Conference PaperPDF Available

Designing mobile health technology for bipolar disorder: A field trial of the MONARCA system

Authors:

Abstract and Figures

An increasing number of pervasive healthcare systems are being designed, that allow people to monitor and get feedback on their health and wellness. To address the challenges of self-management of mental illnesses, we have developed the MONARCA system - a personal monitoring system for bipolar patients. We conducted a 14 week field trial in which 12 patients used the system, and we report findings focusing on their experiences. The results were positive; compared to using paper-based forms, the adherence to self-assessment improved; the system was considered very easy to use; and the perceived usefulness of the system was high. Based on this study, the paper discusses three HCI questions related to the design of personal health technologies; how to design for disease awareness and self-treatment, how to ensure adherence to personal health technologies, and the roles of different types of technology platforms.
Content may be subject to copyright.
A preview of the PDF is not available
... The effectiveness of mHealth apps is still unclear. For instance, some research suggests that mood-tracking apps can reduce the intensity of negative moods and increase the intensity of positive moods [19], while other researchers have found contradictory results [7,16]. Accordingly, conducting longitudinal studies for mHealth apps helps researchers and health practitioners investigate the long-term effects of mHealth apps on individuals' moods. ...
... Bardem et al. [7] developed and evaluated the MONARCA, a personal monitoring system for bipolar patients. a 14-week field trial was conducted where 12 patients used the system. ...
Conference Paper
Full-text available
Recently, mental health applications have gained increasing attention. This is because research shows that users find mental health apps as a good alternative for self-management of mental conditions, especially in the last two years when access to physicians was limited because of the pandemic. Despite the existence of several mobile apps for mental health, most available apps are not supported by empirical and scientific evidence, or they are designed based on experimental rather than real data. This work represents our first step toward designing evidence-based AI-driven mental health applications. In this paper, we show our initial results and discussion of in-the-wild users' interactions and engagement with a mental health app (called Moodie) for a period of two years. Specifically, we investigate the interactions and engagements of 434 users (who used the app for two years) with the mood tracking feature where the app allows users to enter their moods and the corresponding factors associated with the mood. Chi-square analysis showed a significant correlation between moods and factors (or user's activities). The results also show that Home, Work, Relaxation, and Family-related activities are the most common factors that affect moods either positively or negatively and that these factors have a different impact on moods during different times of the year.
... No; other severe mood disorders N/A c ; this is a protocol [25] Users involved in prototype design and evaluation stage with iteration MoodRhythm: smartphone app that can track social rhythms Yes 7 [26,27] Users involved in prototype development and evaluation stage with iteration MONARCA system: combination of passive and active self-monitoring smartphone app Yes 42 (all papers combined) [28][29][30] Users involved in prototype development and evaluation stages with iteration OpenSIMPLE: smartphone-based psychoeducation program Yes 303 (all papers combined) [31][32][33] ...
... Through this design process, users were "involved" in making decisions regard-Patient-Clinician Designer Framework using principles of user-centered design [28][29][30] • Prototype design and deployment stage: 3hour workshops were held for design and iterative prototyping where feedback was incorporated into design ing system features using collaborative design workshops. The design of the MONARCA c system uses a mobile phone app as the main component. ...
... Other mental health apps are designed to improve users' awareness and understanding of their mental health issues by collecting their personal data. For example, Bardram et al. [9] designed an app that allows users with bipolar disorder to track their mood and other factors called "MONARCA" app. They conducted a study to assess the effectiveness of MONARCA app compared with paper-based forms. ...
Chapter
Full-text available
Despite the increasing number of mental health applications (apps), the perceived usability of these apps from the viewpoint of end users has rarely been studied. App usability can impact users’ acceptance and engagement with a self-guided mobile health intervention. This study aims to evaluate the usability of a gratitude application called Be Grateful from the perspective of end-users to identify existing design, functionality, and usability issues and elicit users’ views and experiences with the app. We designed the app and conducted usability testing, a combination of interview and questionnaire study of 14 participants who have experienced mental health issues based on self-diagnosis. Participants used Be Grateful app for ten days, completed the System Usability Scale (SUS) validated measure of system usability, and were interviewed at the end of the study. We found that the end-user appreciated the simplicity, straightforwardness of the app and provided positive feedback about the layout. Participants also gave the system high scores on the SUS usability measure (mean = 83.93). Results indicated that the Be Grateful app is usable and will be more likely to be adopted and used by users. Participants were generally excited about the app and eager to use it. This paper reports the lessons learned from the design and evaluation of the app’s usability. We discuss design implications for future work in the area of designing interactive mobile apps for health and wellness, with a focus on mental health interventions.
