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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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... Finally, research has explored semi-automated monitoring, combining manual self-report with automatic data collection to support patient awareness, long-term engagement, and sense of agency [21,125]. Initial findings from this exploratory work indicate that semi-automated monitoring can be successfully used to monitor sleep and physical activity in mental health interventions [1,6,123], and leveraged in commercial apps [13,79]. ...
... Our longitudinal approach 6 to measuring patient acceptance of the self-report on smartwatch is highly grounded in the technology acceptance literature, and the body of work arguing for considering user acceptance as a multi-stage process [34,42,56,86,92,100,107]. Because of the lack of standardized measurement methods to evaluate acceptance of mental health care technologies, the proposed methodology adopts a mixed-methods approach. ...
Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with smartwatch with those from an additional 34 patients without smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on smartwatch and reflect on the conduct of HCI research evaluating user acceptance of mental health technologies.
... There are several apps developed to monitor remotely via data collecting about mood symptoms and then offer psychoeducation messages and share them with others, such as Brapolar [29], MONARCA [30], SIMPLE, [28], SIMBA [31], and ReMind [32]. Although studies reported that app usage results were satisfactory and acceptable for self-management, users asked for further improvement. ...
... Surveying patients is critical in determining essential features for such apps since the standards must meet the patients' needs. The surveys may promote data integrity [30]. Putting users at the center of product design and development reduces the problems of using apps in the long term [43]. ...
Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project. The features included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug abuse, blood pressure, the patient’s weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application, and standard tests for mood assessment. The first phase of analysis should consider an expert and patient view survey, mood and medication tracking, as well as communication with other people in the same situation are the most features to be considered. The present study has identified the necessity of apps intended to manage and monitor bipolar patients to maximize efficiency and minimize relapse and side effects.
... 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. ...
... Digital phenotyping may provide a much-needed bridge between a patient's symptomatology and behaviors that can be used to assess and monitor Bardram et al. (2013)) psychotic disorders. Among clinical high risk (CHR) and first-episode psychosis (FEP) individuals, digital phenotyping owns a great potential due to an increasing rate of ownership, interest, and technology and smartphone usage among FEP individuals (Camacho et al., 2019;Lal et al., 2020), being indeed represented as an unmet opportunity. ...
... As Wu et al [26] echo, although passive PGHD can capture a wealth of information on patient behavior, it is less clear how this information adds value to improving patient care. Studies often focus on the technical efforts of collecting, deriving signals, and visualizing passive PGHD [12,40,41], but less attention has been paid to translational research showing the value of using PGHD in improving mental health care [42], and trials that have attempted to measure this value thus far have shown mixed results [43,44]. Thus, more evidence on how passive PGHD should be used for clinical decision-making may be required to further engage clinicians. ...
Background
Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used.
Objective
We conducted a qualitative study to understand mental health clinicians’ perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants’ current experiences with and visions for using passive PGHD.
Methods
Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes.
ResultsOverall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven—we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data—participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients’ mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action—participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy—participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use.
Conclusions
Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data–sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).
Depression is a prevailing issue and is an increasing problem in many people’s lives. Due to the lack of physical symptoms, diagnosing depression is a challenge. More importantly, not detecting depression in time might lead to tragic consequences. In this work, we studied the feasibility of depression severity prediction based on passively collected smartphone sensor data. Moreover, we explored the influence of feature extraction window size on classification accuracy and found out that longer window sizes lead to higher prediction accuracy. As a result, we achieved the highest accuracy of 77% with the largest tested window size of two weeks. Furthermore, we analyzed the associations between extracted sensor features and PHQ-9 reported item scores separately for three depression groups and explored similarities and differences among non-depressed, depressed, and severely depressed groups.
Depression is a prevailing issue and is an increasing problem in many people’s lives. Due to the lack of physical symptoms, diagnosing depression is a challenge. More importantly, not detecting depression in time might lead to tragic consequences. In this work, we studied the feasibility of depression severity prediction based on passively collected smartphone sensor data. Moreover, we explored the influence of feature extraction window size on classification accuracy and found out that longer window sizes lead to higher prediction accuracy. As a result, we achieved the highest accuracy of 77% with the largest tested window size of two weeks. Furthermore, we analyzed the associations between extracted sensor features and PHQ-9 reported item scores separately for three depression groups and explored similarities and differences among non-depressed, depressed, and severely depressed groups.
Background:
Alterations and instability in mood and activity/energy has been associated with impaired functioning and risk of relapse in bipolar disorder. The present study aimed to investigate whether mood instability and activity/energy instability are associated, and whether these instability measures are associated with stress, quality of life and functioning in patients with bipolar disorder.
Methods:
Data from two studies were combined for exploratory post hoc analyses. Patients with bipolar disorder provided smartphone-based evaluations of mood and activity/energy levels from day-to-day. In addition, information on functioning, perceived stress and quality of life was collected. A total of 316 patients with bipolar disorder were included.
Results:
A total of 55,968 observations of patient-reported smartphone-based data collected from day-to-day were available. Regardless of the affective state, there was a statistically significant positive association between mood instability and activity/energy instability in all models (all p-values < 0.0001). There was a statistically significant association between mood and activity/energy instability with patient-reported stress and quality of life (e.g., mood instability and stress: B: 0.098, 95 % CI: 0.085; 0.11, p < 0.0001), and between mood instability and functioning (B: 0.045, 95 % CI: 0.0011; 0.0080, p = 0.010).
Limitations:
Findings should be interpreted with caution since the analyses were exploratory and post hoc by nature.
Conclusion:
Mood instability and activity/energy instability is suggested to play important roles in the symptomatology of bipolar disorder. This highlight that monitoring and identifying subsyndromal inter-episodic fluctuations in symptoms is clinically recommended. Future studies investigating the effect of treatment on these measures would be interesting.
Objective:
To investigate (i) the proportions of time with irritability and (ii) the association between irritability and affective symptoms and functioning, stress, and quality of life in patients with bipolar disorder (BD) and unipolar depressive disorder (UD).
Methods:
A total of 316 patients with BD and 58 patients with UD provided self-reported once-a-day data on irritability and other affective symptoms using smartphones for a total of 64,129 days with observations. Questionnaires on perceived stress and quality of life and clinical evaluations of functioning were collected multiple times during the study.
Results:
During a depressive state, patients with UD spent a significantly higher proportion of time with presence of irritability (83.10%) as compared with patients with BD (70.27%) (p = 0.045). Irritability was associated with lower mood, activity level and sleep duration and with increased stress and anxiety level, in both patient groups (p-values<0.008). Increased irritability was associated with impaired functioning and increased perceived stress (p-values<0.024). In addition, in patients with UD, increased irritability was associated with decreased quality of life (p = 0.002). The results were not altered when adjusting for psychopharmacological treatments.
Conclusions:
Irritability is an important part of the symptomatology in affective disorders. Clinicians could have focus on symptoms of irritability in both patients with BD and UD during their course of illness. Future studies investigating treatment effects on irritability would be interesting.
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.
