Using Agents in Mental Health: A Systemto Support the Remote Treatment of

Major Depression

Juan MARTÍNEZ-MIRANDA a,1, Magí LLUCH-ARIET a

aMicroArt S.L., Parc Científic de Barcelona, Spain

Abstract. The use of software agents in mental health is a relatively few exploredfield, but that in the last years has attracted the interest of researchers due to recentstudies that show the effectiveness of computarised psychological therapies. Thetreatment of Major Depression is one of the mental health treatments that needs theactive participation, during all their phases, of the people affected by this illness. Inthis paper we propose the development of a novel computer-based system to sup-port the treatment of major depression by the remote monitoring of patients andthe promotion of healthy behaviours, through a Virtual Agent (VA), in response tomonitored inputs. We describe the general ideas and the first steps done towardsthe development of the three main components in the system. We particularly em-phasise the description of the Virtual Agent, which will act as the virtual peer ofthe patient supporting him/her with specific activities in the treatment that wouldcontribute to an earlier return to normal health and social and economic activity.

Keywords.Virtual Agents, Personal Health Systems, Human-Computer Interaction

Introduction

The Major Depression (MD) is a mental illness that affects between 5% and 10% ofthe population in Western Europe [19]. The study presented in [24] have shown thatin 28 countries (comprising a population of 466 million), 21 million were affected bydepression, representing an estimated cost of 118 billion euros in 2004, or 253 euros perinhabitant. The effect of Major Depression on sufferers’ quality of life and morbidityis similar to the effects of chronic diseases like hypertension, rheumatoid arthritis anddiabetes [3]. Moreover, the World Health Organization reports suicide to be among thetop ten causes of death worldwide, with the lifetime risk of suicide in people with mooddisorders (mainly depression) estimated to be 6-15%.

People with Major Depression (MD) -in particular, those with mild and moderateforms of MD who do not require hospitalisation- are generally treated with pharmaco-logical and psychotherapeutic regimens to be followed as part of their daily activities.Ideally, every patient should be actively involved in all the phases of their treatment. It isparticularly important to identify those situations and aspects of daily life that could lead

1Corresponding Author: MicroArt S.L., Baldiri Reixac, 4-6, 08028 Barcelona, Spain; E-mail:jmartinez@microart.cat.

Artificial Intelligence Research and Development

R. Alquézar et al. (Eds.)

IOS Press, 2010

© 2010 The authors and IOS Press. All rights reserved.

doi:10.3233/978-1-60750-643-0-101

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patients to relapse or experience a critical episode. Mood disorders affect each persondifferently [10]. Therefore, people with mood disorders should develop their own person-alised strategies for staying well and avoiding relapses, strategies that are tailored to theirindividual needs and characteristics [16]. Such strategies include acceptance of diagno-sis, mindfulness, education, recognition of warning signals and make lifestyle changes.

While approximately 40% of patients recover completely from an episode and a fur-ther 20% show marked improvement [8], many patients respond only slowly or incom-pletely or both. The reasons why patients do not respond to treatment are not fully un-derstood but a major factor appears to be premature discontinuation of treatment. Whileboth antidepressant drug and face to face psychological therapies are effective in de-pression, these are not acceptable or available to some patients and a number of novelapproaches for delivering therapy have been developed. In particular computerised cog-nitive behavioural therapy (CCBT) [15] uses stand-alone computer software or a webapplication to encourage patients to complete self-help tasks that involve altering be-haviour and reflecting on and reframing cognitions (well-known examples of these ap-plications include Beating the Blues - http://www.beatingtheblues.co.uk/and MoodGYM - http://moodgym.anu.edu.au/). CCBT appears only weaklyeffective when the motivation to continue is left to the patient, but has greater benefitwhen use is augmented by support; to date this has been provided either face to face orby telephone.