... HCI is increasingly concerned with enhancing therapy for mental illness by creating supportive technologies that emphasize patient engagement. The notion of patient engagement has been explored through a variety of mental health conditions [7,8,56] and technologies [58,69,70]. These supportive technologies emphasize the need for information sharing [58], visualization of treatment progress [21], customization [11,78], and eliciting patient reflections to place at the center of care [55,68]. ...
... Notably, patients with BD are generally open to using smartphones to help them monitor their mental state [23,24]. The usefulness and ease of use of such apps were found to influence patients' satisfaction and adherence [25,26]. Nevertheless, numerous challenges exist, especially concerning safeguarding privacy and ensuring data security [27]. ...
Article
Full-text available
Background: Smartphones allow for real-time monitoring of patients' behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. Methods: BDmon, a dedicated mobile app, was developed and installed on patients' smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (β=-.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=-.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=-1.452, P<.001) and higher in manic states (β=.509, P<.001). Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient's chance of improving in the course of the illness.
... A smartphone-based monitoring system (the Monsenso system) was installed on the participants own smartphones (both iPhone and Android smartphones). The smartphone-based monitoring system developed by the authors was used by the patients with BD on a daily basis to collect fine-grained real-time recordings of mood, activity, and sleep duration (Bardram et al. 2013). Mood was evaluated with scores on a 9-point scale ranging from depressed to manic (− 3, − 2, − 1, − 0.5, 0, 0.5, 1, 2, 3). ...
Article
Full-text available
Background Voice features have been suggested as objective markers of bipolar disorder (BD). Aims To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. Results Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11). Conclusions Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.
Article
Depression is a challenging condition that requires individuals to manage their moods and emotions over time. Within CSCW, there has been an interest in understanding how individuals seek and share support on social media and in online communities. However, less attention has been paid to how collaboration as an aspect of self-management of depression unfolds in people's daily lives. In this paper, we explore the collaborative self-management work of 28 individuals managing depression who live in the United States. Data collection included remote semi-structured interviews with an associated cognitive mapping exercise. Our findings describe who participants turn to for day-to-day collaborative support, how collaborative activities are enacted (across both mood-focused and preventative support practices), and where these often technology-mediated interactions occur across text, phone, video, and picture-based channels. We discuss collaborative self-management in the depression support context, including key characteristics: agency, reciprocity, time, and interaction. We also present a four-step model of how the process occurs over time (awareness, planning, interaction, and reflection). We conclude by discussing how technology ecosystems support individuals' collaborative self-management.
Article
Background It is of crucial importance to be able to discriminate unipolar disorder (UD) from bipolar disorder (BD), as treatments, as well as course of illness, differ between the two disorders. Aims: to investigate whether voice features from naturalistic phone calls could discriminate between 1) UD, BD, and healthy control individuals (HC); 2) different states within UD. Methods Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 48 patients with UD, 121 patients with BD, and 38 HC were included. A total of 115483 voice data entries were collected (UD (n= 16454), BD (n= 78733), and HC (n =20296)). Patients evaluated symptoms daily using a smartphone-based system, making it possible to define illness states within UD and BD. Data were analyzed using random forest algorithms. Results Compared to BD, UD was classified with a specificity of 0.84 (SD 0.07) /AUC of 0.58 (SD 0.07) and compared to HC with a sensitivity of 0.74 (SD 0.10)/ AUC=0.74 (SD 0.06). Compared to BD during euthymia, UD during euthymia was classified with a specificity of 0.79 (SD 0.05)/ AUC=0.43 (SD 0.16). Compared to BD during depression, UD during depression was classified with a specificity of 0.81 (SD 0.09)/ AUC=0.48 (SD 0.12). Within UD, compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.31)/ AUC=0.65 (SD 0.11). In all models the user-dependent models outperformed the user-independent models. Conclusions The results from the present study are promising, but as reflected by the low AUCs, does not support that voice features collected during naturalistic phone calls at the current state of art can be implemented in clinical practice as a supplementary and assisting tool. Further studies are needed.
