Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior

Carretero, Patricia; Campaña-Montes, Juan José; Artés‐Rodríguez, Antonio

doi:10.1007/7854_2020_170

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…For example, future studies should consider exploring the relationship between agitation, sleep quality, and suicidality in a more diverse sample of individuals with eating disorders and explore these relations by studying more specific facets of sleep quality and suicidal behaviors in addition to thoughts. Furthermore, future studies should consider using methods such as ecological momentary assessment and/or wearable actigraph sensors to assess relations between these variables across a shorter time frame, as these methods have promise in assessing short-term suicidal behaviors, but have been underutilized in the suicide literature (Carretero, Campaña-Montes, & Artés-Rodríguez, 2020; Davidson, Anestis, & Gutierrez, 2017). These methods would be useful in delineating relations as it is possible that agitation and sleep quality may be more strongly related to suicidality within an even shorter time frame, such as hours or days.…”

Section: Discussionmentioning

confidence: 99%

Unsound sleep, wound-up mind: a longitudinal examination of acute suicidal affective disturbance features among an eating disorder sample

et al. 2021

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Background Suicide is one of the most commonly reported causes of death in individuals with eating disorders. However, the mechanisms underlying the suicide and disordered eating link are largely unknown, and current assessments are still unable to accurately predict future suicidal thoughts and behaviors. The purpose of this study is to test the utility of two promising proximal risk factors, sleep quality and agitation, in predicting suicidal ideation in a sample of individuals with elevated suicidal thoughts and behaviors, namely those with eating disorders. Methods Women (N = 97) receiving treatment at an eating disorder treatment center completed weekly questionnaires assessing suicidal ideation, agitation, and sleep. General linear mixed models examined whether agitation and/or sleep quality were concurrently or prospectively associated with suicidal ideation across 12 weeks of treatment. Results There was a significant interaction between within-person agitation and sleep quality on suicidal ideation [B(s.e.) = −0.02(0.01), p < 0.05], such that on weeks when an individual experienced both higher than their average agitation and lower than their average sleep quality, they also experienced their highest levels of suicidal ideation. However, neither agitation nor sleep quality prospectively predicted suicidal ideation. Conclusions This study was the first to examine dynamic associations between interpersonal constructs and suicidal ideation in individuals with eating disorders. Results suggest that ongoing assessment for overarousal symptoms, such as agitation and poor sleep quality, in individuals with eating disorders may be warranted in order to manage suicidal ideation among this vulnerable population.

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Section: Discussionmentioning

confidence: 99%

Unsound sleep, wound-up mind: a longitudinal examination of acute suicidal affective disturbance features among an eating disorder sample

et al. 2021

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“…Passive data of participants was collected using eB2 Mindcare ( eb2 evidence-based behaviour, n.d. ; Bonilla-Escribano et al, 2019 ; Carretero et al, 2020 ), an eHealth platform validated for clinical use. The app runs in the background of the individual's smartphone, providing an e-diary for mental health.…”

Section: Methodsmentioning

confidence: 99%

Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation

Sükei

Leon-Martinez

Olmos

et al. 2023

Internet Interventions

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“…From February 1 through May 3, 2020, passive smartphone usage data were collected using eB2 Mindcare [ 24 - 26 ], a clinically validated eHealth platform. On March 14, a country-wide state of emergency was declared due to rising mortality rates from the coronavirus pandemic, and the government mandated a lockdown of all individuals who were not essential workers (ie, they were restricted to their residences, except when purchasing food and medicines or attending emergencies).…”

Section: Methodsmentioning

confidence: 99%

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study

Ryu¹,

Sükei²,

Norbury³

et al. 2021

JMIR Ment Health

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Background Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.

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Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior

Cited by 12 publications

References 32 publications

Unsound sleep, wound-up mind: a longitudinal examination of acute suicidal affective disturbance features among an eating disorder sample

Unsound sleep, wound-up mind: a longitudinal examination of acute suicidal affective disturbance features among an eating disorder sample

Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study

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