The main users of these existent computer-based therapies are those patients whofollow a treatment outside the hospital but that need a constant monitoring to assess theirclinical evolution. A complementary and useful approach would be a system that sup-ports the remote patient’s treatment through the provision of a continuous and person-alised monitoring over specific behaviour patterns to collect relevant data that allow theidentification of current patients’ condition and more important, prevent the recurrenceof symptoms in the future. The first ideas and steps towards the further development ofsuch a system are explained in this paper putting emphasis in the development of a vir-tual agent (VA) which will act as a patient’s assistant by supporting two main types ofspoken interactions: computer-based interviews and enhanced prompts. Computer-basedinterviews will be used to obtain relevant information about the patient’s day, adminis-ter standardised questionnaires about the user’s mood and ask about general subjectivehealth and well-being. Interviews will also contain a diary function that allows patientsto talk freely about their mood, the VA, or their treatment. Enhanced prompts will givemedication reminders and present motivational reminders that encourage the user to en-gage with self-help Cognitive Behaviour Therapy material such as books, worksheetsand internet-based CBT. The proposed system described in this paper will be developedas a coordinated effort between several technological and clinical partners from differentEuropean institutions.

1. Related Work

The great advances in the development of non-invasive, wearable sensors and wirelesscommunications, have contributed to create environments where computers are transpar-ent and seamlessly integrated and connected in everyday-life people’s situations. One ofthe main fields that has benefited from these novel environments is healthcare through

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the development of systems that provide telemedicine/pervasive care capabilities. Sev-eral applications have been developed in the last years where different physiological andphysical data are obtained through sensors that continuously and remotely monitor andassess the patients’ condition.

As an example, the PERFORM project has developed a platform for the monitoringand management of neurodegenerative diseases such as the Parkinson’s and the Amy-otrophic Lateral Sclerosis [2]. The PERFORM platform uses a set of light-weighted andunobtrusive wearable and wireless sensors to detect and quantify patient symptoms dur-ing the day and night, at the patient’s environment, both at home and outside. The sen-sors used in the PERFORM platform include accelerometers and sensors to measurethe patient oxygen saturation among some others. Similarly, the CHRONIOUS initiativeaims to develop a smart wearable platform based on multi-parametric sensors process-ing for monitoring people suffering from chronic diseases such as chronic obstructivepulmonary disease and chronic kidney disease [9]. A third initiative is the HeartCycleproject which also aims to build a multi-parametric monitoring and analysis system ofvital signs to provide a closed-loop disease management for patients suffering from heartfailure and patients with chronic heart disease [23].

Due to the distributed nature of this type of systems, the use of Multi-Agent Systemsis a frequent applied technology to perform specific tasks including the input/output op-erations of the physical devices and their coordination in acquiring the data, the process-ing of the received data, and the pro-active sending of signals and information to both,patients and clinical staff, for a better treatment management among some others (a deepreview of this type of systems can be found in [14]).

A complementary use of software agents applied in telemedicine is the view of theagent entity as an intelligent assistant that provides specific information and recommen-dations to the patients supporting a set of well defined tasks that contribute with theself-management of the treatment. The use of these agents, commonly known as VirtualAgents (VA) have been applied for different purposes in healthcare including the trainingof clinicians [17], explanation of medical documents to patients with low health literacy[6], the treatment of phobias [7] and motivating the execution of physical exercises [4].

Recent works have already introduced the use of virtual agents in computer assistedmental health interventions: in [26] a virtual peer was developed to engage children suf-fering from autism spectrum disorders (ASD) in collaborative narrative to produce con-tingent discourse. Similarly, in [5] a VA to promote medication adherence, through dia-logue, among adults with schizophrenia is presented. Initial results have been focused onpatient attitudes towards the VA, indicating that most of the participants liked and trustedthe agent, and 80% of respondents indicated they would have liked to continue work-ing with the agent at the end of the 30 day intervention. Also [21] describes a VA thatguides the user through the Beck Depression Inventory (BDI), a questionnaire used tomeasure the severity of depression. Through a basic emotional model, the VA generatesand shows empathy (through its facial expression representing sadness and/or happiness)to the user depending on his/her assessed depression level.

In line with these works, we introduce the general ideas and the initial steps towardsthe further development of a computational distributed system to support the remotetreatment of patients with Major Depression (MD). The main components and charac-teristics that the proposed system must implement are presented in the following sectionwith particular emphasis in the description of the Virtual Agent component.