Article
Full-text available
An increasing number of persuasive personal healthcare monitoring systems are being researched, designed and tested. However, most of these systems have targeted somatic diseases and few have targeted mental illness. This paper describes the MONARCA system; a persuasive personal monitoring system for bipolar patients based on an Android mobile phone. The paper describes the user- centered design process behind the system, the user experience, and the technical implementation. This system is one of the first examples of the use of mobile monitoring to support the treatment of metal illness, and we discuss lessons learned and how others can use our experience in the design of such systems for the treatment of this important, yet challenging, patient group.
Article
Full-text available
Doctors often ask patients to recall recent health experiences, such as pain, fatigue, and quality of life.1 Research has shown, however, that recall is unreliable and rife with inaccuracies and biases.2 Recognition of recall's shortcomings has led to the use of diaries, which are intended to capture experiences close to the time of occurrence, thus limiting recall bias and producing more accurate data.3The rationale for using diaries would be undermined if patients failed to complete diaries according to protocol. In this study we used a newly developed paper diary that could objectively record when patients made diary entries in order to compare patients' reported and actual compliance with diary keeping. For comparison, we also used an electronic diary designed to enhance compliance in order to assess what compliance rates might be achieved. Methods and results We recruited 80 adults with chronic pain (pain for ≥3 hours a day and rated ≥4 on a 10 point scale) and assigned 40 to keeping a paper diary and 40 to an electronic diary. On satisfying the eligibility criteria, each patient was assigned to the next training session for which he or she was available, regardless of which diary it was for. We conducted one training session for each diary each week, with each training session for the paper diary matched by time and day of the week with an electronic diary training session. Participants were paid $150 and gave their informed consent; patients given the paper diary were not told that compliance would be recorded electronically. The paper diary comprised diary cards bound into a DayRunner Organizer binder. The cards contained 20 questions drawn from several common pain instruments and included fields to record time and date of completion. The diary binders were unobtrusively fitted with photosensors that detected light and recorded when the binder was opened and closed; these were extensively tested and validated. The electronic diary was a Palm computer with software for data collection in clinical trials and presented identical pain questions via a touch screen and recorded time and date of entries. This system (invivodata) incorporated several features to maximise compliance, including auditory prompts, and has demonstrated good compliance.4 Patients were instructed to complete daily entries at 10 am, 4 pm, and 8 pm within 15 minutes of the target times. With the electronic diary, entries could not be initiated outside the designated 30 minute windows. We considered paper diary entries to be compliant if they were made within the 30 minute windows. A more liberal secondary outcome allowed a 90 minute window around the target times. Reported compliance was based on the time and date that patients recorded on their paper diary cards. Actual compliance was based on the electronic record (from the record of diary binder openings for paper diaries). Paper diary entries were deemed compliant if the binder was opened or closed at any point during the target time window. We also assessed “hoarding” with the paper diary, defined as days when the diary binder was not opened but for which diary cards were completed. Compliance rates for 80 patients' record keeping in paper and electronic diaries
Article
Full-text available
A key challenge for mobile health is to develop new technology that can assist individuals in maintaining a healthy lifestyle by keeping track of their everyday behaviors. Smartphones embedded with a wide variety of sensors are enabling a new generation of personal health applications that can actively monitor, model and promote wellbeing. Automated wellbeing tracking systems available so far have focused on physical fitness and sleep and often require external non-phone based sensors. In this work, we take a step towards a more comprehensive smartphone based system that can track activities that impact physical, social, and mental wellbeing namely, sleep, physical activity, and social interactions and provides intelligent feedback to promote better health. We present the design, implementation and evaluation of BeWell, an automated wellbeing app for the Android smartphones and demonstrate its feasibility in monitoring multi-dimensional wellbeing. By providing a more complete picture of health, BeWell has the potential to empower individuals to improve their overall wellbeing and identify any early signs of decline.
