JMIR Publications

ABSTRACT

Background:

Suicides and suicidal attempts are one of the most common causes of death in the USA. The rates of suicide have increased by 33% in the period 1999-2019. Clinically, a previous history of suicide is the main the risk factor, as well as the presence of comorbid conditions like depression and anxiety. Accurate and real-time prediction of suicidal thoughts may lead to improved management of patients with depression. Prediction of suicidality is difficult due to its day to day variability in relation to mood and anxiety symptoms. This can be overcome with the advent of mobile health (mHealth) technology that can capture granular data scores at a higher frequency than conventional therapeutic visitations.

Objective:

To predict suicidal ideation using self-reported mood, anxiety and baseline demographic variables in those undergoing treatment for depression.

Methods:

This study will utilize data from the ‘DepWatch’ study, a mHealth study that uses the DepWatch app developed by our research group. The objective of this longitudinal study is to develop a mHealth-based, personalized diagnostic prediction system for patients undergoing treatment for depression. Patients are followed over 12 weeks using electronic assessments conducted via the DepWatch app installed on their smart phones. The electronic assessments include the Quick Inventory of Depression Symptomatology-self report (QIDS-SR) conducted on a weekly basis and weekly medication adherence and medication safety and tolerability questionnaires. The assessments include brief mood and anxiety assessments conducted on a daily basis. Logistic regression analyses and analyses of the demographic and clinical characteristic collected at baseline is being conducted to predict the development of suicidal thoughts. The key explanatory variables are the daily mood and anxiety levels. The outcome variable is suicidal ideation (SI) as determined by the self-reported subject response to question 12 on the QIDS-SR scale.

Results:

A total of 34 subjects are in the Interim Analysis Set. The median age is 26 years and 85.3% are female. 64.7% of participants are White. 38.2% either have a college degree or graduate education. 23.5% are unemployed, 41.1% full-time employed. 52.9% earn less than $50,000 annually and 61% have never smoked. Univariate analysis shows statistical significance of gender, race, aggregate anxiety, and employment status at 0.1 significance level. In the multivariate model only gender and employment status are significant at 0.05. Race is marginally insignificant (p=0.068).

Conclusions:

Suicidality and completed suicides is a significant public health problem, especially in patients with depression. The mHealth technology and statistical modelling that captures daily variability in anxiety leading up to SI can help predict suicidality.