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2. A Distributed System to Support the Treatment of Major Depression

The development of a system to remotely assess the condition of patients with majordepression and supporting them with the self-management during specific stages of theirtreatment, would significantly advance the state-of-the-art in the research and develop-ment of remote care systems applied to mental health. The proposed system (distributedbetween the clinical institution and patient’s home) will focus on major depression in itsmoderate form, supporting people who are significantly affected but are still able to liveat home and may also be working. The main focus of the system is on reducing of de-pressive symptoms, improving functioning, and preventing the recurrence of symptomsin the future. More specifically, it addresses the need for:

• Ongoing assessment/monitoring of mood.• Identifying relapse signals.• Increasing treatment compliance.• Providing simple therapeutic interventions (e.g., coping statements, relaxationtraining).

Such kind of system must facilitate the clinical management through the: remote

personal monitoring and the virtual agent components deployed at the site where thepatient spends most time (typically, at the patient’s home). The main functionality of thepersonal monitoring system will be the collection of a relevant set of parameters thatallow the detection of specific patterns in the patient. In particular two types of data areuseful to be collected:

1. Behavioural data including patterns of sleeping, motor activity and speech;2. Subjective data, including brief validated scales to measure mood, cognition andbehaviours. These scales are frequently used in Cognitive Behaviour Therapy(CBT) to identify progress towards recovery and/or the onset of a new depressiveepisode.

The collection of the behavioural data includes the use of existent commercial sen-sors such as under-mattress sensors to detect and assess specific sleeping patterns(seefor example the sensors from S4Sensors: http://s4sensors.com/products.html). Motor activity patterns will be collected through the use of wearable accelerom-eters adapting the received signals to allow the assessment of the collected data and iden-tify depression indicators with the advice of the clinicians.

These identified behaviour patterns can then be used in a first instance by the virtualagent to prompt the patient (when appropriate) to carry out potentially helpful activities,such as relaxation or exercise, or offer the opportunity to add entries to a spoken diary.Suggestions must be made at appropriate times to ensure privacy and convenience. Addi-tionally, in case the patient data suggest a potential treatment failure and/or a suicide risk,the virtual agent needs to urgently alert the clinical site leading to a direct communication(e.g. by phone) between the patient and the clinician.

Immediate feedback provided from the VA to the patient is based on the informationprovided by the personal monitoring system, jointly with specific pieces of informationcollected from the patient’s clinical record. A second purpose in the use of the VA is torecord speech samples from the patient that can yield acoustic and lexical cues to theircurrent mood. This speech data can be used to complement the information obtainedfrom the personal monitoring system to support the assessment of the patient’s condition.

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Figure 1. General architecture of the proposed system to support the remote treatment of major depression

The information collected by the two components at the patient site needs to be se-curely transmitted to the clinical centre, filling a knowledge repository that can be usedby the clinicians to update and complement the patient’s clinical record. The informationcontained in the knowledge repository can be directly accessed by the clinical staff toobjectively assess the patient’s condition and analyse the ongoing effectiveness of his/hertreatment during its different phases. Additionally, this knowledge repository will pro-vide the input to a knowledge engine which contains a reasoning mechanism to providea decision support for treatment management. Both sub-systems, the knowledge repos-itory and the knowledge engine, form the third component of the proposed system: thedecision support system for treatment management deployed at the clinical centre. Thegeneral architecture containing the three main components of the system is shown inFigure 1.

2.1. Interacting with the Virtual Agent

The interaction between the patient and the embodied virtual agent will take place mainlythrough a combination of dialogue interaction and a basic set of body movements andfacial expressions designed to maintain the attention of the patient and help him/her toeffectively manage important stages of his/her treatment. The research work carried outin the development of the system offers a unique opportunity to explore different per-spectives in the development of the VA including the design of the graphical appearancein the agent, the study of adaptive dialogue management, and the generation of a coher-ent behaviour in the agent while interacting with the patient. An alternative touch screengraphical interface, but also containing the VA, can be considered for patients who arehard of hearing, who lack sufficient privacy to speak to the VA, and who prefer visualinteractions (see the VA’s internal architecture in Figure 2). All these features need tobe suitable designed for a non-typical user group which is nevertheless a significant andsensitive part of the general population of users.

Therefore, the main aim of the VA is to support CBT and self-help through:

Enhanced reminder prompts: Reminder prompts can cover life style advice, medi-cation, and self-directed CBT interventions. Self-directed CBT interventions canrange from listening to relaxation exercises provided by the VA to completingworksheets suggested by the clinician. Whenever the VA delivers a reminder or a

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Figure 2. The components of the Virtual Agent

prompt, the user can record comments on compliance or non-compliance, if de-sired. The prompts will be designed to be motivational and non-confrontational.