Article
Full-text available
Sleep is a basic physiological process essential for good health. However, 40 million people in the U.S. are diagnosed with sleep disorders, with many more undiagnosed. To help address this problem, we developed an application, ShutEye, which provides a peripheral display on the wall-paper of the user's mobile phone to promote awareness about recommended activities that promote good sleep quality. Based on preferences about the user's desired bed-time and activities' for example - consuming caffeine or performing vigorous exercise - ShutEye displays guidance about when engaging in those activities is likely to affect sleep without requiring any explicit interaction from the user. In this paper, we describe ShutEye and results from a four-week field study with 12 participants. Results indicate that a simple, recommendation-based peripheral display can be a very low-effort but still effective method for improving awareness of healthy sleep habits. We also provide recommendations about designing peripheral displays and extend insights for designing health-based mobile applications.
Conference Paper
Full-text available
Online mental health interventions can benefit people experiencing a range of psychological difficulties, but attrition is a major problem in real-world deployments. We discuss strategies to reduce attrition, and present SilverCloud, a platform designed to provide more engaging online experiences. The paper presents the results of a practice-based clinical study in which 45 clients and 6 therapists used an online Cognitive Behavioural Therapy programme for depression. Pre and post-treatment assessments, using the Beck Depression Inventory, indicate a statistically significant improvement in depressive symptoms, with a large effect size, for the moderate-to-severe clinical sub-sample receiving standalone online treatment (n=18). This group was the primary target for the intervention. A high level of engagement was also observed compared to a prior online intervention used within the same service. We discuss strategies for design in this area and consider how the quantitative and qualitative results contribute towards our understanding of engagement.
Article
Full-text available
Bipolar disorder remains a serious public health problem with a significant personal and economic burden. In line with the widespread recognition of the value of active patient involvement in their care, daily mood charting may increase the patient's understanding of their condition and improve adherence with complex medication regimes. Knowledge about the course and pattern of an individual's disorder may also allow earlier recognition of new episodes and help determine the optimal treatment strategy. Mood charting is also an essential tool for longitudinal studies of patient outcomes. Traditionally, patients have used paper-based tools for this daily self-assessment, but these forms are associated with problems of data quality, poor compliance, high costs for data entry, and only provide limited feedback for the patient and physician. As computer technology has gained acceptance by the public worldwide, new options are available to automate monitoring of patients with mood disorders. This article will review mood charting and describe our experience with the development, validation and use of ChronoRecord, an automated instrument for mood charting.
Conference Paper
Full-text available
Persuasive personal monitoring and feedback systems could help patients and clinicians manage mental illness. Mental illness is complex, difficult to treat, and carries social stigma. From a review of literature on bipolar disorder and interviews with bipolar disorder experts, we developed a framework for designing a persuasive monitoring system to support management of the illness. The framework applies a user-centered design process that is especially sensitive to the complexity of the illness, the difficulty of treatment, its stigma, and the goals of patients and clinicians. We describe our application of this framework to designing a persuasive mobile phone monitoring system. We discuss how our use of the framework can help overcome the special challenges posed by designing systems for mental illness: (1) accommodating a complex array of symptoms, (2) supporting an uncertain treatment process, and (3) maintaining a high level of sensitivity to the seriousness and darkness of the illness, as well as the social stigma associated with it.
Article
Although pharmacotherapy is essential component of bipolar disorder treatment, specific forms of psychotherapy are also critical components of the treatment plan for many patients. Some studies have suggested that cognitive behavioral therapy may be useful when added to pharmacotherapy during depressive episodes in patients with BP. Also during maintenance phase of the treatment patients with BP are likely to benefit from a concomitant psychotherapy. The identification of early prodromal signs or symptoms can help the patient enhance mastery over his illness and can help to introduce an adequate treatment as early as possible in the course of an episode. Patients receiving psychoeducation experienced a significant reduction of the risk of manic relapses as well as improved social and vocational functioning. Patients with bipolar disorder may also benefit from regular pattern of daily activities, including sleeping, eating, physical activity, and social and emotional stimulation. It is important to recognize distress or dysfunction in the family of a patient wich BP. The cognitive style influences why individuals stop their medication, how they interpret and process prodromal symptoms they experience, and how they cope with the early warning signs of impending relapse.