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Designing mobile health technology for bipolar disorder: A field trial of the MONARCA system
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.
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... The MONARCA system, a personal smartphone-based monitoring system for bipolar disorder patients, collected different subjective self-reported data and objective sensor data, including mood, sleep, activity, and therapy adherence (Bardram et al., 2013;Alvarez-Lozano et al., 2014) (Fig. 2). The MONARCA system has been generally demonstrated to be an effective tool for early recognizing warning patients with a bipolar disorder (Faurholt-Jepsen et al., 2015, 2019. ...
... The MONARCA Android application user interface. (FromBardram et al. (2013)) ...
... Passive sensing-detection research intersects with areas of computational psychiatry interested in the real-time quantification of mental health from passive data, called digital phenotyping [160], digital biomarkers [8], personal sensing [93,114], or behavioral sensing [113]. Passive sensing data have been applied to detect mental health disorders including depression [6,177], schizophrenia [18,168], anxiety [78,79], and bipolar disorder [16,56]. The stated "value" of detection research is to create a low-burden method, using passive sensing, to continuously measure mental health outside of the clinic, and identify and manage emergent symptoms early-on [8,77]. ...
Researchers in ubiquitous computing have long promised that passive sensing will revolutionize mental health measurement by detecting individuals in a population experiencing a mental health disorder or specific symptoms. Recent work suggests that detection tools do not generalize well when trained and tested in more heterogeneous samples. In this work, we contribute a narrative review and findings from two studies with 41 mental health clinicians to understand these generalization challenges. Our findings motivate research on actionable sensing, as an alternative to detection research, studying how passive sensing can augment traditional mental health measures to support actions in clinical care. Specifically, we identify how passive sensing can support clinical actions by revealing patients' presenting problems for treatment and identifying targets for behavior change and symptom reduction, but passive data requires additional contextual information to be appropriately interpreted and used in care. We conclude by suggesting research at the intersection of actionable sensing and mental healthcare, to align technical research in ubiquitous computing with clinical actions and needs.
... Clinicians can gain a general sense of the patient's disposition, prognosis, sleep hygiene, and medication compliance [63][64][65][66]. ...
The COVID-19 pandemic and lockdown have sparked global terror and apprehension. This phenomenon has led to short-term as well as long-term psychosocial and mental health implications for all. This paper aims at reviewing the prevalence, pathophysiology and treatment of various neuropsychiatric disorders in the pandemic era. We conducted a review and collected articles before and during the COVID-19 pandemic and presented a comparative study of the prevalence of neuropsychiatric disorders before COVID-19 and during COVID-19 pandemic. We selected articles and thematically organized them. Prevalence, pathophysiology, and treatment techniques were studied. We can conclude that the time has come to ensure that the special opportunities presented by this pandemic can translate advocacy to resourcing, ensuring that actionable outputs are delivered for better mental health services. This is necessary given the ground level as well as grassroots support for increased funding for mental health services as well as the apparent desire across all sectors for improved service provision. Longitudinal investigations are required for the assessment of long-lived psychological disorders of the COVID-19 pandemic. The findings need comprehensive evaluation and rehabilitation post-COVID-19 to promote quality of life.
... [8] 2. Dengan kemampuannya yang terintegrasi, Teknologi Komputasi Pervasif dapat membantu pengobatan penyakit kronis mulai dari bipolar dengan MONARCA system, yang memungkinkan pemantauan dan manajemen gejala secara real-time, hingga diabetes dengan sistem pengelolaan diabetes berbasis sensor yang memberikan informasi langsung tentang kadar gula darah dan pola makan yang sehat. Dalam konteks pengobatan penyakit kronis, komputasi pervasif menawarkan pendekatan yang proaktif dan terukur, memungkinkan para pasien untuk memantau kondisi mereka secara terus-menerus dan mengambil tindakan preventif atau pengaturan secara tepat waktu untuk meningkatkan kualitas hidup mereka [9], [10], [11] Adapun juga kekurangan pada Teknologi untuk bidang ini yaitu : ...
Background
Mood, activity, and instability in symptomatology hold significant roles in bipolar disorder (BD) and unipolar disorder (UD). The objectives were to examine disparities in these symptoms among patients with BD and UD.
Methods
Data from two studies including patients with BD and UD, respectively, were combined for exploratory analyses. Patients provided daily smartphone‐based evaluations of mood and activity/energy for a 6‐month period. A total of 47 patients with BD and 59 patients with UD were included in the analyses. The dataset contains more than 13,000 patient‐reported evaluations of mood and activity. Daily mood and activity instability measures were calculated using the root squared successive difference method.
Results
In linear mixed effect regression models adjusted for age, sex, and work status, there were statistically significant lower levels of activity in patients with BD as compared with patients with UD overall, during euthymic states and during depressive states ( B : −0.61, 95% CI: −0.98; −0.24, p = 0.001). There were no statistically significant differences in mood instability and activity instability between patients with BD and patients with UD overall, during euthymic states and during depressive states, when accounting for multiple testing ( p > 0.012).
Limitations
Analyses were exploratory and post hoc. Findings should be interpreted with caution. The sample size was modest.
Conclusion
Patients with BD presented with lower level of activity as compared with patients with UD. There were no differences in mood and activity instability between these groups. Future studies including larger sample sizes should investigate differences between BD and UD.
Trial Registration: ClinicalTrials.gov identifier: NCT03033420
Patients' self-reports are crucial for effective care management of clinical conditions involving subjective symptoms. While patients often value the ability to bring in different forms of self-report data to convey their lived experiences, they often struggle to make their data practically usable in clinical settings. To better center patient needs in communicating illness experiences in clinical contexts, we explore the idea of patient annotations to digital clinical self-report measures, specifically in the context of discontinuing antidepressants. Through interviews with 20 patients with AT Annotator, a digital aid to introduce the concept of annotations, we found that participants perceived annotations to digital clinical measures as a means to enrich self-report measures and reduce the cognitive and emotional burden of logging. However, concerns were raised regarding potential disruptions in patient-provider relationships and the sensitive and complex nature of mental health contexts. We discuss opportunities for annotations to promote patient-centered communication by balancing with clinical practicality and incorporating customization support for patients' communication needs.
Stress is an inevitable part of day-to-day life yet many find themselves unable to manage it themselves, particularly when professional or peer support are not always readily available. As self-care becomes increasingly vital for mental well-being, this paper explores the potential of social simulation as a safe, virtual environment for practicing stress relief for everyday situations. Leveraging the immersive capabilities of VR, AR, and LLMs, we developed eight interactive prototypes for various everyday stressful scenarios (e.g. public speaking) then conducted prototype-driven semi-structured interviews with 19 participants. We reveal that people currently lack effective means to support themselves through everyday stress and found that social simulation fills a gap for simulating real environments for training mental health practices. We outline key considerations for future development of simulation for self-care, including risks of trauma from hyper-realism, distrust of LLM-recommended timing for mental health recommendations, and the value of accessibility for self-care interventions.
Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.
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.
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
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.
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.
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.
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.
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.
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.
