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Journal Article

Citation

Barrigón ML, Berrouiguet S, Carballo JJ, Bonal-Giménez C, Fernandez-Navarro P, Pfang B, Delgado-Gomez D, Courtet P, Aroca F, Lopez-Castroman J, Artés-Rodríguez A, Baca-Garcia E, MEmind study group. Int. J. Methods Psychiatr. Res. 2017; 26(1): e1554.

Copyright

(Copyright © 2017, John Wiley and Sons)

DOI

10.1002/mpr.1554

PMID

28276176

PMCID

PMC6877232

Abstract

Ecological momentary assessment (EMA) is gaining importance in psychiatry. This article assesses the characteristics of patients who used a new electronic EMA tool: the MEmind Wellness Tracker. Over one year, 13811 adult outpatients in our Psychiatry Department were asked to use MEmind. We collected information about socio-demographic data, psychiatric diagnoses, illness severity, stressful life events and suicidal thoughts/behavior. We compared active users (N = 2838) and non-active users (N = 10,973) of MEmind and performed a Random Forest analysis to assess which variables could predict its use. Univariate analyses revealed that MEmind-users were younger (42.2 ± 13.5 years versus 48.5 ± 16.3 years; χ2  = 18.85; P < 0.001) and more frequently diagnosed with anxiety related disorders (57.9% versus 46.7%; χ2  = 105.92; P = 0.000) than non-active users. They were more likely to report thoughts about death and suicide (up to 24% of active users expressed wish for death) and had experienced more stressful life events than non-active users (57% versus 48.5%; χ2  = 64.65; P < 0.001). In the Random Forest analysis, 31 variables showed mean decrease accuracy values higher than zero with a 95% confidence interval (CI), including sex, age, suicidal thoughts, life threatening events and several diagnoses. In the light of these results, strategies to improve EMA and e-Mental Health adherence are discussed.


Language: en

Keywords

Humans; Adult; Female; Male; Middle Aged; Internet; suicide; Suicide; internet; Mental Disorders; smartphone; Ecological Momentary Assessment; Medical Informatics Applications; experience sampling methods; health records; Random Forest analysis

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