.Results The multimodal fusion model showed numerically higher recall than the conventional assessment model (0.560 vs 0.289) and a higher F2-score (0.548 vs 0.300). However, the 95% confidence intervals overlapped substantially across models; therefore, these findings should be interpreted as exploratory and hypothesis-generating rather than as confirmatory evidence of model superiority. The fusion model identified C-SSRS-positive records from patients with low admission scores using wearable-derived activity-pattern features, particularly blunted morning activity (08:00-10:00) and nocturnal hyperactivity (22:00-24:00).Conclusion These exploratory findings suggest that wearable-derived sleep and activity features may provide complementary information for daily morning risk triage in psychiatric inpatients. Larger studies are needed to validate whether this approach can support routine clinical review beyond baseline clinical variables.

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