ICT4Depression: Service oriented architecture applied to the treatment of depression

Rocha, Artur; Henriques, Ricardo; Lopes, João Correia; Camacho, Rui; Klein, Michel; Modena, Gabriele; Ven, Pepijn van de; McGovern, Elaine; Tousset, Eric; Gauthier, Thibaut; Warmerdam, Lisanne

doi:10.1109/cbms.2012.6266379

Cited by 7 publications

(3 citation statements)

References 10 publications

(10 reference statements)

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“…MoodBuster was developed in the scope of the ICT4-Depression ( ) [ 11 ] and E-Compared ( ) [ 12 ] projects. These projects intend to provide mental healthcare stakeholders with evidence-based information and recommendations about the clinical and cost-effectiveness of blended depression treatment.…”

Section: Preliminaries and Related Researchmentioning

confidence: 99%

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Teles

Rocha

Silva

et al. 2017

Sensors

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Current mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient’s daily routine (e.g., “studying”, “at work”, “working out”). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations.

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Section: Preliminaries and Related Researchmentioning

confidence: 99%

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Teles

Rocha

Silva

et al. 2017

Sensors

Self Cite

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“…The prevalence of common mental disorders, such as depression, generalised anxiety disorder (GAD), panic disorder, phobias, social anxiety disorder, obsessive-compulsive disorder (OCD), and post-traumatic stress disorder (PTSD), is increasing globally [1], and has become a global public health concern [2]. Mood disorders (such as depression or bipolar disorder), anxiety disorders, personality disorders, psychotic disorders (such as schizophrenia), eating disorders, trauma-related disorders (such as post-traumatic stress disorder), drug abuse disorders, and self-harm affect people of all ages [3].…”

Section: Introductionmentioning

confidence: 99%

Mental Health Mobile Application with Diagnosis, Sentiment Analysis and Chatbot

Gadgil¹,

Jadhav²,

Kumari³

et al. 2022

IJSRCSEIT

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Mobile phones are probably one of the fastest growing and most rapidly adopted technologies in the world. The various apps and their health features are still relatively new, but their popularity is growing rapidly. The purpose of this study is to explore multiple elements of mental health applications. This study examines many aspects of mental health-related applications available on the Google Play Store between 2016 and 2020. We used a list of keywords such as mental health, mental illness, mental illness, mental illness remedies, and mental illness remedies to search for apps in the Google Play store. Various applications and programming tools were used to scrape the data. According to our findings, psychiatric apps primarily address the following symptoms: depression, anxiety, general mental health, stress, post-traumatic stress disorders, bipolar disorders, panic disorders, Sleep disorders, schizophrenia, compulsive disorders, substance abuse (drugs and alcohol), addiction (techniques, etc.). The app, on the other hand, offers different approaches to improving mental health. Relaxation, stress management, symptom tracking, soothing audio, journaling, connecting with mental health resources, interpersonal support, meditation, mood tracking, etc. are one of the approaches. These simple and engaging mental health apps have addressed specific mental health issues. The most common strategy for dealing with these issues is relaxation. It was not possible to predict the reliability of these applications based on their ratings and the number of users rated.

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“…E-COMPARED ( Kleiboer et al, 2016 ) is a European multi-centre trial, comparing face-to-face cognitive behavior therapy (cbt) for depression with so-called blended cbt, in which treatment is provided through a mix of face-to-face and online contact 1 . At 5 out of 8 research sites of E-COMPARED (DE, FR, NL, UK & PL), the on-line component of the blended treatment was delivered through an internet-based system called Moodbuster/ICT4D ( Warmerdam et al, 2012 , Rocha et al, 2012 ), through which detailed logs of system usage were systematically collected, as well as Ecological Momentary Assessments (EMA) ( Shiffman et al, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Rocha

Camacho

Ruwaard

et al. 2018

Internet Interventions

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IntroductionClinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP) data mining approach, using combined trial data of the EU E-COMPARED depression trial.MethodsWe explored the capability of ILP to handle and combine (implicit) multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1) Time reasoning is involved; 2) there is a reasonable amount of explicit useful relations to be analyzed; 3) ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4) both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity), and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks.ResultsE-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much) slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA), PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories.DiscussionThis work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically meaningful predictors of system use and treatment outcome. Strengths and limitations of the ILP approach given this objective will be discussed.

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ICT4Depression: Service oriented architecture applied to the treatment of depression

Cited by 7 publications

References 10 publications

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness

Mental Health Mobile Application with Diagnosis, Sentiment Analysis and Chatbot

Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

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