What is Psychologist in a Pocket?

Psychologist in a Pocket (PiaP) is a mobile application intended to serve as an adjunct to the clinical assessment of depression. PiaP screens for depression symptoms based on the DSM-V and the ICD-10 classification systems. It employs text analysis of electronic data (such as text messages, social network posts and Emails). Current and future research directions include other behavioral indicators such as sleep, movement and speech.

This was the result of interdisciplinary efforts in clinical psychology and computer science. The first prototype was developed in 2011 as part of a computer science student research project at RWTH Aachen University, Germany. From 2014 to the present, there has been broader collaborative work on the PiaP, which includes the Department of Medical Informatics of the RWTH Aachen University Hospital (UKA), Lahore University of Management Sciences (LUMS) (Pakistan) and University of Santo Tomas (UST) (Philippines).

In 2012, the PiaP was among the Five Finalists in the Heritage Open mHealth Challenge, which was sponsored by Heritage Provider, Open mHealth and University of California-Los Angeles (UCLA).

The PiaP Research Team is composed of: Jó Ágila Bitsch (RWTH Aachen University), Roann Munoz Ramos (RWTH Aachen University Hospital), Paula Glenda Ferrer Cheng (University of Santo Tomas), Christian Kohlschein (RWTH Aachen University) and Stephan Michael Jonas (RWTH Aachen University Hospital).

The Team would also like to thank: Tim Ix, Eugen Seljutin, Sarah Winter and Paul Smith (COMSYS, RWTH Aachen University, Germany) Francine Rose de Castro-Bofill (University of the East, Philippines) and Marko Jovanović (mHealth Division, RWTH Aachen University Hospital, Germany) for their contributions to this project.

  • [DOI] F. P. G. Cheng, R. M. Ramos, Á. Jó. Bitsch, S. M. Jonas, T. Ix, Q. P. L. See, and K. Wehrle, “Psychologist in a pocket: lexicon development and content validation of a mobile-based app for depression screening,” Jmir mhealth uhealth, vol. 4, iss. 3, p. e88, 2016.
    [Bibtex]
    @Article{info:doi/10.2196/mhealth.5284,
    author="Cheng, Ferrer Paula Glenda
    and Ramos, Roann Munoz
    and Bitsch, J\'o \'Agila
    and Jonas, Stephan Michael
    and Ix, Tim
    and See, Quetulio Portia Lynn
    and Wehrle, Klaus",
    title="Psychologist in a Pocket: Lexicon Development and Content Validation of a Mobile-Based App for Depression Screening",
    journal="JMIR Mhealth Uhealth",
    year="2016",
    month="Jul",
    day="20",
    volume="4",
    number="3",
    pages="e88",
    keywords="depression",
    keywords="Psychologist in a Pocket",
    keywords="lexicon development",
    keywords="text analysis",
    abstract="Background: Language reflects the state of one's mental health and personal characteristics. It also reveals preoccupations with a particular schema, thus possibly providing insights into psychological conditions. Using text or lexical analysis in exploring depression, negative schemas and self-focusing tendencies may be depicted. As mobile technology has become highly integrated in daily routine, mobile devices have the capacity for ecological momentary assessment (EMA), specifically the experience sampling method (ESM), where behavior is captured in real-time or closer in time to experience in one's natural environment. Extending mobile technology to psychological health could augment initial clinical assessment, particularly of mood disturbances, such as depression and analyze daily activities, such as language use in communication. Here, we present the process of lexicon generation and development and the initial validation of Psychologist in a Pocket (PiaP), a mobile app designed to screen signs of depression through text analysis. Objective: The main objectives of the study are (1) to generate and develop a depressive lexicon that can be used for screening text-input in mobile apps to be used in the PiaP; and (2) to conduct content validation as initial validation. Methods: The first phase of our research focused on lexicon development. Words related to depression and its symptoms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and in the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines classification systems were gathered from focus group discussions with Filipino college students, interviews with mental health professionals, and the review of established scales for depression and other related constructs. Results: The lexicon development phase yielded a database consisting of 13 categories based on the criteria depressive symptoms in the DSM-5 and ICD-10. For the draft of the depression lexicon for PiaP, we were able to gather 1762 main keywords and 9655 derivatives of main keywords. In addition, we compiled 823,869 spelling variations. Keywords included negatively-valenced words like ``sad'', ``unworthy'', or ``tired'' which are almost always accompanied by personal pronouns, such as ``I'', ``I'm'' or ``my'' and in Filipino, ``ako'' or ``ko''. For the content validation, only keywords with CVR equal to or more than 0.75 were included in the depression lexicon test-run version. The mean of all CVRs yielded a high overall CVI of 0.90. A total of 1498 main keywords, 8911 derivatives of main keywords, and 783,140 spelling variations, with a total of 793, 553 keywords now comprise the test-run version. Conclusions: The generation of the depression lexicon is relatively exhaustive. The breadth of keywords used in text analysis incorporates the characteristic expressions of depression and its related constructs by a particular culture and age group. A content-validated mobile health app, PiaP may help augment a more effective and early detection of depressive symptoms. ",
    doi="10.2196/mhealth.5284",
    url="http://mhealth.jmir.org/2016/3/e88/",
    url="http://www.ncbi.nlm.nih.gov/pubmed/27439444"
    }