Spoken diary: Patients will be encouraged to reflect on their day or the past coupleof days using structured prompts adapted from CBT. Patients can record theirthoughts for later review by themselves or, exceptionally, by a clinician.

Computer-based interviews: Computer-based interviews are highly structured system-initiative dialogues where users can enter information about their day, completestandardised assessments recorded daily and keep a spoken diary for recordinggeneral mood and views. These may include monitoring (administered daily ormore often) and review (at longer intervals, such as weekly) interviews. Mon-itoring assessments may include the Clinical Global Impression (CGI), simplevisual analogue scales for mood and anxiety [29] and items from AutomatedThoughts Questionnaire. Review assessments may include longer measures suchas the PHQ-9 [31] and Beck Depression Inventory.

All the characteristics embodied in the VA will be developed in close consultationwith the three main stakeholder groups, patients, caregivers, and clinicians. The stake-holder groups will also be involved in the design of four key aspects of the visual appear-ance of the VA: realism vs. iconicity; face; body and costume design; and visual style[11].

Previous EC-founded research projects, such as the Engaging Media for MentalHealth Applications (EMMA) project have shown the potential of using virtual real-ity environments containing "emotionally charged" scenarios in the treatment of mentalhealth problems [1]. Building on recent research [28], [25], our system will create be-haviourally coherent, and thus more believable characters with emotional characteristicsand personality. Moreover and due to the importance of empathy in providing CBT [12],the design and development of a cognitive-emotional architecture that leads to an em-phatic behaviour in the agent is currently analysed to be implemented in the VA [18]. Al-though there are currently some similar works that have already implemented emotionalarchitectures that generate empathic attitudes in virtual characters for different purposes[20], [22], we will explore the development of a cognitive-emotional model based onthe conceptualisation of therapeutic empathy [27]. Empathic attitude in the VA can beachieved by implementing different personality traits in the agent, putting emphasis (butnot only) in the modelling of those personality types in which empathic traits are facil-

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itated (such as the agreeableness personality from the OCEAN model). Using differentpersonality traits, empathic actions in the VA can be for example activated/deactivatedaccording to the particular characteristics of the modelled personality. Even thought thatour VA does not intend to have the role of a therapist, but more related to a helper/guide,an empathic attitude in the VA would contribute with a better engagement of the patientwith the agent supporting a better and effective self-management of his/her treatment.

Since the use of VAs in mental health must be guided by the "do no harm" clinicalmaxim, a very important consideration in the development of the VA is the prevention ofharmful behaviours. Moreover, the VA needs to be clinically evaluated in terms of safetyand efficacy that contributes in the further translation of this technology into clinicalpractice. The achievement of these objectives for the VA (and also for the other compo-nents in the system) is not a minor issue and needs the deep involvement of the clinicians,patients and eHealth policies and health informatics evaluation experts throughout thecomplete life-cycle of the project. We are adopting a user-centred design methodology

[13] during the development of the system. This type of methodology emphasises the ac-tive engagement of end-users (and other relevant stakeholders) with the aim of maximis-ing the conceptual fit, functional utility and usability of the product. Currently successfulmedical informatics applications have been reported following this methodology [30].

3. Current and Future Work

The development of the ideas and a set of basic system’s functionalities described in theprevious sections, are at the initial stages within a project partially supported by the Cata-lan Government called MADep2 (Monitoratge i Assistència per a la Depressió). Addi-tionally, the Help4Mood project is a recently EC-FP7 well evaluated initiative (currentlyat the negotiation stage and expected to start in the following months) that will providethe opportunity to go deeper in the research activities towards a complete developmentof the proposed system.

In the context of these projects, three clinical institutions (located in Romania, UKand Spain) are involved facilitating the effective implementation of the user-centredmethodology, described above, during all the system’s development. Additionally, theseinstitutions will involve a set of patients for evaluation purposes which will allow a con-tinuous evolution of the system components. Four main evaluation stages are plannedduring the development of the system where each component will be iteratively improvedaccording to the results obtained after each stage. In terms of the VA, the four clinicalevaluation stages include the following:

Initial usability study. A pilot usability study using a set of inpatients provided by theclinical experts of the consortium will be carried out to test the initial operabilityof the components and collect baseline data. Regarding the VA, a first version ofa dumb agent (controlled by a clinician in a Wizard of Oz scenario) with limitedbodymovements and facial expressions will be used. This will allow the collectionof data for the further development of the agent’s appearance and more complexmovements and expressions according to the acceptance in the patients.

2http://madep.cat/

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Pilot uncontrolled study (phase 1). The second stage of evaluation will examine theintegrated monitoring system and the VA, using an emotionally neutral version ofthe agent and with no feedback to patients. In addition to further monitoring andacceptability data, it will gather information on the structure of the dialogue be-tween agent and patient in order to inform the branching of questions and feed-back. This will be tested with patients in at least two of the clinical settings in anuncontrolled usability study before moving to the next stage.

Pilot uncontrolled study (phase 2). The third pilot stage of evaluation will examine theeffect on mood and activity of giving simple conditional feedback advice throughthe VA. It is assumed that by this stage the system can be used independentlyby patients (although a trained clinician will be available). At this stage the VAwill behave autonomously showing more complex body movements and facialexpressions according to the generated emotional state and behave coherently withthe patient’s interaction content. This stage will be uncontrolled - evidence forchange in activity or mood will be sought by examining responses in relation tothe advice provided not by comparison against a control group.

Exploratory pilot trial of the complete system. A complete beta version of the wholesystem will be tested in a pilot effectiveness trial in 3 clinical contexts (inpatient,outpatient specialist care, primary care). Improvements and changes in the VAidentified in the previous evaluation stages will be included for this trial. This willbe a randomised trial in which patients in each context are allocated to either thesystem + usual care (UC) or usual care alone. Sample size will be 10-20 patientsper setting with equal numbers randomized to system+UC and UC. This stage willhave an embedded qualitative evaluation. This study will be designed to (a) givean estimate of effect sizes in order to plan (beyond the duration of the project) adefinitive effectiveness trial and (b) identify potential problems in future trials.

Each evaluation state will be characterised by an iterative process of evaluation anddevelopment in each one of the three system’s components in order to improve perfor-mance, efficacy and efficiency that lead to a further definitive clinical trials.

4. Conclusions

The application of new technologies in telemedicine/pervasive care have been increasedin the last years mainly due to the great advance in the development of non-intrusivesensors and wireless communications. One of the clinical fields where these technologiescan contribute is in the treatment of mental illness through the monitoring of the patient’sevolution complemented with the provision of remote therapy.

We have presented our main ideas of a system which aims to provide a closed loopapproach supporting the control, communication and treatment management of patientswith Major Depression. This approach will be a distributed system with three main com-ponents (the Personal Monitoring system, the Virtual Agent component and a DSS forTreatment Management) deployed in both places: at the patient’s site and at the clinicalinstitution. The monitoring system will combine existing (movement sensor, psycholog-ical ratings) and novel (voice analysis) technologies, as inputs to a pattern recognitionbased decision support system for treatment management.

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In this paper we particularly present the main characteristics of the Virtual Agentcomponent which is an embodied virtual entity that will interact with the patient throughspoken dialogue episodes to support self-treatment, elicit self-reporting and facilitatean immediate direct communication between patient and clinician when necessary. Inaddition, the planned continuous evolution and clinical evaluation of the VA has beenalso presented through four different stages that will take place at different times duringthe development of the system.

Acknowledgements

We would like to thank our partners in the MADep (Hospital Sant Joan de Deu - Serveisde Salut Mental, Fundació I2CAT, the "Centre de Recerca en Enginyeria Biomedica" andthe Soft Computing Research Group, both from the Technical University of Catalonia)and Help4Mood (The University of Edinburgh,Heriot-Watt University, Fundació I2CAT,Babes-Bolyai University, FVA New Media Design, the Technical University of Valen-cia and the Technical University of Catalonia) consortiums for their key contribution toshape the ideas expressed in this paper

This paper reflects only the author’s views. This research is partially funded by theMADep project (RD09-1-0013), under the support of ACC1O (Government of Catalo-nia) and FEDER(Fons Europeu de Desenvolupament Regional en el marc del ProgramaOperatiu de Catalunya 2007-2013).

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